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...

Structural health monitoring feature design by genetic programming

International Nuclear Information System (INIS)

Harvey, Dustin Y; Todd, Michael D

2014-01-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems. (paper)

Feature constrained compressed sensing CT image reconstruction from incomplete data via robust principal component analysis of the database

International Nuclear Information System (INIS)

Wu, Dufan; Li, Liang; Zhang, Li

2013-01-01

In computed tomography (CT), incomplete data problems such as limited angle projections often cause artifacts in the reconstruction results. Additional prior knowledge of the image has shown the potential for better results, such as a prior image constrained compressed sensing algorithm. While a pre-full-scan of the same patient is not always available, massive well-reconstructed images of different patients can be easily obtained from clinical multi-slice helical CTs. In this paper, a feature constrained compressed sensing (FCCS) image reconstruction algorithm was proposed to improve the image quality by using the prior knowledge extracted from the clinical database. The database consists of instances which are similar to the target image but not necessarily the same. Robust principal component analysis is employed to retrieve features of the training images to sparsify the target image. The features form a low-dimensional linear space and a constraint on the distance between the image and the space is used. A bi-criterion convex program which combines the feature constraint and total variation constraint is proposed for the reconstruction procedure and a flexible method is adopted for a good solution. Numerical simulations on both the phantom and real clinical patient images were taken to validate our algorithm. Promising results are shown for limited angle problems. (paper)

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.

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 ...

QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIP OF ANTIMALARIAL COMPOUND OF ARTEMISININ DERIVATIVES USING PRINCIPAL COMPONENT REGRESSION APPROACH

Directory of Open Access Journals (Sweden)

Paul Robert Martin Werfette

2010-06-01

Full Text Available Analysis of quantitative structure - activity relationship (QSAR for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation,  (;;   Keywords: QSAR, antimalarial, artemisinin, principal component regression

Structural features that predict real-value fluctuations of globular proteins.

Science.gov (United States)

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

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.

FeatureMap3D - a tool to map protein features and sequence conservation onto homologous structures in the PDB

DEFF Research Database (Denmark)

Wernersson, Rasmus; Rapacki, Krzysztof; Stærfeldt, Hans Henrik

2006-01-01

FeatureMap3D is a web-based tool that maps protein features onto 3D structures. The user provides sequences annotated with any feature of interest, such as post-translational modifications, protease cleavage sites or exonic structure and FeatureMap3D will then search the Protein Data Bank (PDB) f...

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.

Learning about the internal structure of categories through classification and feature inference.

Science.gov (United States)

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

Science.gov (United States)

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Subsurface structures of buried features in the lunar Procellarum region

Science.gov (United States)

Wang, Wenrui; Heki, Kosuke

2017-07-01

The Gravity Recovery and Interior Laboratory (GRAIL) mission unraveled numbers of features showing strong gravity anomalies without prominent topographic signatures in the lunar Procellarum region. These features, located in different geologic units, are considered to have complex subsurface structures reflecting different evolution processes. By using the GRAIL level-1 data, we estimated the free-air and Bouguer gravity anomalies in several selected regions including such intriguing features. With the three-dimensional inversion technique, we recovered subsurface density structures in these regions.

Principal shapes and squeezed limits in the effective field theory of large scale structure

Energy Technology Data Exchange (ETDEWEB)

Bertolini, Daniele; Solon, Mikhail P., E-mail: dbertolini@lbl.gov, E-mail: mpsolon@lbl.gov [Berkeley Center for Theoretical Physics, University of California, South Hall Road, Berkeley, CA, 94720 (United States)

2016-11-01

We apply an orthogonalization procedure on the effective field theory of large scale structure (EFT of LSS) shapes, relevant for the angle-averaged bispectrum and non-Gaussian covariance of the matter power spectrum at one loop. Assuming natural-sized EFT parameters, this identifies a linear combination of EFT shapes—referred to as the principal shape—that gives the dominant contribution for the whole kinematic plane, with subdominant combinations suppressed by a few orders of magnitude. For the covariance, our orthogonal transformation is in excellent agreement with a principal component analysis applied to available data. Additionally we find that, for both observables, the coefficients of the principal shapes are well approximated by the EFT coefficients appearing in the squeezed limit, and are thus measurable from power spectrum response functions. Employing data from N-body simulations for the growth-only response, we measure the single EFT coefficient describing the angle-averaged bispectrum with Ο (10%) precision. These methods of shape orthogonalization and measurement of coefficients from response functions are valuable tools for developing the EFT of LSS framework, and can be applied to more general observables.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

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

2016-03-01

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

Reduction of symplectic principal R-bundles

International Nuclear Information System (INIS)

Lacirasella, Ignazio; Marrero, Juan Carlos; Padrón, Edith

2012-01-01

We describe a reduction process for symplectic principal R-bundles in the presence of a momentum map. These types of structures play an important role in the geometric formulation of non-autonomous Hamiltonian systems. We apply this procedure to the standard symplectic principal R-bundle associated with a fibration π:M→R. Moreover, we show a reduction process for non-autonomous Hamiltonian systems on symplectic principal R-bundles. We apply these reduction processes to several examples. (paper)

Understanding Structural Features of Microbial Lipases–-An Overview

Directory of Open Access Journals (Sweden)

John Geraldine Sandana Mala

2008-01-01

Full Text Available The structural elucidations of microbial lipases have been of prime interest since the 1980s. Knowledge of structural features plays an important role in designing and engineering lipases for specific purposes. Significant structural data have been presented for few microbial lipases, while, there is still a structure-deficit, that is, most lipase structures are yet to be resolved. A search for ‘lipase structure’ in the RCSB Protein Data Bank ( http://www.rcsb.org/pdb/ returns only 93 hits (as of September 2007 and, the NCBI database ( http://www.ncbi.nlm.nih.gov reports 89 lipase structures as compared to 14719 core nucleotide records. It is therefore worthwhile to consider investigations on the structural analysis of microbial lipases. This review is intended to provide a collection of resources on the instrumental, chemical and bioinformatics approaches for structure analyses. X-ray crystallography is a versatile tool for the structural biochemists and is been exploited till today. The chemical methods of recent interests include molecular modeling and combinatorial designs. Bioinformatics has surged striking interests in protein structural analysis with the advent of innumerable tools. Furthermore, a literature platform of the structural elucidations so far investigated has been presented with detailed descriptions as applicable to microbial lipases. A case study of Candida rugosa lipase (CRL has also been discussed which highlights important structural features also common to most lipases. A general profile of lipase has been vividly described with an overview of lipase research reviewed in the past.

High-Need Schools in Australia: The Leadership of Two Principals

Science.gov (United States)

Gurr, David; Drysdale, Lawrie; Clarke, Simon; Wildy, Helen

2014-01-01

In this article, we report on our initial work with the International School Leadership Development Network. In doing so, we present two cases of principals leading high-need schools, and conclude with some key observations in relation to what is distinctive about leading these schools. The first case features a principal leading a suburban school…

APPRIS 2017: principal isoforms for multiple gene sets

Science.gov (United States)

Rodriguez-Rivas, Juan; Di Domenico, Tomás; Vázquez, Jesús; Valencia, Alfonso

2018-01-01

Abstract The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the ‘principal’ isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants. PMID:29069475

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection.

Science.gov (United States)

Kipli, Kuryati; Kouzani, Abbas Z

2015-07-01

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression. A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation. The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score. DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

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.)

Endogenous Market Structures and Contract Theory. Delegation, principal-agent contracts, screening, franchising and tying

OpenAIRE

Etro Federico

2010-01-01

I study the role of unilateral strategic contracts for firms active in markets with price competition and endogenous entry. Traditional results change substantially when the market structure is endogenous rather than exogenous. They concern 1) contracts of managerial delegation to non-profit maximizers, 2) incentive principal-agent contracts in the presence of moral hazard on cost reducing activities, 3) screening contracts in case of asymmetric information on the productivity of the managers...

Linking structural features of protein complexes and biological function.

Science.gov (United States)

Sowmya, Gopichandran; Breen, Edmond J; Ranganathan, Shoba

2015-09-01

Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions. © 2015 The Protein Society.

The Principal as Instructional Leader: A Practical Handbook. 3rd Edition

Science.gov (United States)

Zepeda, Sally J.

2013-01-01

In the updated third edition of this highly successful book, leadership expert, Sally Zepeda offers savvy advice to both new and seasoned principals and assistant principals. You get practical tools and strategies, along with real-world examples to help you improve teacher effectiveness and boost student achievement. This edition features valuable…

STRUCTURAL FEATURES OF PLANT CHITINASES AND CHITIN-BINDING PROTEINS

NARCIS (Netherlands)

BEINTEMA, JJ

1994-01-01

Structural features of plant chitinases and chitin-binding proteins are discussed. Many of these proteins consist of multiple domains,of which the chitin-binding hevein domain is a predominant one. X-ray and NMR structures of representatives of the major classes of these proteins are available now,

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

Science.gov (United States)

Hara, Kazuo; Shimbo, Masashi; Matsumoto, Yuji

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

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.

Understanding Protein-Protein Interactions Using Local Structural Features

DEFF Research Database (Denmark)

Planas-Iglesias, Joan; Bonet, Jaume; García-García, Javier

2013-01-01

Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...... to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable...

The future of primordial features with large-scale structure surveys

International Nuclear Information System (INIS)

Chen, Xingang; Namjoo, Mohammad Hossein; Dvorkin, Cora; Huang, Zhiqi; Verde, Licia

2016-01-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

The future of primordial features with large-scale structure surveys

Energy Technology Data Exchange (ETDEWEB)

Chen, Xingang; Namjoo, Mohammad Hossein [Institute for Theory and Computation, Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Dvorkin, Cora [Department of Physics, Harvard University, Cambridge, MA 02138 (United States); Huang, Zhiqi [School of Physics and Astronomy, Sun Yat-Sen University, 135 Xingang Xi Road, Guangzhou, 510275 (China); Verde, Licia, E-mail: xingang.chen@cfa.harvard.edu, E-mail: dvorkin@physics.harvard.edu, E-mail: huangzhq25@sysu.edu.cn, E-mail: mohammad.namjoo@cfa.harvard.edu, E-mail: liciaverde@icc.ub.edu [ICREA and ICC-UB, University of Barcelona (IEEC-UB), Marti i Franques, 1, Barcelona 08028 (Spain)

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

Metallacyclopentadienes: structural features and coordination in transition metal complexes

International Nuclear Information System (INIS)

Dolgushin, Fedor M; Yanovsky, Aleksandr I; Antipin, Mikhail Yu

2004-01-01

Results of structural studies of polynuclear transition metal complexes containing the metallacyclopentadiene fragment are overviewed. The structural features of the complexes in relation to the nature of the substituents in the organic moiety of the metallacycles, the nature of the transition metals and their ligand environment are analysed. The main structural characteristics corresponding to different modes of coordination of metallacyclopentadienes to one or two additional metal centres are revealed.

Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different Working Conditions

Directory of Open Access Journals (Sweden)

Dong Wang

2015-01-01

Full Text Available Gears are widely used in gearbox to transmit power from one shaft to another. Gear crack is one of the most frequent gear fault modes found in industry. Identification of different gear crack levels is beneficial in preventing any unexpected machine breakdown and reducing economic loss because gear crack leads to gear tooth breakage. In this paper, an intelligent fault diagnosis method for identification of different gear crack levels under different working conditions is proposed. First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. To reduce the dimensionality of the extracted statistical features and generate new significant low-dimensional statistical features, a simple and effective method called principal component analysis is used. To further improve identification accuracies of different gear crack levels under different working conditions, support vector machine is employed. Three experiments are investigated to show the superiority of the proposed method. Comparisons with other existing gear crack level identification methods are conducted. The results show that the proposed method has the highest identification accuracies among all existing methods.

Job Satisfaction of Elementary Principals in Large Urban Communities

Science.gov (United States)

Mitchell, Cathryn M.

2010-01-01

The purpose of this study was to determine job satisfaction levels of elementary principals in "major urban" districts in Texas and to identify strategies these principals used to cope with the demands of the position. Additionally, the project sought to find structures and supports needed to attract and retain principals in the…

Common Features in Electronic Structure of the Oxypnictide Superconductors from Photoemission Spectroscopy

International Nuclear Information System (INIS)

Xiao-Wen, Jia; Hai-Yun, Liu; Wen-Tao, Zhang; Lin, Zhao; Jian-Qiao, Meng; Guo-Dong, Liu; Xiao-Li, Dong; Zhi-An, Ren; Wei, Yi; Guang-Can, Che; Zhong-Xian, Zhao; Gang, Wu; Rong-Hua, Liu; Xian-Hui, Chen; Gen-Fu, Chen; Nan-Lin, Wang; Yong, Zhu; Xiao-Yang, Wang; Gui-Ling, Wang; Yong, Zhou

2008-01-01

High resolution photoemission measurements are carried out on non-superconducting LaFeAsO parent compound and various superconducting RFeAs(O 1-x F x ) (R=La, Ce and Pr) compounds. It is found that the parent LaFeAsO compound shows a metallic character. By extensive measurements, several common features are identified in the electronic structure of these Fe-based compounds: (1) 0.2 eV feature in the valence band, (2) a universal 13-16 meV feature, (3) near Ef spectral weight suppression with decreasing temperature. These universal features can provide important information about band structure, superconducting gap and pseudogap in these Fe-based materials

Porous Structure of Road Concrete

OpenAIRE

Пшембаев, М. К.; Гиринский, В. В.; Ковалев, Я. Н.; Яглов, В. Н.; Будниченко, С. С.

2016-01-01

Having a great number of concrete structure classifications it is recommended to specify the following three principal types: microstructure – cement stone structure; mesostructure – structure of cement-sand mortar in concrete; macrostucture – two-component system that consists of mortar and coarse aggregate. Every mentioned-above structure has its own specific features which are related to the conditions of their formation. Thus, microstructure of cement stone can be characterized by such st...

Primary School Principals' Experiences with Smartphone Apps

Science.gov (United States)

Çakir, Rahman; Aktay, Sayim

2016-01-01

Smartphones are not just pieces of hardware, they at same time also dip into software features such as communication systems. The aim of this study is to examine primary school principals' experiences with smart phone applications. Shedding light on this subject means that this research is qualitative. Criterion sampling has been intentionally…

Chinese wine classification system based on micrograph using combination of shape and structure features

Science.gov (United States)

Wan, Yi

2011-06-01

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

Directory of Open Access Journals (Sweden)

Chunling DU

2012-03-01

Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

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Noor Abdalrazak Shnain

2017-08-01

Full Text Available Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM, combines the best features of the well-known SSIM (structural similarity index measure and FSIM (feature similarity index measure approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio, using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

The influence of target structure on topographical features produced by ion beam sputtering

International Nuclear Information System (INIS)

Whitton, J.L.; Grant, W.A.

1981-01-01

Ion beam erosion of solid surfaces often results in the development of distinctive topographical features. The relationship between the type of features formed by ion erosion and target structure has been investigated. Single crystals of copper and nickel and the amorphous alloy Metglas have been bombarded to high doses (approx. >=10 19 ions cm -2 ) with 40 keV Ar + and P + . Topography changes were monitored using SEM and structural changes by TEM. Targets that retain their long range crystallinity show sharply defined, regular features that are related to the target structure. Targets that are highly disordered, either intrinsically or as a result of the ion bombardment, produce diffuse, smaller features. Those differences are observed at all stages in topographical evolution. (orig.)

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…

Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis.

Science.gov (United States)

Zou, Bin; Lee, Victor H F; Yan, Hong

2018-03-07

Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib. To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib. We can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery.

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

Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.

Science.gov (United States)

de Pierrefeu, Amicie; Lofstedt, Tommy; Hadj-Selem, Fouad; Dubois, Mathieu; Jardri, Renaud; Fovet, Thomas; Ciuciu, Philippe; Frouin, Vincent; Duchesnay, Edouard

2018-02-01

Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach

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.

Female Principals in Education: Breaking the Glass Ceiling in Spain

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Enrique Javier Diez Gutierrez

Full Text Available Abstract Spanish schools are characterised by having a high proportion of female staff. However, statistics show that a proportionately higher number of men hold leadership positions. The aim of this study was to analyse the reasons why this is so, and to determine the motivations and barriers that women encounter in attaining and exercising these positions of greater responsibility and power. Questionnaires were administered to 2,022 female teachers, 430 female principals and 322 male principals. In addition, semi-structured interviews were held with 60 female principals, 14 focus group discussions were held with female principals and 16 autobiographical narratives were compiled with female principals and school inspectors. The reasons identified were related to structural aspects linked to the patriarchal worldview that is still dominant in our society and culture. Nevertheless, we also found motivations among women for attaining and exercising leadership roles.

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.

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

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Suwicha Jirayucharoensak

2014-01-01

Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

Compressive behavior of pervious concretes and a quantification of the influence of random pore structure features

International Nuclear Information System (INIS)

Deo, Omkar; Neithalath, Narayanan

2010-01-01

Research highlights: → Identified the relevant pore structure features of pervious concretes, provided methodologies to extract those, and quantified the influence of these features on compressive response. → A model for stress-strain relationship of pervious concretes, and relationship between model parameters and parameters of the stress-strain relationship developed. → Statistical model for compressive strength as a function of pore structure features; and a stochastic model for the sensitivity of pore structure features in strength prediction. - Abstract: Properties of a random porous material such as pervious concrete are strongly dependent on its pore structure features, porosity being an important one among them. This study deals with developing an understanding of the material structure-compressive response relationships in pervious concretes. Several pervious concrete mixtures with different pore structure features are proportioned and subjected to static compression tests. The pore structure features such as pore area fractions, pore sizes, mean free spacing of the pores, specific surface area, and the three-dimensional pore distribution density are extracted using image analysis methods. The compressive stress-strain response of pervious concretes, a model to predict the stress-strain response, and its relationship to several of the pore structure features are outlined. Larger aggregate sizes and increase in paste volume fractions are observed to result in increased compressive strengths. The compressive response is found to be influenced by the pore sizes, their distributions and spacing. A statistical model is used to relate the compressive strength to the relevant pore structure features, which is then used as a base model in a Monte-Carlo simulation to evaluate the sensitivity of the predicted compressive strength to the model terms.

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…

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

Science.gov (United States)

Matsen IV, Frederick A.; Evans, Steven N.

2013-01-01

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

Feature Extraction for Structural Dynamics Model Validation

Energy Technology Data Exchange (ETDEWEB)

Farrar, Charles [Los Alamos National Laboratory; Nishio, Mayuko [Yokohama University; Hemez, Francois [Los Alamos National Laboratory; Stull, Chris [Los Alamos National Laboratory; Park, Gyuhae [Chonnam Univesity; Cornwell, Phil [Rose-Hulman Institute of Technology; Figueiredo, Eloi [Universidade Lusófona; Luscher, D. J. [Los Alamos National Laboratory; Worden, Keith [University of Sheffield

2016-01-13

As structural dynamics becomes increasingly non-modal, stochastic and nonlinear, finite element model-updating technology must adopt the broader notions of model validation and uncertainty quantification. For example, particular re-sampling procedures must be implemented to propagate uncertainty through a forward calculation, and non-modal features must be defined to analyze nonlinear data sets. The latter topic is the focus of this report, but first, some more general comments regarding the concept of model validation will be discussed.

Canadian Perspectives on Beginning Principals: Their Role in Building Capacity for Learning Communities

Science.gov (United States)

Sackney, Larry; Walker, Keith

2006-01-01

Purpose: This paper sets out to posit that the new economy places a new set of demands on schools and those who lead. Mindfulness, intentional engagement of people and adaptive confidence are needed developmental features of beginning principal success. The paper examines how beginning principals in Canada respond to the capacity-building work of…

The characteristics of effective secondary math and science instructional facilitators and the necessary support structures as perceived by practitioners and principals

Science.gov (United States)

Mahagan, Vikki Lynn

Instructional facilitators are known by a variety of titles depending on the school district in which they are employed. They are sometimes called instructional coaches, teacher leaders, lead teachers, and instructional specialist (Denton & Hasbrouck, 2009). Throughout this study, the title instructional facilitator was used and will refer to secondary math or science instructional facilitators who are housed at least one day per week on a campus. This study is a mixed-methods descriptive study which has identified character traits, specials skill, and talents possessed by effective secondary math and science instructional facilitators as perceived by practicing facilitators and principals and assistant principals who work along side instructional facilitators. Specific job training to help ensure the success of a facilitator was identified as viewed by both facilitators and principals. Additionally, this study compared the perceptions of practicing facilitators and principals to determine if significant differences exist with respect to perceptions of staff development opportunities, support structures, and resources available for instructional facilitators.

Structural-phenomenological features of the internal picture of doctors’ illnesses

Directory of Open Access Journals (Sweden)

Lazarenko, Victor A.

2016-06-01

Full Text Available The vocational activities of doctors and their social status do not ensure their health. And, falling ill, doctors don’t identify themselves with ordinary patients as they have a deep knowledge of medicine. Thus, the internal picture of a doctor’s illness is both a research and a practical problem: the problem of the psychoprevention of doctors’ illnesses at all stages of their professionalization. The purpose of the research was to study the phenomenological features of the internal picture of doctors’ illnesses using the structural approach. The total number of participants was 132. The experimental group consisted of 66 sick doctors, differentiated according to their stage of professionalization: vocational training (students, professional adaptation (interns, full professionalization (doctors. The control group consisted of 66 people who did not have any medical education. All the control subjects were hospitalized with chronic diseases during the study period. The organization of the research was carried out with the use of clinical-psychological and diagnostic methods, the methods of descriptive statistics, and comparative, multidimensional, and structural analysis. The research revealed the following phenomenological features of the internal picture of doctors’ illnesses: the prevalence of some anxiety in the doctors and high awareness of their health; the doctors’ altruistic orientation; their willingness to work despite difficulties; and their ability to achieve high results in different activities. The structural features of the doctors’ image of their own diseases on the cognitive level were the following: qualitative heterogeneity during in-service activities; a high degree of image integration during in-service activities; and stereotyped perceptions of the disease. The emotional level revealed the emotional distance between doctors and their patients, and the behavioral level revealed doctors’ disregard for the

Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure.

Science.gov (United States)

Foroughi Pour, Ali; Dalton, Lori A

2018-03-21

Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms. The proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study. Bayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.

DroidEnsemble: Detecting Android Malicious Applications with Ensemble of String and Structural Static Features

KAUST Repository

Wang, Wei

2018-05-11

Android platform has dominated the Operating System of mobile devices. However, the dramatic increase of Android malicious applications (malapps) has caused serious software failures to Android system and posed a great threat to users. The effective detection of Android malapps has thus become an emerging yet crucial issue. Characterizing the behaviors of Android applications (apps) is essential to detecting malapps. Most existing work on detecting Android malapps was mainly based on string static features such as permissions and API usage extracted from apps. There also exists work on the detection of Android malapps with structural features, such as Control Flow Graph (CFG) and Data Flow Graph (DFG). As Android malapps have become increasingly polymorphic and sophisticated, using only one type of static features may result in false negatives. In this work, we propose DroidEnsemble that takes advantages of both string features and structural features to systematically and comprehensively characterize the static behaviors of Android apps and thus build a more accurate detection model for the detection of Android malapps. We extract each app’s string features, including permissions, hardware features, filter intents, restricted API calls, used permissions, code patterns, as well as structural features like function call graph. We then use three machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Random Forest (RF), to evaluate the performance of these two types of features and of their ensemble. In the experiments, We evaluate our methods and models with 1386 benign apps and 1296 malapps. Extensive experimental results demonstrate the effectiveness of DroidEnsemble. It achieves the detection accuracy as 95.8% with only string features and as 90.68% with only structural features. DroidEnsemble reaches the detection accuracy as 98.4% with the ensemble of both types of features, reducing 9 false positives and 12 false

The Technology Principal: To Be or Not to Be?

Science.gov (United States)

Anthony, Anika Ball; Patravanich, Supawaree

2014-01-01

This case provides principal licensure candidates a strategic perspective on leading and managing educational technology initiatives. It presents issues related to vision setting, planning, implementation, organizational structure, and decision making. The case narrative is presented from the perspective of a principal, but it can also be used to…

Teachers' Perspectives on Principal Mistreatment

Science.gov (United States)

Blase, Joseph; Blase, Jo

2006-01-01

Although there is some important scholarly work on the problem of workplace mistreatment/abuse, theoretical or empirical work on abusive school principals is nonexistent. Symbolic interactionism was the theoretical structure for the present study. This perspective on social research is founded on three primary assumptions: (1) individuals act…

Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

Science.gov (United States)

Zhang, Shengli; Tang, Jiong

2016-04-01

Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

An Matching Method for Vehicle-borne Panoramic Image Sequence Based on Adaptive Structure from Motion Feature

Directory of Open Access Journals (Sweden)

ZHANG Zhengpeng

2015-10-01

Full Text Available Panoramic image matching method with the constraint condition of local structure from motion similarity feature is an important method, the process requires multivariable kernel density estimations for the structure from motion feature used nonparametric mean shift. Proper selection of the kernel bandwidth is a critical step for convergence speed and accuracy of matching method. Variable bandwidth with adaptive structure from motion feature for panoramic image matching method has been proposed in this work. First the bandwidth matrix is defined using the locally adaptive spatial structure of the sampling point in spatial domain and optical flow domain. The relaxation diffusion process of structure from motion similarity feature is described by distance weighting method of local optical flow feature vector. Then the expression form of adaptive multivariate kernel density function is given out, and discusses the solution of the mean shift vector, termination conditions, and the seed point selection method. The final fusions of multi-scale SIFT the features and structure features to establish a unified panoramic image matching framework. The sphere panoramic images from vehicle-borne mobile measurement system are chosen such that a comparison analysis between fixed bandwidth and adaptive bandwidth is carried out in detail. The results show that adaptive bandwidth is good for case with the inlier ratio changes and the object space scale changes. The proposed method can realize the adaptive similarity measure of structure from motion feature, improves the correct matching points and matching rate, experimental results have shown our method to be robust.

Model features as the basis of preparation of boxers individualization principal level (elite

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O.J. Pavelec

2013-10-01

Full Text Available Purpose to improve the system of training boxers of higher categories (elite. Individualization of the training process using the model characteristics special physical preparedness. Materials : The study was conducted during 2000-2010. Participated boxers national team of Ukraine in the amount of 43 people. Of those honored masters of sport 6, masters of sports of international class 16, masters of sports 21. The average age of the athletes 23.5 years. Results : justified and features a specially designed model of physical fitness boxing class. It is established that the boxers middle weight classes (64 75 kg have an advantage over other boxers weight categories (light and after a hard in the development of speed and strength endurance. The presented model characteristics can guide the professional fitness boxing (elite, as representatives of the sport. Conclusions : It is established that the structure of the special physical training boxers depends on many components, such as weight category, tactical fighter role, skill level, stage of preparation.

Microhabitat features influencing habitat use by Florida black bears

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Dana L. Karelus

2018-01-01

, Microhabitat, Principal components analysis, Compositional features of microhabitat, Structural features of microhabitat, Ursus americanus, Vegetation sampling

Structural conceptual models of water-conducting features at Aespoe

International Nuclear Information System (INIS)

Bossart, P.; Mazurek, M.; Hermansson, Jan

1998-01-01

Within the framework of the Fracture Classification and Characterization Project (FCC), water conducting features (WCF) in the Aespoe tunnel system and on the surface of Aespoe Island are being characterized over a range of scales. The larger-scale hierarchies of WCF are mostly constituted of fault arrays, i.e. brittle structures that accommodated episodes of shear strain. The smaller-scale WCF (contained within blocks 1 m. Structural evidence indicates that the fractures within the TRUE-1 block constitute an interconnected system with a pronounced anisotropy

Analysis of Conserved Structural Features of Selenoprotein K | Al ...

African Journals Online (AJOL)

Selenium plays important roles in human health and these roles may be exerted through its presence in selenoproteins. Among the 25 selenoproteins in human is selenoprotein K (SelK) whose exact function is still unclear. Here, we investigated the conserved structural features of SelK using bioinformatics as an approach ...

The research of structural features of astralens - nanodimensional carbon particles of fulleroid type

International Nuclear Information System (INIS)

Ponomarev, A.N.; Nikitin, V.A.; Rybalko, V.V.

2006-01-01

The article is focused on the research of structural features of astralens - nanodimensional carbonic particles of fulleroid type. Astralens are perspective nanomodificators of properties of materials of different types. The potentials os astralens as modificators depend on their characteristic structural features, and in the first place, on the distribution of nanoparticles by sizes. The typical dimensions of astralens are determined to be within the range of 15-75 nm [ru

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.

Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

Directory of Open Access Journals (Sweden)

Taigang Liu

2015-12-01

Full Text Available The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE. These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

Oral features of a family with benign familial neutropenia.

Science.gov (United States)

Porter, S R; Luker, J; Scully, C; Oakhill, A

1994-05-01

The oral features of three members of a family with familial benign neutropenia (a mother and two children) are detailed. Prepubertal periodontitis, oral ulceration, and angular stomatitis were the principal features.

Structural features of subtype-selective EP receptor modulators.

Science.gov (United States)

Markovič, Tijana; Jakopin, Žiga; Dolenc, Marija Sollner; Mlinarič-Raščan, Irena

2017-01-01

Prostaglandin E2 is a potent endogenous molecule that binds to four different G-protein-coupled receptors: EP1-4. Each of these receptors is a valuable drug target, with distinct tissue localisation and signalling pathways. We review the structural features of EP modulators required for subtype-selective activity, as well as the structural requirements for improved pharmacokinetic parameters. Novel EP receptor subtype selective agonists and antagonists appear to be valuable drug candidates in the therapy of many pathophysiological states, including ulcerative colitis, glaucoma, bone healing, B cell lymphoma, neurological diseases, among others, which have been studied in vitro, in vivo and in early phase clinical trials. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Structural features of nitroaromatics that determine mutagenic activity in Salmonella typhimurium

International Nuclear Information System (INIS)

Vance, W.A.; Levin, D.E.

1984-01-01

Seventeen structurally homologous nitroaromatics were tested for direct-acting mutagenic potency in nine strains of Salmonella typhimurium. The following four structural features were determined to have a strong influence on mutagenic activity: physical dimensions of the aromatic rings, isomeric position of the nitro group, conformation of the nitro group with respect to the plane of the aromatic rings, and ability to resonance-stabilize the utimate electrophile. Progressive addition of five- and six-membered rings to a nitrobenzene nucleus demonstrated that mutagenic activity was a direct function of size. Nitroaromatics with a nitro group oriented along the long axis of symmetry of the molecule were more potent mutagens that those with the nitro group oriented along the short axis. These results are discussed in light of the insertion-denaturation model for intercalation of certain DNA adducts. Finally, structural features that contribute to resonance stabilization of the reactive nitrenium ion enhance mutagenic potency. The predictive value of these structure-activity relationships should permit a first approximation in the assessment of mutagenic potency of nitroaromatics

In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features.

Science.gov (United States)

Ding, Yiliang; Tang, Yin; Kwok, Chun Kit; Zhang, Yu; Bevilacqua, Philip C; Assmann, Sarah M

2014-01-30

RNA structure has critical roles in processes ranging from ligand sensing to the regulation of translation, polyadenylation and splicing. However, a lack of genome-wide in vivo RNA structural data has limited our understanding of how RNA structure regulates gene expression in living cells. Here we present a high-throughput, genome-wide in vivo RNA structure probing method, structure-seq, in which dimethyl sulphate methylation of unprotected adenines and cytosines is identified by next-generation sequencing. Application of this method to Arabidopsis thaliana seedlings yielded the first in vivo genome-wide RNA structure map at nucleotide resolution for any organism, with quantitative structural information across more than 10,000 transcripts. Our analysis reveals a three-nucleotide periodic repeat pattern in the structure of coding regions, as well as a less-structured region immediately upstream of the start codon, and shows that these features are strongly correlated with translation efficiency. We also find patterns of strong and weak secondary structure at sites of alternative polyadenylation, as well as strong secondary structure at 5' splice sites that correlates with unspliced events. Notably, in vivo structures of messenger RNAs annotated for stress responses are poorly predicted in silico, whereas mRNA structures of genes related to cell function maintenance are well predicted. Global comparison of several structural features between these two categories shows that the mRNAs associated with stress responses tend to have more single-strandedness, longer maximal loop length and higher free energy per nucleotide, features that may allow these RNAs to undergo conformational changes in response to environmental conditions. Structure-seq allows the RNA structurome and its biological roles to be interrogated on a genome-wide scale and should be applicable to any organism.

On combining principal components with Fisher's linear discriminants for supervised learning

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.

2006-01-01

"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic increase of computational complexity and classification error in high dimensions. In this paper, principal component analysis (PCA), parametric feature extraction (FE) based on Fisher’s linear

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.

Organization Features and School Performance

OpenAIRE

Atkins, Lois Major

2005-01-01

The purpose of this study was to determine the odds of school organization features predicting schools meeting district or state performance goals. The school organization features were organizational complexity, shared decision making, and leadership behavior. The dependent variable was school performance, operationally defined as a principalâ s yes response or no response to the question, â did your school meet district or state performance goals.â The independent variables representing...

Ownership structures of principal petroleum companies in Canada: company profiles - significant events - takeovers and acquisitions

International Nuclear Information System (INIS)

Anon.

1997-01-01

This reference document on ownership structures of principal petroleum companies identifies 'who owns whom' in the Canadian petroleum industry. The publication consists of three chapters. Chapter one, entitled 'Corporate Structures' includes the equity linkages between the energy enterprise and its parents and subsidiaries, names of directors and officers of the company and their ownership of voting shares. Chapter two under the title of 'Significant Events', provides company incorporation and listing data, outlining information on address of the company's head office, the nature of its business, number of employees in Canada, and stock exchanges on which the company equity is listed, stock symbol, high, low and closing prices as of December 31, 1996. Chapter three, entitled 'Takeovers and Acquisitions 1976-1997, provides a list of purchases, mergers and acquisitions and the estimated value of each, where applicable. All information included is provided by the companies themselves

Structure in the interstellar polarization curve and the nature of the polarizing grains

International Nuclear Information System (INIS)

Wolstencroft, R.D.; Smith, R.J.

1984-01-01

At this workshop the emphasis is on divining the nature of the interstellar grains by using infrared spectral features as the principal diagnostic. Nevertheless other approaches are also contributing to an understanding of the grains and deserve some attention. This paper describes the structure recently found in the interstellar polarization curve, and discusses its relation to the structure seen in the extinction curve and the nature of the grains producing the spectral features. (author)

Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS).

Science.gov (United States)

Kahan, Tracey L; Claudatos, Stephanie

2016-04-01

Self-ratings of dream experiences were obtained from 144 college women for 788 dreams, using the Subjective Experiences Rating Scale (SERS). Consistent with past studies, dreams were characterized by a greater prevalence of vision, audition, and movement than smell, touch, or taste, by both positive and negative emotion, and by a range of cognitive processes. A Principal Components Analysis of SERS ratings revealed ten subscales: four sensory, three affective, one cognitive, and two structural (events/actions, locations). Correlations (Pearson r) among subscale means showed a stronger relationship among the process-oriented features (sensory, cognitive, affective) than between the process-oriented and content-centered (structural) features--a pattern predicted from past research (e.g., Bulkeley & Kahan, 2008). Notably, cognition and positive emotion were associated with a greater number of other phenomenal features than was negative emotion; these findings are consistent with studies of the qualitative features of waking autobiographical memory (e.g., Fredrickson, 2001). Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

Science.gov (United States)

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

2018-02-01

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

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Science.gov (United States)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Structure and Principal Components Analyses Reveal an Intervarietal Fusion in Malaysian Mistletoe Fig (Ficus deltoidea Jack Populations

Directory of Open Access Journals (Sweden)

Birifdzi Zimisuhara

2015-06-01

Full Text Available Genetic structure and biodiversity of the medicinal plant Ficus deltoidea have rarely been scrutinized. To fill these lacunae, five varieties, consisting of 30 F. deltoidea accessions were collected across the country and studied on the basis of molecular and morphological data. Molecular analysis of the accessions was performed using nine Inter Simple Sequence Repeat (ISSR markers, seven of which were detected as polymorphic markers. ISSR-based clustering generated four clusters supporting the geographical distribution of the accessions to some extent. The Jaccard’s similarity coefficient implied the existence of low diversity (0.50–0.75 in the studied population. STRUCTURE analysis showed a low differentiation among the sampling sites, while a moderate varietal differentiation was unveiled with two main populations of F. deltoidea. Our observations confirmed the occurrence of gene flow among the accessions; however, the highest degree of this genetic interference was related to the three accessions of FDDJ10, FDTT16 and FDKT25. These three accessions may be the genetic intervarietal fusion points of the plant’s population. Principal Components Analysis (PCA relying on quantitative morphological characteristics resulted in two principal components with Eigenvalue >1 which made up 89.96% of the total variation. The cluster analysis performed by the eight quantitative characteristics led to grouping the accessions into four clusters with a Euclidean distance ranged between 0.06 and 1.10. Similarly, a four-cluster dendrogram was generated using qualitative traits. The qualitative characteristics were found to be more discriminating in the cluster and PCA analyses, while ISSRs were more informative on the evolution and genetic structure of the population.

Evaluation of physical structural features on influencing enzymatic hydrolysis efficiency of micronized wood

Science.gov (United States)

Jinxue Jiang; Jinwu Wang; Xiao Zhang; Michael Wolcott

2016-01-01

Enzymatic hydrolysis of lignocellulosic biomass is highly dependent on the changes in structural features after pretreatment. Mechanical milling pretreatment is an effective approach to alter the physical structure of biomass and thus improve enzymatic hydrolysis. This study examined the influence of structural characteristics on the enzymatic hydrolysis of micronized...

Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries.

Science.gov (United States)

Wenderski, Todd A; Stratton, Christopher F; Bauer, Renato A; Kopp, Felix; Tan, Derek S

2015-01-01

Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design.

The role of emotion in musical improvisation: an analysis of structural features.

Science.gov (United States)

McPherson, Malinda J; Lopez-Gonzalez, Monica; Rankin, Summer K; Limb, Charles J

2014-01-01

One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor) and emotion (happy vs. sad) do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not utilize any universal rules to convey emotions, but would instead combine heterogeneous musical elements together in order to depict positive and negative emotions. Our findings demonstrate a lack of simple correspondence between emotions and musical features of spontaneous musical improvisation. While improvisations in response to positive emotional cues were more likely to be in major keys, have faster tempos, faster key press velocities and more staccato notes when compared to negative improvisations, there was a wide distribution for each emotion with components that directly violated these primary associations. The finding that musicians often combine disparate features together in order to convey emotion during improvisation suggests that structural diversity may be an essential feature of the ability of music to express a wide range of emotion.

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.

Fault diagnosis of rotating machine by isometric feature mapping

International Nuclear Information System (INIS)

Zhang, Yun; Li, Benwei; Wang, Lin; Wang, Wen; Wang, Zibin

2013-01-01

Principal component analysis (PCA) and linear discriminate analysis (LDA) are well-known linear dimensionality reductions for fault classification. However, since they are linear methods, they perform not well for high-dimensional data that has the nonlinear geometric structure. As kernel extension of PCA, Kernel PCA is used for nonlinear fault classification. However, the performance of Kernel PCA largely depends on its kernel function which can only be empirically selected from finite candidates. Thus, a novel rotating machine fault diagnosis approach based on geometrically motivated nonlinear dimensionality reduction named isometric feature mapping (Isomap) is proposed. The approach can effectively extract the intrinsic nonlinear manifold features embedded in high-dimensional fault data sets. Experimental results with rotor and rolling bearing data show that the proposed approach overcomes the flaw of conventional fault pattern recognition approaches and obviously improves the fault classification performance.

SVM-based glioma grading. Optimization by feature reduction analysis

International Nuclear Information System (INIS)

Zoellner, Frank G.; Schad, Lothar R.; Emblem, Kyrre E.; Harvard Medical School, Boston, MA; Oslo Univ. Hospital

2012-01-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∝87%) while reducing the number of features by up to 98%. (orig.)

SVM-based glioma grading. Optimization by feature reduction analysis

Energy Technology Data Exchange (ETDEWEB)

Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

2012-11-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

Structural characterization of the principal mRNA-export factor Mex67–Mtr2 from Chaetomium thermophilum

Energy Technology Data Exchange (ETDEWEB)

Aibara, Shintaro; Valkov, Eugene; Lamers, Meindert H. [MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH (United Kingdom); Dimitrova, Lyudmila; Hurt, Ed [Biochemie-Zentrum der Universität Heidelberg, Im Neuenheimer Feld 328, 69120 Heidelberg (Germany); Stewart, Murray, E-mail: ms@mrc-lmb.cam.ac.uk [MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH (United Kingdom)

2015-06-27

The crystal structures of the individual domains of the Mex67–Mtr2 complex from C. thermophilum have been determined and their arrangement in solution has been studied by SAXS. Members of the Mex67–Mtr2/NXF–NXT1 family are the principal mediators of the nuclear export of mRNA. Mex67/NXF1 has a modular structure based on four domains (RRM, LRR, NTF2-like and UBA) that are thought to be present across species, although the level of sequence conservation between organisms, especially in lower eukaryotes, is low. Here, the crystal structures of these domains from the thermophilic fungus Chaetomium thermophilum are presented together with small-angle X-ray scattering (SAXS) and in vitro RNA-binding data that indicate that, not withstanding the limited sequence conservation between different NXF family members, the molecules retain similar structural and RNA-binding properties. Moreover, the resolution of crystal structures obtained with the C. thermophilum domains was often higher than that obtained previously and, when combined with solution and biochemical studies, provided insight into the structural organization, self-association and RNA-binding properties of Mex67–Mtr2 that facilitate mRNA nuclear export.

Structural characterization of the principal mRNA-export factor Mex67–Mtr2 from Chaetomium thermophilum

International Nuclear Information System (INIS)

Aibara, Shintaro; Valkov, Eugene; Lamers, Meindert H.; Dimitrova, Lyudmila; Hurt, Ed; Stewart, Murray

2015-01-01

The crystal structures of the individual domains of the Mex67–Mtr2 complex from C. thermophilum have been determined and their arrangement in solution has been studied by SAXS. Members of the Mex67–Mtr2/NXF–NXT1 family are the principal mediators of the nuclear export of mRNA. Mex67/NXF1 has a modular structure based on four domains (RRM, LRR, NTF2-like and UBA) that are thought to be present across species, although the level of sequence conservation between organisms, especially in lower eukaryotes, is low. Here, the crystal structures of these domains from the thermophilic fungus Chaetomium thermophilum are presented together with small-angle X-ray scattering (SAXS) and in vitro RNA-binding data that indicate that, not withstanding the limited sequence conservation between different NXF family members, the molecules retain similar structural and RNA-binding properties. Moreover, the resolution of crystal structures obtained with the C. thermophilum domains was often higher than that obtained previously and, when combined with solution and biochemical studies, provided insight into the structural organization, self-association and RNA-binding properties of Mex67–Mtr2 that facilitate mRNA nuclear export

Comparative study of trusses to determine the influence of the geometry in the structural efficiency, according to the directions of the principal stresses

OpenAIRE

Señís López, Roger; Brufau Niubó, Roberto; Sastre Sastre, Ramon; Carbajal Navarro, Eusebio Carlos

2015-01-01

This study compares flat lattice girders mounted on two supports, based on various design parameters, to determine which have better structural performance and what geometries are more efficient. The fundamental goal is to determine the relationship of performance and structural behavior of each type of framework structure, with respect to the principle of optimization and improvement in the efficiency of the trusses if their geometry adapts to the directions of the principal s...

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

Science.gov (United States)

Eckman, Ellen Wexler; Kelber, Sheryl Talcott

2010-01-01

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

Photonic crystals based on opals and inverse opals: synthesis and structural features

International Nuclear Information System (INIS)

Klimonsky, S O; Abramova, Vera V; Sinitskii, Alexander S; Tretyakov, Yuri D

2011-01-01

Methods of synthesis of photonic crystals based on opals and inverse opals are considered. Their structural features are discussed. Data on different types of structural defects and their influence on the optical properties of opaline materials are systematized. The possibilities of investigation of structural defects by optical spectroscopy, electron microscopy, microradian X-ray diffraction, laser diffraction and using an analysis of Kossel ring patterns are described. The bibliography includes 253 references.

Band structure in 104Ag

International Nuclear Information System (INIS)

Goswami, A.; Saha Sarkar, M.; Datta Pramanik, U.; Banerjee, P.; Basu, P.; Bhattacharya, P.; Bhattacharya, S.; Chatterjee, M.L.; Sen, S.; Dasmahapatra, B.

1995-01-01

The level structure of 104 Ag has been studied through the 103 Rh(α,3nγ) reaction at E α =40 and 45 MeV. The principal features of the proposed level scheme are in agreement with those obtained earlier through heavy ion reaction. A two-quasiparticle-plus-rotor model calculation has been performed, and the results are compared with experimental data. (orig.)

Using Bureaucratic and Cultural Linkages to Improve Instruction: The Principal's Contribution.

Science.gov (United States)

Firestone, William A.; Wilson, Bruce L.

1985-01-01

Principals can influence teachers and instructional behavior by working through linkage mechanisms within the organizational structure of the school. Two types of linkages are identified: bureaucratic and cultural. Principals have access to linkages of both kinds; using linkages effectively, they can generate a common purpose in their schools. (MD)

Aeromagnetic Compensation Algorithm Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

Peilin Wu

2018-01-01

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

Quantification of Lignin and Its Structural Features in Plant Biomass Using

NARCIS (Netherlands)

Erven, Van Gijs; Visser, de Ries; Merkx, Donny W.H.; Strolenberg, Willem; Gijsel, de Peter; Gruppen, Harry; Kabel, Mirjam A.

2017-01-01

Understanding the mechanisms underlying plant biomass recalcitrance at the molecular level can only be achieved by accurate analyses of both the content and structural features of the molecules involved. Current quantification of lignin is, however, majorly based on unspecific gravimetric

Employing Eigenvalue Ratios to Generate Prior Fracture-like Features for Stochastic Hydrogeophysical Characterization of a Fractured Aquifer System

Science.gov (United States)

Brewster, J.; Oware, E. K.

2017-12-01

Groundwater hosted in fractured rocks constitutes almost 65% of the principal aquifers in the US. The exploitation and contaminant management of fractured aquifers require fracture flow and transport modeling, which in turn requires a detailed understanding of the structure of the aquifer. The widely used equivalent porous medium approach to modeling fractured aquifer systems is inadequate to accurately predict fracture transport processes due to the averaging of the sharp lithological contrast between the matrix and the fractures. The potential of geophysical imaging (GI) to estimate spatially continuous subsurface profiles in a minimally invasive fashion is well proven. Conventional deterministic GI strategies, however, produce geologically unrealistic, smoothed-out results due to commonly enforced smoothing constraints. Stochastic GI of fractured aquifers is becoming increasing appealing due to its ability to recover realistic fracture features while providing multiple likely realizations that enable uncertainty assessment. Generating prior spatial features consistent with the expected target structures is crucial in stochastic imaging. We propose to utilize eigenvalue ratios to resolve the elongated fracture features expected in a fractured aquifer system. Eigenvalues capture the major and minor directions of variability in a region, which can be employed to evaluate shape descriptors, such as eccentricity (elongation) and orientation of features in the region. Eccentricity ranges from zero to one, representing a circularly sharped to a line feature, respectively. Here, we apply eigenvalue ratios to define a joint objective parameter consisting of eccentricity (shape) and direction terms to guide the generation of prior fracture-like features in some predefined principal directions for stochastic GI. Preliminary unconditional, synthetic experiments reveal the potential of the algorithm to simulate prior fracture-like features. We illustrate the strategy with a

Structural and compositional features of high-rise buildings: experimental design in Yekaterinburg

Science.gov (United States)

Yankovskaya, Yulia; Lobanov, Yuriy; Temnov, Vladimir

2018-03-01

The study looks at the specifics of high-rise development in Yekaterinburg. High-rise buildings are considered in the context of their historical development, structural features, compositional and imaginative design techniques. Experience of Yekaterinburg architects in experimental design is considered and analyzed. Main issues and prospects of high-rise development within the Yekaterinburg structure are studied. The most interesting and significant conceptual approaches to the structural and compositional arrangement of high-rise buildings are discussed.

Principal axis-based correspondence between multiple cameras for people tracking.

Science.gov (United States)

Hu, Weiming; Hu, Min; Zhou, Xue; Tan, Tieniu; Lou, Jianguang; Maybank, Steve

2006-04-01

Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of pairs of principal axes of people is constructed according to the relationship between "ground-points" of people detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: 1) Camera calibration is not needed. 2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. 3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency, and robustness of our method.

How To Select a Good Assistant Principal.

Science.gov (United States)

Holman, Linda J.

1997-01-01

Notes that a well-structured job profile and interview can provide insight into the key qualities of an effective assistant principal. These include organizational skills, basic accounting knowledge, interpersonal skills, dependability, strong work ethic, effective problem-solving skills, leadership skills, written communication skills,…

The role of emotion in musical improvisation: an analysis of structural features.

Directory of Open Access Journals (Sweden)

Malinda J McPherson

Full Text Available One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor and emotion (happy vs. sad do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not utilize any universal rules to convey emotions, but would instead combine heterogeneous musical elements together in order to depict positive and negative emotions. Our findings demonstrate a lack of simple correspondence between emotions and musical features of spontaneous musical improvisation. While improvisations in response to positive emotional cues were more likely to be in major keys, have faster tempos, faster key press velocities and more staccato notes when compared to negative improvisations, there was a wide distribution for each emotion with components that directly violated these primary associations. The finding that musicians often combine disparate features together in order to convey emotion during improvisation suggests that structural diversity may be an essential feature of the ability of music to express a wide range of emotion.

Principal Component Analysis - A Powerful Tool in Computing Marketing Information

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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.

Structure and origin of Australian ring and dome features with reference to the search for asteroid impact events

Science.gov (United States)

Glikson, Andrew

2018-01-01

Ring, dome and crater features on the Australian continent and shelf include (A) 38 structures of confirmed or probable asteroid and meteorite impact origin and (B) numerous buried and exposed ring, dome and crater features of undefined origin. A large number of the latter include structural and geophysical elements consistent with impact structures, pending test by field investigations and/or drilling. This paper documents and briefly describes 43 ring and dome features with the aim of appraising their similarities and differences from those of impact structures. Discrimination between impact structures and igneous plugs, volcanic caldera and salt domes require field work and/or drilling. Where crater-like morphological patterns intersect pre-existing linear structural features and contain central morphological highs and unique thrust and fault patterns an impact connection needs to tested in the field. Hints of potential buried impact structures may be furnished by single or multi-ring TMI patterns, circular TMI quiet zones, corresponding gravity patterns, low velocity and non-reflective seismic zones.

New Equations for Calculating Principal and Fine-Structure Atomic Spectra for Single and Multi-Electron Atoms

Energy Technology Data Exchange (ETDEWEB)

Surdoval, Wayne A. [National Energy Technology Lab. (NETL), Pittsburgh, PA, (United States); Berry, David A. [National Energy Technology Lab. (NETL), Morgantown, WV (United States); Shultz, Travis R. [National Energy Technology Lab. (NETL), Morgantown, WV (United States)

2018-03-09

A set of equations are presented for calculating atomic principal spectral lines and fine-structure energy splits for single and multi-electron atoms. Calculated results are presented and compared to the National Institute of Science and Technology database demonstrating very good accuracy. The equations do not require fitted parameters. The only experimental parameter required is the Ionization energy for the electron of interest. The equations have comparable accuracy and broader applicability than the single electron Dirac equation. Three Appendices discuss the origin of the new equations and present calculated results. New insights into the special relativistic nature of the Dirac equation and its relationship to the new equations are presented.

Critical Features of Fragment Libraries for Protein Structure Prediction.

Science.gov (United States)

Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

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.

Process Features in Writing: Internal Structure and Incremental Value over Product Features. Research Report. ETS RR-15-27

Science.gov (United States)

Zhang, Mo; Deane, Paul

2015-01-01

In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…

Empowering principals to lead and manage public schools ...

African Journals Online (AJOL)

Mestry, Rajkumar

preparing principals for the educational demands of life and work in the 21st century (Bush, 2005; Russell &. Cranston, 2012). .... the heart of raising standards of teaching, learning ..... the school to work as a collaborative structure … it.

Common structural features of cholesterol binding sites in crystallized soluble proteins.

Science.gov (United States)

Bukiya, Anna N; Dopico, Alejandro M

2017-06-01

Cholesterol-protein interactions are essential for the architectural organization of cell membranes and for lipid metabolism. While cholesterol-sensing motifs in transmembrane proteins have been identified, little is known about cholesterol recognition by soluble proteins. We reviewed the structural characteristics of binding sites for cholesterol and cholesterol sulfate from crystallographic structures available in the Protein Data Bank. This analysis unveiled key features of cholesterol-binding sites that are present in either all or the majority of sites: i ) the cholesterol molecule is generally positioned between protein domains that have an organized secondary structure; ii ) the cholesterol hydroxyl/sulfo group is often partnered by Asn, Gln, and/or Tyr, while the hydrophobic part of cholesterol interacts with Leu, Ile, Val, and/or Phe; iii ) cholesterol hydrogen-bonding partners are often found on α-helices, while amino acids that interact with cholesterol's hydrophobic core have a slight preference for β-strands and secondary structure-lacking protein areas; iv ) the steroid's C21 and C26 constitute the "hot spots" most often seen for steroid-protein hydrophobic interactions; v ) common "cold spots" are C8-C10, C13, and C17, at which contacts with the proteins were not detected. Several common features we identified for soluble protein-steroid interaction appear evolutionarily conserved. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

Relevance feature selection of modal frequency-ambient condition pattern recognition in structural health assessment for reinforced concrete buildings

Directory of Open Access Journals (Sweden)

He-Qing Mu

2016-08-01

Full Text Available Modal frequency is an important indicator for structural health assessment. Previous studies have shown that this indicator is substantially affected by the fluctuation of ambient conditions, such as temperature and humidity. Therefore, recognizing the pattern between modal frequency and ambient conditions is necessary for reliable long-term structural health assessment. In this article, a novel machine-learning algorithm is proposed to automatically select relevance features in modal frequency-ambient condition pattern recognition based on structural dynamic response and ambient condition measurement. In contrast to the traditional feature selection approaches by examining a large number of combinations of extracted features, the proposed algorithm conducts continuous relevance feature selection by introducing a sophisticated hyperparameterization on the weight parameter vector controlling the relevancy of different features in the prediction model. The proposed algorithm is then utilized for structural health assessment for a reinforced concrete building based on 1-year daily measurements. It turns out that the optimal model class including the relevance features for each vibrational mode is capable to capture the pattern between the corresponding modal frequency and the ambient conditions.

Features Of Household Lexics, Their Characteristics And Structural Analysis In The Modern English Language

Directory of Open Access Journals (Sweden)

Aygun Yusifova

2014-04-01

Full Text Available The present paper aims to analyze the most inherent features and characteristics of household lexis in English. Special emphasis has been placed on their names of the objects used in everyday life, kitchen utensils, animal and birds. Lexical units concerning ceremonies, habits and traditions are also among the scope of the paper. Moreover, the study deals with the structural features of the units under consideration. It is believed that the thematic-semantic characterization of every-day lexis can have both pedagogical and linguistic implications, especially when dealing with comparative structures.

Regularized principal covariates regression and its application to finding coupled patterns in climate fields

Science.gov (United States)

Fischer, M. J.

2014-02-01

There are many different methods for investigating the coupling between two climate fields, which are all based on the multivariate regression model. Each different method of solving the multivariate model has its own attractive characteristics, but often the suitability of a particular method for a particular problem is not clear. Continuum regression methods search the solution space between the conventional methods and thus can find regression model subspaces that mix the attractive characteristics of the end-member subspaces. Principal covariates regression is a continuum regression method that is easily applied to climate fields and makes use of two end-members: principal components regression and redundancy analysis. In this study, principal covariates regression is extended to additionally span a third end-member (partial least squares or maximum covariance analysis). The new method, regularized principal covariates regression, has several attractive features including the following: it easily applies to problems in which the response field has missing values or is temporally sparse, it explores a wide range of model spaces, and it seeks a model subspace that will, for a set number of components, have a predictive skill that is the same or better than conventional regression methods. The new method is illustrated by applying it to the problem of predicting the southern Australian winter rainfall anomaly field using the regional atmospheric pressure anomaly field. Regularized principal covariates regression identifies four major coupled patterns in these two fields. The two leading patterns, which explain over half the variance in the rainfall field, are related to the subtropical ridge and features of the zonally asymmetric circulation.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

Science.gov (United States)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

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.

Structural features that optimize high temperature superconductivity

International Nuclear Information System (INIS)

Jorgensen, J.D.; Argonne Nat. Lab., IL; Hinks, D.G.; Argonne Nat. Lab., IL; Chmaissem, O.; Argonne Nat. Lab., IL; Argyriou, D.N.; Argonne Nat. Lab., IL; Mitchell, J.F.; Argonne Nat. Lab., IL; Dabrowski, B.

1996-01-01

Studies of a large number of compounds have provided a consistent picture of what structural features give rise to the highest T c 's in copper-oxide superconductors. For example, various defects can be introduced into the blocking layer to provide the optimum carrier concentration, but defects that form in or adjacent to the CuO 2 layers will lower T c and eventually destroy superconductivity. After these requirements are satisfied, the highest T c 's are observed for compounds (such as the HgBa 2 Ca n-1 Cu n O 2n+2+x family) that have flat and square CuO 2 planes and long apical Cu-O bonds. This conclusion is confirmed by the study of materials in which the flatness of the CuO 2 plane can be varied in a systematic way. In more recent work, attention has focused on how the structure can be modified, for example, by chemical substitution, to improve flux pinning properties. Two strategies are being investigated: (1) Increasing the coupling of pancake vortices to form vortex lines by shortening or ''metallizing'' the blocking layer; and (2) the formation of defects that pin flux. (orig.)

The semiology of febrile seizures: Focal features are frequent.

Science.gov (United States)

Takasu, Michihiko; Kubota, Tetsuo; Tsuji, Takeshi; Kurahashi, Hirokazu; Numoto, Shingo; Watanabe, Kazuyoshi; Okumura, Akihisa

2017-08-01

To clarify the semiology of febrile seizures (FS) and to determine the frequency of FS with symptoms suggestive of focal onset. FS symptoms in children were reported within 24h of seizure onset by the parents using a structured questionnaire consisting principally of closed-ended questions. We focused on events at seizure commencement, including changes in behavior and facial expression, and ocular and oral symptoms. We also investigated the autonomic and motor symptoms developing during seizures. The presence or absence of focal and limbic features was determined for each patient. The associations of certain focal and limbic features with patient characteristics were assessed. Information was obtained on FS in 106 children. Various events were recorded at seizure commencement. Behavioral changes were observed in 35 children, changes in facial expression in 53, ocular symptoms in 78, and oral symptoms in 90. In terms of events during seizures, autonomic symptoms were recognized in 78, and convulsive motor symptoms were recognized in 68 children. Focal features were evident in 81 children; 38 children had two or more such features. Limbic features were observed in 44 children, 9 of whom had two or more such features. There was no significant relationship between any patient characteristic and the numbers of focal or limbic features. The semiology of FS varied widely among children, and symptoms suggestive of focal onset were frequent. FS of focal onset may be more common than is generally thought. Copyright © 2017 Elsevier Inc. All rights reserved.

Creation of Principally New Generation of Switching Technique Elements (Reed Switches) with Nanostructured Contact Surfaces

Energy Technology Data Exchange (ETDEWEB)

Karabanov S M; Zeltser I A; Maizels R M; Moos E N; Arushanov K A, E-mail: zeltseria@rmcip.ru [Russia, Ryazan, 390027, Novaya Str., 51B, Ryazan Metal Ceramics Instrumentation Plant JSC (Russian Federation)

2011-04-01

The cycle of activities of the creation of principally new generation of reed switches with nanostructured contact surfaces was implemented. Experimental justification of the opportunity of reed switches creation with modified contact surface was given (instead of precious metals-based galvanic coating). Principally new technological process of modification of magnetically operated contacts contacting surfaces was developed, based on the usage of the ion-plasma methods of nanolayers and nanostructures forming having specified contact features.

Hydrogeologic framework of the uppermost principal aquifer systems in the Williston and Powder River structural basins, United States and Canada

Science.gov (United States)

Thamke, Joanna N.; LeCain, Gary D.; Ryter, Derek W.; Sando, Roy; Long, Andrew J.

2014-01-01

The glacial, lower Tertiary, and Upper Cretaceous aquifer systems in the Williston and Powder River structural basins within the United States and Canada are the uppermost principal aquifer systems and most accessible sources of groundwater for these energy-producing basins. The glacial aquifer system covers the northeastern part of the Williston structural basin. The lower Tertiary and Upper Cretaceous aquifer systems are present in about 91,300 square miles (mi2) of the Williston structural basin and about 25,500 mi2 of the Powder River structural basin. Directly under these aquifer systems are 800 to more than 3,000 feet (ft) of relatively impermeable marine shale that serves as a basal confining unit. The aquifer systems in the Williston structural basin have a shallow (less than 2,900 ft deep), wide, and generally symmetrical bowl shape. The aquifer systems in the Powder River structural basin have a very deep (as much as 8,500 ft deep), narrow, and asymmetrical shape.

Structural damage identification using damping: a compendium of uses and features

Science.gov (United States)

Cao, M. S.; Sha, G. G.; Gao, Y. F.; Ostachowicz, W.

2017-04-01

The vibration responses of structures under controlled or ambient excitation can be used to detect structural damage by correlating changes in structural dynamic properties extracted from responses with damage. Typical dynamic properties refer to modal parameters: natural frequencies, mode shapes, and damping. Among these parameters, natural frequencies and mode shapes have been investigated extensively for their use in damage characterization by associating damage with reduction in local stiffness of structures. In contrast, the use of damping as a dynamic property to represent structural damage has not been comprehensively elucidated, primarily due to the complexities of damping measurement and analysis. With advances in measurement technologies and analysis tools, the use of damping to identify damage is becoming a focus of increasing attention in the damage detection community. Recently, a number of studies have demonstrated that damping has greater sensitivity for characterizing damage than natural frequencies and mode shapes in various applications, but damping-based damage identification is still a research direction ‘in progress’ and is not yet well resolved. This situation calls for an overall survey of the state-of-the-art and the state-of-the-practice of using damping to detect structural damage. To this end, this study aims to provide a comprehensive survey of uses and features of applying damping in structural damage detection. First, we present various methods for damping estimation in different domains including the time domain, the frequency domain, and the time-frequency domain. Second, we investigate the features and applications of damping-based damage detection methods on the basis of two predominant infrastructure elements, reinforced concrete structures and fiber-reinforced composites. Third, we clarify the influential factors that can impair the capability of damping to characterize damage. Finally, we recommend future research directions

Principal considerations in large energy-storage capacitor banks

International Nuclear Information System (INIS)

Kemp, E.L.

1976-01-01

Capacitor banks storing one or more megajoules and costing more than one million dollars have unique problems not often found in smaller systems. Two large banks, Scyllac at Los Alamos and Shiva at Livermore, are used as models of large, complex systems. Scyllac is a 10-MJ, 60-kV theta-pinch system while Shiva is a 20-MJ, 20-kV energy system for laser flash lamps. A number of design principles are emphasized for expediting the design and construction of large banks. The sensitive features of the charge system, the storage system layout, the switching system, the transmission system, and the design of the principal bank components are presented. Project management and planning must involve a PERT chart with certain common features for all the activities. The importance of the budget is emphasized

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

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.

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.

Features of Inner Structure of Placer Gold of the North-Eastern Part Siberian Platform

Science.gov (United States)

Gerasimov, Boris; Zhuravlev, Anatolii; Ivanov, Alexey

2017-12-01

Mineral and raw material base of placer and ore gold is based on prognosis evaluation, which allows to define promising areas regarding gold-bearing deposit prospecting. But there are some difficulties in gold primary source predicting and prospecting at the North-east Siberian platform, because the studied area is overlapped by thick cover of the Cenozoic deposits, where traditional methods of gold deposit prospecting are ineffective. In this connection, detailed study of typomorphic features of placer gold is important, because it contains key genetic information, necessary for development of mineralogical criteria of prognosis evaluation of ore gold content. Authors studied mineralogical-geochemical features of placer gold of the Anabar placer area for 15 years, with a view to identify indicators of gold, typical for different formation types of primary sources. This article presents results of these works. In placer regions, where primary sources of gold are not identified, there is need to study typomorphic features of placer gold, because it contains important genetic information, necessary for the development of mineralogical criteria of prognosis evaluation of ore gold content. Inner structures of gold from the Anabar placer region are studied, as one of the diagnostic typomorphic criteria as described in prominent method, developed by N.V. Petrovskaya [1980]. Etching of gold was carried out using reagent: HCl + HNO3 + FeCl3 × 6H2O + CrO3 +thioureat + water. Identified inner structures wer studied in details by means of scanning electron microscope JEOL JSM-6480LV. Two types of gold are identified according to the features of inner structure of placer gold of the Anabar region. First type - medium-high karat fine, well processed gold with significantly changed inner structure. This gold is allochthonous, which was redeposited many times from ancient intermediate reservoirs to younger deposits. Second type - low-medium karat, poorly rounded gold with

Principals' and Special Education Teachers' Perceptions of Special Education Teachers' Roles and Responsibilities

Science.gov (United States)

Mott, Japhia

2013-01-01

This explanatory mixed methods study focuses on the perceptions of principals and special education teachers about special education teachers' roles and responsibilities. An online survey was conducted with 11 principals and 41 special education teachers (Resource Specialists and Special Day Class teachers). Independent semi-structured interviews…

Some highlights of the Daresbury nuclear structure programme

International Nuclear Information System (INIS)

Gelletly, W.

1984-01-01

The paper concerns the nuclear structure programme at the Daresbury laboratory, United Kingdom. A description is given of the Nuclear Structure Facility (NSF), along with its principal properties and design features. Some of the latest equipment used at the NSF is discussed, including the isotope separator, recoil separator, magnetic spectrometer and gamma-ray detectors. Uses of this equipment at the NSF to study the nuclear properties at high angular momentum and nuclei far from stability, are also described. (U.K.)

Principal bundles on the projective line

Indian Academy of Sciences (India)

M. Senthilkumar (Newgen Imaging) 1461 1996 Oct 15 13:05:22

LetX be a complete nonsingular curve over the algebraic closurek ofk andGa reductive group over k. Let E → X be a principal G-bundle on X. E is said to be semistable if, for every reduction of structure group EP ⊂ E to a maximal parabolic subgroup P of G, we have degree EP (p) ≤ 0, where p is the Lie algebra of P and EP ...

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...

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

The structure of Lactococcus lactis thioredoxin reductase reveals molecular features of photo-oxidative damage

DEFF Research Database (Denmark)

Skjoldager, Nicklas; Bang, Maria Blanner; Rykær, Martin

2017-01-01

The NADPH-dependent homodimeric flavoenzyme thioredoxin reductase (TrxR) provides reducing equivalents to thioredoxin, a key regulator of various cellular redox processes. Crystal structures of photo-inactivated thioredoxin reductase (TrxR) from the Gram-positive bacterium Lactococcus lactis have...... been determined. These structures reveal novel molecular features that provide further insight into the mechanisms behind the sensitivity of this enzyme toward visible light. We propose that a pocket on the si-face of the isoalloxazine ring accommodates oxygen that reacts with photo-excited FAD...... thus be a widespread feature among bacterial TrxR with the described characteristics, which affords applications in clinical photo-therapy of drug-resistant bacteria....

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…

One-step synthesis and structural features of CdS/montmorillonite nanocomposites.

Science.gov (United States)

Han, Zhaohui; Zhu, Huaiyong; Bulcock, Shaun R; Ringer, Simon P

2005-02-24

A novel synthesis method was introduced for the nanocomposites of cadmium sulfide and montmorillonite. This method features the combination of an ion exchange process and an in situ hydrothermal decomposition process of a complex precursor, which is simple in contrast to the conventional synthesis methods that comprise two separate steps for similar nanocomposite materials. Cadmium sulfide species in the composites exist in the forms of pillars and nanoparticles, the crystallized sulfide particles are in the hexagonal phase, and the sizes change when the amount of the complex for the synthesis is varied. Structural features of the nanocomposites are similar to those of the clay host but changed because of the introduction of the sulfide into the clay.

31 CFR 19.995 - Principal.

Science.gov (United States)

2010-07-01

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

22 CFR 208.995 - Principal.

Science.gov (United States)

2010-04-01

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

22 CFR 1006.995 - Principal.

Science.gov (United States)

2010-04-01

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

2 CFR 180.995 - Principal.

Science.gov (United States)

2010-01-01

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

22 CFR 1508.995 - Principal.

Science.gov (United States)

2010-04-01

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

Principal stratification in causal inference.

Science.gov (United States)

Frangakis, Constantine E; Rubin, Donald B

2002-03-01

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

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...

Principal component structure and sport-specific differences in the running one-leg vertical jump.

Science.gov (United States)

Laffaye, G; Bardy, B G; Durey, A

2007-05-01

The aim of this study is to identify the kinetic principal components involved in one-leg running vertical jumps, as well as the potential differences between specialists from different sports. The sample was composed of 25 regional skilled athletes who play different jumping sports (volleyball players, handball players, basketball players, high jumpers and novices), who performed a running one-leg jump. A principal component analysis was performed on the data obtained from the 200 tested jumps in order to identify the principal components summarizing the six variables extracted from the force-time curve. Two principal components including six variables accounted for 78 % of the variance in jump height. Running one-leg vertical jump performance was predicted by a temporal component (that brings together impulse time, eccentric time and vertical displacement of the center of mass) and a force component (who brings together relative peak of force and power, and rate of force development). A comparison made among athletes revealed a temporal-prevailing profile for volleyball players, and a force-dominant profile for Fosbury high jumpers. Novices showed an ineffective utilization of the force component, while handball and basketball players showed heterogeneous and neutral component profiles. Participants will use a jumping strategy in which variables related to either the magnitude or timing of force production will be closely coupled; athletes from different sporting backgrounds will use a jumping strategy that reflects the inherent demands of their chosen sport.

Executive Compensation and Principal-Agent Theory.

OpenAIRE

Garen, John E

1994-01-01

The empirical literature on executive compensation generally fails to specify a model of executive pay on which to base hypotheses regarding its determinants. In contrast, this paper analyzes a simple principal-agent model to determine how well it explains variations in CEO incentive pay and salaries. Many findings are consistent with the basic intuition of principle-agent models that compensation is structured to trade off incentives with insurance. However, statistical significance for some...

Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree

Science.gov (United States)

Li, Fachao; Jin, Chenxia

In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method; Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short); Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.

Fine structure and analytical quantum-defect wave functions

International Nuclear Information System (INIS)

Kostelecky, V.A.; Nieto, M.M.; Truax, D.R.

1988-01-01

We investigate the domain of validity of previously proposed analytical wave functions for atomic quantum-defect theory. This is done by considering the fine-structure splitting of alkali-metal and singly ionized alkaline-earth atoms. The Lande formula is found to be naturally incorporated. A supersymmetric-type integer is necessary for finite results. Calculated splittings correctly reproduce the principal features of experimental values for alkali-like atoms

How Principals and Peers Influence Teaching and Learning

Science.gov (United States)

Supovitz, Jonathan; Sirinides, Philip; May, Henry

2010-01-01

This paper examines the effects of principal leadership and peer teacher influence on teachers' instructional practice and student learning. Using teacher survey and student achievement data from a mid-sized urban southeastern school district in the United States in 2006-2007, the study employs multilevel structural equation modeling to examine…

Hydrotalcites: relation between structural features, basicity and activity in the Wittig reaction

NARCIS (Netherlands)

Sychev, M.V.; Prihod'ko, R.V.; Erdmann, K.; Mangel, A.; Santen, van R.A.

2001-01-01

Carbonate hydrotalcitrs (HTls) were prepared by coprecipitation of metal nitrate salts and Na2CO3. The structural features of noncalcined, calcined and reconstructed materials were characterized by XRD, Al-27 MAS NMR, AAS and N-2 adsorption. If was shown that reconstruction of the calcined HTls due

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…

An Examination of Pay Facets and Referent Groups for Assessing Pay Satisfaction of Male Elementary School Principals

Science.gov (United States)

Young, I. Phillip; Young, Karen Holsey; Okhremtchouk, Irina; Castaneda, Jose Moreno

2009-01-01

Pay satisfaction was assessed according to different facets (pay level, benefits, pay structure, and pay raises) and potential referent groups (teachers and elementary school principals) for a random sample of male elementary school principals. A structural model approach was used that considers facets of the pay process, potential others as…

Integrated Phoneme Subspace Method for Speech Feature Extraction

Directory of Open Access Journals (Sweden)

Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Directory of Open Access Journals (Sweden)

Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Principal Leadership in an Era of Accountability: A Perspective from the Hong Kong Context

Science.gov (United States)

Walker, Allan; Ko, James

2011-01-01

This article presents the findings of a study into the leadership practices of Hong Kong principals working within an environment of increasing accountability. The study set out to investigate the relationships between sets of principal leadership practices and the levels of "alignment," "coherence and structure" and…

Principals' Administrative Styles and Students' Academic Performance in Taraba State Secondary Schools, Nigeria

Science.gov (United States)

Bello, Suleiman; Ibi, Mustapha Baba; Bukar, Ibrahim Bulama

2016-01-01

The study determined the relationship between principals' administrative styles and students' academic performance in Taraba State secondary schools, Nigeria. The objectives of the study were to determine the relationships between initiative structure of leadership styles, consideration structure of leadership styles, participatory structure of…

Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

Directory of Open Access Journals (Sweden)

Mihail Lucian Birsa

2011-10-01

Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

Fourier transform infrared spectroscopy microscopic imaging classification based on spatial-spectral features

Science.gov (United States)

Liu, Lian; Yang, Xiukun; Zhong, Mingliang; Liu, Yao; Jing, Xiaojun; Yang, Qin

2018-04-01

The discrete fractional Brownian incremental random (DFBIR) field is used to describe the irregular, random, and highly complex shapes of natural objects such as coastlines and biological tissues, for which traditional Euclidean geometry cannot be used. In this paper, an anisotropic variable window (AVW) directional operator based on the DFBIR field model is proposed for extracting spatial characteristics of Fourier transform infrared spectroscopy (FTIR) microscopic imaging. Probabilistic principal component analysis first extracts spectral features, and then the spatial features of the proposed AVW directional operator are combined with the former to construct a spatial-spectral structure, which increases feature-related information and helps a support vector machine classifier to obtain more efficient distribution-related information. Compared to Haralick’s grey-level co-occurrence matrix, Gabor filters, and local binary patterns (e.g. uniform LBPs, rotation-invariant LBPs, uniform rotation-invariant LBPs), experiments on three FTIR spectroscopy microscopic imaging datasets show that the proposed AVW directional operator is more advantageous in terms of classification accuracy, particularly for low-dimensional spaces of spatial characteristics.

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 Fusion Approach to Feature Extraction by Wavelet Decomposition and Principal Component Analysis in Transient Signal Processing of SAW Odor Sensor Array

Directory of Open Access Journals (Sweden)

Prashant SINGH

2011-03-01

Full Text Available This paper presents theoretical analysis of a new approach for development of surface acoustic wave (SAW sensor array based odor recognition system. The construction of sensor array employs a single polymer interface for selective sorption of odorant chemicals in vapor phase. The individual sensors are however coated with different thicknesses. The idea of sensor coating thickness variation is for terminating solvation and diffusion kinetics of vapors into polymer up to different stages of equilibration on different sensors. This is expected to generate diversity in information content of the sensors transient. The analysis is based on wavelet decomposition of transient signals. The single sensor transients have been used earlier for generating odor identity signatures based on wavelet approximation coefficients. In the present work, however, we exploit variability in diffusion kinetics due to polymer thicknesses for making odor signatures. This is done by fusion of the wavelet coefficients from different sensors in the array, and then applying the principal component analysis. We find that the present approach substantially enhances the vapor class separability in feature space. The validation is done by generating synthetic sensor array data based on well-established SAW sensor theory.

Price dependence in the principal EU olive oil markets

Energy Technology Data Exchange (ETDEWEB)

Emmanouilides, C.; Fousekis, P.; Grigoriadis, V.

2014-06-01

The objective of this paper is to assess the degree and the structure of price dependence in the principal EU olive oil markets (Spain, Italy and Greece). To this end, it utilizes monthly olive oil price data and the statistical tool of copulas. The empirical results suggest that prices are likely to boom together but not to crash together; this is especially true for the prices of the two most important players, Italy (importer) and Spain (exporter). The finding of asymmetric price co-movements implies that the three principal spatial olive oil markets in the EU cannot be thought of as one great pool. (Author)

Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.

Directory of Open Access Journals (Sweden)

Yang Zhao

Full Text Available GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM and principal components analysis based approach (PCA using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

Directory of Open Access Journals (Sweden)

Namatēvs Ivars

2017-12-01

Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

RE Rooted in Principal's Biography

NARCIS (Netherlands)

ter Avest, Ina; Bakker, C.

2017-01-01

Critical incidents in the biography of principals appear to be steering in their innovative way of constructing InterReligious Education in their schools. In this contribution, the authors present the biographical narratives of 4 principals: 1 principal introducing interreligious education in a

Clustering of immunological, metabolic and genetic features in latent autoimmune diabetes in adults: evidence from principal component analysis.

Science.gov (United States)

Pes, Giovanni Mario; Delitala, Alessandro Palmerio; Errigo, Alessandra; Delitala, Giuseppe; Dore, Maria Pina

2016-06-01

Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers

Dimensionality reduction of collective motion by principal manifolds

Science.gov (United States)

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

2015-01-01

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

The Role of School Principals in Shaping Children's Values.

Science.gov (United States)

Berson, Yair; Oreg, Shaul

2016-12-01

Instilling values in children is among the cornerstones of every society. There is wide agreement that beyond academic teaching, schools play an important role in shaping schoolchildren's character, imparting in them values such as curiosity, achievement, benevolence, and citizenship. Despite the importance of this topic, we know very little about whether and how schools affect children's values. In this large-scale longitudinal study, we examined school principals' roles in the development of children's values. We hypothesized that relationships exist between principals' values and changes in children's values through the mediating effect of the school climate. To test our predictions, we collected data from 252 school principals, 3,658 teachers, and 49,401 schoolchildren. A multilevel structural-equation-modeling analysis yielded overall support for our hypotheses. These findings contribute to understanding the development of children's values and the far-reaching impact of leaders' values. They also demonstrate effects of schools on children beyond those on academic achievement.

nRC: non-coding RNA Classifier based on structural features.

Science.gov (United States)

Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

2017-01-01

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

Magnitude differences in agronomic, chemical, nutritional, and structural features among different varieties of forage corn grown on dry land and irrigated land.

Science.gov (United States)

Xin, Hangshu; Abeysekara, Samen; Zhang, Xuewei; Yu, Peiqiang

2015-03-11

In this study, eight varieties of corn forage grown in semiarid western Canada (including Pioneer P2501, Pioneer P39m26, Pioneer P7443, Hyland HL3085, Hyland HLBaxxos, Hyland HLR219, Hyland HLSR22, and Pickseed Silex BT) were selected to explore the effect of irrigation implementation in comparison with nonirrigation on (1) agronomic characteristics, (2) basic chemical profiles explored by using a near-infrared reflectance (NIR) system, and (3) protein and carbohydrate internal structural parameters revealed by using an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) system. Also, principal component analysis (PCA) was performed on spectroscopic data for clarification of differences in molecular structural makeup among the varieties. The results showed that irrigation treatment significantly increased (P forages. Significant interactions of irrigation treatment and corn variety were observed on most agronomic characteristics (DM yield, T/ha, days to tasseling, days to silking) and crude fiber (CF) and ether extract (EE) contents as well as some spectral data such as cellulosic compounds (CELC) peak intensity, peak ratios of CHO third peak to CELC, α-helix to β-sheet, and CHO third peak to amide I. Additionally, the spectral ratios of chemical functional groups that related to structural and nonstructural carbohydrates and protein polymers in forages did not remain constant over corn varieties cultivated with and without water treatment. Moreover, different cultivars had different growth, structure, and nutrition performances in this study. Although significant differences could be found in peak intensities, PCA results indicated some structural similarities existed between two treated corn forages with the exception of HL3085 and HLBaxxos. In conclusion, irrigation and corn variety had interaction effects on agronomic, chemical, nutritional, and structural features. Further study on the optimum level of irrigation for corn forage

The sequence, structure and evolutionary features of HOTAIR in mammals

Science.gov (United States)

2011-01-01

. Conclusions HOTAIR exists in mammals, has poorly conserved sequences and considerably conserved structures, and has evolved faster than nearby HoxC genes. Exons of HOTAIR show distinct evolutionary features, and a 239 bp domain in the 1804 bp exon6 is especially conserved. These features, together with the absence of some exons and sequences in mouse, rat and kangaroo, suggest ab initio generation of HOTAIR in marsupials. Structure prediction identifies two fragments in the 5' end exon1 and the 3' end domain B of exon6, with sequence and structure invariably occurring in various predicted structures of exon1, the domain B of exon6 and the full HOTAIR. PMID:21496275

Redesigning Principal Internships: Practicing Principals' Perspectives

Science.gov (United States)

Anast-May, Linda; Buckner, Barbara; Geer, Gregory

2011-01-01

Internship programs too often do not provide the types of experiences that effectively bridge the gap between theory and practice and prepare school leaders who are capable of leading and transforming schools. To help address this problem, the current study is directed at providing insight into practicing principals' views of the types of…

Group-wise Principal Component Analysis for Exploratory Data Analysis

NARCIS (Netherlands)

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

2017-01-01

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

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.

The Future of Principal Evaluation

Science.gov (United States)

Clifford, Matthew; Ross, Steven

2012-01-01

The need to improve the quality of principal evaluation systems is long overdue. Although states and districts generally require principal evaluations, research and experience tell that many state and district evaluations do not reflect current standards and practices for principals, and that evaluation is not systematically administered. When…

Preparing Principals as Instructional Leaders: Perceptions of University Faculty, Expert Principals, and Expert Teacher Leaders

Science.gov (United States)

Taylor Backor, Karen; Gordon, Stephen P.

2015-01-01

Although research has established links between the principal's instructional leadership and student achievement, there is considerable concern in the literature concerning the capacity of principal preparation programs to prepare instructional leaders. This study interviewed educational leadership faculty as well as expert principals and teacher…

School Principals' Emotional Coping Process

Science.gov (United States)

Poirel, Emmanuel; Yvon, Frédéric

2014-01-01

The present study examines the emotional coping of school principals in Quebec. Emotional coping was measured by stimulated recall; six principals were filmed during a working day and presented a week later with their video showing stressful encounters. The results show that school principals experience anger because of reproaches from staff…

Pedestrian count estimation using texture feature with spatial distribution

Directory of Open Access Journals (Sweden)

Hongyu Hu

2016-12-01

Full Text Available We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.

Statistical intercomparison of global climate models: A common principal component approach with application to GCM data

International Nuclear Information System (INIS)

Sengupta, S.K.; Boyle, J.S.

1993-05-01

Variables describing atmospheric circulation and other climate parameters derived from various GCMs and obtained from observations can be represented on a spatio-temporal grid (lattice) structure. The primary objective of this paper is to explore existing as well as some new statistical methods to analyze such data structures for the purpose of model diagnostics and intercomparison from a statistical perspective. Among the several statistical methods considered here, a new method based on common principal components appears most promising for the purpose of intercomparison of spatio-temporal data structures arising in the task of model/model and model/data intercomparison. A complete strategy for such an intercomparison is outlined. The strategy includes two steps. First, the commonality of spatial structures in two (or more) fields is captured in the common principal vectors. Second, the corresponding principal components obtained as time series are then compared on the basis of similarities in their temporal evolution

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

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

Principal Time Management Skills: Explaining Patterns in Principals' Time Use, Job Stress, and Perceived Effectiveness

Science.gov (United States)

Grissom, Jason A.; Loeb, Susanna; Mitani, Hajime

2015-01-01

Purpose: Time demands faced by school principals make principals' work increasingly difficult. Research outside education suggests that effective time management skills may help principals meet job demands, reduce job stress, and improve their performance. The purpose of this paper is to investigate these hypotheses. Design/methodology/approach:…

Subsurface mapping of Rustenburg Layered Suite (RLS), Bushveld Complex, South Africa: Inferred structural features using borehole data and spatial analysis

Science.gov (United States)

Bamisaiye, O. A.; Eriksson, P. G.; Van Rooy, J. L.; Brynard, H. M.; Foya, S.; Billay, A. Y.; Nxumalo, V.

2017-08-01

Faults and other structural features within the mafic-ultramafic layers of the Bushveld Complex have been a major issue mainly for exploration and mine planning. This study employed a new approach in detecting faults with both regional and meter scale offsets, which was not possible with the usually applied structure contour mapping. Interpretations of faults from structural and isopach maps were previously based on geological experience, while meter-scale faults were virtually impossible to detect from such maps. Spatial analysis was performed using borehole data primarily. This resulted in the identification of previously known structures and other hitherto unsuspected structural features. Consequently, the location, trends, and geometry of faults and some regional features within the Rustenburg Layered Suite (RLS) that might not be easy to detect through field mapping are adequately described in this study.

Principal Self-Efficacy, Teacher Perceptions of Principal Performance, and Teacher Job Satisfaction

Science.gov (United States)

Evans, Molly Lynn

2016-01-01

In public schools, the principal's role is of paramount importance in influencing teachers to excel and to keep their job satisfaction high. The self-efficacy of leaders is an important characteristic of leadership, but this issue has not been extensively explored in school principals. Using internet-based questionnaires, this study obtained…

Design, construction and operation features of high-rise structures

Science.gov (United States)

Mylnik, Alexey; Mylnik, Vladimir; Zubeeva, Elena; Mukhamedzhanova, Olga

2018-03-01

The article considers design, construction and operation features of high-rise facilities. The analysis of various situations, that come from improper designing, construction and operation of unique facilities, is carried out. The integrated approach is suggested, when the problems of choosing acceptable constructional solutions related to the functional purpose, architectural solutions, methods of manufacturing and installation, operating conditions for unique buildings and structures are being tackled. A number of main causes for the emergency destruction of objects under construction and operation is considered. A number of measures are proposed on the basis of factor classification in order to efficiently prevent the situations, when various negative options of design loads and emergency impacts occur.

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy.

Science.gov (United States)

Wang, Jiazhou; Jin, Xiance; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Studenski, Matthew; Hu, Weigang

2015-02-01

To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients. Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

Consumers’ Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis

Directory of Open Access Journals (Sweden)

Christine E. Kistler

2017-06-01

Full Text Available To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9. We conducted five individual older adult interviews (aged 68–80. Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1 user experience; (2 social acceptability; (3 cost; (4 health risks/benefits; (5 ease of use; (6 flavors; (7 smoking cessation aid; (8 nicotine content; (9 modifiability; (10 ENDS regulation; (11 bridge between tobacco cigarettes; (12 collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.

Polarized spectral features of human breast tissues through wavelet ...

Indian Academy of Sciences (India)

Abstract. Fluorescence characteristics of human breast tissues are investigated through wavelet transform and principal component analysis (PCA). Wavelet transform of polar- ized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate different tissue types.

Legal Problems of the Principal.

Science.gov (United States)

Stern, Ralph D.; And Others

The three talks included here treat aspects of the law--tort liability, student records, and the age of majority--as they relate to the principal. Specifically, the talk on torts deals with the consequences of principal negligence in the event of injuries to students. Assurance is given that a reasonable and prudent principal will have a minimum…

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.

Mapping the structural and dynamical features of kinesin motor domains.

Directory of Open Access Journals (Sweden)

Guido Scarabelli

Full Text Available Kinesin motor proteins drive intracellular transport by coupling ATP hydrolysis to conformational changes that mediate directed movement along microtubules. Characterizing these distinct conformations and their interconversion mechanism is essential to determining an atomic-level model of kinesin action. Here we report a comprehensive principal component analysis of 114 experimental structures along with the results of conventional and accelerated molecular dynamics simulations that together map the structural dynamics of the kinesin motor domain. All experimental structures were found to reside in one of three distinct conformational clusters (ATP-like, ADP-like and Eg5 inhibitor-bound. These groups differ in the orientation of key functional elements, most notably the microtubule binding α4-α5, loop8 subdomain and α2b-β4-β6-β7 motor domain tip. Group membership was found not to correlate with the nature of the bound nucleotide in a given structure. However, groupings were coincident with distinct neck-linker orientations. Accelerated molecular dynamics simulations of ATP, ADP and nucleotide free Eg5 indicate that all three nucleotide states could sample the major crystallographically observed conformations. Differences in the dynamic coupling of distal sites were also evident. In multiple ATP bound simulations, the neck-linker, loop8 and the α4-α5 subdomain display correlated motions that are absent in ADP bound simulations. Further dissection of these couplings provides evidence for a network of dynamic communication between the active site, microtubule-binding interface and neck-linker via loop7 and loop13. Additional simulations indicate that the mutations G325A and G326A in loop13 reduce the flexibility of these regions and disrupt their couplings. Our combined results indicate that the reported ATP and ADP-like conformations of kinesin are intrinsically accessible regardless of nucleotide state and support a model where neck

Implementing Comprehensive School Health in Alberta, Canada: the principal's role.

Science.gov (United States)

Roberts, Erica; McLeod, Nicole; Montemurro, Genevieve; Veugelers, Paul J; Gleddie, Doug; Storey, Kate E

2016-12-01

Comprehensive School Health (CSH) is an internationally recognized framework that moves beyond the individual to holistically address school health, leading to the development of health-enhancing behaviors while also improving educational outcomes. Previous research has suggested that principal support for CSH implementation is essential, but this role has yet to be explored. Therefore, the purpose of this research was to examine the role of the principal in the implementation of a CSH project aimed at creating a healthy school culture. This research was guided by the grounded ethnography method. Semi-structured interviews were conducted with APPLE School principals (n = 29) to qualitatively explore their role in creating a healthy school culture. A model consisting of five major themes emerged, suggesting that the principal played a fluid role throughout the CSH implementation process. Principals (i) primed the cultural change; (ii) communicated the project's importance to others; (iii) negotiated concerns and collaboratively planned; (iv) held others accountable to the change, while enabling them to take ownership and (v) played an underlying supportive role, providing positive recognition and establishing ongoing commitment. This research provides recommendations to help establish effective leadership practices in schools, conducive to creating a healthy school culture. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Principals Who Think Like Teachers

Science.gov (United States)

Fahey, Kevin

2013-01-01

Being a principal is a complex job, requiring quick, on-the-job learning. But many principals already have deep experience in a role at the very essence of the principalship. They know how to teach. In interviews with principals, Fahey and his colleagues learned that thinking like a teacher was key to their work. Part of thinking the way a teacher…

Light-harvesting features revealed by the structure of plant Photosystem I

CERN Document Server

Ben-Shem, A; Nelson, N; 10.1023/B:PRES.0000036881.23512.42

2004-01-01

Oxygenic photosynthesis is driven by two multi-subunit membrane protein complexes, Photosystem I and Photosystem II. In plants and green algae, both complexes are composed of two moieties: a reaction center (RC), where light-induced charge translocation occurs, and a peripheral antenna that absorbs light and funnels its energy to the reaction center. The peripheral antenna of PS I (LHC I) is composed of four gene products (Lhca 1-4) that are unique among the chlorophyll a/b binding proteins in their pronounced long-wavelength absorbance and in their assembly into dimers. The recently determined structure of plant Photosystem I provides the first relatively high- resolution structural model of a super-complex containing a reaction center and its peripheral antenna. We describe some of the structural features responsible for the unique properties of LHC I and discuss the advantages of the particular LHC I dimerization mode over monomeric or trimeric forms. In addition, we delineate some of the interactions betw...

Trust Me, Principal, or Burn Out! The Relationship between Principals' Burnout and Trust in Students and Parents

Science.gov (United States)

Ozer, Niyazi

2013-01-01

The purpose of this study was to determine the primary school principals' views on trust in students and parents and also, to explore the relationships between principals' levels of professional burnout and their trust in students and parents. To this end, Principal Trust Survey and Friedman Principal Burnout scales were administered on 119…

Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration

International Nuclear Information System (INIS)

Yao, Ruigen; Pakzad, Shamim N

2014-01-01

In the past few decades many types of structural damage indices based on structural health monitoring signals have been proposed, requiring performance evaluation and comparison studies on these indices in a quantitative manner. One tool to help accomplish this objective is analytical sensitivity analysis, which has been successfully used to evaluate the influences of system operational parameters on observable characteristics in many fields of study. In this paper, the sensitivity expressions of two damage features, namely the Mahalanobis distance of autoregressive coefficients and the Cosh distance of autoregressive spectra, will be derived with respect to both structural damage and measurement noise level. The effectiveness of the proposed methods is illustrated in a numerical case study on a 10-DOF system, where their results are compared with those from direct simulation and theoretical calculation. (paper)

Using ATR-FT/IR to detect carbohydrate-related molecular structure features of carinata meal and their in situ residues of ruminal fermentation in comparison with canola meal

Science.gov (United States)

Xin, Hangshu; Yu, Peiqiang

2013-10-01

ruminal degradation in both carinata meal and canola meal. Although carinata meal differed from canola meal in some carbohydrate spectral parameters, multivariate results from agglomerative hierarchical cluster analysis and principal component analysis showed that both original and in situ residues of two meals were not fully distinguished from each other within carbohydrate spectral regions. It was concluded that carbohydrate structural conformation could be detected in carinata meal by using ATR-FT/IR techniques and further study is needed to explore more information on molecular spectral features of other functional group such as protein structure profile and their association with potential nutrient supply and availability of carinata meal in animals.

Detecting Structural Features in Metallic Glass via Synchrotron Radiation Experiments Combined with Simulations

Directory of Open Access Journals (Sweden)

Gu-Qing Guo

2015-11-01

Full Text Available Revealing the essential structural features of metallic glasses (MGs will enhance the understanding of glass-forming mechanisms. In this work, a feasible scheme is provided where we performed the state-of-the-art synchrotron-radiation based experiments combined with simulations to investigate the microstructures of ZrCu amorphous compositions. It is revealed that in order to stabilize the amorphous state and optimize the topological and chemical distribution, besides the icosahedral or icosahedral-like clusters, other types of clusters also participate in the formation of the microstructure in MGs. This cluster-level co-existing feature may be popular in this class of glassy materials.

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

A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring

Science.gov (United States)

Tibaduiza, D.-A.; Torres-Arredondo, M.-A.; Mujica, L. E.; Rodellar, J.; Fritzen, C.-P.

2013-12-01

This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time-frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.

Monitoring of surface-fatigue crack propagation in a welded steel angle structure using guided waves and principal component analysis

Science.gov (United States)

Lu, Mingyu; Qu, Yongwei; Lu, Ye; Ye, Lin; Zhou, Limin; Su, Zhongqing

2012-04-01

An experimental study is reported in this paper demonstrating monitoring of surface-fatigue crack propagation in a welded steel angle structure using Lamb waves generated by an active piezoceramic transducer (PZT) network which was freely surface-mounted for each PZT transducer to serve as either actuator or sensor. The fatigue crack was initiated and propagated in welding zone of a steel angle structure by three-point bending fatigue tests. Instead of directly comparing changes between a series of specific signal segments such as S0 and A0 wave modes scattered from fatigue crack tips, a variety of signal statistical parameters representing five different structural status obtained from marginal spectrum in Hilbert-huang transform (HHT), indicating energy progressive distribution along time period in the frequency domain including all wave modes of one wave signal were employed to classify and distinguish different structural conditions due to fatigue crack initiation and propagation with the combination of using principal component analysis (PCA). Results show that PCA based on marginal spectrum is effective and sensitive for monitoring the growth of fatigue crack although the received signals are extremely complicated due to wave scattered from weld, multi-boundaries, notch and fatigue crack. More importantly, this method indicates good potential for identification of integrity status of complicated structures which cause uncertain wave patterns and ambiguous sensor network arrangement.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

Directory of Open Access Journals (Sweden)

Junbao Zheng

2012-03-01

Full Text Available Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor as well as its parallel channels (inner factor. The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Double-loop Learning: A Coaching Protocol for Enhancing Principal Instructional Leadership

Directory of Open Access Journals (Sweden)

Gary W. Houchens

2012-10-01

Full Text Available Executive coaching has become increasingly commonplace in both the corporate and non-profit sectors as a means of improving professional effectiveness but there is a dearth of empirically-based protocols geared specifically toward the growth needs of school principals. This qualitative case study explores the implementation of a principal coaching protocol using a theories of practice framework based on concepts originally articulated by Argyris and Schön (1974 and further explicated by the authors in previous publications. This study examined the extent to which a coaching protocol based on theories of practice enhanced principals’ self-perceived capacity for reflection and effective instructional leadership. Findings suggest that principals valued the structure, feedback, and reflective dimensions of the protocol and found their confidence level about an important instructional leadership problem – how to support and assist struggling teachers improve their teaching practice – was greatly enhanced. Implications for further iterations of the coaching protocol, as well as future directions of research on principal professional growth, are discussed.

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction.

Science.gov (United States)

Hao, Xiaohu; Zhang, Guijun; Zhou, Xiaogen

2018-04-01

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploring the Impact of Applicants' Gender and Religion on Principals' Screening Decisions for Assistant Principal Applicants

Science.gov (United States)

Bon, Susan C.

2009-01-01

In this experimental study, a national random sample of high school principals (stratified by gender) were asked to evaluate hypothetical applicants whose resumes varied by religion (Jewish, Catholic, nondenominational) and gender (male, female) for employment as assistant principals. Results reveal that male principals rate all applicants higher…

Local appearance features for robust MRI brain structure segmentation across scanning protocols

DEFF Research Database (Denmark)

Achterberg, H.C.; Poot, Dirk H. J.; van der Lijn, Fedde

2013-01-01

Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require...... a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution...... with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multiatlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition...

Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

Directory of Open Access Journals (Sweden)

TjongWan Sen

2009-11-01

Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

Features of structure formation in the low modulus quasi-single crystal from Zr-25%Nb alloy at cold rolling

Science.gov (United States)

Isaenkova, M.; Perlovich, Yu.; Fesenko, V.; Babich, Y.; Zaripova, M.; Krapivka, N.

2018-05-01

The paper presents the results of investigation of the regularities of the structure and texture formation during rolling of single crystals of Zr-25%Nb alloy differing in their initial orientations relative to the external principal directions in the rolled plate: normal (ND) and rolling directions (RD). The features of rolled single crystals with initial orientations of planes {001}, {011} or {111} parallel to the rolling plane and different crystallographic directions along RD are considered. A comparison of the peculiarities of plastic deformation in a polycrystalline alloy of the same composition is made. For the samples studied, a decrease in the lattice parameter of the β-phase has been recorded, the minimum of the parameter being observed for different degrees of deformation, varying from 20 to 50%. Observed decrease in the unit cell parameter can be connected with the precipitation of the α(α')-Zr phase from the deformed nonequilibrium β-phase of the Zr-25%Nb alloy, i.e. change in the composition of the solid solution. Distributions of the increase in the dimensions of the deformed single crystal along RD and the transverse direction (TD) with its deformation up to 30% in thickness, which indicate the anisotropy of the plasticity of single crystals during their rolling, are constructed on stereographic projection. It is shown, that the deformation of single crystals occurs practically without increasing of their dimensions in the direction with a total thickness deformation of up to 30%. Direction is characterized by maximum hardening (microhardness) with indentation along it, which causes low plasticity of deformed and annealed foils from Zr-25%Nb alloy at the stretching along and across RD, that is connected with the features of their crystallographic texture.

Determining characteristic principal clusters in the “cluster-plus-glue-atom” model

International Nuclear Information System (INIS)

Du, Jinglian; Wen, Bin; 2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" data-affiliation=" (M2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" >Melnik, Roderick; Kawazoe, Yoshiyuki

2014-01-01

The “cluster-plus-glue-atom” model can easily describe the structure of complex metallic alloy phases. However, the biggest obstacle limiting the application of this model is that it is difficult to determine the characteristic principal cluster. In the case when interatomic force constants (IFCs) inside the cluster lead to stronger interaction than the interaction between the clusters, a new rule for determining the characteristic principal cluster in the “cluster-plus-glue-atom” model has been proposed on the basis of IFCs. To verify this new rule, the alloy phases in Cu–Zr and Al–Ni–Zr systems have been tested, and our results indicate that the present new rule for determining characteristic principal clusters is effective and reliable

Inkjet Printing of Functional and Structural Materials: Fluid Property Requirements, Feature Stability, and Resolution

Science.gov (United States)

Derby, Brian

2010-08-01

Inkjet printing is viewed as a versatile manufacturing tool for applications in materials fabrication in addition to its traditional role in graphics output and marking. The unifying feature in all these applications is the dispensing and precise positioning of very small volumes of fluid (1-100 picoliters) on a substrate before transformation to a solid. The application of inkjet printing to the fabrication of structures for structural or functional materials applications requires an understanding as to how the physical processes that operate during inkjet printing interact with the properties of the fluid precursors used. Here we review the current state of understanding of the mechanisms of drop formation and how this defines the fluid properties that are required for a given liquid to be printable. The interactions between individual drops and the substrate as well as between adjacent drops are important in defining the resolution and accuracy of printed objects. Pattern resolution is limited by the extent to which a liquid drop spreads on a substrate and how spreading changes with the overlap of adjacent drops to form continuous features. There are clearly defined upper and lower bounds to the width of a printed continuous line, which can be defined in terms of materials and process variables. Finer-resolution features can be achieved through appropriate patterning and structuring of the substrate prior to printing, which is essential if polymeric semiconducting devices are to be fabricated. Low advancing and receding contact angles promote printed line stability but are also more prone to solute segregation or “coffee staining” on drying.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Science.gov (United States)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Elastic properties of uniaxial-fiber reinforced composites - General features

Science.gov (United States)

Datta, Subhendu; Ledbetter, Hassel; Lei, Ming

The salient features of the elastic properties of uniaxial-fiber-reinforced composites are examined by considering the complete set of elastic constants of composites comprising isotropic uniaxial fibers in an isotropic matrix. Such materials exhibit transverse-isotropic symmetry and five independent elastic constants in Voigt notation: C(11), C(33), C(44), C(66), and C(13). These C(ij) constants are calculated over the entire fiber-volume-fraction range 0.0-1.0, using a scattered-plane-wave ensemple-average model. Some practical elastic constants such as the principal Young moduli and the principal Poisson ratios are considered, and the behavior of these constants is discussed. Also presented are the results for the four principal sound velocities used to study uniaxial-fiber-reinforced composites: v(11), v(33), v(12), and v(13).

Principals' Perceptions of Politics

Science.gov (United States)

Tooms, Autumn K.; Kretovics, Mark A.; Smialek, Charles A.

2007-01-01

This study is an effort to examine principals' perceptions of workplace politics and its influence on their productivity and efficacy. A survey was used to explore the perceptions of current school administrators with regard to workplace politics. The instrument was disseminated to principals serving public schools in one Midwestern state in the…

Key structural features of nonsteroidal ligands for binding and activation of the androgen receptor.

Science.gov (United States)

Yin, Donghua; He, Yali; Perera, Minoli A; Hong, Seoung Soo; Marhefka, Craig; Stourman, Nina; Kirkovsky, Leonid; Miller, Duane D; Dalton, James T

2003-01-01

The purposes of the present studies were to examine the androgen receptor (AR) binding ability and in vitro functional activity of multiple series of nonsteroidal compounds derived from known antiandrogen pharmacophores and to investigate the structure-activity relationships (SARs) of these nonsteroidal compounds. The AR binding properties of sixty-five nonsteroidal compounds were assessed by a radioligand competitive binding assay with the use of cytosolic AR prepared from rat prostates. The AR agonist and antagonist activities of high-affinity ligands were determined by the ability of the ligand to regulate AR-mediated transcriptional activation in cultured CV-1 cells, using a cotransfection assay. Nonsteroidal compounds with diverse structural features demonstrated a wide range of binding affinity for the AR. Ten compounds, mainly from the bicalutamide-related series, showed a binding affinity superior to the structural pharmacophore from which they were derived. Several SARs regarding nonsteroidal AR binding were revealed from the binding data, including stereoisomeric conformation, steric effect, and electronic effect. The functional activity of high-affinity ligands ranged from antagonist to full agonist for the AR. Several structural features were found to be determinative of agonist and antagonist activities. The nonsteroidal AR agonists identified from the present studies provided a pool of candidates for further development of selective androgen receptor modulators (SARMs) for androgen therapy. Also, these studies uncovered or confirmed numerous important SARs governing AR binding and functional properties by nonsteroidal molecules, which would be valuable in the future structural optimization of SARMs.

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.

Structural and phylogenetic analysis of Rhodobacter capsulatus NifF: uncovering general features of nitrogen-fixation (nif)-flavodoxins.

Science.gov (United States)

Pérez-Dorado, Inmaculada; Bortolotti, Ana; Cortez, Néstor; Hermoso, Juan A

2013-01-09

Analysis of the crystal structure of NifF from Rhodobacter capsulatus and its homologues reported so far reflects the existence of unique structural features in nif flavodoxins: a leucine at the re face of the isoalloxazine, an eight-residue insertion at the C-terminus of the 50's loop and a remarkable difference in the electrostatic potential surface with respect to non-nif flavodoxins. A phylogenetic study on 64 sequences from 52 bacterial species revealed four clusters, including different functional prototypes, correlating the previously defined as "short-chain" with the firmicutes flavodoxins and the "long-chain" with gram-negative species. The comparison of Rhodobacter NifF structure with other bacterial flavodoxin prototypes discloses the concurrence of specific features of these functional electron donors to nitrogenase.

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.

Lagrangian motion, coherent structures, and lines of persistent material strain.

Science.gov (United States)

Samelson, R M

2013-01-01

Lagrangian motion in geophysical fluids may be strongly influenced by coherent structures that support distinct regimes in a given flow. The problems of identifying and demarcating Lagrangian regime boundaries associated with dynamical coherent structures in a given velocity field can be studied using approaches originally developed in the context of the abstract geometric theory of ordinary differential equations. An essential insight is that when coherent structures exist in a flow, Lagrangian regime boundaries may often be indicated as material curves on which the Lagrangian-mean principal-axis strain is large. This insight is the foundation of many numerical techniques for identifying such features in complex observed or numerically simulated ocean flows. The basic theoretical ideas are illustrated with a simple, kinematic traveling-wave model. The corresponding numerical algorithms for identifying candidate Lagrangian regime boundaries and lines of principal Lagrangian strain (also called Lagrangian coherent structures) are divided into parcel and bundle schemes; the latter include the finite-time and finite-size Lyapunov exponent/Lagrangian strain (FTLE/FTLS and FSLE/FSLS) metrics. Some aspects and results of oceanographic studies based on these approaches are reviewed, and the results are discussed in the context of oceanographic observations of dynamical coherent structures.

Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

Science.gov (United States)

Ling, X.; Snow, J. E.; Chin, W.

2017-12-01

The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The

Boosting Discriminant Learners for Gait Recognition Using MPCA Features

Directory of Open Access Journals (Sweden)

Haiping Lu

2009-01-01

Full Text Available This paper proposes a boosted linear discriminant analysis (LDA solution on features extracted by the multilinear principal component analysis (MPCA to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.

Renewing the Principal Pipeline

Science.gov (United States)

Turnbull, Brenda J.

2015-01-01

The work principals do has always mattered, but as the demands of the job increase, it matters even more. Perhaps once they could maintain safety and order and call it a day, but no longer. Successful principals today must also lead instruction and nurture a productive learning community for students, teachers, and staff. They set the tone for the…

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

Science.gov (United States)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Principal Ports

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Principal Ports are defined by port limits or US Army Corps of Engineers (USACE) projects, these exclude non-USACE projects not authorized for publication. The...

Structural features of the San Andreas fault at Tejon Pass, California

Science.gov (United States)

Dewers, T. A.; Reches, Z.; Brune, J. N.

2002-12-01

We mapped a 2 km belt along the San Andreas fault (SAF) in the Tejon Pass area where road cuts provide fresh exposures of the fault zone and surrounding rocks. Our 1:2,000 structural mapping is focused on analysis of faulting processes and is complementary to regional mapping at 1:12,000 scale by Ramirez (M.Sc., UC Santa Barbara, 1984). The dominant rock units are the Hungry Valley Formation of Pliocene age (clastic sediments) exposed south of the SAF, and the Tejon Lookout granite (Cretaceous) and Neenach Volcanic Formation exposed north of it. Ramirez (1983) deduced ~220 km of post-Miocene lateral slip. The local trend of the SAF is about N60W and it includes at least three main, subparallel segments that form a 200 m wide zone. The traces of the segments are quasi-linear, discontinuous, and they are stepped with respect to each other, forming at least five small pull-aparts and sag ponds in the mapping area. The three segments were not active semi-contemporaneously and the southern segment is apparently the oldest. The largest pull-apart, 60-70 m wide, displays young (Quaternary?) silt and shale layers. We found two rock bodies that are suspected as fault-rocks. One is a 1-2 m thick sheet-like body that separates the Tejon Lookout granite from young (Recent?) clastic rocks. In the field, it appears as a gouge zone composed of poorly cemented, dark clay size grains; however, the microstructure of this rock does not reveal clear shear features. The second body is the 80-120 m wide zone of Tejon Lookout granite that extends for less than 1 km along the SAF in the mapped area. It is characterized by three structural features: (1) pulverization into friable, granular material by multitude of grain-crossing fractures; (2) abundance of dip-slip small faults that are gently dipping toward and away from the SAF; and (3) striking lack of evidence for shear parallel to the SAF. The relationships between these features and the large right-lateral shear along the SAF are

Perceptions of Beginning Public School Principals.

Science.gov (United States)

Lyons, James E.

1993-01-01

Summarizes a study to determine principal's perceptions of their competency in primary responsibility areas and their greatest challenges and frustrations. Beginning principals are challenged by delegating responsibilities and becoming familiar with the principal's role, the local school, and school operations. Their major frustrations are role…

Andragogical Practices of School Principals in Developing the Leadership Capacities of Assistant Principals

Science.gov (United States)

McDaniel, Luther

2017-01-01

The purpose of this mixed methods study was to assess school principals' perspectives of the extent to which they apply the principles of andragogy to the professional development of assistant principals in their schools. This study was conducted in school districts that constitute a RESA area in a southeastern state. The schools in these…

The Role of Emotion in Musical Improvisation: An Analysis of Structural Features

OpenAIRE

McPherson, Malinda J.; Lopez-Gonzalez, Monica; Rankin, Summer K.; Limb, Charles J.

2014-01-01

One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor) and emotion (happy vs. sad) do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not u...

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Directory of Open Access Journals (Sweden)

Fan Wu

2015-05-01

Full Text Available Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN and minimum distance classifier (MDC. In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated.

Structural features of various kinds of carbon fibers as determined by small-angle X-ray scattering

Energy Technology Data Exchange (ETDEWEB)

Li, Denghua; Du, Sujun [Shanxi Transportation Research Institute, National and Local Joint Engineering Laboratory of Advanced Road Materials, Taiyuan (China); Lu, Chunxiang; Wu, Gangping; Yang, Yu; Wang, Lina [Chinese Academy of Sciences, National Engineering Laboratory for Carbon Fiber Technology, Institute of Coal Chemistry, Taiyuan (China)

2016-11-15

The structural features of polyacrylonitrile and pitch-based carbon fibers were analyzed from a comprehensive point of view by X-ray measurements and related techniques. The results indicated that the undulating graphite ribbon with embedded microvoid was the main structural unit for graphitic fibers. The void's parameters for these fibers could be obtained directly by small-angle X-ray scattering following the classic method deduced based on the typical two-phase system (i.e., Porod's law, Guinier's law and Debye's law). The non-graphitic fibers, however, were composed of two-dimensional turbostratic crystallites in the aggregation of microfibril and thus had a quasi two-phase structure (microfibril, interfibrillar amorphous structure and microvoid embedded within the microfibril). The extended Debye or Beaucage model in this case should be applied in order to obtain the structural parameters. It also revealed that the quasi two-phase system would complete its transformation to two-phase system during high-temperature graphitization. Therefore, the degree of graphitization was speculated to be the essential indicator distinguishing graphitic fibers from non-graphitic ones and would be helpful in understanding the transformation of structural features during the graphitization of carbon fibers. (orig.)

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

41 CFR 105-68.995 - Principal.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 3 2010-07-01 2010-07-01 false Principal. 105-68.995 Section 105-68.995 Public Contracts and Property Management Federal Property Management Regulations System...-GOVERNMENTWIDE DEBARMENT AND SUSPENSION (NONPROCUREMENT) Definitions § 105-68.995 Principal. Principal means— (a...

Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

OpenAIRE

Moon Young Kang; Byungho Park

2018-01-01

Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy) in corporate social media on customer...

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

Science.gov (United States)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Structural and Phylogenetic Analysis of Rhodobacter capsulatus NifF: Uncovering General Features of Nitrogen-fixation (nif-Flavodoxins

Directory of Open Access Journals (Sweden)

Inmaculada Pérez-Dorado

2013-01-01

Full Text Available Analysis of the crystal structure of NifF from Rhodobacter capsulatus and its homologues reported so far reflects the existence of unique structural features in nif flavodoxins: a leucine at the re face of the isoalloxazine, an eight-residue insertion at the C-terminus of the 50’s loop and a remarkable difference in the electrostatic potential surface with respect to non-nif flavodoxins. A phylogenetic study on 64 sequences from 52 bacterial species revealed four clusters, including different functional prototypes, correlating the previously defined as “short-chain” with the firmicutes flavodoxins and the “long-chain” with gram-negative species. The comparison of Rhodobacter NifF structure with other bacterial flavodoxin prototypes discloses the concurrence of specific features of these functional electron donors to nitrogenase.

Principal-Counselor Collaboration and School Climate

Science.gov (United States)

Rock, Wendy D.; Remley, Theodore P.; Range, Lillian M.

2017-01-01

Examining whether principal-counselor collaboration and school climate were related, researchers sent 4,193 surveys to high school counselors in the United States and received 419 responses. As principal-counselor collaboration increased, there were increases in counselors viewing the principal as supportive, the teachers as regarding one another…

12 CFR 561.39 - Principal office.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 5 2010-01-01 2010-01-01 false Principal office. 561.39 Section 561.39 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY DEFINITIONS FOR REGULATIONS AFFECTING ALL SAVINGS ASSOCIATIONS § 561.39 Principal office. The term principal office means the home...

Teacher Supervision Practices and Principals' Characteristics

Science.gov (United States)

April, Daniel; Bouchamma, Yamina

2015-01-01

A questionnaire was used to determine the individual and collective teacher supervision practices of school principals and vice-principals in Québec (n = 39) who participated in a research-action study on pedagogical supervision. These practices were then analyzed in terms of the principals' sociodemographic and socioprofessional characteristics…

[Simultaneous separation and detection of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate by RP-HPLC and structure confirmation].

Science.gov (United States)

Zhao, Yan-Yan; Liu, Li-Yan; Han, Yuan-Yuan; Li, Yue-Qiu; Wang, Yan; Shi, Min-Jian

2013-08-01

A simple, fast and sensitive analytical method for the simultaneous separation and detection of 18alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B by RP-HPLC and drug quality standard was established. The structures of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate have been confirmed. Reference European Pharmacopoeia EP7.0 version, British Pharmacopoeia 2012 version, National Drug Standards of China (WS 1-XG-2002), domestic and international interrelated literature were referred to select the composition of mobile phase. The experimental parameters including salt concentration, pH, addition quantities of organic solvent, column temperature and flow rate were optimized. Finally, the assay was conducted on a Durashell-C18 column (250 mm x 4.6 mm, 5 microm) with 0.01 mol x mL(-1) ammonium perchlorate (add ammonia to adjust the pH value to 8.2) -methanol (48 : 52) as mobile phase at the flow rate of 0.8 mL x min(-1), and the detection wavelength was set at 254 nm. The column temperature was 50 degrees C and the injection volume was 10 microL. The MS, NMR, UV and RP-HPLC were used to confirm the structures of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate. Under the optimized separation conditions, the calibration curves of 18 alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B showed good linearity within the concentration of 0.50-100 microg x mL(-1) (r = 0.999 9). The detection limits for 18alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B were 0.15, 0.10, 0.10, 0.15 microg x mL(-1) respectively. The method is sensitive, reproducible and the results are accurate and reliable. It can be used for chiral resolution of 18alpha-glycyrrhizinic acid, 18Pbeta-glycyrrhizinic acid, and detection content of principal component and

Structural and sequence features of two residue turns in beta-hairpins.

Science.gov (United States)

Madan, Bharat; Seo, Sung Yong; Lee, Sun-Gu

2014-09-01

Beta-turns in beta-hairpins have been implicated as important sites in protein folding. In particular, two residue β-turns, the most abundant connecting elements in beta-hairpins, have been a major target for engineering protein stability and folding. In this study, we attempted to investigate and update the structural and sequence properties of two residue turns in beta-hairpins with a large data set. For this, 3977 beta-turns were extracted from 2394 nonhomologous protein chains and analyzed. First, the distribution, dihedral angles and twists of two residue turn types were determined, and compared with previous data. The trend of turn type occurrence and most structural features of the turn types were similar to previous results, but for the first time Type II turns in beta-hairpins were identified. Second, sequence motifs for the turn types were devised based on amino acid positional potentials of two-residue turns, and their distributions were examined. From this study, we could identify code-like sequence motifs for the two residue beta-turn types. Finally, structural and sequence properties of beta-strands in the beta-hairpins were analyzed, which revealed that the beta-strands showed no specific sequence and structural patterns for turn types. The analytical results in this study are expected to be a reference in the engineering or design of beta-hairpin turn structures and sequences. © 2014 Wiley Periodicals, Inc.

Detection and analysis of unusual features in the structural model and structure-factor data of a birch pollen allergen

International Nuclear Information System (INIS)

Rupp, Bernhard

2012-01-01

The structure factors deposited with PDB entry 3k78 show properties inconsistent with experimentally observed diffraction data, and without uncertainty represent calculated structure factors. The refinement of the model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019). Physically improbable features in the model of the birch pollen structure Bet v 1d are faithfully reproduced in electron density generated with the deposited structure factors, but these structure factors themselves exhibit properties that are characteristic of data calculated from a simple model and are inconsistent with the data and error model obtained through experimental measurements. The refinement of the model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019). The abnormal refinement is compared with normal refinement of an isomorphous variant structure of Bet v 1l. A variety of analytical tools, including the application of Diederichs plots, Rσ plots and bulk-solvent analysis are discussed as promising aids in validation. The examination of the Bet v 1d structure also cautions against the practice of indicating poorly defined protein chain residues through zero occupancies. The recommendation to preserve diffraction images is amplified

Developing Principal Instructional Leadership through Collaborative Networking

Science.gov (United States)

Cone, Mariah Bahar

2010-01-01

This study examines what occurs when principals of urban schools meet together to learn and improve their instructional leadership in collaborative principal networks designed to support, sustain, and provide ongoing principal capacity building. Principal leadership is considered second only to teaching in its ability to improve schools, yet few…

A novel automated spike sorting algorithm with adaptable feature extraction.

Science.gov (United States)

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Sustainability Assessment of the Natural Gas Industry in China Using Principal Component Analysis

Directory of Open Access Journals (Sweden)

Xiucheng Dong

2015-05-01

Full Text Available Under pressure toward carbon emission reduction and air protection, China has accelerated energy restructuring by greatly improving the supply and consumption of natural gas in recent years. However, several issues with the sustainable development of the natural gas industry in China still need in-depth discussion. Therefore, based on the fundamental ideas of sustainable development, industrial development theories and features of the natural gas industry, a sustainable development theory is proposed in this thesis. The theory consists of five parts: resource, market, enterprise, technology and policy. The five parts, which unite for mutual connection and promotion, push the gas industry’s development forward together. Furthermore, based on the theoretical structure, the Natural Gas Industry Sustainability Index in China is established and evaluated via the Principal Component Analysis (PCA method. Finally, a conclusion is reached: that the sustainability of the natural gas industry in China kept rising from 2008 to 2013, mainly benefiting from increasing supply and demand, the enhancement of enterprise profits, technological innovation, policy support and the optimization and reformation of the gas market.

Numerical evaluation of state boundary surface through rotation of principal stress axes in sand

International Nuclear Information System (INIS)

Sadrnejad, S. A.

2001-01-01

In applying shear stress to saturated soil with arbitrary stress paths, the prediction of the exact value of strains is difficult because of mainly its stress path dependent nature. Rotation of the principal stress axes during shearing of the soil is a feature of stress paths associated with many field loading situations. A proper understanding of the effects of principal stress rotation on soil behavior can be provided if the anisotropy existing prior to stress rotation and induced anisotropy due to plastic flow in soil are clearly understood and modeled. A multi laminate based model for soil is developed and used to compute and present the influence of rotation of principal stress axes on the plastic behavior of soil. This is fulfilled by distributing the effects of boundary condition changes into several predefined sampling orientations at one point and summing the micro-results up as the macro-result. The validity of the presented model examined by comparing numerical and test results showing the mentioned aspect. In this paper, the state boundary surface is numerically obtained by a multi laminate based model capable of predicting the behavior of sand under the influences of rotation of the direction of principal stress axes and induced anisotropy. the predicted numerical results are tally in agreement with experiments

A principal-agent Model of corruption

NARCIS (Netherlands)

Groenendijk, Nico

1997-01-01

One of the new avenues in the study of political corruption is that of neo-institutional economics, of which the principal-agent theory is a part. In this article a principal-agent model of corruption is presented, in which there are two principals (one of which is corrupting), and one agent (who is

HYDROTHEMAL ALTERATION MAPPING USING FEATURE-ORIENTED PRINCIPAL COMPONENT SELECTION (FPCS METHOD TO ASTER DATA:WIKKI AND MAWULGO THERMAL SPRINGS, YANKARI PARK, NIGERIA

Directory of Open Access Journals (Sweden)

A. J. Abubakar

2017-10-01

Full Text Available Geothermal systems are essentially associated with hydrothermal alteration mineral assemblages such as iron oxide/hydroxide, clay, sulfate, carbonate and silicate groups. Blind and fossilized geothermal systems are not characterized by obvious surface manifestations like hot springs, geysers and fumaroles, therefore, they could not be easily identifiable using conventional techniques. In this investigation, the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER were evaluated in discriminating hydrothermal alteration minerals associated with geothermal systems as a proxy in identifying subtle Geothermal systems at Yankari Park in northeastern Nigeria. The area is characterized by a number of thermal springs such as Wikki and Mawulgo. Feature-oriented Principal Component selection (FPCS was applied to ASTER data based on spectral characteristics of hydrothermal alteration minerals for a systematic and selective extraction of the information of interest. Application of FPCS analysis to bands 5, 6 and 8 and bands 1, 2, 3 and 4 datasets of ASTER was used for mapping clay and iron oxide/hydroxide minerals in the zones of Wikki and Mawulgo thermal springs in Yankari Park area. Field survey using GPS and laboratory analysis, including X-ray Diffractometer (XRD and Analytical Spectral Devices (ASD were carried out to verify the image processing results. The results indicate that ASTER dataset reliably and complementarily be used for reconnaissance stage of targeting subtle alteration mineral assemblages associated with geothermal systems.

Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

Directory of Open Access Journals (Sweden)

Khader Shameer

2010-06-01

Full Text Available 3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neuro-degenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping.

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.

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.

From Vision to Reality: Views of Primary School Principals on Inclusive Education in New South Wales, Australia

Science.gov (United States)

Graham, Linda J.; Spandagou, Ilektra

2011-01-01

This paper discusses the findings of a research study that used semi-structured interviews to explore the views of primary school principals on inclusive education in New South Wales, Australia. Content analysis of the transcript data indicates that principals' attitudes towards inclusive education and their success in engineering inclusive…

Environmental forensic principals for sources allocation of polycyclic aromatic hydrocarbons

International Nuclear Information System (INIS)

O'Sullivan, G.; Martin, E.; Sandau, C.D.

2008-01-01

Polycyclic aromatic hydrocarbons (PAH) are organic compounds which include only carbon and hydrogen with a fused ring structure containing at least two six-sided benzene rings but may also contain additional fused rings that are not six-sided. The environmental forensic principals for sources allocation of PAHs were examined in this presentation. Specifically, the presentation addressed the structure and physiochemical properties of PAHs; sources and sinks; fate and behaviour; analytical techniques; conventional source identification techniques; and toxic equivalent fingerprinting. It presented a case study where residents had been allegedly exposed to dioxins, PAHs and metals released from a railroad tie treatment plant. The classification of PAHs is governed by thermodynamic properties such as biogenic, petrogenic, and pyrogenic properties. A number of techniques were completed, including chemical fingerprinting; molecular diagnostic ratios; cluster analysis; principal component analysis; and TEF fingerprinting. These techniques have shown that suspected impacted sites do not all share similar PAH signatures indicating the potential for various sources. Several sites shared similar signatures to background locations. tabs., figs

Influence of continuous electron irradiation and different modes of mechanic-thermal treatment on structure-phase composition of alloys 36NKhTYu and 40KhNYu

International Nuclear Information System (INIS)

Alontseva, D.L.; Suslov, S.E.; Kupchishin, A.I.; Plotnikov, S.V.; Petrov, V.A.

2002-01-01

Principal regularities of structure formation in strongly deformed alloys 36NKhTYu and 40KhNYu under aging in certain temperature range and after electron irradiation are revealed. Morphological features of precipitating phases with purpose of development of methods for getting of optimal structural states providing essential properties rate were determined. Data of electron microscopic examinations of structure-phase composition are compared with data on mechanical properties

Features and Recursive Structure

Directory of Open Access Journals (Sweden)

Kuniya Nasukawa

2015-01-01

Full Text Available Based on the cross-linguistic tendency that weak vowels are realized with a central quality such as ə, ɨ, or ɯ, this paper attempts to account for this choice by proposing that the nucleus itself is one of the three monovalent vowel elements |A|, |I| and |U| which function as the building blocks of melodic structure. I claim that individual languages make a parametric choice to determine which of the three elements functions as the head of a nuclear expression. In addition, I show that elements can be freely concatenated to create melodic compounds. The resulting phonetic value of an element compound is determined by the specific elements it contains and by the head-dependency relations between those elements. This concatenation-based recursive mechanism of melodic structure can also be extended to levels above the segment, thus ultimately eliminating the need for syllabic constituents. This approach reinterprets the notion of minimalism in phonology by opposing the string-based flat structure.

Low-Dimensional Feature Representation for Instrument Identification

Science.gov (United States)

Ihara, Mizuki; Maeda, Shin-Ichi; Ikeda, Kazushi; Ishii, Shin

For monophonic music instrument identification, various feature extraction and selection methods have been proposed. One of the issues toward instrument identification is that the same spectrum is not always observed even in the same instrument due to the difference of the recording condition. Therefore, it is important to find non-redundant instrument-specific features that maintain information essential for high-quality instrument identification to apply them to various instrumental music analyses. For such a dimensionality reduction method, the authors propose the utilization of linear projection methods: local Fisher discriminant analysis (LFDA) and LFDA combined with principal component analysis (PCA). After experimentally clarifying that raw power spectra are actually good for instrument classification, the authors reduced the feature dimensionality by LFDA or by PCA followed by LFDA (PCA-LFDA). The reduced features achieved reasonably high identification performance that was comparable or higher than those by the power spectra and those achieved by other existing studies. These results demonstrated that our LFDA and PCA-LFDA can successfully extract low-dimensional instrument features that maintain the characteristic information of the instruments.

How District Leaders Use Knowledge Management to Influence Principals' Instructional Leadership

Science.gov (United States)

McGloughlin, Denise Marie

2016-01-01

The study of knowledge management, an integrated system of an organization's culture, conditions, and structure, as applied to educational institutions is limited. It was not known how district leaders use knowledge management to influence principals' instructional leadership performance. The purpose of this qualitative single-case study was to…

Triangulating Principal Effectiveness: How Perspectives of Parents, Teachers, and Assistant Principals Identify the Central Importance of Managerial Skills. Working Paper 35

Science.gov (United States)

Grissom, Jason A.; Loeb, Susanna

2009-01-01

While the importance of effective principals is undisputed, few studies have addressed what specific skills principals need to promote school success. This study draws on unique data combining survey responses from principals, assistant principals, teachers and parents with rich administrative data to identify which principal skills matter most…

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

Measuring Principal Performance: How Rigorous Are Commonly Used Principal Performance Assessment Instruments? A Quality School Leadership Issue Brief

Science.gov (United States)

Condon, Christopher; Clifford, Matthew

2010-01-01

This brief reviews the publicly available principal assessments and points superintendents and policy makers toward strong instruments to measure principal performance. Specifically, the measures included in this review are expressly intended to evaluate principal performance and have varying degrees of publicly available evidence of psychometric…

Face-iris multimodal biometric scheme based on feature level fusion

Science.gov (United States)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

A comparative study of image low level feature extraction algorithms

Directory of Open Access Journals (Sweden)

M.M. El-gayar

2013-07-01

Full Text Available Feature extraction and matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods for assessing the performance of popular image matching algorithms are presented and rely on costly descriptors for detection and matching. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. This study addresses the limitations of the existing comparative tools and delivers a generalized criterion to determine beforehand the level of efficiency expected from a matching algorithm given the type of images evaluated. The algorithms and the respective images used within this work are divided into two groups: feature-based and texture-based. And from this broad classification only three of the most widely used algorithms are assessed: color histogram, FAST (Features from Accelerated Segment Test, SIFT (Scale Invariant Feature Transform, PCA-SIFT (Principal Component Analysis-SIFT, F-SIFT (fast-SIFT and SURF (speeded up robust features. The performance of the Fast-SIFT (F-SIFT feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. F-SIFT is the fastest one with good performance as the same as SURF, SIFT, PCA-SIFT show its advantages in rotation and illumination changes.

Computational Tools for RF Structure Design

CERN Document Server

Jensen, E

2004-01-01

The Finite Differences Method and the Finite Element Method are the two principally employed numerical methods in modern RF field simulation programs. The basic ideas behind these methods are explained, with regard to available simulation programs. We then go through a list of characteristic parameters of RF structures, explaining how they can be calculated using these tools. With the help of these parameters, we introduce the frequency-domain and the time-domain calculations, leading to impedances and wake-fields, respectively. Subsequently, we present some readily available computer programs, which are in use for RF structure design, stressing their distinctive features and limitations. One final example benchmarks the precision of different codes for calculating the eigenfrequency and Q of a simple cavity resonator.

Featureous: infrastructure for feature-centric analysis of object-oriented software

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2010-01-01

The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand how user-observable program features are implemented and how their implementations relate to each other. It is worthwhile to improve this situation, since feature-centric program...... understanding and modification are essential during software evolution and maintenance. In this paper, we present an infrastructure built on top of the NetBeans IDE called Featureous that allows for rapid construction of tools for feature-centric analysis of object-oriented software. Our infrastructure...... encompasses a lightweight feature location mechanism, a number of analytical views and an API allowing for addition of third-party extensions. To form a common conceptual framework for future feature-centric extensions, we propose to structure feature centric analysis along three dimensions: perspective...

Structure of potato starch

DEFF Research Database (Denmark)

Bertoft, Eric; Blennow, Andreas

2016-01-01

Potato starch granules consist primarily of two tightly packed polysaccharides, amylose and amylopectin. Amylose, which amount for 20-30%, is the principal linear component, but a fraction is in fact slightly branched. Amylopectin is typically the major component and is extensively branched...... chains extending from the clusters. A range of enzymes is involved in the biosynthesis of the cluster structures and linear segments. These are required for sugar activation, chain elongation, branching, and trimming of the final branching pattern. As an interesting feature, potato amylopectin...... is substituted with low amounts of phosphate groups monoesterified to the C-3 and the C-6 carbons of the glucose units. They seem to align well in the granular structure and have tremendous effects on starch degradation in the potato and functionality of the refined starch. A specific dikinase catalyzes...

Innovation Management Perceptions of Principals

Science.gov (United States)

Bakir, Asli Agiroglu

2016-01-01

This study is aimed to determine the perceptions of principals about innovation management and to investigate whether there is a significant difference in this perception according to various parameters. In the study, descriptive research model is used and universe is consisted from principals who participated in "Acquiring Formation Course…

Numerical Simulation of Flow Features and Energy Exchange Physics in Near-Wall Region with Fluid-Structure Interaction

Science.gov (United States)

Zhang, Lixiang; Wang, Wenquan; Guo, Yakun

Large eddy simulation is used to explore flow features and energy exchange physics between turbulent flow and structure vibration in the near-wall region with fluid-structure interaction (FSI). The statistical turbulence characteristics in the near-wall region of a vibrating wall, such as the skin frictional coefficient, velocity, pressure, vortices, and the coherent structures have been studied for an aerofoil blade passage of a true three-dimensional hydroturbine. The results show that (i) FSI greatly strengthens the turbulence in the inner region of y+ < 25; and (ii) the energy exchange mechanism between the flow and the vibration depends strongly on the vibration-induced vorticity in the inner region. The structural vibration provokes a frequent action between the low- and high-speed streaks to balance the energy deficit caused by the vibration. The velocity profile in the inner layer near the vibrating wall has a significant distinctness, and the viscosity effect of the fluid in the inner region decreases due to the vibration. The flow features in the inner layer are altered by a suitable wall vibration.

Time Management for New Principals

Science.gov (United States)

Ruder, Robert

2008-01-01

Becoming a principal is a milestone in an educator's professional life. The principalship is an opportunity to provide leadership that will afford students opportunities to thrive in a nurturing and supportive environment. Despite the continuously expanding demands of being a new principal, effective time management will enable an individual to be…

Structural features facilitating tumor cell targeting and internalization by bleomycin and its disaccharide.

Science.gov (United States)

Yu, Zhiqiang; Paul, Rakesh; Bhattacharya, Chandrabali; Bozeman, Trevor C; Rishel, Michael J; Hecht, Sidney M

2015-05-19

We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide-cytotoxin conjugates.

Age-related changes in dermal fiber-like structures in facial cheeks.

Science.gov (United States)

Mizukoshi, K; Hirayama, K

2017-08-01

Despite recent progress in non-invasive measurement methods, such as in vivo laser confocal microscopy (CLSM), it is difficult to quantitatively measure age-related changes in dermal fibrous structures in the face using these methods and qualitative characteristics. We used characteristics extracted from the analysis of CLSM images to quantitatively investigate the effects of aging on dermal fibrous structures in the face. CLSM images of dermal fibrous structures were obtained from 90 Japanese females, ranging in age from 20 to 60 years. The feature values of CLSM images were extracted using image analysis methods, such as short-line segment-matching processing and spatial frequency analysis. The qualitative characteristics of the dermal fibrous structures in the CLSM images were obtained by principal component analysis (PCA) of these feature values. The fibrous structures were scored on the basis of qualitative characteristics and then age-related changes in the scores among the subjects were quantitatively evaluated. The PCA results showed that there were two characteristics in the images of fibrous structures: clearness and directionality. The clearness of fibrous structures decreased and directionality isotropy increased with age. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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)

The Discourse Structure and Linguistic Features of Research Article Abstracts in English by Indonesian Academics

Science.gov (United States)

Arsyad, Safnil

2014-01-01

To effectively teach university lecturers or students to write a good research article (RA) abstract for publication in international journals, instructors need to know the present characteristics of abstracts written published in such journals. This study examines the discourse structure and linguistic features of RA abstracts written in English…

Bureaucratic Control and Principal Role.

Science.gov (United States)

Bezdek, Robert; And Others

The purposes of this study were to determine the manner in which the imposition of increased bureaucratic control over principals influenced their allocation of time to tasks and to investigate principals' perceptions of the changes in their roles brought about by this increased control. The specific bureaucratic control system whose effects were…

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2014-01-01

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

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.

School Principals' Sources of Knowledge

Science.gov (United States)

Perkins, Arland Early

2014-01-01

The purpose of this study was to determine what sources of professional knowledge are available to principals in 1 rural East Tennessee school district. Qualitative research methods were applied to gain an understanding of what sources of knowledge are used by school principals in 1 rural East Tennessee school district and the barriers they face…

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.

New Principal Coaching as a Safety Net

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Celoria, Davide; Roberson, Ingrid

2015-01-01

This study examines new principal coaching as an induction process and explores the emotional dimensions of educational leadership. Twelve principal coaches and new principals--six of each--participated in this qualitative study that employed emergent coding (Creswell, 2008; Denzin, 2005; Glaser & Strauss, 1998; Spradley, 1979). The major…

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 ...

Importance of an Effective Principal-Counselor Relationship

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Edwards, LaWanda; Grace, Ronald; King, Gwendolyn

2014-01-01

An effective relationship between the principal and school counselor is essential when improving student achievement. To have an effective relationship, there must be communication, trust and respect, leadership, and collaborative planning between the principal and school counselor (College Board, 2011). Principals and school counselors are both…

Feature Binding in Zebrafish

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P Neri

2012-07-01

Full Text Available Binding operations are primarily ascribed to cortex or similarly complex avian structures. My experiments show that the zebrafish, a lower vertebrate lacking cortex, supports visual feature binding of form and motion for the purpose of social behavior. These results challenge the notion that feature binding may require highly evolved neural structures and demonstrate that the nervous system of lower vertebrates can afford unexpectedly complex computations.

Management Of Indiscipline Among Teachers By Principals Of ...

African Journals Online (AJOL)

This study compared the management of indiscipline among teachers by public and private school principals in Akwa Ibom State. The sample comprised four hundred and fifty (450) principals/vice principals randomly selected from a population of one thousand, four hundred and twenty eight (1,428) principals. The null ...

What Do Effective Principals Do?

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Protheroe, Nancy

2011-01-01

Much has been written during the past decade about the changing role of the principal and the shift in emphasis from manager to instructional leader. Anyone in education, and especially principals themselves, could develop a mental list of responsibilities that fit within each of these realms. But research makes it clear that both those aspects of…

Chemistry and Structure-Activity Relationships of Psychedelics.

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Nichols, David E

2018-01-01

This chapter will summarize structure-activity relationships (SAR) that are known for the classic serotonergic hallucinogens (aka psychedelics), focusing on the three chemical types: tryptamines, ergolines, and phenethylamines. In the brain, the serotonin 5-HT 2A receptor plays a key role in regulation of cortical function and cognition, and also appears to be the principal target for hallucinogenic/psychedelic drugs such as LSD. It is one of the most extensively studied of the 14 known types of serotonin receptors. Important structural features will be identified for activity and, where possible, those that the psychedelics have in common will be discussed. Because activation of the 5-HT 2A receptor is the principal mechanism of action for psychedelics, compounds with 5-HT 2A agonist activity generally are quickly discarded by the pharmaceutical industry. Thus, most of the research on psychedelics can be related to activation of 5-HT 2A receptors. Therefore, much of the discussion will include not only clinical or anecdotal studies, but also will consider data from animal models as well as a certain amount of molecular pharmacology where it is known.

The Principal as Academician: The Renewed Voice.

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McAvoy, Brenda, Ed.

This collection of essays was written by principals who participated in the 1986-87 Humanities Seminar sponsored by the Principals' Institute of Georgia State University. The focus was "The Evolution of Intellectual Leadership." The roles of the principal as philosopher, historian, ethnician, writer and team member are examined through…

Relevance of echo-structure and texture features

DEFF Research Database (Denmark)

Karemore, Gopal; Mullick, Jhinuk Basu; KV, Dr. Rajagopal

2010-01-01

Aim: Echostructure is an essential parameter for the evaluation of circumscribed lesions and can be described as a texture feature on ultrasound images. Present study evaluates the possibility of distinguishing between benign and malignant breast tumors using various texture features. Materials...... and Methods: 58 cases of breast tumor (29 each from benign and malignant) were documented under standardized conditions using a linear array machine and 7.5 MHz transducer. In each sonographic image, ROI of tumor was marked and then subjected to the evaluation of tumor status using five parameters of second...... performance ROC= 0.78(pbenign and malignant tumors. It also reveals that when evaluating images of a breast tumor...

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Leadership Coaching for Principals: A National Study

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Wise, Donald; Cavazos, Blanca

2017-01-01

Surveys were sent to a large representative sample of public school principals in the United States asking if they had received leadership coaching. Comparison of responses to actual numbers of principals indicates that the sample represents the first national study of principal leadership coaching. Results indicate that approximately 50% of all…

Riccati transformations and principal solutions of discrete linear systems

International Nuclear Information System (INIS)

Ahlbrandt, C.D.; Hooker, J.W.

1984-01-01

Consider a second-order linear matrix difference equation. A definition of principal and anti-principal, or recessive and dominant, solutions of the equation are given and the existence of principal and anti-principal solutions and the essential uniqueness of principal solutions is proven

Feature selection and nearest centroid classification for protein mass spectrometry

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Levner Ilya

2005-03-01

Full Text Available Abstract Background The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. Results This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. Conclusion This study tested a number of popular feature

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

Science.gov (United States)

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Principals' Collaborative Roles as Leaders for Learning

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Kitchen, Margaret; Gray, Susan; Jeurissen, Maree

2016-01-01

This article draws on data from three multicultural New Zealand primary schools to reconceptualize principals' roles as leaders for learning. In doing so, the writers build on Sinnema and Robinson's (2012) article on goal setting in principal evaluation. Sinnema and Robinson found that even principals hand-picked for their experience fell short on…

Crystal structure and nanotopographical features on the surface of heat-treated and anodized porous titanium biomaterials produced using selective laser melting

Energy Technology Data Exchange (ETDEWEB)

Amin Yavari, S., E-mail: s.aminyavari@tudelft.nl [Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD Delft (Netherlands); FT Innovations BV, Braamsluiper 1, 5831 PW Boxmeer (Netherlands); Wauthle, R. [KU Leuven, Department of Mechanical Engineering, Section Production Engineering, Machine Design and Automation (PMA), Celestijnenlaan 300B, 3001 Leuven (Belgium); LayerWise NV, Kapeldreef 60, Leuven (Belgium); Böttger, A.J. [Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD Delft (Netherlands); Schrooten, J. [Department of Metallurgy and Materials Engineering, KU Leuven, Kasteelpark Arenberg 44 PB 2450, 3001 Heverlee (Belgium); Weinans, H. [Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD Delft (Netherlands); Department of Orthopedics and Department of Rheumatology, UMC Utrecht, Heidelberglaan 100, 3584 CX Utrecht (Netherlands); Zadpoor, A.A. [Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD Delft (Netherlands)

2014-01-30

Porous titanium biomaterials manufactured using additive manufacturing techniques such as selective laser melting are considered promising materials for orthopedic applications where the biomaterial needs to mimic the properties of bone. Despite their appropriate mechanical properties and the ample pore space they provide for bone ingrowth and osseointegration, porous titanium structures have an intrinsically bioinert surface and need to be subjected to surface bio-functionalizing procedures to enhance their in vivo performance. In this study, we used a specific anodizing process to build a hierarchical oxide layer on the surface of porous titanium structures made by selective laser melting of Ti6Al4V ELI powder. The hierarchical structure included both nanotopographical features (nanotubes) and micro-features (micropits). After anodizing, the biomaterial was heat treated in Argon at different temperatures ranging between 400 and 600 °C for either 1 or 2 h to improve its bioactivity. The effects of applied heat treatment on the crystal structure of TiO{sub 2} nanotubes and the nanotopographical features of the surface were studied using scanning electron microscopy and X-ray diffraction. It was shown that the transition from the initial crystal structure, i.e. anatase, to rutile occurs between 500 and 600 °C and that after 2 h of heat treatment at 600 °C the crystal structure is predominantly rutile. The nanotopographical features of the surface were found to be largely unchanged for heat treatments carried out at 500 °C or below, whereas they were partially or largely disrupted after heat treatment at 600 °C. The possible implications of these findings for the bioactivity of porous titanium structures are discussed.

Value of Coaching in Building Leadership Capacity of Principals in Urban Schools

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Farver, Anita R.; Holt, Carleton R.

2015-01-01

The purpose of this qualitative case study was to understand how coaching support structures enabled and sustained leadership practices of urban principals. The study investigated how the intervention of coaching for academic leaders can serve as evidence-based professional development for building leadership capacity. The central focus was on…

Structural Features Facilitating Tumor Cell Targeting and Internalization by Bleomycin and Its Disaccharide

Science.gov (United States)

2016-01-01

We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide–cytotoxin conjugates. PMID:25905565

Medium-range structural properties of vitreous germania obtained through first-principles analysis of vibrational spectra.

Science.gov (United States)

Giacomazzi, Luigi; Umari, P; Pasquarello, Alfredo

2005-08-12

We analyze the principal vibrational spectra of vitreous GeO(2) and derive therefrom structural properties referring to length scales beyond the basic tetrahedral unit. We generate a model structure that yields a neutron structure factor in accord with experiment. The inelastic-neutron, the infrared, and the Raman spectra, calculated within a density-functional approach, also agree with respective experimental spectra. The accord for the Raman spectrum supports a Ge-O-Ge angle distribution centered at 135 degrees. The Raman feature X(2) is found to result from vibrations in three-membered rings, and therefore constitutes a distinctive characteristic of the medium-range structure.

SoftSearch: integration of multiple sequence features to identify breakpoints of structural variations.

Directory of Open Access Journals (Sweden)

Steven N Hart

Full Text Available BACKGROUND: Structural variation (SV represents a significant, yet poorly understood contribution to an individual's genetic makeup. Advanced next-generation sequencing technologies are widely used to discover such variations, but there is no single detection tool that is considered a community standard. In an attempt to fulfil this need, we developed an algorithm, SoftSearch, for discovering structural variant breakpoints in Illumina paired-end next-generation sequencing data. SoftSearch combines multiple strategies for detecting SV including split-read, discordant read-pair, and unmated pairs. Co-localized split-reads and discordant read pairs are used to refine the breakpoints. RESULTS: We developed and validated SoftSearch using real and synthetic datasets. SoftSearch's key features are 1 not requiring secondary (or exhaustive primary alignment, 2 portability into established sequencing workflows, and 3 is applicable to any DNA-sequencing experiment (e.g. whole genome, exome, custom capture, etc.. SoftSearch identifies breakpoints from a small number of soft-clipped bases from split reads and a few discordant read-pairs which on their own would not be sufficient to make an SV call. CONCLUSIONS: We show that SoftSearch can identify more true SVs by combining multiple sequence features. SoftSearch was able to call clinically relevant SVs in the BRCA2 gene not reported by other tools while offering significantly improved overall performance.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Science.gov (United States)

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

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...

Principals as Assessment Leaders in Rural Schools

Science.gov (United States)

Renihan, Patrick; Noonan, Brian

2012-01-01

This article reports a study of rural school principals' assessment leadership roles and the impact of rural context on their work. The study involved three focus groups of principals serving small rural schools of varied size and grade configuration in three systems. Principals viewed assessment as a matter of teacher accountability and as a…

Principals: Learn P.R. Survival Skills.

Science.gov (United States)

Reep, Beverly B.

1988-01-01

School building level public relations depends on the principal or vice principal. Strategies designed to enhance school public relations programs include linking school and community, working with the press, and keeping morale high inside the school. (MLF)

THE GEOMORPHOLOGIC FEATURES OF INTRUSIVE MAGMATIC STRUCTURES FROM BÂRGĂU MOUNTAINS (EASTERN CARPATHIANS, ROMANIA

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Ioan Bâca

2016-08-01

Full Text Available Igneous intrusive structures from Bârgău Mountains belong to the group of central Neogene volcanic chain of the Eastern Carpathians of Romania. The evolution of the relief developed on these structures are three main stages: the stage of injection of structures (Pannonian, the stage of uncovering of igneous intrusive bodies from Oligo-Miocene sedimentary cover (Pliocene, and the stage of subaerial modeling of magmatic bodies (Pliocene-current.In those circumstances, the geodiversity of intrusive magmatic structures from Bârgău Mountains is represented by several types of landforms such as: polycyclic landforms (erosional levels, structural landforms (the configuration of igneous intrusive structures, petrographic landforms (andesites, lithological contact, fluvial landforms (valleys, slopes, ridges, periglacial landforms (cryogenic and crionival landforms, biogenic and anthropogenic landforms. This study highlights certain features of the landforms modeled on igneous intrusive bodies with the aim of developing some strategy for tourism recovery by local and county authorities.

Principal minors and rhombus tilings

International Nuclear Information System (INIS)

Kenyon, Richard; Pemantle, Robin

2014-01-01

The algebraic relations between the principal minors of a generic n × n matrix are somewhat mysterious, see e.g. Lin and Sturmfels (2009 J. Algebra 322 4121–31). We show, however, that by adding in certain almost principal minors, the ideal of relations is generated by translations of a single relation, the so-called hexahedron relation, which is a composition of six cluster mutations. We give in particular a Laurent-polynomial parameterization of the space of n × n matrices, whose parameters consist of certain principal and almost principal minors. The parameters naturally live on vertices and faces of the tiles in a rhombus tiling of a convex 2n-gon. A matrix is associated to an equivalence class of tilings, all related to each other by Yang–Baxter-like transformations. By specializing the initial data we can similarly parameterize the space of Hermitian symmetric matrices over R,C or H the quaternions. Moreover by further specialization we can parametrize the space of positive definite matrices over these rings. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras mathematical physics’. (paper)

Social inequality and the principal-agent problem

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S. A. Barkov

2014-01-01

Full Text Available Social inequality has a lot of reasons. One of them is managerial. Managerial duties are paid so high that it set the stage for discontent not only within individual organizations, but also entire countries. The principles (the people in the state and shareholders in the corporation because the specific structure of their competencies can’t totally control agents (officials and managers. As to agents, the moral imperative to act for the good of the social system and reputation considerations (to be remembered as a good ruler or a genius manager can easily rejected when there is an opportunity to make millions dollars without special efforts. As a result hundreds thousands of people across the globe in the corporate and government structures are enriched through specific solutions to the principal-agent problem, and social inequality becomes an integral inevitable part of the modern economy.

Differential impairment of social cognition factors in bipolar disorder with and without psychotic features and schizophrenia.

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Thaler, Nicholas S; Allen, Daniel N; Sutton, Griffin P; Vertinski, Mary; Ringdahl, Erik N

2013-12-01

While it is well-established that patients with schizophrenia and bipolar disorder exhibit deficits in social cognition, few studies have separately examined bipolar disorder with and without psychotic features. The current study addressed this gap by comparing patients with bipolar disorder with (BD+) and without (BD-) psychotic features, patients with schizophrenia (SZ), and healthy controls (NC) across social cognitive measures. Principal factor analysis on five social cognition tasks extracted a two-factor structure comprised of social/emotional processing and theory of mind. Factor scores were compared among the four groups. Results identified differential patterns of impairment between the BD+ and BD- group on the social/emotional processing factor while all clinical groups performed poorer than controls on the theory of mind factor. This provides evidence that a history of psychosis should be taken into account while evaluating social cognition in patients with bipolar disorder and also raises hypotheses about the relationship between social cognition and psychosis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

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Walsh Ian

2006-09-01

Full Text Available Abstract Background We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Cα traces for short proteins (up to 200 amino acids. Results The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA, 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Cα traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348. The majority of the servers, including the Cα trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. Conclusion All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/.

Statewide Data on Supply and Demand of Principals after Policy Changes to Principal Preparation in Illinois

Science.gov (United States)

Haller, Alicia; Hunt, Erika

2016-01-01

Research has demonstrated that principals have a powerful impact on school improvement and student learning. Principals play a vital role in recruiting, developing, and retaining effective teachers; creating a school-wide culture of learning; and implementing a continuous improvement plan aimed at increasing student achievement. Leithwood, Louis,…

Structure in galactic soft X-ray features

International Nuclear Information System (INIS)

Davelaar, J.

1979-01-01

Observations are described of the soft X-ray background in a part of the northern hemisphere in the energy range 0.06 - 3.0 keV. The X-ray instruments, placed onboard a sounding rocket, are a one-dimensional focusing collector with multi-cell proportional counters in the focal plane and eight large area counters on deployable panels. A description of the instruments and their preflight calibration is given. Precautions were taken to prevent UV sensitivity of the X-ray instruments. The observation program, which consisted of a number of pre-programmed slow scans, is outlined. The spectral date on the soft X-ray background in these and previous observations showed that at least two components of different temperature are present. A low temperature component of approximately (3-10)x10 5 is found all over the sky. Components of higher temperature approximately 3x10 6 K are found in regions of soft X-ray enhancement; The North Polar Spur has been observed in two scans at the galactic latitude b=25 0 and b=75 0 . The X-ray ridge structure is found to be strongly energy dependent. The low energy data ( 0 reveals two separate emission features on the ridge, both probably of finite extensions (approximately equal to 0 0 .5). A wider X-ray ridge (approximately equal to 10 0 ) is observed above 0.4 keV. (Auth.)

An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

Science.gov (United States)

Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

2015-12-01

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

A Study of Feature Extraction Using Divergence Analysis of Texture Features

Science.gov (United States)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Feature Inference Learning and Eyetracking

Science.gov (United States)

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

Directory of Open Access Journals (Sweden)

R. Youmaran

2012-01-01

Full Text Available This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal-Component Analysis- (PCA- and Independent-Component Analysis- (ICA- based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278 bits for PCA and 288 bits for ICA iris features using Masek's iris recognition scheme. This value approximately matches previous estimates of iris information content.

The Deputy Principal Instructional Leadership Role and Professional Learning: Perceptions of Secondary Principals, Deputies and Teachers

Science.gov (United States)

Leaf, Ann; Odhiambo, George

2017-01-01

Purpose: The purpose of this paper is to report on a study examining the perceptions of secondary principals, deputies and teachers, of deputy principal (DP) instructional leadership (IL), as well as deputies' professional learning (PL) needs. Framed within an interpretivist approach, the specific objectives of this study were: to explore the…

Measuring Principals' Effectiveness: Results from New Jersey's First Year of Statewide Principal Evaluation. REL 2016-156

Science.gov (United States)

Herrmann, Mariesa; Ross, Christine

2016-01-01

States and districts across the country are implementing new principal evaluation systems that include measures of the quality of principals' school leadership practices and measures of student achievement growth. Because these evaluation systems will be used for high-stakes decisions, it is important that the component measures of the evaluation…

Structural and functional features of lysine acetylation of plant and animal tubulins.

Science.gov (United States)

Rayevsky, Alexey V; Sharifi, Mohsen; Samofalova, Dariya A; Karpov, Pavel A; Blume, Yaroslav B

2017-10-10

The study of the genome and the proteome of different species and representatives of distinct kingdoms, especially detection of proteome via wide-scaled analyses has various challenges and pitfalls. Attempts to combine all available information together and isolate some common features for determination of the pathway and their mechanism of action generally have a highly complicated nature. However, microtubule (MT) monomers are highly conserved protein structures, and microtubules are structurally conserved from Homo sapiens to Arabidopsis thaliana. The interaction of MT elements with microtubule-associated proteins and post-translational modifiers is fully dependent on protein interfaces, and almost all MT modifications are well described except acetylation. Crystallography and interactome data using different approaches were combined to identify conserved proteins important in acetylation of microtubules. Application of computational methods and comparative analysis of binding modes generated a robust predictive model of acetylation of the ϵ-amino group of Lys40 in α-tubulins. In turn, the model discarded some probable mechanisms of interaction between elements of interest. Reconstruction of unresolved protein structures was carried out with modeling by homology to the existing crystal structure (PDBID: 1Z2B) from B. taurus using Swiss-model server, followed by a molecular dynamics simulation. Docking of the human tubulin fragment with Lys40 into the active site of α-tubulin acetyltransferase, reproduces the binding mode of peptidomimetic from X-ray structure (PDBID: 4PK3). © 2017 International Federation for Cell Biology.

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

Science.gov (United States)

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Principals' Perceptions of School Public Relations

Science.gov (United States)

Morris, Robert C.; Chan, Tak Cheung; Patterson, Judith

2009-01-01

This study was designed to investigate school principals' perceptions on school public relations in five areas: community demographics, parental involvement, internal and external communications, school council issues, and community resources. Findings indicated that principals' concerns were as follows: rapid population growth, change of…

Principal-vector-directed fringe-tracking technique.

Science.gov (United States)

Zhang, Zhihui; Guo, Hongwei

2014-11-01

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

Solubility on compact subsets for differential equations with real principal pencil of symbols

International Nuclear Information System (INIS)

Shananin, N A

2006-01-01

The central result is a theorem on the solubility on compact subsets for differential equations of quasiprincipal type with real principal pencil of symbols. The proof is based on the analysis of the microlocal structure of the singularities of solutions of equations in this class.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

Science.gov (United States)

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Whose Perception of Principal Instructional Leadership? Principal-Teacher Perceptual (Dis)agreement and Its Influence on Teacher Collaboration

Science.gov (United States)

Park, Joo-Ho; Ham, Seung-Hwan

2016-01-01

This study examines teacher collaboration across three Asia-Pacific countries (Australia, Malaysia, and South Korea), focusing on the possibility that principal-teacher perceptual disagreement regarding principal instructional leadership performance may impede progress toward a school organizational condition conducive to collaborative teacher…

Students' Demand for Smartphones: Structural Relationships of Product Features, Brand Name, Product Price and Social Infuence

Science.gov (United States)

Suki, Norazah Mohd

2013-01-01

Purpose: The study aims to examine structural relationships of product features, brand name, product price and social influence with demand for Smartphones among Malaysian students'. Design/methodology/approach: Data collected from 320 valid pre-screened university students studying at the pubic higher learning institution in Federal Territory of…

Guilt by Association: The 13 Micron Dust Emission Feature and Its Correlation to Other Gas and Dust Features

Science.gov (United States)

Sloan, G. C.; Kraemer, Kathleen E.; Goebel, J. H.; Price, Stephan D.

2003-09-01

A study of all full-scan spectra of optically thin oxygen-rich circumstellar dust shells in the database produced by the Short Wavelength Spectrometer on ISO reveals that the strength of several infrared spectral features correlates with the strength of the 13 μm dust feature. These correlated features include dust features at 19.8 and 28.1 μm and the bands produced by warm carbon dioxide molecules (the strongest of which are at 13.9, 15.0, and 16.2 μm). The database does not provide any evidence for a correlation of the 13 μm feature with a dust feature at 32 μm, and it is more likely that a weak emission feature at 16.8 μm arises from carbon dioxide gas rather than dust. The correlated dust features at 13, 20, and 28 μm tend to be stronger with respect to the total dust emission in semiregular and irregular variables associated with the asymptotic giant branch than in Mira variables or supergiants. This family of dust features also tends to be stronger in systems with lower infrared excesses and thus lower mass-loss rates. We hypothesize that the dust features arise from crystalline forms of alumina (13 μm) and silicates (20 and 28 μm). Based on observations with the ISO, a European Space Agency (ESA) project with instruments funded by ESA member states (especially the Principal Investigator countries: France, Germany, the Netherlands, and the United Kingdom) and with the participation of the Institute of Space and Astronautical Science (ISAS) and the National Aeronautics and Space Administration (NASA).

Featureous: an Integrated Approach to Location, Analysis and Modularization of Features in Java Applications

DEFF Research Database (Denmark)

Olszak, Andrzej

, it is essential that features are properly modularized within the structural organization of software systems. Nevertheless, in many object-oriented applications, features are not represented explicitly. Consequently, features typically end up scattered and tangled over multiple source code units......, such as architectural layers, packages and classes. This lack of modularization is known to make application features difficult to locate, to comprehend and to modify in isolation from one another. To overcome these problems, this thesis proposes Featureous, a novel approach to location, analysis and modularization...... quantitative and qualitative results suggest that Featureous succeeds at efficiently locating features in unfamiliar codebases, at aiding feature-oriented comprehension and modification, and at improving modularization of features using Java packages....

Including the gifted learner: perceptions of South African teachers and principals

Directory of Open Access Journals (Sweden)

Marietjie Oswald

2013-01-01

Full Text Available We report the findings of a qualitative study embedded in an interpretive paradigm to determine the perceptions of South African primary school teachers and principals regarding the inclusion of learners considered gifted. Eight principals and 16 classroom teachers in the Foundation Phase (Grades 1-3 in public primary schools situated in communities that were representative of the different socio-economic and language groups in the Western Cape province participated in the study. Qualitative data collection methods included in-depth individual semi-structured interviews with the eight principals and two semi-structuredfocus group interviews with the 16 classroom teachers. Qualitative content analysis revealed the following themes: inclusive education and the learner who is gifted; curriculum differentiation; obstacles to curriculum differentiation; and possible solutions for more effectively including the gifted learner. Despite their diversity in terms of culture, language and positioning by the previous apartheid regime, the participants acknowledged the marginalisation by default of gifted learners. Gifted learners were most often those who were not receiving appropriate education and support and data suggested that a particular drive for the inclusion of gifted learners was absent in the agenda of education authorities.

New pulser for principal PO power

International Nuclear Information System (INIS)

Coudert, G.

1984-01-01

The pulser of the principal power of the PS is the unit that makes it possible to generate the reference function of the voltage of the principal magnet. This function depends on time and on the magnetic field of the magnet. It also generates various synchronization and reference pulses

An Examination of Principal Job Satisfaction

Science.gov (United States)

Pengilly, Michelle M.

2010-01-01

As education continues to succumb to deficits in budgets and increasingly high levels of student performance to meet the federal and state mandates, the quest to sustain and retain successful principals is imperative. The National Association of School Boards (1999) portrays effective principals as "linchpins" of school improvement and…

The Succession of a School Principal.

Science.gov (United States)

Fauske, Janice R.; Ogawa, Rodney T.

Applying theory from organizational and cultural perspectives to succession of principals, this study observes and records the language and culture of a small suburban elementary school. The study's procedures included analyses of shared organizational understandings as well as identification of the principal's influence on the school. Analyses of…

Social Media Strategies for School Principals

Science.gov (United States)

Cox, Dan; McLeod, Scott

2014-01-01

The purpose of this qualitative study was to describe, analyze, and interpret the experiences of school principals who use multiple social media tools with stakeholders as part of their comprehensive communications practices. Additionally, it examined why school principals have chosen to communicate with their stakeholders through social media.…

Synchronous machines. General principles and structures; Machines synchrones. Principes generaux et structures

Energy Technology Data Exchange (ETDEWEB)

Ben Ahmed, H.; Feld, G.; Multon, B. [Ecole Normale Superieure de Cachan, Lab. SATIE, Systemes et Applications des Technologies de l' Information et de l' Energie, UMR CNRS 8029, 94 (France); Bernard, N. [Institut Universitaire de Saint-Nazaire, Institut de Recherche en Electrotechnique et Electronique de Nantes Atlantique (IREENA), 44 - Nantes (France)

2005-10-01

Power generation is mainly performed by synchronous rotating machines which consume about a third of the world primary energy. Electric motors used in industrial applications convert about two thirds of this electricity. Therefore, synchronous machines are present everywhere at different scales, from micro-actuators of few micro-watts to thermo-mechanical production units of more than 1 GW, and represent a large variety of structures which have in common the synchronism between the frequency of the power supply currents and the relative movement of the fixed part with respect to the mobile part. Since several decades, these machines are more and more used as variable speed motors with permanent magnets. The advances in power electronics have contributed to the widening of their use in various applications with a huge range of powers. This article presents the general principle of operation of electromechanical converters of synchronous type: 1 - electromechanical conversion in electromagnetic systems: basic laws and elementary structures (elementary structure, energy conversion cycle, case of a system working in linear magnetic regime), rotating fields structure (magneto-motive force and Ferraris theorem, superficial air gap permeance, air gap magnetic induction, case of a permanent magnet inductor, magnetic energy and electromagnetic torque, conditions for reaching a non-null average torque, application to common cases); 2 - constitution, operation modes and efficiency: constitution and main types of synchronous machines, efficiency - analysis by similarity laws (other expression of the electromagnetic torque, thermal limitation in permanent regime, scale effects, effect of pole pairs number, examples of efficiencies and domains of use), operation modes. (J.S.)

Slow feature analysis: unsupervised learning of invariances.

Science.gov (United States)

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Dominant color and texture feature extraction for banknote discrimination

Science.gov (United States)

Wang, Junmin; Fan, Yangyu; Li, Ning

2017-07-01

Banknote discrimination with image recognition technology is significant in many applications. The traditional methods based on image recognition only recognize the banknote denomination without discriminating the counterfeit banknote. To solve this problem, we propose a systematical banknote discrimination approach with the dominant color and texture features. After capturing the visible and infrared images of the test banknote, we first implement the tilt correction based on the principal component analysis (PCA) algorithm. Second, we extract the dominant color feature of the visible banknote image to recognize the denomination. Third, we propose an adaptively weighted local binary pattern with "delta" tolerance algorithm to extract the texture features of the infrared banknote image. At last, we discriminate the genuine or counterfeit banknote by comparing the texture features between the test banknote and the benchmark banknote. The proposed approach is tested using 14,000 banknotes of six different denominations from Chinese yuan (CNY). The experimental results show 100% accuracy for denomination recognition and 99.92% accuracy for counterfeit banknote discrimination.

Infrared face recognition based on LBP histogram and KW feature selection

Science.gov (United States)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

The Interdependence of Principal School Leadership and Student Achievement

Science.gov (United States)

Soehner, David; Ryan, Thomas

2011-01-01

This review illuminated principal school leadership as a variable that impacted achievement. The principal as school leader and manager was explored because these roles were thought to impact student achievement both directly and indirectly. Specific principal leadership behaviors and principal effectiveness were explored as variables potentially…

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

Science.gov (United States)

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

District Leadership for Effective Principal Evaluation and Support

Science.gov (United States)

Kimball, Steven M.; Arrigoni, Jessica; Clifford, Matthew; Yoder, Maureen; Milanowski, Anthony

2015-01-01

Research demonstrating principals' impact on student learning outcomes has fueled the shift from principals as facilities managers to an emphasis on instructional leadership (Hallinger & Heck, 1996; Leithwood, Louis, Anderson, & Wahlstrom, 2004; Marzano, Waters, & McNulty, 2005). Principals are under increasing pressure to carry out…

School Restructuring and the Dilemmas of Principals' Work.

Science.gov (United States)

Wildy, Helen; Louden, William

2000-01-01

The complexity of principals' work may be characterized according to three dilemmas: accountability, autonomy, and efficiency. Narrative vignettes of 74 Australian principals revealed that principals were fair and inclusive. When faced with restructuring dilemmas, however, they favored strong over shared leadership, efficiency over collaboration,…

Do Principals Fire the Worst Teachers?

Science.gov (United States)

Jacob, Brian A.

2011-01-01

This article takes advantage of a unique policy change to examine how principals make decisions regarding teacher dismissal. In 2004, the Chicago Public Schools (CPS) and Chicago Teachers Union signed a new collective bargaining agreement that gave principals the flexibility to dismiss probationary teachers for any reason and without the…

Revising the Role of Principal Supervisor

Science.gov (United States)

Saltzman, Amy

2016-01-01

In Washington, D.C., and Tulsa, Okla., districts whose efforts are supported by the Wallace Foundation, principal supervisors concentrate on bolstering their principals' work to improve instruction, as opposed to focusing on the managerial or operational aspects of running a school. Supervisors oversee fewer schools, which enables them to provide…

The Principal's Guide to Grant Success.

Science.gov (United States)

Bauer, David G.

This book provides principals of public and private elementary and middle schools with a step-by-step approach for developing a system that empowers faculty, staff, and the school community in attracting grant funds. Following the introduction, chapter 1 discusses the principal's role in supporting grantseeking. Chapter 2 describes how to…

Principals, agents and research programmes

OpenAIRE

Elizabeth Shove

2003-01-01

Research programmes appear to represent one of the more powerful instruments through which research funders (principals) steer and shape what researchers (agents) do. The fact that agents navigate between different sources and styles of programme funding and that they use programmes to their own ends is readily accommodated within principal-agent theory with the help of concepts such as shirking and defection. Taking a different route, I use three examples of research programming (by the UK, ...

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

Negligence--When Is the Principal Liable? A Legal Memorandum.

Science.gov (United States)

Stern, Ralph D., Ed.

Negligence, a tort liability, is defined, discussed, and reviewed in relation to several court decisions involving school principals. The history of liability suits against school principals suggests that a reasonable, prudent principal can avoid legal problems. Ten guidelines are presented to assist principals in avoiding charges of negligence.…

The Principal and the Law. Elementary Principal Series No. 7.

Science.gov (United States)

Doverspike, David E.; Cone, W. Henry

Developments over the past 25 years in school-related legal issues in elementary schools have significantly changed the principal's role. In 1975, a decision of the U.S. Supreme Court established three due-process guidelines for short-term suspension. The decision requires student notification of charges, explanation of evidence, and an informal…

The Structure of Neurexin 1[alpha] Reveals Features Promoting a Role as Synaptic Organizer

Energy Technology Data Exchange (ETDEWEB)

Chen, Fang; Venugopal, Vandavasi; Murray, Beverly; Rudenko, Gabby (Michigan)

2014-10-02

{alpha}-Neurexins are essential synaptic adhesion molecules implicated in autism spectrum disorder and schizophrenia. The {alpha}-neurexin extracellular domain consists of six LNS domains interspersed by three EGF-like repeats and interacts with many different proteins in the synaptic cleft. To understand how {alpha}-neurexins might function as synaptic organizers, we solved the structure of the neurexin 1{alpha} extracellular domain (n1{alpha}) to 2.65 {angstrom}. The L-shaped molecule can be divided into a flexible repeat I (LNS1-EGF-A-LNS2), a rigid horseshoe-shaped repeat II (LNS3-EGF-B-LNS4) with structural similarity to so-called reelin repeats, and an extended repeat III (LNS5-EGF-B-LNS6) with controlled flexibility. A 2.95 {angstrom} structure of n1{alpha} carrying splice insert SS3 in LNS4 reveals that SS3 protrudes as a loop and does not alter the rigid arrangement of repeat II. The global architecture imposed by conserved structural features enables {alpha}-neurexins to recruit and organize proteins in distinct and variable ways, influenced by splicing, thereby promoting synaptic function.

New Features in the Lipid A Structure of Brucella suis and Brucella abortus Lipopolysaccharide

Science.gov (United States)

Casabuono, Adriana C.; Czibener, Cecilia; Del Giudice, Mariela G.; Valguarnera, Ezequiel; Ugalde, Juan E.; Couto, Alicia S.

2017-12-01

Brucellaceae are Gram-negative bacteria that cause brucellosis, one of the most distributed worldwide zoonosis, transmitted to humans by contact with either infected animals or their products. The lipopolysaccharide exposed on the cell surface has been intensively studied and is considered a major virulence factor of Brucella. In the last years, structural studies allowed the determination of new structures in the core oligosaccharide and the O-antigen of this lipopolysaccharide. In this work, we have reinvestigated the lipid A structure isolated from B. suis and B. abortus lipopolysaccharides. A detailed study by MALDI-TOF mass spectrometry in the positive and negative ion modes of the lipid A moieties purified from both species was performed. Interestingly, a new feature was detected: the presence of a pyrophosphorylethanolamine residue substituting the backbone. LID-MS/MS analysis of some of the detected ions allowed assurance that the Lipid A structure composed by the diGlcN3N disaccharide, mainly hexa-acylated and penta-acylated, bearing one phosphate and one pyrophosphorylethanolamine residue. [Figure not available: see fulltext.

Endotoxin Structures in the Psychrophiles Psychromonas marina and Psychrobacter cryohalolentis Contain Distinctive Acyl Features

Directory of Open Access Journals (Sweden)

Charles R. Sweet

2014-07-01

Full Text Available Lipid A is the essential component of endotoxin (Gram-negative lipopolysaccharide, a potent immunostimulatory compound. As the outer surface of the outer membrane, the details of lipid A structure are crucial not only to bacterial pathogenesis but also to membrane integrity. This work characterizes the structure of lipid A in two psychrophiles, Psychromonas marina and Psychrobacter cryohalolentis, and also two mesophiles to which they are related using MALDI-TOF MS and fatty acid methyl ester (FAME GC-MS. P. marina lipid A is strikingly similar to that of Escherichia coli in organization and total acyl size, but incorporates an unusual doubly unsaturated tetradecadienoyl acyl residue. P. cryohalolentis also shows structural organization similar to a closely related mesophile, Acinetobacter baumannii, however it has generally shorter acyl constituents and shows many acyl variants differing by single methylene (-CH2- units, a characteristic it shares with the one previously reported psychrotolerant lipid A structure. This work is the first detailed structural characterization of lipid A from an obligate psychrophile and the second from a psychrotolerant species. It reveals distinctive structural features of psychrophilic lipid A in comparison to that of related mesophiles which suggest constitutive adaptations to maintain outer membrane fluidity in cold environments.

Should Principals Know More about Law?

Science.gov (United States)

Doctor, Tyrus L.

2013-01-01

Educational law is a critical piece of the education conundrum. Principals reference law books on a daily basis in order to address the wide range of complex problems in the school system. A principal's knowledge of law issues and legal decision-making are essential to provide effective feedback for a successful school.

Influence of landscape structure on reef fish assemblages

Science.gov (United States)

Grober-Dunsmore, R.; Frazer, T.K.; Beets, J.P.; Lindberg, W.J.; Zwick, P.; Funicelli, N.A.

2008-01-01

Management of tropical marine environments calls for interdisciplinary studies and innovative methodologies that consider processes occurring over broad spatial scales. We investigated relationships between landscape structure and reef fish assemblage structure in the US Virgin Islands. Measures of landscape structure were transformed into a reduced set of composite indices using principal component analyses (PCA) to synthesize data on the spatial patterning of the landscape structure of the study reefs. However, composite indices (e.g., habitat diversity) were not particularly informative for predicting reef fish assemblage structure. Rather, relationships were interpreted more easily when functional groups of fishes were related to individual habitat features. In particular, multiple reef fish parameters were strongly associated with reef context. Fishes responded to benthic habitat structure at multiple spatial scales, with various groups of fishes each correlated to a unique suite of variables. Accordingly, future experiments should be designed to test functional relationships based on the ecology of the organisms of interest. Our study demonstrates that landscape-scale habitat features influence reef fish communities, illustrating promise in applying a landscape ecology approach to better understand factors that structure coral reef ecosystems. Furthermore, our findings may prove useful in design of spatially-based conservation approaches such as marine protected areas (MPAs), because landscape-scale metrics may serve as proxies for areas with high species diversity and abundance within the coral reef landscape. ?? 2007 Springer Science+Business Media B.V.

PCA Fault Feature Extraction in Complex Electric Power Systems

Directory of Open Access Journals (Sweden)

ZHANG, J.

2010-08-01

Full Text Available Electric power system is one of the most complex artificial systems in the world. The complexity is determined by its characteristics about constitution, configuration, operation, organization, etc. The fault in electric power system cannot be completely avoided. When electric power system operates from normal state to failure or abnormal, its electric quantities (current, voltage and angles, etc. may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of phasor measurement unit, based on principal components analysis technology, we have extracted successfully the distinct features of fault component. Of course, because of the complexity of different types of faults in electric power system, there still exists enormous problems need a close and intensive study.

Principal Ports and Facilities

Data.gov (United States)

California Natural Resource Agency — The Principal Port file contains USACE port codes, geographic locations (longitude, latitude), names, and commodity tonnage summaries (total tons, domestic, foreign,...

Principal Ports and Facilities

Data.gov (United States)

California Department of Resources — The Principal Port file contains USACE port codes, geographic locations (longitude, latitude), names, and commodity tonnage summaries (total tons, domestic, foreign,...

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

Science.gov (United States)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Promoting principals' managerial involvement in instructional improvement.

Science.gov (United States)

Gillat, A

1994-01-01

Studies of school leadership suggest that visiting classrooms, emphasizing achievement and training, and supporting teachers are important indicators of the effectiveness of school principals. The utility of a behavior-analytic program to support the enhancement of these behaviors in 2 school principals and the impact of their involvement upon teachers' and students' performances in three classes were examined in two experiments, one at an elementary school and another at a secondary school. Treatment conditions consisted of helping the principal or teacher to schedule his or her time and to use goal setting, feedback, and praise. A withdrawal design (Experiment 1) and a multiple baseline across classrooms (Experiment 2) showed that the principal's and teacher's rates of praise, feedback, and goal setting increased during the intervention, and were associated with improvements in the academic performance of the students. In the future, school psychologists might analyze the impact of involving themselves in supporting the principal's involvement in improving students' and teachers' performances or in playing a similar leadership role themselves.

Primary School Principals' Self-Monitoring Skills

Science.gov (United States)

Konan, Necdet

2015-01-01

The aim of the present study is to identify primary school principals' self-monitoring skills. The study adopted the general survey model and its population comprised primary school principals serving in the city of Diyarbakir, Turkey, while 292 of these constituted the sample. Self-Monitoring Scale was used as the data collection instrument. In…

How Not to Prepare School Principals

Science.gov (United States)

Davis, Stephen H.; Leon, Ronald J.

2011-01-01

Instead of focusing on how principals should be trained, an contrarian view is offered, grounded upon theoretical perspectives of experiential learning, and in particular, upon the theory of andragogy. A brief parable of the DoNoHarm School of Medicine is used as a descriptive analog for many principal preparation programs in America. The…

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.

Evaluating the Effectiveness of Traditional and Alternative Principal Preparation Programs

Science.gov (United States)

Pannell, Summer; Peltier-Glaze, Bernnell M.; Haynes, Ingrid; Davis, Delilah; Skelton, Carrie

2015-01-01

This study sought to determine the effectiveness on increasing student achievement of principals trained in a traditional principal preparation program and those trained in an alternate route principal preparation program within the same Mississippi university. Sixty-six Mississippi principals and assistant principals participated in the study. Of…

Principal Turnover: Upheaval and Uncertainty in Charter Schools?

Science.gov (United States)

Ni, Yongmei; Sun, Min; Rorrer, Andrea

2015-01-01

Purpose: Informed by literature on labor market and school choice, this study aims to examine the dynamics of principal career movements in charter schools by comparing principal turnover rates and patterns between charter schools and traditional public schools. Research Methods/Approach: This study uses longitudinal data on Utah principals and…

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

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.

Looked after or Left Behind: The Effectiveness of Principal Preparation Programs as Perceived by Generation Y Principals

Science.gov (United States)

Sledge, Chandra

2013-01-01

This research study intended to discover the perceptions of 10 Illinois Generation Y novice high school principals pertaining to the effectiveness of their principal preparation programs in terms of how well it prepared them to lead in the first three years of their principalship, and what subsequent professional development they deemed necessary…

Automated measurement of CT noise in patient images with a novel structure coherence feature

International Nuclear Information System (INIS)

Chun, Minsoo; Kim, Jong Hyo; Choi, Young Hun

2015-01-01

While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCC within   =  0.75, PCC between   =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine. (paper)

An object-oriented feature-based design system face-based detection of feature interactions

International Nuclear Information System (INIS)

Ariffin Abdul Razak

1999-01-01

This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)

Stepping stones: Principal career paths and school outcomes.

Science.gov (United States)

Béteille, Tara; Kalogrides, Demetra; Loeb, Susanna

2012-07-01

More than one out of every five principals leaves their school each year. In some cases, these career changes are driven by the choices of district leadership. In other cases, principals initiate the move, often demonstrating preferences to work in schools with higher achieving students from more advantaged socioeconomic backgrounds. Principals often use schools with many poor or low-achieving students as stepping stones to what they view as more desirable assignments. We use longitudinal data from one large urban school district to study the relationship between principal turnover and school outcomes. We find that principal turnover is, on average, detrimental to school performance. Frequent turnover of school leadership results in lower teacher retention and lower student achievement gains. Leadership changes are particularly harmful for high poverty schools, low-achieving schools, and schools with many inexperienced teachers. These schools not only suffer from high rates of principal turnover but are also unable to attract experienced successors. The negative effect of leadership changes can be mitigated when vacancies are filled by individuals with prior experience leading other schools. However, the majority of new principals in high poverty and low-performing schools lack prior leadership experience and leave when more attractive positions become available in other schools. Copyright © 2012 Elsevier Inc. All rights reserved.

Oxide-ion and proton conducting electrolyte materials for clean energy applications: structural and mechanistic features.

Science.gov (United States)

Malavasi, Lorenzo; Fisher, Craig A J; Islam, M Saiful

2010-11-01

This critical review presents an overview of the various classes of oxide materials exhibiting fast oxide-ion or proton conductivity for use as solid electrolytes in clean energy applications such as solid oxide fuel cells. Emphasis is placed on the relationship between structural and mechanistic features of the crystalline materials and their ion conduction properties. After describing well-established classes such as fluorite- and perovskite-based oxides, new materials and structure-types are presented. These include a variety of molybdate, gallate, apatite silicate/germanate and niobate systems, many of which contain flexible structural networks, and exhibit different defect properties and transport mechanisms to the conventional materials. It is concluded that the rich chemistry of these important systems provides diverse possibilities for developing superior ionic conductors for use as solid electrolytes in fuel cells and related applications. In most cases, a greater atomic-level understanding of the structures, defects and conduction mechanisms is achieved through a combination of experimental and computational techniques (217 references).

Line-feature-based calibration method of structured light plane parameters for robot hand-eye system

Science.gov (United States)

Qi, Yuhan; Jing, Fengshui; Tan, Min

2013-03-01

For monocular-structured light vision measurement, it is essential to calibrate the structured light plane parameters in addition to the camera intrinsic parameters. A line-feature-based calibration method of structured light plane parameters for a robot hand-eye system is proposed. Structured light stripes are selected as calibrating primitive elements, and the robot moves from one calibrating position to another with constraint in order that two misaligned stripe lines are generated. The images of stripe lines could then be captured by the camera fixed at the robot's end link. During calibration, the equations of two stripe lines in the camera coordinate system are calculated, and then the structured light plane could be determined. As the robot's motion may affect the effectiveness of calibration, so the robot's motion constraints are analyzed. A calibration experiment and two vision measurement experiments are implemented, and the results reveal that the calibration accuracy can meet the precision requirement of robot thick plate welding. Finally, analysis and discussion are provided to illustrate that the method has a high efficiency fit for industrial in-situ calibration.

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

Science.gov (United States)

Hou, T J; Wang, J M; Liao, N; Xu, X J

1999-01-01

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

Complete fold annotation of the human proteome using a novel structural feature space.

Science.gov (United States)

Middleton, Sarah A; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

Structural features in Ni-Al alloys

International Nuclear Information System (INIS)

Abylkalykova, R.B.; Kveglis, L.I.; Rakhimova, U.A.; Nasokhova, Sh.B.; Tazhibaeva, G.B.

2007-01-01

Purpose of the work is study of structural transformations under diverse memory effect in Ni-Al alloys. Examination were conducted in following composition samples: Ni -75 at.% and Al - 25 at.%. The work is devoted to clarification reasons both formation atom-ordered structures in inter-grain boundaries of bulk samples under temperature action and static load. Revealed inter-grain inter-boundary layers in Ni-Al alloy both bulk and surface state have complicated structure

A Review of the Literature on Principal Turnover

Science.gov (United States)

Snodgrass Rangel, Virginia

2018-01-01

Among the many challenges facing public schools are high levels of principal turnover. Given the important role that principals play and are expected to play in the improvement process, concerns about principal turnover have resulted in a growing body of research on its causes and consequences. The purpose of this review is to take stock of what…

Aerobic Physical Activity and the Leadership of Principals

Science.gov (United States)

Kiser, Kari

2016-01-01

The purpose of this study was to explore if there was a connection between regular aerobic physical activity and the stress and energy levels of principals as they reported it. To begin the research, the current aerobic physical activity level of principals was discovered. Additionally, the energy and stress levels of the principals who do engage…

Diagnosis of multidetector spiral CT and its reconstruction techniques in trachea and principal bronchus tumors

Energy Technology Data Exchange (ETDEWEB)

Mingyue, Luo; Hong, Shan; Zaibo, Jiang; Lufang, Li; Jiansheng, Zhang [Zhongshan Univ., Guangzhou (China). The Third Univ. Hospital, Dept. of Radiology; Lijia, Gu; Shaohong, Huang; Yi, Jin; Zhiqiang, Hou

2003-12-01

Objective: To investigate the clinical diagnostic value of multidetector spiral CT (MSCT) and its reconstruction techniques including multiplanar volume reformation (MPVR), volume rendering (VR), and virtual bronchoscopy (VB) in the trachea and principal bronchus tumors. Methods: Thin slice MSCT scanning was performed in 31 patients with suspected trachea or principal bronchus tumors, and image reconstruction data were formed after retro-reconstructing of initial scanning data. MPVR, VR, and VB images were obtained respectively by postprocessing of image reconstruction data with MPVR, VR, and VB image processing software in AW workstation. The findings of MSCT initial axial images, MPVR, VR, and VB images were compared with surgical and pathological results. Results: MSCT initial axial images combined with MPVR, VR, and VB images displayed the locations (tracheae, n=19; right principal bronchi, n=6; left principal bronchi, n=6), morphologies (endoluminal nodular tumors with narrow bases, n=2; endoluminal nodular tumors with wide bases, n=13; intraluminal and extraluminal massive tumors, n=16), internal features (1 had homogeneous density, 1 had low density, they both without obvious enhancement; 23 squamous cell carcinomas and 3 adenocarcinomas had fairly homogeneous density and rather obvious enhancement; 1 had homogeneous density, 1 had inhomogeneous density, 1 had punctate calcification, all with obvious enhancement), extramural invasion situations (broke through only serous membrane, n=1; no clear border with right atelectatic lung tissue, n=1; ranges of extramural invasion 4-56 mm, n=14), morphologies of luminal stenoses (eccentric, n=1; irregular, n=26; circular, n=3; conical interruption, n=1), extents (mild, n=5; moderate, n=7; severe, n=19); measured longitudinal invasion ranges (only 3 mm, n=1; invaded the whole right principal bronchus wall and carina, n=1; 5-68 mm, n=29), and distances between principal bronchus tumors and carina (invaded carina, n=1

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.

Surface analysis the principal techniques

CERN Document Server

Vickerman, John C

2009-01-01

This completely updated and revised second edition of Surface Analysis: The Principal Techniques, deals with the characterisation and understanding of the outer layers of substrates, how they react, look and function which are all of interest to surface scientists. Within this comprehensive text, experts in each analysis area introduce the theory and practice of the principal techniques that have shown themselves to be effective in both basic research and in applied surface analysis. Examples of analysis are provided to facilitate the understanding of this topic and to show readers how they c

OR TEP-II: a FORTRAN Thermal-Ellipsoid Plot Program for crystal structure illustrations

International Nuclear Information System (INIS)

Johnson, C.K.

1976-03-01

A computer program is described for drawing crystal structure illustrations using a mechanical plotter. Ball-and-stick type illustrations of a quality suitable for publication are produced with either spheres or thermal-motion probability ellipsoids on the atomic sites. The program can produce stereoscopic pairs of illustrations which aid in the visualization of complex packing arrangements of atoms and thermal motion patterns. Interatomic distances, bond angles, and principal axes of thermal motion are also calculated to aid the structural study. The most recent version of the program, OR TEP-II, has a hidden-line-elimination feature to omit those portions of atoms or bonds behind other atoms or bonds

OR TEP-II: a FORTRAN Thermal-Ellipsoid Plot Program for crystal structure illustrations

Energy Technology Data Exchange (ETDEWEB)

Johnson, C.K.

1976-03-01

A computer program is described for drawing crystal structure illustrations using a mechanical plotter. Ball-and-stick type illustrations of a quality suitable for publication are produced with either spheres or thermal-motion probability ellipsoids on the atomic sites. The program can produce stereoscopic pairs of illustrations which aid in the visualization of complex packing arrangements of atoms and thermal motion patterns. Interatomic distances, bond angles, and principal axes of thermal motion are also calculated to aid the structural study. The most recent version of the program, OR TEP-II, has a hidden-line-elimination feature to omit those portions of atoms or bonds behind other atoms or bonds.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Directory of Open Access Journals (Sweden)

Alessandra Caggiano

2018-03-01

Full Text Available Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA is proposed. PCA allowed to identify a smaller number of features (k = 2 features, the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Science.gov (United States)

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

Science.gov (United States)

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

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.)

Subjective performance evaluations and reciprocity in principal-agent relations

DEFF Research Database (Denmark)

Sebald, Alexander Christopher; Walzl, Markus

2014-01-01

. In contrast to existing models of reciprocity, we find that agents tend to sanction whenever the feedback of principals is below their subjective self-evaluations even if agents' pay-offs are independent of it. In turn, principals provide more positive feedback (relative to their actual performance assessment......We conduct a laboratory experiment with agents working on, and principals benefiting from, a real effort task in which the agents' performance can only be evaluated subjectively. Principals give subjective performance feedback to agents, and agents have an opportunity to sanction principals...... of the agent) if this does not affect their pay-off....

A New Feature Selection Algorithm Based on the Mean Impact Variance

Directory of Open Access Journals (Sweden)

Weidong Cheng

2014-01-01

Full Text Available The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2 the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA algorithm and a back propagation (BP network is constructed, and (4 the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.

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.

Perceptions of Kentucky High School Principals and Superintendents on the Role of the Superintendent Influencing Principal Instructional Leadership

Science.gov (United States)

Hamilton, Charles L., Jr.

2011-01-01

This exploratory study surveyed the promotion of instructional leadership of high school principals by superintendents, as perceived by self and the principals they supervise. The two-phased study included an initial questionnaire administered to both study groups and comparisons of responses analyzed. All superintendents (N = 173), except the…

Role Perceptions and Job Stress among Special Education School Principals: Do They Differ from Principals of Regular Schools?

Science.gov (United States)

Gaziel, Haim Henry; Cohen-Azaria, Yael; Ermenc, Klara Skubic

2012-01-01

The objective of the present study was to compare principals' perceptions of their leadership roles in regular (Dovno, 1999) versus special education (Zaretzky, Faircloth & Moreau, 2005) schools, and how these perceptions affect feelings of job stress (Friedman, 2001; Margalit, 1999). We predicted that regular school principals would differ in…

The changes of macroscopic features and microscopic structures of water under influence of magnetic field

International Nuclear Information System (INIS)

Pang Xiaofeng; Deng Bo

2008-01-01

Influences of magnetic field on microscopic structures and macroscopic properties of water are studied by the spectrum techniques of infrared, Raman, visible, ultraviolet lights and X-ray. From these investigations, we know that the magnetic fields change the distribution of molecules and electrons, cause displacements and polarization of molecules and atoms, result in changes of dipole-moment transition and vibrational states of molecules and variation of transition probability of electrons, but does not alter the constitution of molecules and atoms. These are helpful in seeking the mechanism of magnetization of water. Meanwhile, we also measure the changed rules of the surface tension force, soaking effect or angle of contact, viscosity, rheology features, refraction index, dielectric constant and electric conductivity of magnetized water relative to that of pure water. The results show that the magnetic fields increase the soaking degree and hydrophobicity of water to materials, depress its surface-tension force, diminish the viscosity of war, enhance the feature of plastic flowing of water, and increase the refraction index, dielectric constant and electric conductivity of water after magnetization. These changes are caused by the above changes of microscopic structures under the action of magnetic field. Therefore, our studies are significant in science and has practical value of applications

Topographic features over the continental shelf off Visakhapatnam

Digital Repository Service at National Institute of Oceanography (India)

Rao, T.C.S.; Machado, T.; Murthy, K.S.R.

water depth and the continental shelfedge several interesting topographic features such as Terraces, Karstic structures associated with pinnacles and troughs and smooth dome shaped reef structures are recorded. The nature of these features...

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

Directory of Open Access Journals (Sweden)

Rockson Dobgegah

2011-03-01

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

Investigating the effect of empowerment aspects on the competence level and success of primary school principals

Directory of Open Access Journals (Sweden)

Hamidreza Rezazadeh Bahadoran

2018-05-01

Full Text Available The purpose of this study is to investigate the effect of empowerment aspects on the competence level and success of the primary school principals in Pakdasht city in Iran. This research is a descriptive-survey method and in terms purpose is practical. The statistical population of this study consisted of principals of the primary schools in Pakdasht city. The total number of primary school principals in this city is 135 people (75 male schools and 60 female schools. As data gathering tool, a researcher-made questionnaire was used. In order to analyze the validity of the questionnaire in this study, the Content Validation Method was used in which the questionnaires were first examined by the experts and the necessary corrections were made. The reliability of the questionnaire was evaluated using Cronbach's alpha coefficient. Research hypotheses were tested using structural equation modeling and AMOS software. The results show that competence and effectiveness aspects affect competence and success of principals of the Pakdasht elementary schools. Autonomy affects the competence level of elementary school principals in Pakdasht city but does not affect principals' success. Meaningfulness aspect did not affect principals’ competency however it is effective on principals’ success in Pakdasht elementary schools. Trust aspect was not effective on the Pakdasht elementary schools principals’ competence and success.

Deformation quantization of principal fibre bundles

International Nuclear Information System (INIS)

Weiss, S.

2007-01-01

Deformation quantization is an algebraic but still geometrical way to define noncommutative spacetimes. In order to investigate corresponding gauge theories on such spaces, the geometrical formulation in terms of principal fibre bundles yields the appropriate framework. In this talk I will explain what should be understood by a deformation quantization of principal fibre bundles and how associated vector bundles arise in this context. (author)

Feature Extraction and Selection Strategies for Automated Target Recognition

Science.gov (United States)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Development of multiple source data processing for structural analysis at a regional scale. [digital remote sensing in geology

Science.gov (United States)

Carrere, Veronique

1990-01-01

Various image processing techniques developed for enhancement and extraction of linear features, of interest to the structural geologist, from digital remote sensing, geologic, and gravity data, are presented. These techniques include: (1) automatic detection of linear features and construction of rose diagrams from Landsat MSS data; (2) enhancement of principal structural directions using selective filters on Landsat MSS, Spacelab panchromatic, and HCMM NIR data; (3) directional filtering of Spacelab panchromatic data using Fast Fourier Transform; (4) detection of linear/elongated zones of high thermal gradient from thermal infrared data; and (5) extraction of strong gravimetric gradients from digitized Bouguer anomaly maps. Processing results can be compared to each other through the use of a geocoded database to evaluate the structural importance of each lineament according to its depth: superficial structures in the sedimentary cover, or deeper ones affecting the basement. These image processing techniques were successfully applied to achieve a better understanding of the transition between Provence and the Pyrenees structural blocks, in southeastern France, for an improved structural interpretation of the Mediterranean region.

Artful Dodges Principals Use to Beat Bureaucracy.

Science.gov (United States)

Ficklen, Ellen

1982-01-01

A study of Chicago (Illinois) principals revealed many ways principals practiced "creative insubordination"--avoiding following instructions but still getting things done. Among the dodges are deliberately missing deadlines, following orders literally, ignoring channels to procure teachers or materials, and using community members to…

Principal spectra describing magnetooptic permittivity tensor in cubic crystals

Energy Technology Data Exchange (ETDEWEB)

Hamrlová, Jana [Nanotechnology Centre, VSB – Technical University of Ostrava, listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic); IT4Innovations Centre, VSB – Technical University of Ostrava, listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic); Legut, Dominik [IT4Innovations Centre, VSB – Technical University of Ostrava, listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic); Veis, Martin [Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague, 121 16 Czech Republic (Czech Republic); Pištora, Jaromír [Nanotechnology Centre, VSB – Technical University of Ostrava, listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic); Hamrle, Jaroslav, E-mail: jaroslav.hamrle@vsb.cz [IT4Innovations Centre, VSB – Technical University of Ostrava, listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic); Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague, 121 16 Czech Republic (Czech Republic); Department of Physics, VSB – Technical University of Ostrava, 17. listopadu 15, Ostrava, 708 33 Czech Republic (Czech Republic)

2016-12-15

We provide unified phenomenological description of magnetooptic effects being linear and quadratic in magnetization. The description is based on few principal spectra, describing elements of permittivity tensor up to the second order in magnetization. Each permittivity tensor element for any magnetization direction and any sample surface orientation is simply determined by weighted summation of the principal spectra, where weights are given by crystallographic and magnetization orientations. The number of principal spectra depends on the symmetry of the crystal. In cubic crystals owning point symmetry we need only four principal spectra. Here, the principal spectra are expressed by ab initio calculations for bcc Fe, fcc Co and fcc Ni in optical range as well as in hard and soft x-ray energy range, i.e. at the 2p- and 3p-edges. We also express principal spectra analytically using modified Kubo formula.

Structural and sequence variants in patients with Silver-Russell syndrome or similar features-Curation of a disease database

DEFF Research Database (Denmark)

Tümer, Zeynep; López-Hernández, Julia Angélica; Netchine, Irène

2018-01-01

data of these patients. The clinical features are scored according to the Netchine-Harbison clinical scoring system (NH-CSS), which has recently been accepted as standard by consensus. The structural and sequence variations are reviewed and where necessary redescribed according to recent...

Feature Selection pada Dataset Faktor Kesiapan Bencana pada Provinsi di Indonesia Menggunakan Metode PCA (Principal Component Analysis

Directory of Open Access Journals (Sweden)

Septa Firmansyah Putra

2017-01-01

Full Text Available Penelitian ini bertujuan untuk mengetahui atribut-atribut apa yang akan digunakan untuk klasterisasi provinsi di Indonesia berdasarkan faktor kesiapan dalam menghadapi bencana. Data yang digunakan terdiri dari tiga kelompok data yaitu data jumlah kejadian bencana yang terdiri dari 19 sub-atribut, data jumlah fasilitas kesehatan yang terdiri dari 14 sub-atribut dan data jumlah tenaga kesehatan yang terdiri dari 11 sub atribut. Penelitian ini dapat menjadi gambaran tentang bagaimana melakukan pembersihan dan pemilihan data sebelum digunakan dalam proses klasterisasi. Data-data ini akan dibersihkan dan dipilih sebelum nantinya digunakan pada proses klasterisasi. Proses pembersihan dan pemilihan data dilakukan dengan bantuan PCA (Principal Component Analysis namun sebelumnya dibersihkan telebih dahulu dengan cara manual. Penelitian dibagi menjadi 3 percobaan. Pada percobaan pertama didapatkan 31 sub-atribut yang siap digunakan, percobaan kedua didapatkan 29 sub-atribut yang siap digunakan dan pada percobaan ketiga didapatkan 24 sub-atribut yang siap digunakan.

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…

The DRE-Principal Partnership: Making It Work.

Science.gov (United States)

Davis, Barbara; Elliott, Karen

1995-01-01

Discusses the roles of the director of religious education (DRE) and the school principal at Catholic schools, viewing them as complimentary rather than competitive. Provides examples of positive cooperation between the principal and DRE at Most Pure Heart of Mary Parish, in Shelby, Ohio. (KP)

New Principals' Perspectives of Their Multifaceted Roles

Science.gov (United States)

Gentilucci, James L.; Denti, Lou; Guaglianone, Curtis L.

2013-01-01

This study utilizes Symbolic Interactionism to explore perspectives of neophyte principals. Findings explain how these perspectives are modified through complex interactions throughout the school year, and they also suggest preparation programs can help new principals most effectively by teaching "soft" skills such as active listening…

Leading Learning: First-Year Principals' Reflections on Instructional Leadership

Science.gov (United States)

O'Doherty, Ann; Ovando, Martha N.

2013-01-01

This qualitative study examined the instructional leadership perceptions of four first-year principals. Findings illuminate five themes drawn from the data: definitions of instructional leadership, challenges that first-year principals faced, how these principals addressed these challenges, how the novice principals plan to enact their…

Linguistic Features and Schematic Textual Structure in Look-Good Advertisements in the Indian Print Media in English

Science.gov (United States)

Singh, Sukhdev; Bedi, Navkiran Kaur

2013-01-01

Every text has a communicative purpose that it performs by dividing itself into generic stages. These stages are assigned specific goals and have differing linguistic structures. This paper makes an attempt to investigate whether there is a definable co-relation between linguistic features and stages in the genre of look-good advertisements. It…

Software reference for SaTool - a Tool for Structural Analysis of Automated Systems

DEFF Research Database (Denmark)

Lorentzen, Torsten; Blanke, Mogens

2004-01-01

This software reference details the functions of SaTool – a tool for structural analysis of technical systems. SaTool is intended used as part of an industrial systems design cycle. Structural analysis is a graph-based technique where principal relations between variables express the system’s...... of the graph. SaTool makes analysis of the structure graph to provide knowledge about fundamental properties of the system in normal and faulty conditions. Salient features of SaTool include rapid analysis of possibility to diagnose faults and ability to make autonomous recovery should faults occur........ The list of such variables and functional relations constitute the system’s structure graph. Normal operation means all functional relations are intact. Should faults occur, one or more functional relations cease to be valid. In a structure graph, this is seen as the disappearance of one or more nodes...

Modality prediction of biomedical literature images using multimodal feature representation

Directory of Open Access Journals (Sweden)

Pelka, Obioma

2016-08-01

Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

Science.gov (United States)

Nixon, Mark S.; Komogortsev, Oleg V.

2017-01-01

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before. PMID:28575030

Novel Fluorinated Indanone, Tetralone and Naphthone Derivatives: Synthesis and Unique Structural Features

Directory of Open Access Journals (Sweden)

Joseph C. Sloop

2012-02-01

Full Text Available Several fluorinated and trifluoromethylated indanone, tetralone and naphthone derivatives have been prepared via Claisen condensations and selective fluorinations in yields ranging from 22–60%. In addition, we report the synthesis of new, selectively fluorinated bindones in yields ranging from 72–92%. Of particular interest is the fluorination and trifluoroacetylation regiochemistry observed in these fluorinated products. We also note unusual transformations including a novel one pot, dual trifluoroacetylation, trifluoroacetylnaphthone synthesis via a deacetylation as well as an acetyl-trifluoroacetyl group exchange. Solid-state structural features exhibited by these compounds were investigated using crystallographic methods. Crystallographic results, supported by spectroscopic data, show that trifluoroacetylated ketones prefer a chelated cis-enol form whereas fluorinated bindone products exist primarily as the cross-conjugated triketo form.

Honouring Roles: The Story of a Principal and a Student

Directory of Open Access Journals (Sweden)

Jerome Cranston

2012-11-01

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

The Principal's Role in Site-Based Management.

Science.gov (United States)

Drury, William R.

1993-01-01

In existing school-based management models, the principal's role ranges from chairing the local council to being a coach/facilitator. With teachers and parents assuming greater control over governance, curriculum, and budgeting, paranoid principals may establish more formal bargaining relationships with district boards. Caution is advised, because…

Leadership Standards in Action: The School Principal as Servant-Leader

Science.gov (United States)

Brumley, Cade

2011-01-01

"Leadership Standards In Action: The School Principal as Servant-Leader" is a powerful resource for aspiring principals, practicing principals, district leadership, and university faculty. The book responsibly unpacks the metaphor of principal as servant leader to the school's people and purpose. As a framework, the six ISLLC Standards of…

The Factor Structure in Equity Options

DEFF Research Database (Denmark)

Christoffersen, Peter; Fournier, Mathieu; Jacobs, Kris

2018-01-01

Equity options display a strong factor structure. The first principal components of the equity volatility levels, skews, and term structures explain a substantial fraction of the crosssectional variation. Furthermore, these principal components are highly correlated with the S&P 500 index option...... volatility, skew, and term structure, respectively. We develop an equity option valuation model that captures this factor structure. The model predicts that firms with higher market betas have higher implied volatilities, steeper moneyness slopes, and a term structure that covaries more with the market...

Do Qualification, Experience and Age Matter for Principals Leadership Styles?

OpenAIRE

Muhammad Javed Sawati; Saeed Anwar; Muhammad Iqbal Majoka

2013-01-01

The main focus of present study was to find out the prevalent leadership styles of principals in government schools of Khyber Pakhtunkhwa and to find relationship of leadership styles with qualifications, age and experience of the principals. On the basis of analyzed data, four major leadership styles of the principals were identified as Eclectic, Democratic, Autocratic, and Free-rein. However, a small proportion of the principal had no dominant leadership style. This study shows that princip...

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.

Women principals' reflections of curriculum management challenges ...

African Journals Online (AJOL)

This study reports the reflections of grade 6 rural primary principals in Mpumalanga province. A qualitative method of inquiry was used in this article, where data were collected using individual interviews with three principals and focus group discussions with the school management teams (SMTs) of three primary schools.

What Effective Principals Do to Improve Instruction and Increase Student Achievement

Science.gov (United States)

Turner, Elizabeth Anne

2013-01-01

The purposes of this mixed method study were to (a) Examine the relationships among principal effectiveness, principal instructional leadership, and student achievement; (b) examine the differences among principal effectiveness, principal instructional leadership and student achievement; and (c) investigate what effective principals do to improve…

Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

Science.gov (United States)

Iqbal, Abdullah; Valous, Nektarios A; Sun, Da-Wen; Allen, Paul

2011-02-01

Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Effect of processing on structural features of anodic aluminum oxides

Science.gov (United States)

Erdogan, Pembe; Birol, Yucel

2012-09-01

Morphological features of the anodic aluminum oxide (AAO) templates fabricated by electrochemical oxidation under different processing conditions were investigated. The selection of the polishing parameters does not appear to be critical as long as the aluminum substrate is polished adequately prior to the anodization process. AAO layers with a highly ordered pore distribution are obtained after anodizing in 0.6 M oxalic acid at 20 °C under 40 V for 5 minutes suggesting that the desired pore features are attained once an oxide layer develops on the surface. While the pore features are not affected much, the thickness of the AAO template increases with increasing anodization treatment time. Pore features are better and the AAO growth rate is higher at 20 °C than at 5 °C; higher under 45 V than under 40 V; higher with 0.6 M than with 0.3 M oxalic acid.

Prostate cancer multi-feature analysis using trans-rectal ultrasound images

International Nuclear Information System (INIS)

Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

2005-01-01

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

Analysis of forest structure using thematic mapper simulator data

Science.gov (United States)

Peterson, D. L.; Westman, W. E.; Brass, J. A.; Stephenson, N. J.; Ambrosia, V. G.; Spanner, M. A.

1986-01-01

The potential of Thematic Mapper Simulator (TMS) data for sensing forest structure information has been explored by principal components and feature selection techniques. In a survey of forest structural properties conducted for 123 field sites of the Sequoia National Park, the canopy closure could be well estimated (r = 0.62 to 0.69) by a variety of channel bands and band ratios, without reference to the forest type. Estimation of the basal area was less successful (r = 0.51 or less) on the average, but could be improved for certain forest types when data were stratified by floristic composition. To achieve such a stratification, individual sites were ordinated by a detrended correspondence analysis based on the canopy of dominant species. The analysis of forest structure in the Sequoia data suggests that total basal area can be best predicted in stands of lower density, and in younger even-aged managed stands.

Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

Directory of Open Access Journals (Sweden)

Moon Young Kang

2018-04-01

Full Text Available Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy in corporate social media on customers’ purchase intentions, mediated by brand attitude and corporate trust. Especially, social media platforms are believed to provide a good marketing platform for small and medium enterprises (SMEs by providing access to huge audiences at a very low cost. The findings from this study based on a structural equation model suggest that brand attitude and corporate trust have larger impacts on purchase intention for SMEs than large firms. This implies that SMEs with little to no presence in the market should pay more attention to building corporate trust and brand attitude for their sustainable growth.

Individual psychological features of law enforcement officers convicted of crimes

Directory of Open Access Journals (Sweden)

Lyutykh V.A.

2016-06-01

Full Text Available The relevance of this topic is caused by a significant number of crimes committed by law enforcement officers and the necessity of active prevention. The aim of the study was to determine the individual psychological characteristics of law enforcement officers convicted of intentional crimes. The hypothesis was suggested that the main difference of individual psychological characteristics of law enforcement officers convicted of intentional crimes from individual psychological characteristics of law-abiding law enforcement officers is the difference between the principal values of the person both the main motives of activity adopted by an individual and the structure and the hierarchy of these values. This article describes the progress and results of empirical research conducted on the materials of psychodiagnostic examination of: employees who have been convicted of intentional crimes; law-abiding employees; people entering an internal affairs agency. Test subjects - men 18-46 years old, 90 people. Recommendations for practical psychologist of internal affairs agencies on detection of individual psychological personality features typical for law enforcement officers convicted of intentional crimes are formulated based on the obtained results.

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.

Contemporary Challenges and Changes: Principals' Leadership Practices in Malaysia

Science.gov (United States)

Jones, Michelle; Adams, Donnie; Joo, Mabel Tan Hwee; Muniandy, Vasu; Perera, Corinne Jaqueline; Harris, Alma

2015-01-01

This article outlines the findings from a contemporary study of principals' leadership practices in Malaysia as part of the 7 System Leadership Study. Recent policy developments within Malaysia have increased principals' accountability and have underlined the importance of the role of the principals in transforming school performance and student…

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

Science.gov (United States)

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

3D face recognition with asymptotic cones based principal curvatures

KAUST Repository

Tang, Yinhang

2015-05-01

The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.

3D face recognition with asymptotic cones based principal curvatures

KAUST Repository

Tang, Yinhang; Sun, Xiang; Huang, Di; Morvan, Jean-Marie; Wang, Yunhong; Chen, Liming

2015-01-01

The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.

Structural features of lignohumic acids

Czech Academy of Sciences Publication Activity Database

Novák, František; Šestauberová, Martina; Hrabal, R.

2015-01-01

Roč. 1093, August (2015), s. 179-185 ISSN 0022-2860 Institutional support: RVO:60077344 Keywords : C-13 NMR * FTIR * humic acids * lignohumate * lignosulfonate * structure Subject RIV: DF - Soil Science Impact factor: 1.780, year: 2015

The Factor Structure in Equity Options

DEFF Research Database (Denmark)

Christoffersen, Peter; Fournier, Mathieu; Jacobs, Kris

Equity options display a strong factor structure. The first principal components of the equity volatility levels, skews, and term structures explain a substantial fraction of the cross-sectional variation. Furthermore, these principal components are highly correlated with the S&P500 index option...... volatility, skew, and term structure respectively. We develop an equity option valuation model that captures this factor structure. The model predicts that firms with higher market betas have higher implied volatilities, steeper moneyness slopes, and a term structure that co-varies more with the market...

Principal bundles the classical case

CERN Document Server

Sontz, Stephen Bruce

2015-01-01

This introductory graduate level text provides a relatively quick path to a special topic in classical differential geometry: principal bundles.  While the topic of principal bundles in differential geometry has become classic, even standard, material in the modern graduate mathematics curriculum, the unique approach taken in this text presents the material in a way that is intuitive for both students of mathematics and of physics. The goal of this book is to present important, modern geometric ideas in a form readily accessible to students and researchers in both the physics and mathematics communities, providing each with an understanding and appreciation of the language and ideas of the other.

Extra-zonal beech forests in Tuscany: structure, diversity and synecologic features

Directory of Open Access Journals (Sweden)

Viciani D

2011-07-01

Full Text Available The present paper focuses on the structural, synecological and floristic diversity features of beech-dominated forest communities in four major areas of the Antiapenninic Tyrrhenian system in Tuscany: Metalliferous hills, mountains to the south of Mt. Amiata, volcanic area of the upper Lente valley and Mt. Cetona. These are relict woodlands of Holo-Pleistocene origin with a special ecological and conservation value due to their extrazonal location in lowland submediterranean areas. Results show substantial among-area differences in structure, synecology and plant species composition, but in general a potential for coppices to reach the tall forest stage, as demonstrated by the old-growth stands of Pietraporciana and Sassoforte. Compared with montane Apenninic beechwoods, the relatively rich flora of the studied communities include thermophilous species with a southern Apennine-Balkan distribution, making their syntaxonomical position unclear. Closer affinities are found with the calcicolous Beech Forests of the association and with the silicicolous ones of the . Based on the Natura 2000 system, all the examined communities belong to the priority Habitat “Apennine beech forests with and ” (code: 9210*. Due their relict nature, these biotopes appear vulnerable to climate changes and to a production-oriented forest management. Criteria of naturalistic silviculture should instead promote the dynamic development of these communities towards tall forests and their natural regeneration.

Taking a Distributed Perspective to the School Principal's Workday

Science.gov (United States)

Spillane, James P.; Camburn, Eric M.; Pareja, Amber Stitziel

2007-01-01

Focusing on the school principal's day-to-day work, we examine who leads curriculum and instruction- and administration-related activities when the school principal is not leading but participating in the activity. We also explore the prevalence of coperformance of management and leadership activities in the school principal's workday. Looking…

Principal Preparation in Special Education: Building an Inclusive Culture

Science.gov (United States)

Hofreiter, Deborah

2017-01-01

The importance of principal preparation in special education has increased since the Education for All Handicapped Children Act was passed in 1975. There are significant financial reasons for preparing principals in the area of special education. Recent research also shows that all children learn better in an inclusive environment. Principals who…

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…

Feature extraction for ultrasonic sensor based defect detection in ceramic components

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

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…

Principals: Human Capital Managers at Every School

Science.gov (United States)

Kimball, Steven M.

2011-01-01

Being a principal is more than just being an instructional leader. Principals also must manage their schools' teaching talent in a strategic way so that it is linked to school instructional improvement strategies, to the competencies needed to enact the strategies, and to success in boosting student learning. Teacher acquisition and performance…

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

Directory of Open Access Journals (Sweden)

Yi An

2013-01-01

Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

Radiological features of familial Gorlin-Goltz syndrome.

Science.gov (United States)

Hegde, Shruthi; Shetty, Shishir Ram

2012-03-01

Gorlin-Goltz syndrome is an autosomal dominant disorder principally characterized by cutaneous basal cell carcinomas, multiple keratocystic odontogenic tumors, and skeletal anomalies. This syndrome may be diagnosed early by dentist because keratocystic odontogenic tumors are usually one of the first manifestations of the syndrome. Early diagnosis and treatment are of utmost importance in reducing the severity of long term sequelae of this syndrome. This report presents a rare event of Gorlin-Goltz syndrome occurring in a 39-year-old male and his 8-year-old daughter. The clinical and investigative features of this familial disorder has been described in detail.

Radiological features of familial Gorlin-Goltz syndrome

Energy Technology Data Exchange (ETDEWEB)

Hegde, Shruthi; Shetty, Shishir Ram [AB Shetty Memorial Institute of Dental Sciences, Nitte University, Mangalore (India)

2012-03-15

Gorlin-Goltz syndrome is an autosomal dominant disorder principally characterized by cutaneous basal cell carcinomas, multiple keratocystic odontogenic tumors, and skeletal anomalies. This syndrome may be diagnosed early by dentist because keratocystic odontogenic tumors are usually one of the first manifestations of the syndrome. Early diagnosis and treatment are of utmost importance in reducing the severity of long term sequelae of this syndrome. This report presents a rare event of Gorlin-Goltz syndrome occurring in a 39-year-old male and his 8-year-old daughter. The clinical and investigative features of this familial disorder has been described in detail.

Radiological features of familial Gorlin-Goltz syndrome

International Nuclear Information System (INIS)

Hegde, Shruthi; Shetty, Shishir Ram

2012-01-01

Gorlin-Goltz syndrome is an autosomal dominant disorder principally characterized by cutaneous basal cell carcinomas, multiple keratocystic odontogenic tumors, and skeletal anomalies. This syndrome may be diagnosed early by dentist because keratocystic odontogenic tumors are usually one of the first manifestations of the syndrome. Early diagnosis and treatment are of utmost importance in reducing the severity of long term sequelae of this syndrome. This report presents a rare event of Gorlin-Goltz syndrome occurring in a 39-year-old male and his 8-year-old daughter. The clinical and investigative features of this familial disorder has been described in detail.

Structural features and in-service inspection of the LTHR-200 pressure vessel

International Nuclear Information System (INIS)

Xiong Dunshi; He Shuyan; Liu Junjie; Yu Suyuan

1993-01-01

LTHR-200 is a low temperature district-heating reactor. It adopts double-shell design pressure vessel and metal containment. Because of the safety and structural features of the reactor, the in-service inspection of the pressure vessel can be simplified greatly. LTHR-200 is an integrated arrangement. Both its core components and the main heat exchangers are contained in the reactor pressure vessel. The coolant of the main loop is run by a full-power natural circulation and there need no main pumps and pipes. Thus, the reactor pressure vessel constitutes the pressure boundary of the reactor's main loop coolant. In regard to these features, a small-sized containment is designed for the reactor. The metal safety container with a small volume is placed closely around the reactor pressure vessel. Outside the metal containment, there is a large reinforced concrete construction for the reactor. Their main operation and design parameters are as follows: The pressure vessel: operation pressure = 2.4 MPa; design pressure = 3.0 MPa; design temperature = 250 deg C; 40 year fast neutron (E>1MeV) fluence in the belt-line region = < 10E16n/cm; internal diameter = 5000 mm; material SA516-70; shell thickness 65 mm; The metal containment: maximum operation pressure = 1.8 MPa; design pressure = 1.8 MPa; design temperature = 250 deg. C; upper internal diameter 7000 mm; lower internal diameter = 5600 mm; material = SA516-70; shell thickness, upper part = 80 mm; lower part = 50 mm. All penetrating pipes through the pressure vessel are located at the top penetration section of the shell. All the internal diameters of penetrating pipes are less than 50 mm. Inside and outside the metal containment wall respectively, isolating valves are connected to the reactor coolant pipe which passes through the containment. These two isolating valves use different driving methods. Every penetrating part of the reactor construction uses a proper form of structure according to safety requirements

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

Science.gov (United States)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

The Effects of Reform in Principal Selection on Leadership Behavior of General and Vocational High School Principals in Taiwan

Science.gov (United States)

Hsiao, Hsi-Chi; Lee, Ming-Chao; Tu, Ya-Ling

2013-01-01

Deregulation has formed the primary core of education reform in Taiwan in the past decade. The principal selection system was one of the specific recommendations in the deregulation of education. The method of designation of senior high school principals has changed from being "appointed" to being "selected." The issue as to…