WorldWideScience

Sample records for survey discriminant analysis

  1. Social Status, Discrimination, and Minority Individuals' Mental Health: a Secondary Analysis of US National Surveys.

    Science.gov (United States)

    Lo, Celia C; Cheng, Tyrone C

    2017-08-15

    Our study measured minority individuals' social status factors and frequency of discrimination experiences, in order to delineate social mechanisms linking race/ethnicity to mental status (specifically, to current mood/anxiety disorder and self-rated mental health). In this nationally representative secondary research, our data analyses drew on the cross-sectional "Collaborative Psychiatric Epidemiology Surveys," dating 2001-2003. The sample for the final model numbered 9368 respondents (2016 Asians, 2676 Latinos, 4676 blacks). Across races/ethnicities, better mental health was associated with male gender, higher income, marriage, more education, and less-frequent discrimination experiences; discrimination experiences could impair health, especially among blacks. Marriage's strong contribution to Asians' mental health did not hold among blacks; education's contribution to Latinos' mental health did not hold among blacks either. Blacks' mental health was unaffected by immigration status, but Asian and Latino immigrants showed less-robust mental health than native-born counterparts. Across the three racial/ethnic groups studied, differences were noted in relationships between self-reported mental health status and the employed social status and discrimination factors.

  2. Unsupervised Linear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

  3. The discriminative power of patient experience surveys.

    NARCIS (Netherlands)

    Boer, D. de; Delnoij, D.; Rademakers, J.

    2011-01-01

    Background: Comparisons of patient experiences between providers are increasingly used as an index of performance. The present study describes the ability of patient experience surveys to discriminate between healthcare providers for various patient groups and quality aspects, and reports the sample

  4. Discriminant Analysis on Land Grading

    Institute of Scientific and Technical Information of China (English)

    LIU Yaolin; HOU Yajuan

    2004-01-01

    This paper proposes the discriminant analysis on land grading after analyzing the common methods and discussing the Fisher's discriminant in detail. Actually this method deduces the dimension from multi to single, thus it makes the feature vectors in n-dimension change to a scalar, and use this scalar to classify samples. This paper illustrates the result by giving an example of the residential land grading by the discriminant analysis.

  5. Discriminant Incoherent Component Analysis.

    Science.gov (United States)

    Georgakis, Christos; Panagakis, Yannis; Pantic, Maja

    2016-05-01

    Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and l1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings.

  6. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  7. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  8. Does Attending Worship Mitigate Racial/Ethnic Discrimination in Influencing Health Behaviors? Results from an Analysis of the California Health Interview Survey

    Science.gov (United States)

    Caldwell, Julia T.; Takahashi, Lois M.

    2014-01-01

    Existing research suggests that religious institutions play a significant role in improving the health of communities, particularly those coping with racial and ethnic discrimination. Using the California Health Interview Survey, this article examines the relationship of self-reported experiences of racial/ethnic discrimination, worship…

  9. Does Attending Worship Mitigate Racial/Ethnic Discrimination in Influencing Health Behaviors? Results from an Analysis of the California Health Interview Survey

    Science.gov (United States)

    Caldwell, Julia T.; Takahashi, Lois M.

    2014-01-01

    Existing research suggests that religious institutions play a significant role in improving the health of communities, particularly those coping with racial and ethnic discrimination. Using the California Health Interview Survey, this article examines the relationship of self-reported experiences of racial/ethnic discrimination, worship…

  10. Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008-09: analysis of national survey data.

    Science.gov (United States)

    Cunningham, Joan; Paradies, Yin C

    2013-07-01

    There is now considerable evidence that racism is a pernicious and enduring social problem with a wide range of detrimental outcomes for individuals, communities and societies. Although indigenous people worldwide are subjected to high levels of racism, there is a paucity of population-based, quantitative data about the factors associated with their reporting of racial discrimination, about the settings in which such discrimination takes place, and about the frequency with which it is experienced. Such information is essential in efforts to reduce both exposure to racism among indigenous people and the harms associated with such exposure. Weighted data on self-reported racial discrimination from over 7,000 Indigenous Australian adults participating in the 2008-09 National Aboriginal and Torres Strait Islander Survey, a nationally representative survey conducted by the Australian Bureau of Statistics, were analysed by socioeconomic, demographic and cultural factors. More than one in four respondents (27%) reported experiencing racial discrimination in the past year. Racial discrimination was most commonly reported in public (41% of those reporting any racial discrimination), legal (40%) and work (30%) settings. Among those reporting any racial discrimination, about 40% experienced this discrimination most or all of the time (as opposed to a little or some of the time) in at least one setting. Reporting of racial discrimination peaked in the 35-44 year age group and then declined. Higher reporting of racial discrimination was associated with removal from family, low trust, unemployment, having a university degree, and indicators of cultural identity and participation. Lower reporting of racial discrimination was associated with home ownership, remote residence and having relatively few Indigenous friends. These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public, legal and work settings identified as

  11. Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008–09: analysis of national survey data

    Science.gov (United States)

    2013-01-01

    Background There is now considerable evidence that racism is a pernicious and enduring social problem with a wide range of detrimental outcomes for individuals, communities and societies. Although indigenous people worldwide are subjected to high levels of racism, there is a paucity of population-based, quantitative data about the factors associated with their reporting of racial discrimination, about the settings in which such discrimination takes place, and about the frequency with which it is experienced. Such information is essential in efforts to reduce both exposure to racism among indigenous people and the harms associated with such exposure. Methods Weighted data on self-reported racial discrimination from over 7,000 Indigenous Australian adults participating in the 2008–09 National Aboriginal and Torres Strait Islander Survey, a nationally representative survey conducted by the Australian Bureau of Statistics, were analysed by socioeconomic, demographic and cultural factors. Results More than one in four respondents (27%) reported experiencing racial discrimination in the past year. Racial discrimination was most commonly reported in public (41% of those reporting any racial discrimination), legal (40%) and work (30%) settings. Among those reporting any racial discrimination, about 40% experienced this discrimination most or all of the time (as opposed to a little or some of the time) in at least one setting. Reporting of racial discrimination peaked in the 35–44 year age group and then declined. Higher reporting of racial discrimination was associated with removal from family, low trust, unemployment, having a university degree, and indicators of cultural identity and participation. Lower reporting of racial discrimination was associated with home ownership, remote residence and having relatively few Indigenous friends. Conclusions These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public

  12. Employer Learning and Statistical Discrimination. National Longitudinal Surveys Discussion Paper.

    Science.gov (United States)

    Altonji, Joseph G.; Pierret, Charles R.

    The relationship between employer learning and statistical discrimination was explored through a statistical analysis that included a test for statistical discrimination or "rational" stereotyping in environments where agents learn over time. The test is used to study the working hypothesis that, because firms have only limited information about…

  13. Discriminant and Proximity Analysis in Intercultural Investigation.

    Science.gov (United States)

    Laveault, Dany

    1982-01-01

    Discriminant analysis is applied to data from previous research dealing with assessing the particularities of cognitive development in young (four to nine years old) Montagnais Indians and French Canadians. The most important future contribution of discriminant analysis to intercultural research will be its ability to conceptualize group…

  14. Semisupervised Sparse Multilinear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    黄锴; 张丽清

    2014-01-01

    Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size (SSS) and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms (ECGs), another problem must be overcome;the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis (SSSMDA) method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation algorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation (STFT) to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

  15. Perceived discrimination and self-rated health in Europe: evidence from the European Social Survey (2010.

    Directory of Open Access Journals (Sweden)

    Javier Alvarez-Galvez

    Full Text Available INTRODUCTION: Studies have shown that perceived discrimination has an impact on our physical and mental health. A relevant part of literature has highlighted the influence of discrimination based on race or ethnicity on mental and physical health outcomes. However, the influence of other types of discrimination on health has been understudied. This study is aimed to explore how different types of discrimination are related to our subjective state of health, and so to compare the intensity of these relationships in the European context. METHODS: We have performed a multilevel ordered analysis on the fifth wave of the European Social Survey (ESS 2010. This dataset has 52,458 units at individual level that are grouped in 26 European countries. In this study, the dependent variable is self-rated health (SRH that is analyzed in relationship to ten explanatory variables of perceived discrimination: color or race, nationality, religion, language, ethnic group, age, gender, sexuality, disability and others. RESULTS: The model identifies statistically significant differences in the effect that diverse types of perceived discrimination can generate on the self-rated health of Europeans. Specifically, this study identifies three well-defined types of perceived discrimination that can be related to poor health outcomes: (1 age discrimination; (2 disability discrimination; and (3 sexuality discrimination. In this sense, the effect on self-rated health of perceived discrimination related to aging and disabilities seems to be more relevant than other types of discrimination in the European context with a longer tradition in literature (e.g. ethnic and/or race-based. CONCLUSION: The present study shows that the relationship between perceived discrimination and health inequities in Europe are not random, but systematically distributed depending on factors such as age, sexuality and disabilities. Therefore the future orientation of EU social policies should aim

  16. Perceived discrimination and self-rated health in Europe: evidence from the European Social Survey (2010).

    Science.gov (United States)

    Alvarez-Galvez, Javier; Salvador-Carulla, Luis

    2013-01-01

    Studies have shown that perceived discrimination has an impact on our physical and mental health. A relevant part of literature has highlighted the influence of discrimination based on race or ethnicity on mental and physical health outcomes. However, the influence of other types of discrimination on health has been understudied. This study is aimed to explore how different types of discrimination are related to our subjective state of health, and so to compare the intensity of these relationships in the European context. We have performed a multilevel ordered analysis on the fifth wave of the European Social Survey (ESS 2010). This dataset has 52,458 units at individual level that are grouped in 26 European countries. In this study, the dependent variable is self-rated health (SRH) that is analyzed in relationship to ten explanatory variables of perceived discrimination: color or race, nationality, religion, language, ethnic group, age, gender, sexuality, disability and others. The model identifies statistically significant differences in the effect that diverse types of perceived discrimination can generate on the self-rated health of Europeans. Specifically, this study identifies three well-defined types of perceived discrimination that can be related to poor health outcomes: (1) age discrimination; (2) disability discrimination; and (3) sexuality discrimination. In this sense, the effect on self-rated health of perceived discrimination related to aging and disabilities seems to be more relevant than other types of discrimination in the European context with a longer tradition in literature (e.g. ethnic and/or race-based). The present study shows that the relationship between perceived discrimination and health inequities in Europe are not random, but systematically distributed depending on factors such as age, sexuality and disabilities. Therefore the future orientation of EU social policies should aim to reduce the impact of these social determinants on health

  17. Legitimating Racial Discrimination: Emotions, Not Beliefs, Best Predict Discrimination in a Meta-Analysis

    OpenAIRE

    Talaska, Cara A.; Fiske, Susan T.; Chaiken, Shelly

    2008-01-01

    Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as...

  18. Incremental Discriminant Analysis in Tensor Space

    Science.gov (United States)

    Chang, Liu; Weidong, Zhao; Tao, Yan; Qiang, Pu; Xiaodan, Du

    2015-01-01

    To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently. PMID:26339229

  19. Variable Selection Strategies in Discriminate Analysis.

    Science.gov (United States)

    Tanguma, Jesus

    This paper presents three variable selection strategies in discriminate analysis (all variables in the model, use of stepwise methods, and all possible subsets). All three methods are illustrated through examples. Although the all variables in the model and the stepwise methods are the most widely used, B. Thompson (1996) and C. Huberty (1994)…

  20. Efficient Global Programming Model for Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    M.ANGULAKSHMI

    2011-03-01

    Full Text Available Conventional statistical analysis includes the capacity to systematically assign individuals to groups. We suggest alternative assignment procedures, utilizing a set of interrelated goal programming formulations. This paper represents an effort to suggest ways by which the discriminant problem might reasonably be addressed via straightforward linear goal programming formulations. Simple and direct, such formulations may ultimately compete with conventional approaches - free of the classical assumptions and possessing a stronger intuitive appeal. We further demonstrate via simple illustration the potential of these procedures to play a significant part in addressing the discriminant problem, and indicate fundamental ideas that lay the foundation for other more sophisticated approaches.

  1. The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

    Science.gov (United States)

    Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

    2014-08-01

    Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

  2. Direct Neighborhood Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Miao Cheng

    2008-01-01

    Full Text Available Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA, which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.

  3. A Computational Discriminability Analysis on Twin Fingerprints

    Science.gov (United States)

    Liu, Yu; Srihari, Sargur N.

    Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

  4. Face Recognition Using Kernel Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.

  5. EM-63 Decay Curve Analysis for UXO Discrimination

    Science.gov (United States)

    2016-06-13

    EM-63 Decay Curve Analysis for UXO Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7...07 SEP 2001 2. REPORT TYPE 3. DATES COVERED 00-00-2001 to 00-00-2001 4. TITLE AND SUBTITLE EM-63 Decay Curve Analysis for UXO Discrimination ... Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7, 2001 Table Of Contents 1 Introduction

  6. DISCRIMINANT ANALYSIS OF BANK PROFITABILITY LEVELS

    Directory of Open Access Journals (Sweden)

    Ante Rozga

    2013-02-01

    Full Text Available Discriminant analysis has been employed in this paper in order to identify and explain key features of bank profitability levels. Bank profitability is set up in the form of two categorical variables: profit or loss recorded and above or below average return on equity. Predictor variables are selected from various groups of financial indicators usually included in the empirical work on microeconomic determinants of bank profitability. The data from the Croatian banking sector is analyzed using the Enter method. General recommendations for a more profitable business of banking found in the bank management literature and existing empirical framework such as rationalization of overhead costs, asset growth, increase of non-interest income by expanding scale and scope of financial products proved to be important for classification of banks in different profitability levels. A higher market share may bring additional advantages. Classification results, canonical correlation and Wilks’ Lambda test confirm statistical significance of research results. Altogether, discriminant analysis turns out to be a suitable statistical method for solving presented research problem and moving forward from the bankruptcy, credit rating or default issues in finance.

  7. Discriminant analysis with errors in variables

    CERN Document Server

    Loustau, Sébastien

    2012-01-01

    The effect of measurement error in discriminant analysis is investigated. Given observations $Z=X+\\epsilon$, where $\\epsilon$ denotes a random noise, the goal is to predict the density of $X$ among two possible candidates $f$ and $g$. We suppose that we have at our disposal two learning samples. The aim is to approach the best possible decision rule $G^*$ defined as a minimizer of the Bayes risk. In the free-noise case $(\\epsilon=0)$, minimax fast rates of convergence are well-known under the margin assumption in discriminant analysis (see \\cite{mammen}) or in the more general classification framework (see \\cite{tsybakov2004,AT}). In this paper we intend to establish similar results in the noisy case, i.e. when dealing with errors in variables. In particular, we discuss two possible complexity assumptions that can be set on the problem, which may alternatively concern the regularity of $f-g$ or the boundary of $G^*$. We prove minimax lower bounds for these both problems and explain how can these rates be atta...

  8. Global pattern of experienced and anticipated discrimination against people with schizophrenia: a cross-sectional survey.

    Science.gov (United States)

    Thornicroft, Graham; Brohan, Elaine; Rose, Diana; Sartorius, Norman; Leese, Morven

    2009-01-31

    Many people with schizophrenia experience stigma caused by other people's knowledge, attitudes, and behaviour; this can lead to impoverishment, social marginalisation, and low quality of life. We aimed to describe the nature, direction, and severity of anticipated and experienced discrimination reported by people with schizophrenia. We did a cross-sectional survey in 27 countries, in centres affiliated to the INDIGO Research Network, by use of face-to-face interviews with 732 participants with schizophrenia. Discrimination was measured with the newly validated discrimination and stigma scale (DISC), which produces three subscores: positive experienced discrimination; negative experienced discrimination; and anticipated discrimination. Negative discrimination was experienced by 344 (47%) of 729 participants in making or keeping friends, by 315 (43%) of 728 from family members, by 209 (29%) of 724 in finding a job, 215 (29%) of 730 in keeping a job, and by 196 (27%) of 724 in intimate or sexual relationships. Positive experienced discrimination was rare. Anticipated discrimination affected 469 (64%) in applying for work, training, or education and 402 (55%) looking for a close relationship; 526 (72%) felt the need to conceal their diagnosis. Over a third of participants anticipated discrimination for job seeking and close personal relationships when no discrimination was experienced. Rates of both anticipated and experienced discrimination are consistently high across countries among people with mental illness. Measures such as disability discrimination laws might, therefore, not be effective without interventions to improve self-esteem of people with mental illness.

  9. Discrimination in Recruitment: An Empirical Analysis.

    Science.gov (United States)

    Newman, Jerry M.

    1978-01-01

    To investigate whether recruitment practices of companies with affirmative action programs discriminated against Blacks or resulted in reverse discrimination, qualifications and race of fictitious job applicants were manipulated on resumes sent to a sample of employers. Responses strongly indicate discrimination, with Black applicants favored…

  10. Comparing two methods of univariate discriminant analysis for sex discrimination in an Iberian population.

    Science.gov (United States)

    Jiménez-Arenas, Juan Manuel; Esquivel, José Antonio

    2013-05-10

    This study assesses the performance of two analytical approaches to sex discrimination based on single linear variables: discriminant analysis and the Lubischew's test. Ninety individuals from an archaeological population (La Torrecilla-Arenas del Rey, Granada, southern Spain) and 17 craniometrical variables were included in the analyses. Most craniometrical variables were higher for men. The bizygomatic breadth enabled the highest level of discrimination: 87.5% and 88.5%, using discriminant analysis and Lubischew's test, respectively. Bizygomatic breadth proved highly dimorphic in comparison to other populations reported in the literature. Lubischew's test raised the discrimination percentage in specific craniometrical variables, while others showed a superior performance by means of the discriminant analysis. The inconsistent results across statistical methods resulted from the specific formulation of each procedure. Discriminant analysis accounts both for within-group and between-group variance, while Lubischew's test emphasizes between-group variation only. Therefore, both techniques are recommended, as they provide different means of achieving optimal discrimination percentages.

  11. The Discriminant Analysis Flare Forecasting System (DAFFS)

    Science.gov (United States)

    Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

    2016-05-01

    The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

  12. Stochastic model updating using distance discrimination analysis

    Institute of Scientific and Technical Information of China (English)

    Deng Zhongmin; Bi Sifeng; Sez Atamturktur

    2014-01-01

    This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element (FE) models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis (DDA) and advanced Monte Carlo (MC) technique to (1) enable more efficient MC by using a response surface model, (2) calibrate parameters with an iterative test-analysis correlation based upon DDA, and (3) utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed sto-chastic method makes it possible to obtain a precise model updating outcome with acceptable cal-culation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influ-ences the iterative calibration process and thus, the calibration outcome, indicating that an integra-tion of different metrics might yield improved results.

  13. PERCEPTION OF SOCIAL DISCRIMINATION IN RESULTS OF THE EUROPEAN SOCIAL SURVEY

    Directory of Open Access Journals (Sweden)

    Mária HOMIŠINOVÁ

    2014-09-01

    Full Text Available Empirical indices concerning social discrimination were applied repeatedly in the extensive sociological research (within The European Social Survey. They were applied in individual six rounds (in two-year cycles. The aim was to determinate the rate of generally perceived discrimination and to find particular reasons (forms of discrimination (race, nationality, religion, language, ethnicity, age, gender, sexuality. The aim of the study is to inform technical community on the knowledge in the socioscientific field (perception of social discrimination in Slovakia and in other European countries and to contribute to the enrichment of information base in the research sphere as well as to bring near sciences of different orientation.

  14. Discrimination

    National Research Council Canada - National Science Library

    Midtbøen, Arnfinn H; Rogstad, Jon

    2012-01-01

    ... of discrimination in the labour market as well as to the mechanisms involved in discriminatory hiring practices. The design has several advantages compared to -‘single-method’ approaches and provides a more substantial understanding of the processes leading to ethnic inequality in the labour market.

  15. Discrimination against people with severe mental illness and their access to social capital: findings from the Viewpoint survey.

    Science.gov (United States)

    Webber, M; Corker, E; Hamilton, S; Weeks, C; Pinfold, V; Rose, D; Thornicroft, G; Henderson, C

    2014-06-01

    Aims. Discrimination against people with severe mental illness is an international problem. It is associated with reduced social contact and hinders recovery. This paper aims to evaluate if experienced or anticipated discrimination is associated with social capital, a known correlate of mental health. Methods. Data from the annual viewpoint cross-sectional survey of people with severe mental illness (n = 1016) were analysed. Exploratory univariate analysis was used to identify correlates of social capital in the sample, which were then evaluated in linear regression models. Additional hypotheses were tested using t tests. Results. Experienced discrimination made a modest contribution to the explained variance of social capital. Experienced discrimination from friends and immediate family was associated with reduced access to social capital from these groups, but this was not found for wider family, neighbours or mental health staff. Experience of discrimination in finding or keeping a job was also associated with reduced access to social capital. Conclusions. Further longitudinal research is needed to determine how resources within people's networks can help to build resilience, which reduces the harmful effect of discrimination on mental health.

  16. PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Olga Smirnova

    2015-07-01

    Full Text Available This paper analyzes the contemporary theoretical and empirical research in the field of impact assessment of market power and conclusions about the possibilities of the company to implement price discrimination in different market structures. The results of the analysis allow to evaluate current approaches to antitrust regulation of price discrimination.

  17. Racial Discrimination and Ethnic Disparities in Sleep Disturbance: the 2002/03 New Zealand Health Survey.

    Science.gov (United States)

    Paine, Sarah-Jane; Harris, Ricci; Cormack, Donna; Stanley, James

    2016-02-01

    Research on the relationship between racial discrimination and sleep is limited. The aims of this study were to: (1) examine the independent relationship between ethnicity, sex, age, socioeconomic position, experience of racial discrimination and self-reported sleep disturbances, and (2) determine the statistical contribution of experience of racial discrimination to ethnic disparities in sleep disturbances. The study used data from the 2002/03 New Zealand Health Survey, a nationally-representative, population-based survey of New Zealand adults (≥ 15 years). The sample included 4,108 self-identified Māori (indigenous New Zealanders) and 6,261 European adults. Outcome variables were difficulty falling asleep, frequent nocturnal awakenings, and early morning awakenings. Experiences of racial discrimination across five domains were used to assess overall racial discrimination "ever" and the level of exposure to racial discrimination. Socioeconomic position was measured using neighborhood deprivation, education, and equivalized household income. Māori had a higher prevalence of each sleep disturbance item than Europeans. Reported experiences of racial discrimination were independently associated with each sleep disturbance item, adjusted for ethnicity, sex, age group, and socioeconomic position. Sequential logistic regression models showed that racial discrimination and socioeconomic position explained most of the disparity in difficulty falling asleep and frequent nocturnal awakening between Māori and Europeans; however, ethnic differences in early morning awakenings remained. Racial discrimination may play an important role in ethnic disparities in sleep disturbances in New Zealand. Activities to improve the sleep health of non-dominant ethnic groups should consider the potentially multifarious ways in which racial discrimination can disturb sleep. © 2016 Associated Professional Sleep Societies, LLC.

  18. Risk analysis methodology survey

    Science.gov (United States)

    Batson, Robert G.

    1987-01-01

    NASA regulations require that formal risk analysis be performed on a program at each of several milestones as it moves toward full-scale development. Program risk analysis is discussed as a systems analysis approach, an iterative process (identification, assessment, management), and a collection of techniques. These techniques, which range from simple to complex network-based simulation were surveyed. A Program Risk Analysis Handbook was prepared in order to provide both analyst and manager with a guide for selection of the most appropriate technique.

  19. A Bayesian Predictive Discriminant Analysis with Screened Data

    OpenAIRE

    Hea-Jung Kim

    2015-01-01

    In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSS...

  20. Cloud type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Crawford, Melba M.; Hutchison, Keith D.; Khazenie, Nahid

    1993-09-01

    One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multi-spectral rule based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture-based method for feature identification has been investigated. This method uses a set of localized spatial filters known as two dimensional Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a two-dimensional Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determined by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA-advanced very high resolution radiometer (AVHRR) data for cloud type discrimination.

  1. Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

    Science.gov (United States)

    Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young

    2007-01-01

    A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

  2. Small visible energy scalar top iterative discriminant analysis

    Indian Academy of Sciences (India)

    A Sopczak; A Finch; A Freitas; C Milsténe; M Schimtt

    2007-12-01

    Light scalar top quarks with a small mass difference with respect to the neutralino mass are of particular cosmological interest. This study uses an iterative discriminant analysis method to optimize the expected selection efficiency at the international linear collider (ILC).

  3. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  4. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  5. On discriminant analysis techniques and correlation structures in high dimensions

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    This paper compares several recently proposed techniques for performing discriminant analysis in high dimensions, and illustrates that the various sparse methods dier in prediction abilities depending on their underlying assumptions about the correlation structures in the data. The techniques...... generally focus on two things: Obtaining sparsity (variable selection) and regularizing the estimate of the within-class covariance matrix. For high-dimensional data, this gives rise to increased interpretability and generalization ability over standard linear discriminant analysis. Here, we group...

  6. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhan Wang

    2015-01-01

    Full Text Available Local Fisher discriminant analysis (LFDA was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA for face recognition. The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint. Then, DSLFDA reformulates the objective function as a regression-type optimization problem. The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to a l1 penalty. Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.

  7. Race/ethnicity and workplace discrimination: results of a national survey of physicians.

    Science.gov (United States)

    Nunez-Smith, Marcella; Pilgrim, Nanlesta; Wynia, Matthew; Desai, Mayur M; Jones, Beth A; Bright, Cedric; Krumholz, Harlan M; Bradley, Elizabeth H

    2009-11-01

    Promoting racial/ethnic diversity within the physician workforce is a national priority. However, the extent of racial/ethnic discrimination reported by physicians from diverse backgrounds in today's health-care workplace is unknown. To determine the prevalence of physician experiences of perceived racial/ethnic discrimination at work and to explore physician views about race and discussions regarding race/ethnicity in the workplace. Cross-sectional, national survey conducted in 2006-2007. Practicing physicians (total n = 529) from diverse racial/ethnic backgrounds in the United States. We examined physicians' experience of racial/ethnic discrimination over their career course, their experience of discrimination in their current work setting, and their views about race/ethnicity and discrimination at work. The proportion of physicians who reported that they had experienced racial/ethnic discrimination "sometimes, often, or very often" during their medical career was substantial among non-majority physicians (71% of black physicians, 45% of Asian physicians, 63% of "other" race physicians, and 27% of Hispanic/Latino(a) physicians, compared with 7% of white physicians, all p workplace. Opportunities exist for health-care organizations and diverse physicians to work together to improve the climate of perceived discrimination where they work.

  8. EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Kamel

    2012-04-01

    Full Text Available Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT with the Regulated Fisher Linear Discriminant (RFLD classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

  9. Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

    Institute of Scientific and Technical Information of China (English)

    Yan-jun HONG; Xiao-dan WANG; David SHEN; Su ZENG

    2008-01-01

    Aim:A discrimination analysis has been explored for the probabilistic classifica-tion of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).Methods:The method employs data normalization,clustering,and a linear discriminant analysis on surface-enhanced laser desorp-tion ionization (SELDI) time-of-flight MS data.The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra.Cross-validation and training/testing data-split experi-ments are conducted to verify the optimal discriminant and demonstrate the accu-racy and robustness of the method.Results:The cluster discrimination method achieves excellent performance.The sensitivity,specificity,and positive predic-tive values are above 97% on ovarian cancer.The protein fraction peaks,which significantly contribute to the classification,can be available from the analysis process.Conclusion:The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.

  10. Kernel-based fisher discriminant analysis for hyperspectral target detection

    Institute of Scientific and Technical Information of China (English)

    GU Yan-feng; ZHANG Ye; YOU Di

    2007-01-01

    A new method based on kernel Fisher discriminant analysis (KFDA) is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis (LFDA), and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space. All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

  11. Application of Discriminant Analysis on Romanian Insurance Market

    Directory of Open Access Journals (Sweden)

    Constantin Anghelache

    2008-11-01

    Full Text Available Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8 relevant variables: gross written premium (GR_WRI_PRE, net mathematical reserves (NET_M_PES, gross claims paid (GR_CL_PAID, net premium reserves (NET_PRE_RES, net claim reserves (NET_CL_RES, net income (NE—_INCOME, share capital (SHARE_CAP and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED. Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters that emerge from the data.

  12. Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

    Science.gov (United States)

    Byrd, Christy M; Carter Andrews, Dorinda J

    2016-08-01

    Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  13. High-Order Supervised Discriminant Analysis for Visual Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Ling Xia; Hang-Hui Huang

    2014-01-01

    In practical applications, we often have to deal with high-order data, for example, a grayscale image and a video clip are intrinsically a 2nd-order tensor and a 3rd-order tensor, respectively. In order to satisty these high-order data, it is conventional to vectorize these data in advance, which often destroys the intrinsic structures of the data and includes the curse of dimensionality. For this reason, we consider the problem of high-order data representation and classification, and propose a tensor based fisher discriminant analysis (FDA), which is a generalized version of FDA, named as GFDA. Experimental results show our GFDA outperforms the existing methods, such as the 2-directional 2-dimensional principal component analysis ((2D)2PCA), 2-directional 2-dimensional linear discriminant analysis ((2D)2LDA), and multilinear discriminant analysis (MDA), in high-order data classification under a lower compression ratio.

  14. Discriminant analysis for repeated measures data: a review

    Directory of Open Access Journals (Sweden)

    Lisa Lix

    2010-09-01

    Full Text Available Discriminant analysis (DA encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis. In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Specifically, DA procedures have been developed for repeated measures data characterized by missing observations and/or unbalanced measurement occasions, as well as high-dimensional data in which measurements are collected repeatedly on two or more variables. This paper reviews the literature on DA procedures for univariate and multivariate repeated measures data, focusing on covariance pattern and linear mixed-effects models. A numeric example illustrates their implementation using SAS software.

  15. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

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

    2016-01-01

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

  16. A Bayesian Predictive Discriminant Analysis with Screened Data

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2015-09-01

    Full Text Available In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSSMN model, which makes provision for flexible modeling of the screened observations. An Markov chain Monte Carlo (MCMC method using the Gibbs sampler and the Metropolis–Hastings algorithm within the Gibbs sampler is used to perform a Bayesian inference on the HSSMN models and to approximate the optimal predictive rule. A simulation study is given to demonstrate the performance of the proposed predictive discrimination procedure.

  17. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

    Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  18. Discrimination and numerical analysis of human pathogenic ...

    African Journals Online (AJOL)

    SERVER

    2008-02-19

    Feb 19, 2008 ... Numerical analysis of whole-cell protein profiles of all strains revealed 2 .... average linkage method and correlation coefficient distance. ... distance yielded a dendrogam, consisting of two basic .... Candida glabrata: review of.

  19. Visual category recognition using Spectral Regression and Kernel Discriminant Analysis

    NARCIS (Netherlands)

    Tahir, M.A.; Kittler, J.; Mikolajczyk, K.; Yan, F.; van de Sande, K.E.A.; Gevers, T.

    2009-01-01

    Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been s

  20. A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia

    Science.gov (United States)

    Lukes, Robin; Bangs, Joann

    2014-01-01

    This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…

  1. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    LIU QingShan (刘青山); HUANG Rui (黄锐); LU HanQing (卢汉清); MA SongDe (马颂德)

    2003-01-01

    Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA).First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.

  2. ODVBA: optimally-discriminative voxel-based analysis.

    Science.gov (United States)

    Zhang, Tianhao; Davatzikos, Christos

    2011-08-01

    Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

  3. Insulation Defects Discrimination in GIS by Fisher Discriminant Analysis of Partial Discharge

    Institute of Scientific and Technical Information of China (English)

    DING Dengwei; GAO Wensheng; LIU Weidong

    2013-01-01

    To monitor the condition of online power apparatus accurately and provide appropriate guidance on their maintenance,a fundamental ultra-high frequency (UHF) database of partial discharges corresponding to different types of defects is presented for the observation of insulation state of gas insulated switchgear (GIS).In order to imitate the defects in a GIS online,five types of typical partial discharge (PD) sources (i.e.floating metal,protrusion,bouncing particles,void in spacer,and particle on the surface of solid insulation) were designed and fabricated.A wideband UHF sensor and an amplifier were applied to obtain UHF signals.Based on the characteristics of the five different defects,nine meaningful parameters which were independent of the phase of the applied voltage were extracted from the PD samples and then discussed.In this paper,Fisher discriminant analysis (FDA),a pattern recognition algorithm,was applied for the purpose of classifying the defects.In this way,there was no need to set complicated parameters and kernel function,and the total discrimination accuracy of test samples was 97.6%.It indicates that based on the nine especial parameters,the typical defects in GIS can be identified exactly by means of FDA.Hence the analysis method could be possibly regarded as a universal classification method.

  4. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    ) was performed in parallel with videometer image snapshots and sensory analysis. Odour and colour characteristics of meat were determined by a test panel and attributed into three pre-characterized quality classes, namely Fresh; Semi Fresh and Spoiled during the days of its shelf life. So far, different...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples regarding the spoilage process as evaluated from sensory as well as from microbiological data. The support vector classification (SVC) model outperformed other models. Specifically, the misclassification error rate (MER), derived from odour characteristics, was 18% for both aerobic and MAP meat...

  5. Forensic discrimination of dyed hair color: II. Multivariate statistical analysis.

    Science.gov (United States)

    Barrett, Julie A; Siegel, Jay A; Goodpaster, John V

    2011-01-01

    This research is intended to assess the ability of UV-visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty-five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two-dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.

  6. Gender-biased behavior at work: what can surveys tell us about the link between sexual harassment and gender discrimination?

    OpenAIRE

    Antecol, Heather; Barcus, Vanessa E.; Cobb-Clark, Deborah A.

    2007-01-01

    This paper examines the links between survey-based reports of sexual harassment and gender discrimination. In particular, we are interested in assessing whether these concepts measure similar forms of gender-biased behavior and whether they have the same effect on workers' job satisfaction and intentions to leave their jobs. Our results provide little support for the notion that survey-based measures of sexual harassment and gender discrimination capture the same underlying behavior. Responde...

  7. Experiences of Stigma and Discrimination among Caregivers of Persons with Schizophrenia in China: A Field Survey

    Science.gov (United States)

    Yin, Yi; Zhang, Weijun; Hu, Zhenyu; Jia, Fujun; Li, Yafang; Xu, Huiwen; Zhao, Shuliang; Guo, Jing; Tian, Donghua; Qu, Zhiyong

    2014-01-01

    In China, caregivers for family members with schizophrenia play an important role in treatment and recovery but may experience stigma and discrimination simply because of their family relationship. The object of this study was to measure the degrees and correlates of stigma and discrimination experiences among this group. Four hundred twenty-seven caregivers participated in this hospital-based and cross-sectional study in Ningbo and Guangzhou, China. Data were collected by trained interviewers using fixed questionnaires. Stigma and discrimination experiences were measured by the Modified Consumer Experiences of Stigma Questionnaire (MCESQ). Caregivers’ social support was measured by the Social Support Rating Scale. Parametric analysis, nonparametric analysis and multivariate linear regression were used. The mean (SD) score of MCESQ was 2.44(0.45), 2.91(0.71) for stigma experiences and 1.97(0.37) for discrimination experiences on a five-point score (“1 = never” and “5 = very often”). Approximately 65% of caregivers reported that they tried to conceal their family members’ illness, and 71% lacked the support of friends. The experience of stigma was significantly negatively associated with the perceived social support of caregivers (standard β = −0.2,pstigmas than other (standard β = −0.18, pstigmas. In addition, stigma and discrimination was more experienced in Zhejiang than in Guangdong (pstigmas and rare discrimination and found the relations with social support, kinship, patient’s educational level and regional differences. More interventions and supports should been given to caregivers who are lack of social support, who live in rural area and who are the patients’ parents, spouses or siblings. PMID:25259732

  8. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    with corresponding sensory data would be of great interest. The purpose of this research was to produce a method capable of quantifying and/or predicting the spoilage status (e.g. express in TVC counts as well as on sensory evaluation) using a multi spectral image of a meat sample and thereby avoid any time...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples. In the case where all data were taken together the misclassification error amounted to 16%. When spoilage status was based on visual sensory data, the model produced a MER of 22% for the combined dataset. These results suggest that it is feasible to employ a multi spectral image...

  9. Quark/gluon jet discrimination: a reproducible analysis using R

    CERN Document Server

    CERN. Geneva

    2017-01-01

    The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method that combines information theory and graph analytics will be outlined. This method has found new variables that promise significant improvements in discrimination power. The prototype jet tagger is simple, interpretable, parsimonious, and computationally extremely cheap, and therefore might be suitable for use in trigger systems for real-time data processing. Nested stratified k-fold cross validation was used to generate robust estimates of model performance. The data analysis was performed entirely in the R statistical programming language, and is fully reproducible. The entire analysis workflow is data-driven, automated a...

  10. Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Xiao-Zhang Liu

    2013-02-01

    Full Text Available Recent applications and developments based on support vector machines (SVMs have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA method with multiple kernels. This paper proposes a multiple kernel construction method for kernel‐based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC, we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD achieves high recognition performance, compared with single‐kernel‐based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel‐based FDA to some extent.

  11. Discrimination and content analysis of fritillaria using near infrared spectroscopy.

    Science.gov (United States)

    Meng, Yu; Wang, Shisheng; Cai, Rui; Jiang, Bohai; Zhao, Weijie

    2015-01-01

    Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

  12. BUSINESS FAILURE PREDICTION FOR ROMANIAN SMES USING MULTIVARIATE DISCRIMINANT ANALYSIS

    OpenAIRE

    2012-01-01

    Business failure prediction is one of special importance for small and medium sized enterprises (SMEs) due to their increased vulnerability. Consequently, the purpose of this paper is to investigate the utility of financial ratios and other non-financial variables to predict business failure using a sample of Romanian SMEs and applying multiple discriminant analysis. The process that leads to failure is analyzed on a three year time horizon prior to failure and the results showed that failure...

  13. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  14. Quantum discriminant analysis for dimensionality reduction and classification

    Science.gov (United States)

    Cong, Iris; Duan, Luming

    2016-07-01

    We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O({log}(N)). Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O(p {polylog}({MN})/{ε }3), where ɛ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O({log}({MN})/{ε }3).

  15. Rapid discrimination between four seagrass species using hybrid analysis.

    Science.gov (United States)

    Osathanunkul, M; Madesis, P; Ounjai, S; Suwannapoom, C; Jampeetong, A

    2015-04-27

    Biological species are traditionally identified based on their morphological features and the correct identification of species is critical in biological studies. However, some plant types, such as seagrass, are taxonomically problematic and difficult to identify. Furthermore, closely related seagrass species, such as Halophila spp, form a taxonomically unresolved complex. Although some seagrass taxa are easy to recognize, most species are difficult to identify without skilled taxonomic or molecular techniques. Barcoding coupled with High Resolution Melting analysis (BAR-HRM) offers a potentially reliable, rapid, and cost-effective method to confirm species. Here, DNA information of two chloroplast loci was used in combination with HRM analysis to discriminate four species of seagrass collected off the southern coast of Thailand. A distinct melting curve presenting one inflection point was generated for each species using rbcL primers. While the melting profiles of Cymodocea rotundata and Cymodocea serrulata were not statistically different, analysis of the normalized HRM curves produced with the rpoC primers allowed for their discrimination. The Bar-HRM technique showed promise in discriminating seagrass species and with further adaptations and improvements, could make for an effective and power tool for confirming seagrass species.

  16. Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

    Science.gov (United States)

    Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

    2016-03-01

    Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

  17. Discriminant analysis of bronchial asthma by linear discriminant function with parameters of flow-volumes: discriminant analysis of bronchial asthma in young male non-smokers

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1978-10-01

    Full Text Available With the parameters of a flow-volume and a volume-time curve, the discriminant analysis of bronchial asthma is described. The subjects were classified into three groups (healthy adults, mild asthmatic patients and moderates ones. The difference of the mean vectors of the parameters of the three groups was made clear by the selection methods of the discriminant analysis between any two of the groups both with 6 parameters (%FVC, FEV1.0%, peak flow rate (PF, flow rate at 50% of FVC (V50, flow rate at 25% of FVC (V25, and V50/V25 and with 8 (6 parameters mentioned above and V75, V10. Forced expiratory volume in 1 second percent (FEV1.0% or V50 was selected at the first step with 6 parameters, and V75 was selected at the first step with 8 parameters. Probabilities of misclassification with 8 parameters were lower than those with 6 ones and the probability of misclassification at the discriminant analysis between healthy adults and mild asthmatic patients with 8 parameters was 15.75% at the final step.

  18. Use of discriminant analysis to identify propensity for purchasing properties

    Directory of Open Access Journals (Sweden)

    Ricardo Floriani

    2015-03-01

    Full Text Available Properties usually represent a milestone for people and families due to the high added-value when compared with family income. The objective of this study is the proposition of a discrimination model, by a discriminant analysis of people with characteristics (according to independent variables classified as potential buyers of properties, as well as to identify the interest in the use of such property, if it will be assigned to housing or leisure activities such as a cottage or beach house, and/or for investment. Thus, the following research question is proposed: What are the characteristics that better describe the profile of people which intend to acquire properties? The study justifies itself by its economic relevance in the real estate industry, as well as to the players of the real estate Market that may develop products based on the profile of potential customers. As a statistical technique, discriminant analysis was applied to the data gathered by questionnaire, which was sent via e-mail. Three hundred and thirty four responses were gathered. Based on this study, it was observed that it is possible to identify the intention for acquired properties, as well the purpose for acquiring it, for housing or investments.

  19. Discriminating dysplasia: Optical tomographic texture analysis of colorectal polyps.

    Science.gov (United States)

    Li, Wenqi; Coats, Maria; Zhang, Jianguo; McKenna, Stephen J

    2015-12-01

    Optical projection tomography enables 3-D imaging of colorectal polyps at resolutions of 5-10 µm. This paper investigates the ability of image analysis based on 3-D texture features to discriminate diagnostic levels of dysplastic change from such images, specifically, low-grade dysplasia, high-grade dysplasia and invasive cancer. We build a patch-based recognition system and evaluate both multi-class classification and ordinal regression formulations on a 90 polyp dataset. 3-D texture representations computed with a hand-crafted feature extractor, random projection, and unsupervised image filter learning are compared using a bag-of-words framework. We measure performance in terms of error rates, F-measures, and ROC surfaces. Results demonstrate that randomly projected features are effective. Discrimination was improved by carefully manipulating various important aspects of the system, including class balancing, output calibration and approximation of non-linear kernels.

  20. A Direct Estimation Approach to Sparse Linear Discriminant Analysis

    CERN Document Server

    Cai, Tony

    2011-01-01

    This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\\O$ and the difference $\\de$ of the mean vectors, we introduce a simple and effective classifier by estimating the product $\\O\\de$ directly through constrained $\\ell_1$ minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule. The LPD rule is shown to have desirable theoretical and numerical properties. It exploits the approximate sparsity of $\\O\\de$ and as a consequence allows cases where it can still perform well even when $\\O$ and/or $\\de$ cannot be estimated consistently. Asymptotic properties of the LPD rule are investigated and consistency and rate of convergence results are given. The LPD classifier has superior finite sample performance and significant computational advantages over the existing methods that req...

  1. Sex determination by discriminant function analysis of lumbar vertebrae.

    Science.gov (United States)

    Ostrofsky, Kelly R; Churchill, Steven E

    2015-01-01

    Sex determination is critical for developing the biological profile of unidentified skeletal remains. When more commonly used elements (os coxa, cranium) for sexing are not available, methods utilizing other skeletal elements are needed. This study aims to assess the degree of sexual dimorphism of the lumbar vertebrae and develop discriminant functions for sex determination from them, using a sample of South African blacks from the Raymond A. Dart Collection (47 males, 51 females). Eleven variables at each lumbar level were subjected to univariate and multivariate discriminant function analyses. Univariate equations produced classification rates ranging from 57.7% to 83.5%, with the highest accuracies associated with dimensions of the vertebral body. Multivariate stepwise analysis generated classification rates ranging from 75.9% to 88.7%. These results are comparable to other methods for sexing the skeleton and indicate that measures of the lumbar vertebrae can be used as an effective tool for sex determination.

  2. Perception of police on discrimination in Serbia - results of the survey on the attitudes of public order and traffic police officers

    Directory of Open Access Journals (Sweden)

    Zekavica Radomir

    2016-01-01

    Full Text Available The paper analyzes the results of the research on the attitudes of the public order and traffic police officers in seven regional police departments in Serbia - Belgrade, Novi Sad, Subotica, Novi Sad, Niš, Kragujevac and Zaječar. The subject of the research is the analysis of the police attitudes on discrimination, recognition of its essence, the scope of vulnerability of particular social groups and recognition of the hate speech. Also, the subject of the research is: determination of relationship with measures which should improve the position of vulnerable groups and the relationship with the institutions in terms of their responsibility for the appearance of discrimination and its impact on the reduction, then the personal experience of discrimination and analysis of attitudes regarding certain claims of stereotypical character. The results of this research are given in the comparative analysis with the results of the research on the attitudes of members of the criminal police conducted in 2014, so we have indication of perception of discrimination by the police in all three key operating police areas. In regard to some issues, a comparative analysis of the results from the survey of citizens’ attitudes towards discrimination conducted in 2013 by CESID is provided. [Projekat Ministarstva nauke Republike Srbije: Kriminalitet u Srbiji i instrumenti državne reakcije

  3. Sparse discriminant analysis for breast cancer biomarker identification and classification

    Institute of Scientific and Technical Information of China (English)

    Yu Shi; Daoqing Dai; Chaochun Liu; Hong Yan

    2009-01-01

    Biomarker identification and cancer classification are two important procedures in microarray data analysis. We propose a novel uni-fied method to carry out both tasks. We first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost, and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer classification. Moreover, we give a mathematical justification about automatic biomarker identification. Experimental results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

  4. Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

    Directory of Open Access Journals (Sweden)

    Ting-quan Deng

    2014-01-01

    Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

  5. Discrimination in Macedonian companies: Case studies research and analysis of current discrimination grounds, forms and trends

    OpenAIRE

    Ananiev, Jovan; Poposka, Zaneta

    2013-01-01

    Discrimination which is evident in in companies in the Republic of Macedonia is mostly done by the owners or management and on the ground of personal status, gender, ethnicity and age and mostly in the form of harassment and direct discrimination.

  6. Isokinetic evaluation of knee muscles in soccer players: discriminant analysis

    Directory of Open Access Journals (Sweden)

    Bruno Fles Mazuquin

    2015-10-01

    Full Text Available ABSTRACTIntroduction:Muscle activity in soccer players can be measured by isokinetic dynamometer, which is a reliable tool for assessing human performance.Objectives:To perform isokinetic analyses and to determine which variables differentiate the under-17 (U17 soccer category from the professional (PRO.Methods:Thirty four players were assessed (n=17 for each category. The isokinetic variables used for the knee extension-flexion analysis were: peak torque (Nm, total work (J, average power (W, angle of peak torque (deg., agonist/ antagonist ratio (%, measured for three velocities (60°/s, 120°/s and 300°/s, with each series containing five repetitions. Three Wilks' Lambda discriminant analyses were performed, to identify which variables were more significant for the definition of each of the categories.Results:The discriminative variables at 60°/s in the PRO category were: extension peak torque, flexion total work, extension average power and agonist/antagonist ratio; and for the U17s were: extension total work, flexion peak torque and flexion average power. At 120°/s for the PRO category the discriminant variables were: flexion peak torque and extension average power; for the U17s they were: extension total work and flexion average power. Finally at 300°/s, the variables found in the PRO and U17 categories respectively were: extension average power and extension total work.Conclusion:Isokinetic variables for flexion and extension knee muscles were able to significantly discriminate between PRO and U17 soccer players.

  7. A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Bo Yang

    2014-01-01

    Discriminant Analysis (GmGcDA just based on globality alone, GmLcDA, and LmGcDA, we suggest that the joint of locally constructed intraclass and globally constructed interclass graphs is more discriminant.

  8. Discrimination of healthy and carious teeth using laser-induced breakdown spectroscopy and partial least square discriminant analysis.

    Science.gov (United States)

    Gazmeh, Meisam; Bahreini, Maryam; Tavassoli, Seyed Hassan

    2015-01-01

    In the laser drilling of teeth, a microplasma is generated which may be utilized for elemental analysis of ablated tissue via a laser-induced breakdown spectroscopy (LIBS) technique. In this study, LIBS is used to investigate the possibility of discrimination of healthy and carious tooth tissues. This possibility is examined using multivariate statistical analysis called partial least square discriminant analysis (PLS-DA) based on atomic and ionic emission lines of teeth LIBS spectra belonging to P, Ca, Mg, Zn, K, Sr, C, Na, H, and O elements. Results show an excellent discrimination and prediction of unknown tooth tissues. It is shown that using the PLS-DA method, the spectroscopic analysis of plasma emission during the laser drilling, would be a promising technique for caries detection.

  9. Employment discrimination and HIV stigma: survey results from civil society organisations and people living with HIV in Africa.

    Science.gov (United States)

    Sprague, Laurel; Simon, Sara; Sprague, Courtenay

    2011-01-01

    The article presents findings from three surveys of people living with HIV (PLHIV) and civil society organisations about the experience of employment discrimination and stigma in the workplace. The work seeks to contribute to efforts by businesses and other organisations to effectively respond to the HIV epidemic within the world of work, and to deepen our understanding of the ways in which HIV stigma and employment discrimination persist in the workplace. The findings of global and regional surveys indicate the existence of high levels of employment discrimination based on HIV status worldwide, including forced disclosure of HIV status, exclusion in the workplace, refusals to hire or promote, and terminations of people known to be living with HIV. The survey findings show that employment discrimination based on HIV status is experienced in all African subregions. Country-level surveys conducted in Kenya and Zambia indicated that PLHIV face marked barriers to employment, including discrimination in hiring, loss of promotions, and termination because of HIV status. Additionally, large variances were found in the degree of support versus discrimination that employees living with HIV in those two countries received following their disclosure. The discussion emphasises the importance of the workplace as a site for intervention and behaviour change. To address this, we introduce a conceptual framework - the employment continuum - that maps multiple points of entry within the workplace to address HIV-related stigma and discrimination. Additional recommendations include: actions to ensure equal opportunity in hiring for PLHIV; ensuring that HIV testing is voluntary, never mandatory, and that disclosure is not necessary for employment; ensuring confidentiality of HIV status; communicating and enforcing HIV-related antidiscrimination policies; establishing support groups in the workplace; providing safe and confidential processes for resolving complaints of employment

  10. Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds.

    Science.gov (United States)

    Wu, Quan-Jin; Dong, Qing-Hua; Sun, Wei-Jiang; Huang, Yan; Wang, Qiong-Qiong; Zhou, Wei-Long

    2014-09-24

    This study aimed to construct objective and accurate analytical models of tea categories based on their polyphenols and caffeine. A total of 522 tea samples of 4 commonly consumed teas with different fermentation degrees (green tea, white tea, oolong tea, and black tea) were analyzed by high-performance liquid chromatography (HPLC) coupled with spectrophotometry, utilizing ISO 14502, as analytical tools. The content of polyphenols and caffeine varied significantly according to differently fermented teas, indicating that these active constituents may discriminate fermentation degrees effectively. By principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA), the vast majority of tea samples could be successfully differentiated according to their chemical markers. This study yielded three discriminant functions with the capacity to simultaneously discriminate the four tea categories with a 97.8% correct rate. In classification of oolong and other teas, there were one discriminant function and two equations with best discriminant capacity. Furthermore, the classification of different degrees of fermentation of oolong and external validation achieved the desired results. It is suggested that polyphenols and caffeine are the distinct variables to establish internationally recognized models of teas.

  11. Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shanshan; SU Rongguo; DUAN Yali; ZHANG Cui; SONG Zhijie; WANG Xiulin

    2012-01-01

    The discrete excitation-emission-matrix fluorescence spectra(EEMS)at 12 excitation wavelengths (400,430,450,460,470,490,500,510,525,550,570,and 590 nm)and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species.A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed.For laboratory simulatively mixed samples,the samples mixed from 43 algal species(the algae of one division accounted for 25%,50%,75%,85%,and 100% of the gross biomass,respectively),the average discrimination rates at the level of division were 65.0%,87.5%,98.6%,99.0%,and 99.1%,with average relative contents of 18.9%,44.5%,68.9%,73.4%,and 82.9%,respectively;the samples mixed from 32 red tide algal species(the dominant species accounted for 60%,70%,80%,90%,and 100% of the gross biomass,respectively),the average correct discrimination rates of the dominant species at the level of genus were 63.3%,74.2%,78.8%,83.4%,and 79.4%,respectively.For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass(chlorophyll),the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus,respectively.For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007,the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level;for the 12 samples obtained from Jiaozhou Bay in August 2007,the dominant species of all the 12 samples were recognized at the division level.The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for

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

    Science.gov (United States)

    Kim, Isok

    2014-01-01

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

  13. Price Discrimination

    OpenAIRE

    Armstrong, Mark

    2008-01-01

    This paper surveys recent economic research on price discrimination, both in monopoly and oligopoly markets. Topics include static and dynamic forms of price discrimination, and both final and input markets are considered. Potential antitrust aspects of price discrimination are highlighted throughout the paper. The paper argues that the informational requirements to make accurate policy are very great, and with most forms of price discrimination a laissez-faire policy may be the best availabl...

  14. Testing LMC Microlensing Scenarios: The Discrimination Power of the SuperMACHO Microlensing Survey

    Energy Technology Data Exchange (ETDEWEB)

    Rest, A; Stubbs, C; Becker, A C; Miknaitis, G A; Miceli, A; Covarrubias, R; Hawley, S L; Smith, C; Suntzeff, N B; Olsen, K; Prieto, J; Hiriart, R; Welch, D L; Cook, K; Nikolaev, S; Proctor, G; Clocchiatti, A; Minniti, D; Garg, A; Challis, P; Keller, S C; Scmidt, B P

    2004-05-27

    Characterizing the nature and spatial distribution of the lensing objects that produce the observed microlensing optical depth toward the Large Magellanic Cloud (LMC) remains an open problem. They present an appraisal of the ability of the SuperMACHO Project, a next-generation microlensing survey pointed toward the LMC, to discriminate between various proposed lensing populations. they consider two scenarios: lensing by a uniform foreground screen of objects and self-lensing of LMC stars. The optical depth for ''screen-lensing'' is essentially constant across the face of the LMC; whereas, the optical depth for self-lensing shows a strong spatial dependence. they have carried out extensive simulations, based upon actual data obtained during the first year of the project, to assess the SuperMACHO survey's ability to discriminate between these two scenarios. In the simulations they predict the expected number of observed microlensing events for each of their fields by adding artificial stars to the images and estimating the spatial and temporal efficiency of detecting microlensing events using Monte-Carlo methods. They find that the event rate itself shows significant sensitivity to the choice of the LMC luminosity function shape and other parameters, limiting the conclusions which can be drawn from the absolute rate. By instead determining the differential event rate across the LMC, they can decrease the impact of these systematic uncertainties rendering the conclusions more robust. With this approach the SuperMACHO Project should be able to distinguish between the two categories of lens populations and provide important constraints on the nature of the lensing objects.

  15. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  16. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  17. Discriminant possibilities of the Hamilton depression scale: ROC analysis

    Directory of Open Access Journals (Sweden)

    Novović Zdenka

    2005-01-01

    Full Text Available The purpose of this study is to compare discrimination power of original and reconstructed version of Hamilton’s depression scale in separation of depressive vs. anxious patients and to suggest some possibilities which offer ROC analysis. The subjects of the study were 119 patients of Psychiatric clinic in Novi Sad. 67 of them were diagnosed with some of the forms of affective disorders and 52 with an anxious-phobic diagnosis. Results of ROC analysis suggest that both instruments can be used in distinguishing depressive from anxious patients, but reconstructed version shows greater sensitivity and specificity with optimal cut-off score. It also has more significant AUC, which refers to probability of prediction on the basis of the whole spectrum of the results. These data is commented in relation with current debates, between unitaristic and pluralistic oriented authors, about the nature of the anxious-depression relationship.

  18. THE BOLOCAM GALACTIC PLANE SURVEY. VIII. A MID-INFRARED KINEMATIC DISTANCE DISCRIMINATION METHOD

    Energy Technology Data Exchange (ETDEWEB)

    Ellsworth-Bowers, Timothy P.; Glenn, Jason; Battersby, Cara; Ginsburg, Adam; Bally, John [CASA, University of Colorado, UCB 389, University of Colorado, Boulder, CO 80309 (United States); Rosolowsky, Erik [Department of Physics and Astronomy, University of British Columbia Okanagan, 3333 University Way, Kelowna, BC V1V 1V7 (Canada); Mairs, Steven [Department of Physics and Astronomy, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 1A1 (Canada); Evans, Neal J. II [Department of Astronomy, University of Texas, 1 University Station C1400, Austin, TX 78712 (United States); Shirley, Yancy L., E-mail: timothy.ellsworthbowers@colorado.edu [Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States)

    2013-06-10

    We present a new distance estimation method for dust-continuum-identified molecular cloud clumps. Recent (sub-)millimeter Galactic plane surveys have cataloged tens of thousands of these objects, plausible precursors to stellar clusters, but detailed study of their physical properties requires robust distance determinations. We derive Bayesian distance probability density functions (DPDFs) for 770 objects from the Bolocam Galactic Plane Survey in the Galactic longitude range 7. Degree-Sign 5 {<=} l {<=} 65 Degree-Sign . The DPDF formalism is based on kinematic distances, and uses any number of external data sets to place prior distance probabilities to resolve the kinematic distance ambiguity (KDA) for objects in the inner Galaxy. We present here priors related to the mid-infrared absorption of dust in dense molecular regions and the distribution of molecular gas in the Galactic disk. By assuming a numerical model of Galactic mid-infrared emission and simple radiative transfer, we match the morphology of (sub-)millimeter thermal dust emission with mid-infrared absorption to compute a prior DPDF for distance discrimination. Selecting objects first from (sub-)millimeter source catalogs avoids a bias towards the darkest infrared dark clouds (IRDCs) and extends the range of heliocentric distance probed by mid-infrared extinction and includes lower-contrast sources. We derive well-constrained KDA resolutions for 618 molecular cloud clumps, with approximately 15% placed at or beyond the tangent distance. Objects with mid-infrared contrast sufficient to be cataloged as IRDCs are generally placed at the near kinematic distance. Distance comparisons with Galactic Ring Survey KDA resolutions yield a 92% agreement. A face-on view of the Milky Way using resolved distances reveals sections of the Sagittarius and Scutum-Centaurus Arms. This KDA-resolution method for large catalogs of sources through the combination of (sub-)millimeter and mid-infrared observations of molecular

  19. SEM-EDS analysis and discrimination of forensic soil.

    Science.gov (United States)

    Cengiz, Salih; Cengiz Karaca, Ali; Cakir, Ismail; Bülent Uner, H; Sevindik, Aytekin

    2004-04-20

    Soils vary among different areas, and have some characteristics because of the natural effects and transfers made by human and other living beings in time. So that forensic examination of soil is not only concerned with the analysis of naturally occurring rocks, minerals, vegetation, and animal matter. It also includes the detection of such manufactured materials such as ions from synthetic fertilizers and from different environments (e.g., nitrate, phosphate, and sulfate) as environmental artifacts (e.g., lead or objects as glass, paint chips, asphalt, brick fragments, and cinders) whose presence may impart soil with characteristics that will make it unique to a particular location. Many screening and analytical methods have been applied for determining the characteristics which differentiate and discriminate the forensic soil samples but none of them easily standardized. Some of the methods that applied in forensic laboratories in forensic soil discrimination are the color comparison of the normal air-dried (dehumidified) and overheated soil samples, macroscopic observation, and low-power stereo-microscopic observation, determination of anionic composition by capillary electrophoresis (CE), and the elemental composition by scanning electron microscope (SEM)-energy dispersive X-ray spectrometer (EDS) and other high sensitivity techniques. The objective of this study was to show the effect of the application of 9 tonnes/cm2 pressure on the elemental compositions obtained by SEM-EDS technique and comparing the discrimination power of the pressed-homogenized and not homogenized forensic soil samples. For this purpose soil samples from 17 different locations of Istanbul were collected. Aliquots of the well mixed samples were dried in an oven at 110-120 degrees C and sieved by using 0.5 mm sieve and then the undersieve fraction(JEO-JSM-5600 equipped with an energy dispersive X-ray spectrometer OXFORD Link-ISIS-300. The samples from top of the sieves were examined with

  20. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  1. Multitask linear discriminant analysis for view invariant action recognition.

    Science.gov (United States)

    Yan, Yan; Ricci, Elisa; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Nicu

    2014-12-01

    Robust action recognition under viewpoint changes has received considerable attention recently. To this end, self-similarity matrices (SSMs) have been found to be effective view-invariant action descriptors. To enhance the performance of SSM-based methods, we propose multitask linear discriminant analysis (LDA), a novel multitask learning framework for multiview action recognition that allows for the sharing of discriminative SSM features among different views (i.e., tasks). Inspired by the mathematical connection between multivariate linear regression and LDA, we model multitask multiclass LDA as a single optimization problem by choosing an appropriate class indicator matrix. In particular, we propose two variants of graph-guided multitask LDA: 1) where the graph weights specifying view dependencies are fixed a priori and 2) where graph weights are flexibly learnt from the training data. We evaluate the proposed methods extensively on multiview RGB and RGBD video data sets, and experimental results confirm that the proposed approaches compare favorably with the state-of-the-art.

  2. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  3. Linear discriminant analysis of character sequences using occurrences of words

    KAUST Repository

    Dutta, Subhajit

    2014-02-01

    Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

  4. The convergent and discriminant validity of burnout measures in sport: a multi-trait/multi-method analysis.

    Science.gov (United States)

    Cresswell, Scott L; Eklund, Robert C

    2006-02-01

    Athlete burnout research has been hampered by the lack of an adequate measurement tool. The Athlete Burnout Questionnaire (ABQ) and the Maslach Burnout Inventory General Survey (MBI-GS) are two recently developed self-report instruments designed to assess burnout. The convergent and discriminant validity of the ABQ and MBI-GS were assessed through multi-trait/multi-method analysis with a sporting population. Overall, the ABQ and the MBI-GS displayed acceptable convergent validity with matching subscales highly correlated, and satisfactory internal discriminant validity with lower correlations between non-matching subscales. Both scales also indicated an adequate discrimination between the concepts of burnout and depression. These findings add support to previous findings in non-sporting populations that depression and burnout are separate constructs. Based on the psychometric results, construct validity analysis and practical considerations, the results support the use of the ABQ to assess athlete burnout.

  5. Anti-discrimination Analysis Using Privacy Attack Strategies

    KAUST Repository

    Ruggieri, Salvatore

    2014-09-15

    Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

  6. Discriminating topology in galaxy distributions using network analysis

    Science.gov (United States)

    Hong, Sungryong; Coutinho, Bruno C.; Dey, Arjun; Barabási, Albert-L.; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-07-01

    The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ξ(r) ˜ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.

  7. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest......We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis...... accuracy. Numerical experiments based on four data sets of different types of images show that RGED has competitive classification performance with existing multidimensional and sparse techniques of discriminant analysis....

  8. An Analysis of Discrimination by Real Estate Brokers.

    Science.gov (United States)

    Yinger, John

    This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…

  9. Racial Discrimination and Asian Mental Health: A Meta-Analysis

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2011-01-01

    Although research on racial discrimination and mental health has proliferated, findings are varied and dispersed. This study explored the critical question of how Asians, in particular, deal with discrimination and how this relates to Asian mental health. With 99 correlations from 23 independent studies, the overall relationship between racial…

  10. Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

    Science.gov (United States)

    Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

    2017-04-15

    HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran.

  11. Is Knowledge Regarding Tuberculosis Associated with Stigmatising and Discriminating Attitudes of General Population towards Tuberculosis Patients? Findings from a Community Based Survey in 30 Districts of India

    Science.gov (United States)

    Sagili, Karuna D.; Satyanarayana, Srinath; Chadha, Sarabjit S.

    2016-01-01

    Background Stigmatising and discriminating attitudes may discourage tuberculosis (TB) patients from actively seeking medical care, hide their disease status, and discontinue treatment. It is expected that appropriate knowledge regarding TB should remove stigmatising and discriminating attitudes. In this study we assessed the prevalence of stigmatising and discriminating attitudes towards TB patients among general population and their association with knowledge regarding TB. Method A cross-sectional knowledge, attitude and practice survey was conducted in 30 districts of India in January-March 2011. A total of 4562 respondents from general population were interviewed using semi-structured questionnaires which contained items to measure stigma, discrimination and knowledge on TB. Result Of the 4562 interviewed, 3823 were eligible for the current analysis. Of these, 73% (95% CI 71.4–74.2) had stigmatising and 98% (95% CI 97.4–98.3) had discriminating attitude towards TB patients. Only 17% (95% CI 15.6–18.0) of the respondents had appropriate knowledge regarding TB with even lower levels observed amongst females, rural areas and respondents from low income groups. Surprisingly stigmatising (adjusted OR 1.31 (0.78–2.18) and discriminating (adjusted OR 0.79 (0.43–1.44) attitudes were independent of knowledge regarding TB. Conclusion Stigmatising and discriminating attitudes towards TB patients remain high among the general population in India. Since these attitudes were independent of the knowledge regarding TB, it is possible that the current disseminated knowledge regarding TB which is mainly from a medical perspective may not be adequately addressing the factors that lead to stigma and discrimination towards TB patients. Therefore, there is an urgent need to review the messages and strategies currently used for disseminating knowledge regarding TB among general population and revise them appropriately. The disseminated knowledge should include medical

  12. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  13. Exploring Perceived Discrimination Among LGBT Individuals in Turkey in Education, Employment, and Health Care: Results of an Online Survey.

    Science.gov (United States)

    Göçmen, İpek; Yılmaz, Volkan

    2017-01-01

    This article explores the discrimination practices encountered by lesbian, gay, bisexual, and transgender (LGBT) individuals in education, income, employment, and health care in Turkey. Limited quantitative data on LGBT individuals are available in Turkey. This study collected data from 2,875 LGBT individuals through a Web-based survey. The findings suggest that LGBT individuals report perceived direct and indirect discrimination in accessing education, employment, and health care. In a country where LGBT rights are not yet recognized and antidiscrimination legislation covering sexual orientation and gender identity is still nonexistent, findings demonstrate perceived discrimination of LGBTs rarely turns into a legal complaint. Even when they do, most LGBTs in our sample report that they did not feel that the justice system addressed their grievances.

  14. Sex determination of the Acadian Flycatcher using discriminant analysis

    Science.gov (United States)

    Wilson, R.R.

    1999-01-01

    I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.

  15. Discrimination in relation to parenthood reported by community psychiatric service users in the UK: a framework analysis.

    Science.gov (United States)

    Jeffery, Debra; Clement, Sarah; Corker, Elizabeth; Howard, Louise M; Murray, Joanna; Thornicroft, Graham

    2013-04-20

    Experienced discrimination refers to an individual's perception that they have been treated unfairly due to an attribute and is an important recent focus within stigma research. A significant proportion of mental health service users report experiencing mental illness-based discrimination in relation to parenthood. Existing studies in this area have not gone beyond prevalence, therefore little is known about the nature of experienced discrimination in relation to parenthood, and how is it constituted. This study aims to generate a typology of community psychiatric service users' reports of mental illness-based discrimination in relation to becoming or being a parent. A secondary aim is to assess the prevalence of these types of experienced discrimination. In a telephone survey 2026 community psychiatric service users in ten UK Mental Health service provider organisations (Trusts) were asked about discrimination experienced in the previous 12 months using the Discrimination and Stigma Scale (DISC). The sample were asked if, due to their mental health problem, they had been treated unfairly in starting a family, or in their role as a parent, and gave examples of this. Prevalence is reported and the examples of experienced discrimination in relation to parenthood were analysed using the framework method of qualitative analysis. Three hundred and four participants (73% female) reported experienced discrimination, with prevalences of 22.5% and 28.3% for starting a family and for the parenting role respectively. Participants gave 89 examples of discrimination about starting a family and 228 about parenting, and these occurred in social and professional contexts. Ten themes were identified. These related to being seen as an unfit parent; people not being understanding; being stopped from having children; not being allowed to see their children; not getting the support needed; children being affected; children avoiding their parents; children's difficulties being blamed

  16. Unbiased bootstrap error estimation for linear discriminant analysis.

    Science.gov (United States)

    Vu, Thang; Sima, Chao; Braga-Neto, Ulisses M; Dougherty, Edward R

    2014-12-01

    Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.

  17. Discriminating Topology in Galaxy Distributions using Network Analysis

    CERN Document Server

    Hong, Sungryong; Dey, Arjun; Barabási, Albert -L; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-01-01

    (abridged) The large-scale distribution of galaxies is generally analyzed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a L\\'evy walk. For the cosmological simulation, we adopt the redshift $z = 0.58$ slice from Illustris (Vogelsberger et al. 2014A) and select galaxies with stellar masses greater than $10^8$$M_\\odot$. The two point correlation function of these simulated galaxies follows a single power-law, $\\xi(r) \\sim r^{-1.5}$. Then, we generate L\\'evy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point d...

  18. Genetic mapping of complex discrete human diseases by discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1~2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.

  19. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis.

    Science.gov (United States)

    Fang, Shu; Li, Jia; Tian, Yonghong; Huang, Tiejun; Chen, Xiaowu

    2017-05-01

    In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.

  20. Face Recognition Using Holistic Features and Simplified Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gou Koutaki

    2012-08-01

    Full Text Available This paper proposed an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified Linear Discriminant Analysis (LDA. The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data was registered into the training data set. The holistic features of face image were proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment was proposed to decrease time complexity of retraining process. In order to know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D2DLDA, 2DPCA, and (2D22DPCA. The experimental results show that the proposed method can solve the retraining problem of the conventional LDA indicated by requiring short retraining time and stable recognition rate.

  1. Sparse linear discriminant analysis by thresholding for high dimensional data

    CERN Document Server

    Shao, Jun; Deng, Xinwei; Wang, Sijian; 10.1214/10-AOS870

    2011-01-01

    In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the probability distribution of the variables is usually unknown, the rule of classification is constructed using a training sample. The well-known linear discriminant analysis (LDA) works well for the situation where the number of variables used for classification is much smaller than the training sample size. Because of the advance in technologies, modern statistical studies often face classification problems with the number of variables much larger than the sample size, and the LDA may perform poorly. We explore when and why the LDA has poor performance and propose a sparse LDA that is asymptotically optimal under some sparsity conditions on the unknown parameters. For illustration of application, we discuss an example of classifying human cancer into two classes of leukemia based on a set of 7,129 genes and a training ...

  2. Are racist attitudes related to experiences of racial discrimination? Within sample testing utilising nationally representative survey data.

    Science.gov (United States)

    Habtegiorgis, Amanuel E; Paradies, Yin C; Dunn, Kevin M

    2014-09-01

    Although the relationship between an individual's racist attitudes and discriminatory behaviours has been widely studied, the association between racist attitudes among perpetrators and experiences of racism among targets has been under-examined. Based on data from the 2001-8 Australian Challenging Racism Project survey, this paper details a novel method to investigate the link between racist attitudes and experiences of discrimination utilising two separate models linked by nomination of cultural or ethnic groups who do not fit into Australian society (i.e., out-groups). Those identified as out-groups were more likely to report experiences of discrimination than those who were not nominated as out-groups. Overall, out-group nomination by those with racist attitudes strongly predict experiences of discrimination among these same target out-groups, OR=2.2, F(6, 12,348)=78.61, pracial discrimination among targets. This study demonstrates that attitudes not only affect majority group behaviour but also drive the resulting experiences of discrimination for minority group members.

  3. Viewpoint survey of mental health service users' experiences of discrimination in England 2008-2014.

    Science.gov (United States)

    Corker, E; Hamilton, S; Robinson, E; Cotney, J; Pinfold, V; Rose, D; Thornicroft, G; Henderson, C

    2016-08-01

    Discrimination reported by mental health service users in England is high. The study aims to determine changes in mental health-related discrimination from 2008 to 2014. Samples of mental health service users were interviewed from 2008 to 2014 using the Discrimination and Stigma Scale version 12. Social capital in terms of access to social resources is a marker of discrimination in terms of effects on social connections, and so from 2011, social capital also measured using the Resource Generator-UK. Fewer participants reported discrimination in one or more life areas in 2014 compared to 2008 (OR: 0.58, 95% CI 0.36 to 0.94 P = 0.03). A weighted multiple regression model found a decrease in overall discrimination in 2014 compared to 2008 (mean difference: -13.55, 95% CI: -17.32 to -9.78, P discrimination decline between each year. No differences in access to social resources were found. Discrimination has fallen significantly over 2008-2014, although there was not a consistent decline between years. There is no evidence that social capital has increased. © 2016 The Authors. Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.

  4. Optimal discrimination of multiple quantum systems: controllability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Turinici, Gabriel [INRIA Rocquencourt, BP 105, 78153 Le Chesnay Cedex (France); Ramakhrishna, Viswanath [Department of Mathematical Sciences and Center for Signals, Systems and Communications, University of Texas at Dallas, PO Box 830688, Richardson, TX 75083 (United States); Li Baiqing [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States); Rabitz, Herschel [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States)

    2004-01-09

    A theoretical study is presented concerning the ability to dynamically discriminate between members of a set of different (but possibly similar) quantum systems. This discrimination is analysed in terms of independently and simultaneously steering about the wavefunction of each component system to a target state of interest using a tailored control (i.e. laser) field. Controllability criteria are revealed and their applicability is demonstrated in simple cases. Discussion is also presented in some uncontrollable cases.

  5. A comparison of two follow-up analyses after multiple analysis of variance, analysis of variance, and descriptive discriminant analysis: A case study of the program effects on education-abroad programs

    Science.gov (United States)

    Alvin H. Yu; Garry. Chick

    2010-01-01

    This study compared the utility of two different post-hoc tests after detecting significant differences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheffé method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-...

  6. Perceptions of genetic discrimination among people at risk for Huntington’s disease: a cross sectional survey

    OpenAIRE

    Bombard, Yvonne; Veenstra, Gerry; FRIEDMAN, JAN M.; Creighton, Susan; Currie, Lauren; Paulsen, Jane S.; Joan L. Bottorff; Hayden, Michael R.

    2009-01-01

    Objective To assess the nature and prevalence of genetic discrimination experienced by people at risk for Huntington’s disease who had undergone genetic testing or remained untested. Design Cross sectional, self reported survey. Setting Seven genetics and movement disorders clinics servicing rural and urban communities in Canada. Participants 233 genetically tested and untested asymptomatic people at risk for Huntington’s disease (response rate 80%): 167 underwent testing (83 had the Huntingt...

  7. Links between patterns of racial socialization and discrimination experiences and psychological adjustment: a cluster analysis.

    Science.gov (United States)

    Ajayi, Alex A; Syed, Moin

    2014-10-01

    This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  8. Qualitative analysis of mental health service users' reported experiences of discrimination.

    Science.gov (United States)

    Hamilton, S; Pinfold, V; Cotney, J; Couperthwaite, L; Matthews, J; Barret, K; Warren, S; Corker, E; Rose, D; Thornicroft, G; Henderson, C

    2016-08-01

    To better understand mental health service users' experiences of stigma and discrimination in different settings. An annual telephone survey of people with a mental health diagnosis conducted to evaluate the Time to Change antistigma campaign in England. Of 985 people who participated in 2013, 84 took part in a qualitative interview which was audio recorded. Of these, 50 interviews were transcribed and thematically analysed to explore accounts of discrimination. We analysed common types of behaviour; motivations ascribed to the discriminators; expectations of what fair treatment would have been; and the impact of discrimination on participants. Discrimination was most common in five contexts: welfare benefits, mental health care, physical health care, family and friends. Participants often found it hard to assess whether a behaviour was discriminatory or not. Lack of support, whether by public services or by friends and family, was often experienced as discrimination, reflecting an expectation that positive behaviours and reasonable adjustments should be offered in response to mental health needs. The impact of discrimination across different settings was often perceived by participants as aggravating their mental health, and there is thus a need to treat discrimination as a health issue, not just a social justice issue. © 2016 The Authors. Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.

  9. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    Verdessi BD

    2000-01-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  10. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    BD Verdessi

    2000-12-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  11. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data

    Directory of Open Access Journals (Sweden)

    He Miao

    2009-12-01

    Full Text Available Abstract Background More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA and its modification methods for the classification of cancer based on gene expression data. Methods The classification performance of linear discriminant analysis (LDA and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA, prediction analysis for microarrays (PAM, shrinkage centroid regularized discriminant analysis (SCRDA, shrinkage linear discriminant analysis (SLDA and shrinkage diagonal discriminant analysis (SDDA. The procedures were performed by software R 2.80. Results PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method. Conclusions The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.

  12. 稀疏判别分析%Sparse discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    陈小冬; 林焕祥

    2012-01-01

    Methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. To address these problems, a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis (SEDA) was proposed. Firstly, SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. Secondly, it applied both sparse graph and Fisher criterion to seek the optimal projection. The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.%针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,提出一种稀疏判别分析算法(SEDA).首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用Fisher判别准则,能够得到最优的投影.在一组高维数据集上的实验结果表明,SEDA是非常有效的半监督降维方法.

  13. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  14. Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  15. Optimal measurement position estimation by discriminant analysis based on Wilks' lambda for myoelectric hand control.

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    2011-01-01

    This paper describes an optimal measurement position estimation by the discriminant analysis based on Wilks' lambda for the myoelectric hand control. In the past studies, the myoelectric signals were measured from the same positions for the motions discrimination. However, the optimal measurement positions of the myoelectric signals for the motion discrimination are different according to the remaining muscle situation of amputees. Therefore the purpose of this study is to estimate the optimal and fewer measurement positions for the precise motion discrimination of the human forearm. This study proposes the estimation method of the optimal measurement positions by the discriminant analysis based on Wilks' lambda among the myoelectric signal measured from multiple positions. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  16. Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower (7%) error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority (1% in terms of recognition rate) to other polynomial kernel models.

  17. An approach for mechanical fault classification based on generalized discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LI Wei-hua; SHI Tie-lin; YANG Shu-zi

    2006-01-01

    To deal with pattern classification of complicated mechanical faults,an approach to multi-faults classification based on generalized discriminant analysis is presented.Compared with linear discriminant analysis (LDA),generalized discriminant analysis (GDA),one of nonlinear discriminant analysis methods,is more suitable for classifying the linear non-separable problem.The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed.KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms.When the proposed method is applied to air compressor condition classification and gear fault classification,an excellent performance in complicated multi-faults classification is presented.

  18. Block-diagonal discriminant analysis and its bias-corrected rules.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Ng, Michael

    2013-06-01

    High-throughput expression profiling allows simultaneous measure of tens of thousands of genes at once. These data have motivated the development of reliable biomarkers for disease subtypes identification and diagnosis. Many methods have been developed in the literature for analyzing these data, such as diagonal discriminant analysis, support vector machines, and k-nearest neighbor methods. The diagonal discriminant methods have been shown to perform well for high-dimensional data with small sample sizes. Despite its popularity, the independence assumption is unlikely to be true in practice. Recently, a gene module based linear discriminant analysis strategy has been proposed by utilizing the correlation among genes in discriminant analysis. However, the approach can be underpowered when the samples of the two classes are unbalanced. In this paper, we propose to correct the biases in the discriminant scores of block-diagonal discriminant analysis. In simulation studies, our proposed method outperforms other approaches in various settings. We also illustrate our proposed discriminant analysis method for analyzing microarray data studies.

  19. Cloud-type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Hutchison, Keith D.; Crawford, Melba M.; Khazenie, Nahid

    1994-04-01

    In recent years, with the development of satellite and computer technology, Earth observation and atmospheric research have become highly dependent on digital imagery. One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multispectral rule-based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture- based method for feature identification has been investigated. This method uses a set of localized spatial filters known as 2-D Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a 2-D Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determine by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA Advanced Very High Resolution Radiometer data for cloud-type discrimination. Results show that additional texture information improves discrimination between cloud types (especially thin cirrus).

  20. An Analysis of Visible Racial Discrimination In Invisible Man

    Institute of Scientific and Technical Information of China (English)

    LUO Hui

    2016-01-01

    Invisible Man by Ralph Ellison is regarded as one of the classical works in contemporary Black American Literature. By briefly analyzing the causes and effects of racial discrimination, the study aims to explore the blacks’responses to it. It is concluded that the hero, as an“invisible man”, should accept his black identity, admitting the blacks’tradition and cultural iden-tity, then he can find his self-belonging in the black community.

  1. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  2. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  3. Mu opioid mediated discriminative-stimulus effects of tramadol: an individual subjects analysis.

    Science.gov (United States)

    Strickland, Justin C; Rush, Craig R; Stoops, William W

    2015-03-01

    Drug discrimination procedures use dose-dependent generalization, substitution, and pretreatment with selective agonists and antagonists to evaluate receptor systems mediating interoceptive effects of drugs. Despite the extensive use of these techniques in the nonhuman animal literature, few studies have used human participants. Specifically, human studies have not routinely used antagonist administration as a pharmacological tool to elucidate the mechanisms mediating the discriminative stimulus effects of drugs. This study evaluated the discriminative-stimulus effects of tramadol, an atypical analgesic with monoamine and mu opioid activity. Three human participants first learned to discriminate 100 mg tramadol from placebo. A range of tramadol doses (25 to 150 mg) and hydromorphone (4 mg) with and without naltrexone pretreatment (50 mg) were then administered to participants after they acquired the discrimination. Tramadol produced dose-dependent increases in drug-appropriate responding and hydromorphone partially or fully substituted for tramadol in all participants. These effects were attenuated by naltrexone. Individual participant records indicated a relationship between mu opioid activity (i.e., miosis) and drug discrimination performance. Our findings indicate that mu opioid activity may mediate the discriminative-stimulus effects of tramadol in humans. The correspondence of generalization, substitution, and pretreatment findings with the animal literature supports the neuropharmacological specificity of the drug discrimination procedure. © Society for the Experimental Analysis of Behavior.

  4. The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: selecting emission line galaxies using the Fisher discriminant

    Science.gov (United States)

    Raichoor, A.; Comparat, J.; Delubac, T.; Kneib, J.-P.; Yèche, Ch.; Zou, H.; Abdalla, F. B.; Dawson, K.; de la Macorra, A.; Fan, X.; Fan, Z.; Jiang, Z.; Jing, Y.; Jouvel, S.; Lang, D.; Lesser, M.; Li, C.; Ma, J.; Newman, J. A.; Nie, J.; Palanque-Delabrouille, N.; Percival, W. J.; Prada, F.; Shen, S.; Wang, J.; Wu, Z.; Zhang, T.; Zhou, X.; Zhou, Z.

    2016-01-01

    We present a new selection technique of producing spectroscopic target catalogues for massive spectroscopic surveys for cosmology. This work was conducted in the context of the extended Baryon Oscillation Spectroscopic Survey (eBOSS), which will use ~200 000 emission line galaxies (ELGs) at 0.6 ≤ zspec ≤ 1.0 to obtain a precise baryon acoustic oscillation measurement. Our proposed selection technique is based on optical and near-infrared broad-band filter photometry. We used a training sample to define a quantity, the Fisher discriminant (linear combination of colours), which correlates best with the desired properties of the target: redshift and [Oii] flux. The proposed selections are simply done by applying a cut on magnitudes and this Fisher discriminant. We used public data and dedicated SDSS spectroscopy to quantify the redshift distribution and [Oii] flux of our ELG target selections. We demonstrate that two of our selections fulfil the initial eBOSS/ELG redshift requirements: for a target density of 180 deg-2, ~70% of the selected objects have 0.6 ≤ zspec ≤ 1.0 and only ~1% of those galaxies in the range 0.6 ≤ zspec ≤ 1.0 are expected to have a catastrophic zspec estimate. Additionally, the stacked spectra and stacked deep images for those two selections show characteristic features of star-forming galaxies. The proposed approach using the Fisher discriminant could, however, be used to efficiently select other galaxy populations, based on multi-band photometry, providing that spectroscopic information isavailable. This technique could thus be useful for other future massive spectroscopic surveys such as PFS, DESI, and 4MOST.

  5. Gender Discrimination in Higher Education in Pakistan: A Survey of University Faculty

    Science.gov (United States)

    Shaukat, Sadia; Siddiquah, Aishah; Pell, Anthony William

    2014-01-01

    Problem statement: Gender disparity is a worldwide phenomenon. This disparity is not only with respect to opportunities and resources but also in rewards, and exists in all regions and classes. Gender disparity exists in the field of education as well. Females experience overt and subtle gender discrimination to some extent nearly at every stage…

  6. Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

    Science.gov (United States)

    Li, Quanbao; Wei, Fajie; Zhou, Shenghan

    2017-05-01

    The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

  7. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

    Science.gov (United States)

    Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

    2015-02-15

    Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination.

  8. ATTRIBUTE BASED PERCEPTUAL MAPPING USING DISCRIMINANT ANALYSIS REGARDING SELECTED BRANDS OF MOBILE HANDSETS: AN EMPIRICAL INVESTIGATION IN HARYANA

    Directory of Open Access Journals (Sweden)

    Dr. Ashutosh Nigam

    2012-09-01

    Full Text Available Indian mobile handset industry has seen explosive growth backed by liberalization and heavy investment in the telecommunication infrastructure. With growth it becomes extremely important for mobile handset brands to position their brands properly. This paperattempts to know how customers perceive different brands of mobile handset in the light of attributes considered by consumers in making purchase decision. A questionnaire survey of 300 respondents was collected in Haryana. Discriminant analysis is used to draw perceptual maps showing selected mobile handset companies. The results showed that selected mobile handset brands do not differsignificantly with regard to given attributes.

  9. Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Sang-Il Choi

    2016-01-01

    Full Text Available This paper proposes a gas classification method for an electronic nose (e-nose system, for which combined features that have been configured through discriminant analysis are used. First, each global feature is extracted from the entire measurement section of the data samples, while the same process is applied to the local features of the section that corresponds to the stabilization, exposure, and purge stages. The discriminative information amounts in the individual features are then measured based on the discriminant analysis, and the combined features are subsequently composed by selecting the features that have a large amount of discriminative information. Regarding a variety of volatile organic compound data, the results of the experiment show that, in a noisy environment, the proposed method exhibits classification performance that is relatively excellent compared to the other feature types.

  10. Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

    Directory of Open Access Journals (Sweden)

    Zeyu Lin

    2017-01-01

    Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.

  11. Perceived discrimination and self-rated health in South Korea: a nationally representative survey.

    Directory of Open Access Journals (Sweden)

    Seung-Sup Kim

    Full Text Available BACKGROUND: There is mounting evidence that discriminatory experiences can harm health. However, previous research has mainly focused on the health effects of racial discrimination in U.S. or European countries although there is pervasive discrimination by gender, age, education and other factors in Asian countries. METHODS: We analyzed the data from the 7th wave of Korean Labor and Income Panel Study to investigate the association between perceived discriminatory experience and poor self-rated health in South Korea. Perceived discriminatory experiences were measured in eight situations through a modified Experience of Discrimination questionnaire. In each of eight situations, the lifetime prevalence of perceived discriminatory experience was compared between men and women and the main causes of those experiences were identified separately by gender. After adjusting for potential confounders, we examined the association between perceived discriminatory experience and poor self-rated health in each of eight social situations and also checked the association using the number of situations of perceived discriminatory experiences. RESULTS: For both men and women, education level and age were the main sources of work-related perceived discriminatory experiences. Gender was one of the main causes among women across eight situations and more than 90% of women reported their gender as a main cause of discriminatory experience in getting higher education and at home. Discriminatory experiences in four situations were positively associated with poor self-rated health. The odds ratio for poor self-rated health for those exposed to one, two, three or four or more social situations of perceived discrimination were respectively 1.06 (95% CI : 0.87-1.29, 1.15 (95% CI : 0.96-1.55, 1.59 (95% CI : 1.19-2.14, and 1.78 (95% CI :1.26-2.51. CONCLUSION: There is consistent association between perceived discriminatory experience and poor self-rated health across eight

  12. The SDSS-IV extended Baryonic Oscillation Spectroscopic Survey: selecting Emission Line Galaxies using the Fisher Discriminant

    CERN Document Server

    Raichoor, A; Delubac, T; Kneib, J -P; Yèche, C; Zou, H; Abdalla, F B; Dawson, K; Fan, X; Fan, Z; Jiang, Z; Jing, Y; Jouvel, S; Lang, D; Lesser, M; Li, C; Ma, J; Newman, J A; Nie, J; Olszewski, E; Palanque-Delabrouille, N; Percival, W; Prada, F; Shen, S; Wang, J; Wu, Z; Zhang, T; Zhou, X; Zhou, Z

    2015-01-01

    We present a new selection technique to produce spectroscopic target catalogues for massive spectroscopic surveys for cosmology. This work was conducted in the context of the extended Baryon Oscillation Spectroscopic Survey (eBOSS), which will use 200,000 emission line galaxies (ELGs) at 0.6discriminant (linear combination of colours), which correlates best with the desired properties of the target: redshift and [Oii] flux. The proposed selections are simply done by applying a cut on magnitudes and this Fisher discriminant. We used public data and dedicated SDSS spectroscopy to quantify the redshift distribution and [Oii] flux of our ELG target selections. We demonstrate that two of our selections fulfill the initial eBOSS/ELG redshift requirements: for a target density of 180 d...

  13. Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

    Science.gov (United States)

    Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

    2016-06-01

    Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.

  14. Mixture subclass discriminant analysis link to restricted Gaussian model and other generalizations.

    Science.gov (United States)

    Gkalelis, Nikolaos; Mezaris, Vasileios; Kompatsiaris, Ioannis; Stathaki, Tania

    2013-01-01

    In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.

  15. Electron - nuclear recoil discrimination by pulse shape analysis

    CERN Document Server

    Elbs, J; Collin, E; Godfrin, H; Suvorova, O

    2007-01-01

    In the framework of the ``ULTIMA'' project, we use ultra cold superfluid 3He bolometers for the direct detection of single particle events, aimed for a future use as a dark matter detector. One parameter of the pulse shape observed after such an event is the thermalization time constant. Until now it was believed that this parameter only depends on geometrical factors and superfluid 3He properties, and that it is independent of the nature of the incident particles. In this report we show new results which demonstrate that a difference for muon- and neutron events, as well as events simulated by heater pulses exist. The possibility to use this difference for event discrimination in a future dark matter detector will be discussed.

  16. Discrimination, attribution, and racial group identification: implications for psychological distress among Black Americans in the National Survey of American Life (2001-2003).

    Science.gov (United States)

    Chae, David H; Lincoln, Karen D; Jackson, James S

    2011-10-01

    There is increasing evidence that experiencing discrimination may contribute to poor mental health among Black Americans. However, few studies have distinguished between discrimination attributed to race versus other forms of discrimination or have compared differences in their psychological implications. Using nationally representative data on 5,191 Black Americans in the National Survey of American Life (NSAL; 2001-2003), this study examined serious psychological distress (SPD) in relation to discrimination attributed to racial versus nonracial causes and also investigated whether racial group identification may be a buffer. We found that discrimination was associated with greater odds of SPD, regardless of attribution. Racial attributions were associated with higher odds of SPD compared with attributions to nonracial causes for each level of discrimination. High racial group identification buffered the negative effect of moderate levels of both racially and nonracially attributed discrimination. Our results provide evidence for the negative influence of discrimination on SPD among Black Americans and indicate that high racial group identification may somewhat mitigate their negative mental health effects. Our study suggests that discrimination and racial group identification should be addressed to protect against psychological distress among Black Americans. © 2011 American Orthopsychiatric Association.

  17. Justifying direct discrimination: an analysis of the scope for a general justification defence in cases of direct sex discrimination

    OpenAIRE

    Moran, E. R.

    2000-01-01

    The prospect of a justification defence in cases of direct sex discrimination is universally criticised by academic commentators on the ground that it would subvert the goal of equality that underlies sex discrimination and equal treatment legislation. At the outset the thesis examines the differences between the sexes, how these differences can be used to explain the distinction between direct and indirect sex discrimination and considers various concepts of equality. Building...

  18. Heliostat glass survey and analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lind, M.A.; Rusin, J.M.

    1978-09-01

    A comprehensive survey of both foreign and domestic sources of low distortion, high transmission flat glass with a nominal thickness of 3 mm was undertaken. The purpose of the survey was to determine the characteristics, availability and cost of glass for use in second surface heliostat mirrors for the Barstow pilot plant and future commercial central receiver plants. Information obtained from the manufacturers and the results of investigations performed at Sandia Laboratories at Albuquerque and Livermore were compiled with the PNL characterization data to generate the specifications for the GFE glass to be used in the Barstow pilot plant. During the course of the survey, nine of the major glass manufacturers were contacted for information and assistance. These manufacturers were PPG, Corning, Ford, LOF, CE, ASG, Fourco, Schott, and Guardian. Eleven different flat glass samples from seven of the domestic sources and one foreign source were characterized for solar transmittance, flatness and durability. The samples were representative of four different manufacturing processes: float, fusion, rolled, and twin ground. The results and implications of these glass characterization studies and a brief summary of the manufacturer survey are presented.

  19. Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wei; LI Xi-bing; GONG Feng-qiang

    2008-01-01

    Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.

  20. Baseline drift effect on the performance of neutron and gamma ray discrimination using frequency gradient analysis

    CERN Document Server

    Liu, Guofu; Yang, Jun; Lin, Cunbao; Hu, Qingqing; Peng, Jinxian

    2013-01-01

    Frequency gradient analysis (FGA) effectively discriminates neutrons and gamma rays by examining the frequency-domain features of the photomultiplier tube anode signal. This approach is insensitive to noise but is inevitably affected by the baseline drift, similar to other pulse shape discrimination methods. The baseline drift effect is attributed to the factors such as power line fluctuation, dark current, noise disturbances, hum, and pulse tail in front-end electronics. This effect needs to be elucidated and quantified before the baseline shift can be estimated and removed from the captured signal. Therefore, the effect of baseline shift on the discrimination performance of neutrons and gamma rays with organic scintillation detectors using FGA is investigated in this paper. The relationship between the baseline shift and discrimination parameters of FGA is derived and verified by an experimental system consisting of an americium-beryllium source, a BC501A liquid scintillator detector, and a 5 GSPS 8-bit osc...

  1. Performance Evaluation of Discriminant Analysis and Decision Tree, for Weed Classification of Potato Fields

    Directory of Open Access Journals (Sweden)

    Farshad Vesali

    2012-09-01

    Full Text Available In present study we tried to recognizing weeds in potato fields to effective use from herbicides. As we know potato is one of the crops which is cultivated vastly all over the world and it is a major world food crop that is consumed by over one billion people world over, but it is threated by weed invade, because of row cropping system applied in potato tillage. Machine vision is used in this research for effective application of herbicides in field. About 300 color images from 3 potato farms of Qorveh city and 2 farms of Urmia University-Iran, was acquired. Images were acquired in different illumination condition from morning to evening in sunny and cloudy days. Because of overlap and shading of plants in farm condition it is hard to use morphologic parameters. In method used for classifying weeds and potato plants, primary color components of each plant were extracted and the relation between them was estimated for determining discriminant function and classifying plants using discrimination analysis. In addition the decision tree method was used to compare results with discriminant analysis. Three different classifications were applied: first, Classification was applied to discriminate potato plant from all other weeds (two groups, the rate of correct classification was 76.67% for discriminant analysis and 83.82% for decision tree; second classification was applied to discriminate potato plant from separate groups of each weed (6 groups, the rate of correct classification was 87%. And the third, Classification of potato plant versus weed species one by one. As the weeds were different, the results of classification were different in this composition. The decision tree in all conditions showed the better result than discriminant analysis.

  2. Determination of sex by discriminant function analysis of mandibles from a Central Indian population

    Directory of Open Access Journals (Sweden)

    Kanchankumar P Wankhede

    2015-01-01

    Full Text Available Context: Identification of sex from skeletal remains is one of the important forensic considerations. Discriminant function analysis is increasingly used to determine the sex from skeleton. Aims: To develop discriminant function to determine sex from mandible in a Central Indian population. Settings and Design: This was a prospective study done at the Department of Anatomy. Materials and Methods: The mandibles used in the present study were from the museum specimens. Only 82 adult mandibles (55 male and 27 female that had been preserved were selected. Ten mandibular parameters were measured. Statistical Analysis Used: Statistical analysis was conducted using Statistical Package for Social Sciences (SPSS for Windows, version 16. The level of statistical significance was set at P < 0.05. Results: Using stepwise discriminant function analysis, only six variables were selected as the best discriminant between sexes, with the projection length of corpus mandibulae being the most dimorphic. It was observed that sex classification accuracy of the discriminant functions ranged from 57.3 to 80.5% for the individual variables, 81.7% for the stepwise method, and 85.4% for the direct method. Conclusion: The results of the study show that mandibles can be used for determining sex and the results are comparable with other similar studies. The studied mandibular variables showed sexual dimorphism with an accuracy comparable with other skeletal remains, next to cranium and pelvis.

  3. ALE meta-analysis reveals dissociable networks for affective and discriminative aspects of touch.

    Science.gov (United States)

    Morrison, India

    2016-04-01

    Emotionally-laden tactile stimulation-such as a caress on the skin or the feel of velvet-may represent a functionally distinct domain of touch, underpinned by specific cortical pathways. In order to determine whether, and to what extent, cortical functional neuroanatomy supports a distinction between affective and discriminative touch, an activation likelihood estimate (ALE) meta-analysis was performed. This meta-analysis statistically mapped reported functional magnetic resonance imaging (fMRI) activations from 17 published affective touch studies in which tactile stimulation was associated with positive subjective evaluation (n = 291, 34 experimental contrasts). A separate ALE meta-analysis mapped regions most likely to be activated by tactile stimulation during detection and discrimination tasks (n = 1,075, 91 experimental contrasts). These meta-analyses revealed dissociable regions for affective and discriminative touch, with posterior insula (PI) more likely to be activated for affective touch, and primary somatosensory cortices (SI) more likely to be activated for discriminative touch. Secondary somatosensory cortex had a high likelihood of engagement by both affective and discriminative touch. Further, meta-analytic connectivity (MCAM) analyses investigated network-level co-activation likelihoods independent of task or stimulus, across a range of domains and paradigms. Affective-related PI and discriminative-related SI regions co-activated with different networks, implicated in dissociable functions, but sharing somatosensory co-activations. Taken together, these meta-analytic findings suggest that affective and discriminative touch are dissociable both on the regional and network levels. However, their degree of shared activation likelihood in somatosensory cortices indicates that this dissociation reflects functional biases within tactile processing networks, rather than functionally and anatomically distinct pathways.

  4. Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition

    Science.gov (United States)

    Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

    2011-03-01

    Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

  5. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  6. Discriminant analysis of some east Tennessee forest herb niches. Environmental Sciences Division Publication No. 752

    Energy Technology Data Exchange (ETDEWEB)

    Mann, L.K.; Shugart, H.H.; Kitchings, J.T.

    1978-03-01

    The purpose of this study was to evaluate the effectiveness of using discriminant analysis in assessing plant niches. As a component of research by the Environmental Research Park Project at Oak Ridge, Tennessee, five sites were inventoried for herbaceous species. From this inventory, four sympatric species of Galium and seventeen co-occurring herbaceous species were selected for discriminant analysis. The four species of Galium were treated as two data sets: one was composed of information collected at one site (a mesic hardwood area) and the other contained data from two cedar sites of shallow soil over limestone bedrock. The seventeen herbaceous species all occurred in the mesic hardwood area.

  7. Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

    2017-08-15

    The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins.

    Science.gov (United States)

    Perrière, Guy; Thioulouse, Jean

    2003-02-01

    Correspondence discriminant analysis (CDA) is a multivariate statistical method derived from discriminant analysis which can be used on contingency tables. We have used CDA to separate Gram negative bacteria proteins according to their subcellular location. The high resolution of the discrimination obtained makes this method a good tool to predict subcellular location when this information is not known. The main advantage of this technique is its simplicity. Indeed, by computing two linear formulae on amino acid composition, it is possible to classify a protein into one of the three classes of subcellular location we have defined. The CDA itself can be computed with the ADE-4 software package that can be downloaded, as well as the data set used in this study, from the Pôle Bio-Informatique Lyonnais (PBIL) server at http://pbil.univ-lyon1.fr.

  9. The Bolocam Galactic Plane Survey. VIII. A Mid-Infrared Kinematic Distance Discrimination Method

    CERN Document Server

    Ellsworth-Bowers, Timothy P; Rosolowsky, Erik; Mairs, Steven; Evans, Neal J; Battersby, Cara; Ginsburg, Adam; Shirley, Yancy L; Bally, John

    2013-01-01

    We present a new distance estimation method for dust-continuum-identified molecular cloud clumps. Recent (sub-)millimeter Galactic plane surveys have cataloged tens of thousands of these objects, but detailed study of their physical properties requires robust distance determinations. We derive Bayesian distance probability density functions (DPDFs) for 770 objects from the Bolocam Galactic Plane Survey in the longitude range 7.5 < l < 65. The DPDF formalism is based on kinematic distances, and uses external data sets to place prior distance probabilities to resolve the kinematic distance ambiguity (KDA) for objects in the inner Galaxy. We present priors related to the mid-infrared absorption of dust in dense molecular regions and the Galactic distribution of molecular gas. By assuming a numerical model of Galactic mid-infrared emission and simple radiative transfer, we match the morphology of millimeter thermal dust emission with mid-infrared absorption to compute a prior DPDF for distance discriminatio...

  10. Competition, small business financing, and discrimination: evidence from a new survey

    OpenAIRE

    Ken Cavalluzzo; Linda Cavalluzzo; John D. Wolken

    1999-01-01

    Using data from the 1993 National Survey of Small Business Finances, we examine some of the factors influencing differences in small business credit market experiences across demographic groups. We analyze credit applications, loan denials, and interest rates paid across gender, race and ethnicity of small business owners. In addition, we analyze data gathered from small business owners who said they did not apply for credit because they believed that their application would have been turned ...

  11. A survey on AIDS discrimination among medical college students%医学院校学生艾滋病歧视现状调查

    Institute of Scientific and Technical Information of China (English)

    刘家虹; 姜红英; 陈红; 廖清华; 付俊; 卢飞豹; 刘伟新; 李悦

    2009-01-01

    Objective To understand the related knowledge, discrimination attitudes toward HIV/ AIDS among medical college students, and to provide scientific evidence for further HIV/AIDS anti-discrimination intervention. Methods By means of stratified cluster sannpling to classes, 2844 undergraduate students were randomly selected from medical colleges. A self-deaigned and serf-administered questionnaire survey was conducted, and SPSS 13.0 software was used for data analysis. Results 2501 valid questionnaires had been collected. The overall HIV/AIDS knowledge convera.ge rate of the respondents was 73.1% (1828/2501) ; The HIV/AIDS discrimination rates in different questions were varying, the discrimination rate of infected with AIDS by bad sex and sharing needles was 83.1% (2078/2501) and 77.7% (1943/2501)respectively,the discrimination rates in term of contacting with HIV patients and their daily necessities, sharing desks, personal social were all exceeding 40% . Conclusion The medical students held serious discrimination attitudes to HIV infected persons and panients; it is necessary to strengthen anti-discrimination education about HIV/AIDS among medical students.%目的 了解医学院校学生对艾滋病相关知识、歧视态度情况,为进一步开展艾滋病反歧视干预工作提供科学依据.方法 以班级为单位采用分层整群抽样法,从某医学院本科生中抽取2844名,采用自行设计的问卷以学生自填方式进行调查,采用SPSS 13.0软件进行统计分析.结果 共收集有效问卷2501份.调查对象艾滋病相关知识知晓率为73.1%(1828/2501);对HIV感染者和AIDS患者不同问题的歧视率不同,对不良性行为、共用针具感染艾滋病的歧视态度高达83.1%(2078/2501)、77.7%(1943/2501),对于身体接触、接触其生活用品、共用课桌、个人社交的歧视率均在40%以上.结论 医学院校学生对HIV感染者/AIDS患者歧视严重,亟须加强医学院校学生艾滋病反歧视教育.

  12. Complex surveys analysis of categorical data

    CERN Document Server

    Mukhopadhyay, Parimal

    2016-01-01

    The primary objective of this book is to study some of the research topics in the area of analysis of complex surveys which have not been covered in any book yet. It discusses the analysis of categorical data using three models: a full model, a log-linear model and a logistic regression model. It is a valuable resource for survey statisticians and practitioners in the field of sociology, biology, economics, psychology and other areas who have to use these procedures in their day-to-day work. It is also useful for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. The importance of sample surveys today cannot be overstated. From voters’ behaviour to fields such as industry, agriculture, economics, sociology, psychology, investigators generally resort to survey sampling to obtain an assessment of the behaviour of the population they are interested in. Many large-scale sample surveys collect data using complex survey designs like multistage stratified cluster designs. The o...

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

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

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

  14. ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

    Directory of Open Access Journals (Sweden)

    Tarn Duong

    2007-09-01

    Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

  15. Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Müller, Andreas

    This paper describes canonical discriminant analysis and sketches an iterative version which is then applied to obtain optimal separation between a region, here examplified by either “water” or “wood/trees” and the rest of a HyMap image. We show that the iterative version greatly enhances the sep...

  16. Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

    DEFF Research Database (Denmark)

    Jensen, P O; Larsen, J K; Christensen, I J

    1994-01-01

    Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogona...

  17. A Research Project Using the Safran Student Interest Inventory (SSII): Discriminant Analysis of University Majors.

    Science.gov (United States)

    Nichols, K. E.

    Safran Student Interest Inventory (SSII) data was gathered on 135 university students registered in five different faculties. A discriminant analysis of the data indicated that the SSII was a good test for separating students into faculties and therefore would make a good counselling instrument. Some results are also present using Differential…

  18. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Directory of Open Access Journals (Sweden)

    U. Amato

    2014-06-01

    Full Text Available We introduce a classification method (Cumulative Discriminant Analysis of the Discriminant Analysis type to discriminate between cloudy and clear sky satellite observations in the thermal infrared. The tool is intended for the high spectral resolution infrared sounder (IRS planned for the geostationary METEOSAT (Meteorological Satellite Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer data as a proxy. The Cumulative Discriminant Analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A Principal Component Analysis prior step is also introduced which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer and SEVIRI (Spinning Enhanced Visible and Infrared Imager imagers. The agreement with these independent cloud masks is generally well above 80%, except at high latitudes in their winter seasons.

  19. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Science.gov (United States)

    Amato, U.; Lavanant, L.; Liuzzi, G.; Masiello, G.; Serio, C.; Stuhlmann, R.; Tjemkes, S. A.

    2014-10-01

    We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

  20. On the Variable Selection Problem in Multiple Group Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.

    This study was concerned with various schemes for reducing the number of variables in a multivariate analysis. Two sets of illustrative data were used; the numbers of criterion groups were 3 and 5. The proportion of correct classifications was employed as an index of discriminatory power of each subset of variables selected. Of the four procedures…

  1. Comparative analysis of pulse shape discrimination methods in a {sup 6}Li loaded plastic scintillator

    Energy Technology Data Exchange (ETDEWEB)

    Balmer, Matthew J.I., E-mail: m.balmer@lancaster.ac.uk [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Gamage, Kelum A.A. [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Taylor, Graeme C. [Neutron Metrology Group, National Physical Laboratory, Teddington, TW11 0LW (United Kingdom)

    2015-07-11

    Three algorithms for discriminating between fast neutrons, thermal neutrons and gamma rays in a {sup 6}Li loaded plastic scintillator have been compared. Following a literature review of existing pulse shape discrimination techniques, the performance of the charge comparison method, triangular filtering and frequency gradient analysis were investigated in this work. The scintillator was exposed to three different mixed gamma/neutron radiation fields. The figure of merit of neutron/gamma separation was investigated over a broad energy range, as well as for the neutron capture energy region. After optimisation, all three methods were found to perform similarly in terms of neutron/gamma separation.

  2. Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

    Science.gov (United States)

    Mishra, Spandan; Vanli, O. Arda; Huffer, Fred W.; Jung, Sungmoon

    2016-04-01

    In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

  3. Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Zhao, Hongyu

    2009-12-01

    High-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.

  4. Underreporting Discrimination among Arab American and Muslim American Community College Students: Using Focus Groups to Unravel the Ambiguities within the Survey Data

    Science.gov (United States)

    Shammas, Diane

    2017-01-01

    Using a mixed methods approach, the researcher gathered a set of narrative responses from focus groups that supported the claim of underreporting campus discrimination on a survey. Multiple studies have shown that underrepresented minority students are likely to bond with same-ethnic peers in a racially tense campus climate. This mixed method is a…

  5. Underreporting Discrimination among Arab American and Muslim American Community College Students: Using Focus Groups to Unravel the Ambiguities within the Survey Data

    Science.gov (United States)

    Shammas, Diane

    2017-01-01

    Using a mixed methods approach, the researcher gathered a set of narrative responses from focus groups that supported the claim of underreporting campus discrimination on a survey. Multiple studies have shown that underrepresented minority students are likely to bond with same-ethnic peers in a racially tense campus climate. This mixed method is a…

  6. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis.

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  7. Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System

    Directory of Open Access Journals (Sweden)

    Shailendra Singh

    2009-10-01

    Full Text Available This Generalized Discriminant Analysis (GDA has provided an extremely powerful approach to extracting non-linear features. The network traffic data provided for the design of intrusion detection system always are large with ineffective information, thus we need to remove the worthless information from the original high dimensional database. To improve the generalization ability, we usually generate a small set of features from the original input variables by feature extraction. The conventional Linear Discriminant Analysis (LDA feature reduction technique has its limitations. It is not suitable for non-linear dataset. Thus we propose an efficient algorithm based on the Generalized Discriminant Analysis (GDA feature reduction technique which is novel approach used in the area of cyber attack detection. This not only reduces the number of the input features but also increases the classification accuracy and reduces the training and testing time of the classifiers by selecting most discriminating features. We use Artificial Neural Network (ANN and C4.5 classifiers to compare the performance of the proposed technique. The result indicates the superiority of algorithm.

  8. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  9. Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

    Science.gov (United States)

    Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

    2016-09-01

    Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds.

  10. Free choice profiling sensory analysis to discriminate coffees

    Directory of Open Access Journals (Sweden)

    Cíntia Sorane Good Kitzberger

    2016-12-01

    Full Text Available Sensory attributes were evaluated from Arabica coffee genotypes growing in two places in Brazil, Mandaguari and Londrina. Post-harvest and roasted process was standardized. Free choice profiling sensory analysis was apply to investigate the influence of genetic variability and local cultivation (Londrina and Mandaguari, Brazil on the sensory characteristics of coffee genotypes. A sensory panel evaluated coffees from Mandaguari in two groups: one (Sarchimor derived, IPR100, IPR102, IPR105, IPR106 characterized by transparency, coffee colour, green aroma, taste (green, bitter, fermented, astringent and a watery texture, another group (Catuaí, Sarchimor derived, IPR101, IPR103 was characterized by coffee colour, brightness, aroma (coffee, acid, sweet, chocolate, acidity, bitterness, burnt aroma, sweetness and full-bodied. Coffees from Londrina presented brightness, coffee colour, sweet, green, burnt aroma, astringent, bitter, fermented, green taste; and watery texture (Catuaí, IPR97, IPR98, IPR100. Another group (Sarchimor derived, IPR101, IPR102, IPR103, IPR105, IPR106 were associated with turbidity, aroma (green, coffee, sweet, acidity, astringency, bitterness, sweetness and full-bodied. Catuaí, Iapar59, IPR99, IPR101, IPR103 and IPR108 exhibited positive attributes when grown in either locale. Edaphoclimatic conditions play a major role in the sensory profiles of coffee.

  11. Sentiment analysis algorithms and applications: A survey

    Directory of Open Access Journals (Sweden)

    Walaa Medhat

    2014-12-01

    Full Text Available Sentiment Analysis (SA is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.

  12. Over-excavation forecast of underground opening by using Bayes discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    GONG Feng-qiang; LI Xi-bing; ZHANG Wei

    2008-01-01

    A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA) theory was presented. The Bayes discriminant analysis theory was introduced. Based on an engineering example, the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model, and the prior information about over-excavation of underground opening was also taken into consideration. Five parameters influencing the over-excavation of opening, including 2 groups of joints, 1 group of layer surface, extension and space between structure faces were selected as geometric parameters. Engineering data in an underground opening were used as the training samples. The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained. Data in an underground engineering were used to test the discriminant ability of BDA model. The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.

  13. Multivariate Analysis of Laser-Induced Breakdown Spectroscopy for Discrimination between Explosives and Plastics

    Institute of Scientific and Technical Information of China (English)

    WANG Qian-Qian; LIU Kai; ZHAO Hua

    2012-01-01

    A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed. A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method. Seven types of plastics and one explosive are used as samples to test the model. The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods. The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.%A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed.A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method.Seven types of plastics and one explosive are used as samples to test the model.The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods.The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.

  14. Comparative analysis of cyanobacterial superoxide dismutases to discriminate canonical forms

    Directory of Open Access Journals (Sweden)

    Prabaharan Dharmar

    2007-11-01

    Full Text Available Abstract Background Superoxide dismutases (SOD are ubiquitous metalloenzymes that catalyze the disproportion of superoxide to peroxide and molecular oxygen through alternate oxidation and reduction of their metal ions. In general, SODs are classified into four forms by their catalytic metals namely; FeSOD, MnSOD, Cu/ZnSOD and NiSOD. In addition, a cambialistic form that uses Fe/Mn in its active site also exists. Cyanobacteria, the oxygen evolving photosynthetic prokaryotes, produce reactive oxygen species that can damage cellular components leading to cell death. Thus, the co-evolution of an antioxidant system was necessary for the survival of photosynthetic organisms with SOD as the initial enzyme evolved to alleviate the toxic effect. Cyanobacteria represent the first oxygenic photoautotrophs and their SOD sequences available in the databases lack clear annotation. Hence, the present study focuses on structure and sequence pattern of subsets of cyanobacterial superoxide dismutases. Result The sequence conservation and structural analysis of Fe (Thermosynechococcus elongatus BP1 and MnSOD (Anabaena sp. PCC7120 reveal the sharing of N and C terminal domains. At the C terminal domain, the metal binding motif in cyanoprokaryotes is DVWEHAYY while it is D-X-[WF]-E-H-[STA]-[FY]-[FY] in other pro- and eukaryotes. The cyanobacterial FeSOD differs from MnSOD at least in three ways viz. (i FeSOD has a metal specific signature F184X3A188Q189.......T280......F/Y303 while, in Mn it is R184X3G188G189......G280......W303, (ii aspartate ligand forms a hydrogen bond from the active site with the outer sphere residue of W243 in Fe where as it is Q262 in MnSOD; and (iii two unique lysine residues at positions 201 and 255 with a photosynthetic role, found only in FeSOD. Further, most of the cyanobacterial Mn metalloforms have a specific transmembrane hydrophobic pocket that distinguishes FeSOD from Mn isoform. Cyanobacterial Cu/ZnSOD has a copper domain and two

  15. The specter of discrimination: Fear of interpersonal racial discrimination among adolescents in Chicago.

    Science.gov (United States)

    Herda, Daniel

    2016-01-01

    This analysis examines fear of interpersonal racial discrimination among Black, Hispanic, and White adolescents. The extent and correlates of these concerns are examined using survey data from the Project for Human Development in Chicago Neighborhoods. Borrowing from the fear-of-crime literature, the contact hypothesis, and group threat theory, several hypotheses are developed linking discrimination fear to direct personal experience with discrimination, indirect or vicarious experience, and environmental signals of discrimination. Results show that about half of Blacks and Hispanics have feared discrimination in the past year. Multivariate results indicate that fear is most likely if one has experienced victimization first-hand and when one's parent is affected by discrimination. Further, a larger presence neighborhood outgroups produces greater fear. Overall, discrimination fear constitutes an additional obstacle for minority adolescents as they transition to adulthood. The phenomenon warrants increased scholarly attention and represents a fruitful avenue for future research.

  16. Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-07-01

    Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

  17. Digital discrimination of neutrons and gamma-rays in organic scintillation detectors using moment analysis

    Science.gov (United States)

    Xie, Xufei; Zhang, Xing; Yuan, Xi; Chen, Jinxiang; Li, Xiangqing; Zhang, Guohui; Fan, Tieshuan; Yuan, Guoliang; Yang, Jinwei; Yang, Qingwei

    2012-09-01

    Digital discrimination of neutron and gamma-ray events in an organic scintillator has been investigated by moment analysis. Signals induced by an americium-beryllium (Am/Be) isotropic neutron source in a stilbene crystal detector have been sampled with a flash analogue-to-digital converter (ADC) of 1 GSamples/s sampling rate and 10-bit vertical resolution. Neutrons and gamma-rays have been successfully discriminated with a threshold corresponding to gamma-ray energy about 217 keV. Moment analysis has also been verified against the results assessed by a time-of-flight (TOF) measurement. It is shown that the classification of neutrons and gamma-rays afforded by moment analysis is consistent with that achieved by digital TOF measurement. This method has been applied to analyze the data acquired from the stilbene crystal detector in mixed radiation field of the HL-2A tokamak deuterium plasma discharges and the results are described.

  18. Using cluster analysis to explore survey data.

    Science.gov (United States)

    Spencer, Llinos; Roberts, Gwerfyl; Irvine, Fiona; Jones, Peter; Baker, Colin

    2007-01-01

    Llinos Haf Spencer reports on the use of the cluster analysis statistical technique in nursing research and uses data from the Welsh Language Awareness in Healthcare Provision in Wales survey as an exemplar She concludes that cluster analysis is a valuable tool to tease out patterns in data that are not initially evident in bivariate analyses and thus should be considered as a viable option for nursing research.

  19. Geothermal industry employment: Survey results & analysis

    Energy Technology Data Exchange (ETDEWEB)

    2005-09-01

    The Geothermal Energy Association (GEA) is ofteh asked about the socioeconomic and employment impact of the industry. Since available literature dealing with employment involved in the geothermal sector appeared relatively outdated, unduly focused on certain activities of the industry (e.g. operation and maintenance of geothermal power plants) or poorly reliable, GEA, in consultation with the DOE, decided to conduct a new employment survey to provide better answers to these questions. The main objective of this survey is to assess and characterize the current workforce involved in geothermal activities in the US. Several initiatives have therefore been undertaken to reach as many organizations involved in geothermal activities as possible and assess their current workforce. The first section of this document describes the methodology used to contact the companies involved in the geothermal sector. The second section presents the survey results and analyzes them. This analysis includes two major parts. The first part analyzes the survey responses, presents employment numbers that were captured and describes the major characteristics of the industry that have been identified. The second part of the analysis estimates the number of workers involved in companies that are active in the geothermal business but did not respond to the survey or could not be reached. Preliminary conclusions and the study limits and restrictions are then presented. The third section addresses the potential employment impact related to manufacturing and construction of new geothermal power facilities. Indirect and induced economic impacts related with such investment are also investigated.

  20. Forensic discrimination of blue ballpoint pen inks based on thin layer chromatography and image analysis.

    Science.gov (United States)

    Djozan, Djavanshir; Baheri, Tahmineh; Karimian, Ghader; Shahidi, Masomeh

    2008-08-06

    This article aims to provide a new and fast method for differentiation of inks on a questioned document. The data acquisition was carried out by designing specific image analysis software for evaluating thin layer chromatograms (TLC-IA). The ink spot was extracted from the document using methanol and separated by TLC using plastic sheet silica gel 60 without fluorescent indicator, and a mixture of ethyl acetate, ethanol, and water (70:35:30, v/v/v) as mobile phase. To discriminate between different pen inks, new software was designed on the basis of intensity profile of red, green, and blue (RGB) characteristic. In practice, after development of chromatogram, the chromatograms were scanned by ordinary office scanner, intensity profiles of RGB characteristics on the development straight of each sample were produced and compared with the mentioned software. RGB profiles of ballpoint inks from various manufacturers showed that the patterns in most cases were distinctly different from each other. This new method allowed discriminating among different pen inks with a high reliability and the discriminating power of 92.8%. Blue ballpoint pen inks of 41 different samples available on the local market were successfully analyzed and discriminated.

  1. Optical spectroscopic analysis for the discrimination of extra-virgin olive-oil (Conference Presentation)

    Science.gov (United States)

    McReynolds, Naomi; Auñón Garcia, Juan M.; Guengerich, Zoe; Smith, Terry K.; Dholakia, Kishan

    2017-02-01

    We present an optical spectroscopic technique, making use of both Raman signals and fluorescence spectroscopy, for the identification of five brands of commercially available extra-virgin olive-oil (EVOO). We demonstrate our technique on both a `bulk-optics' free-space system and a compact device. Using the compact device, which is capable of recording both Raman and fluorescence signals, we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach demonstrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs which obviates the need to use centralised laboratories and opens up the prospect of in-field testing. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. One of the main challenges facing Raman spectroscopy for use in quality control of EVOOs is that the oxidation of EVOO, which naturally occurs due to aging, causes shifts in Raman spectra with time, which implies regular retraining would be necessary. We present a potential method of analysis to minimize the effect that aging has on discrimination efficiency; we show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency thus improving the robustness of our technique.

  2. Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation

    CERN Document Server

    Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T

    2010-01-01

    In this paper a novel efficient method for representation of facial action units by encoding an image sequence as a fourth-order tensor is presented. The multilinear tensor-based extension of the biased discriminant analysis (BDA) algorithm, called multilinear biased discriminant analysis (MBDA), is first proposed. Then, we apply the MBDA and two-dimensional BDA (2DBDA) algorithms, as the dimensionality reduction techniques, to Gabor representations and the geometric features of the input image sequence respectively. The proposed scheme can deal with the asymmetry between positive and negative samples as well as curse of dimensionality dilemma. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method for representation of the subtle changes and the temporal information involved in formation of the facial expressions. As an accurate tool, this representation can be applied to many areas such as recognition of spontaneous and deliberate facial expressions, multi modal/media huma...

  3. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  4. Facial Expression Representation Using Heteroscedastic Linear Discriminant Analysis and Gabor Wavelets

    CERN Document Server

    Khademi, Mahmoud; Manzuri, Mohammad T

    2010-01-01

    In this paper, a novel representation for facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. Then, we perform a one-dimensional LDA on the new features. This combined method can alleviate the small sample size problem and instability encountered by HLDA. The proposed method is robust to illumination changes and can represent temporal information as well as subtle changes in facial muscles properly. Also, employing both geometric and appearance features and using support vector machines (SVMs) classifier, we provide experiments on Cohn-Kanade datab...

  5. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis

    Science.gov (United States)

    Shao, Zhenfeng; Zhang, Lei

    2014-09-01

    This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

  6. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Science.gov (United States)

    Zhang, Rui; Xu, Peng; Guo, Lanjin; Zhang, Yangsong; Li, Peiyang; Yao, Dezhong

    2013-01-01

    Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  7. Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

    Directory of Open Access Journals (Sweden)

    Khattab M.Ali Alheeti

    2017-08-01

    Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

  8. Fisher's Discriminant and Relevant Component Analysis for static facial expression classification

    OpenAIRE

    Sorci, Matteo; Antonini, Gianluca; Thiran, Jean-Philippe

    2007-01-01

    This paper addresses the issue of automatic classification of the six universal emotional categories (joy, surprise, fear, anger, disgust, sadness) in the case of static images. Appearance parameters are extracted by an active appearance model(AAM) representing the input for the classification step. We show how Relevant Component Analysis (RCA) in combination with Fisher's Linear Discriminant (FLD) provides a good "plug-\\&-play" classifier in the context of facial expression recognitio...

  9. Origin of uncontrolled water emissions in Alicante: use of discriminant analysis

    OpenAIRE

    Chinchón Payá, Servando; Blas Bravo, Isabel de; Nueda Roldán, María José; García Andreu, Francisco

    2010-01-01

    A model has been developed to predict the origin of uncontrolled water flows. For this purpose, we analysed a total of 52 elements using ICP techniques on 72 water samples in all: 40 of them from leaks in the drinking water supply network and the remaining 32 from groundwater outcrops. The study focused on the cases registered within the Alicante town limits (Spain). The use of multivariate statistical classification tools such as discriminant analysis made it possible not only to reduce the ...

  10. 2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

    OpenAIRE

    Hafez, Samir F.; Selim, Mazen M.; Hala H. Zayed

    2015-01-01

    We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for ...

  11. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  12. How Many Genes Are Needed for a Discriminant Microarray Data Analysis ?

    CERN Document Server

    Li, W; Li, Wentian; Yang, Yaning

    2001-01-01

    The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis (also called a supervised learning). Among thousands of genes whose expression levels are measured, not all are needed for discriminant analysis: a gene may either not contribute to the separation of two types of tissues/cancers, or it may be redundant because it is highly correlated with other genes. There are two theoretical frameworks in which variable selection (or gene selection in our case) can be addressed. The first is model selection, and the second is model averaging. We have carried out model selection using Akaike information criterion and Bayesian information criterion with logistic regression (discrimination, prediction, or classification) to determine the number of genes that provide the best model. These model selection criteria set upper limits of 22-25 and 12-13 genes for this data set with 38 samples, and the best model consists of only one (no.4847, zyxin) or two genes. We have also carried out model aver...

  13. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  14. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  15. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

    Directory of Open Access Journals (Sweden)

    Balloux François

    2010-10-01

    Full Text Available Abstract Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC, a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than

  16. Ethnic discrimination in recruitment and decision makers' features: Evidence from laboratory experiment and survey data using a student sample

    NARCIS (Netherlands)

    Blommaert, E.C.C.A.; Coenders, M.T.A.; Tubergen, F.A. van

    2014-01-01

    This article examines which individual-level factors are related to people's likelihood of discriminating against ethnic minority job applicants. It moves beyond describing to what extent discrimination occurs by examining the role of individuals' interethnic contacts, education and religion in shap

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

    CERN Document Server

    Vassali, M R

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components. (6 refs).

  18. UV-vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila

    Science.gov (United States)

    Barbosa-García, O.; Ramos-Ortíz, G.; Maldonado, J. L.; Pichardo-Molina, J. L.; Meneses-Nava, M. A.; Landgrave, J. E. A.; Cervantes-Martínez, J.

    2007-01-01

    Based on the UV-vis absorption spectra of commercially bottled tequilas, and with the aid of multivariate analysis, it is proved that different brands of white tequila can be identified from such spectra, and that 100% agave and mixed tequilas can be discriminated as well. Our study was done with 60 tequilas, 58 of them purchased at liquor stores in various Mexican cities, and two directly acquired from a distillery. All the tequilas were of the "white" type, that is, no aged spirits were considered. For the purposes of discrimination and quality control of tequilas, the spectroscopic method that we present here offers an attractive alternative to the traditional methods, like gas chromatography, which is expensive and time-consuming.

  19. Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River.

    Science.gov (United States)

    Wang, Xiaojing; Zou, Zhihong; Zou, Hui

    2013-10-01

    Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t (2) (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.

  20. Theoretical analysis and experimental research on port/starboard discrimination in towed line array

    Institute of Scientific and Technical Information of China (English)

    DU Xuanmin; ZHU Daizhu; ZHAO Rongrong; YAO Lan

    2001-01-01

    The theoretical analysis and experimental research on Port/Starboard (P/S) discrimination in towed line array are proposed. Two methods resolving the P/S ambiguity with hydrophone triplets are introduced. By processing experimental data, the theoretical analysis is verified. The processing algorithm is extended to broadband signal. The research results show that the method based on optimum beamforming with triplets can be used to remove the port/starboard ambiguity. Also because of the simplicity of the method, it is expected to be implemented in practical towed line array sonar.

  1. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    Science.gov (United States)

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  2. Discriminating between cultivars and treatments of broccoli using mass spectral fingerprinting and analysis of variance-principal component analysis.

    Science.gov (United States)

    Luthria, Devanand L; Lin, Long-Ze; Robbins, Rebecca J; Finley, John W; Banuelos, Gary S; Harnly, James M

    2008-11-12

    Metabolite fingerprints, obtained with direct injection mass spectrometry (MS) with both positive and negative ionization, were used with analysis of variance-principal components analysis (ANOVA-PCA) to discriminate between cultivars and growing treatments of broccoli. The sample set consisted of two cultivars of broccoli, Majestic and Legacy, the first grown with four different levels of Se and the second grown organically and conventionally with two rates of irrigation. Chemical composition differences in the two cultivars and seven treatments produced patterns that were visually and statistically distinguishable using ANOVA-PCA. PCA loadings allowed identification of the molecular and fragment ions that provided the most significant chemical differences. A standardized profiling method for phenolic compounds showed that important discriminating ions were not phenolic compounds. The elution times of the discriminating ions and previous results suggest that they were common sugars and organic acids. ANOVA calculations of the positive and negative ionization MS fingerprints showed that 33% of the variance came from the cultivar, 59% from the growing treatment, and 8% from analytical uncertainty. Although the positive and negative ionization fingerprints differed significantly, there was no difference in the distribution of variance. High variance of individual masses with cultivars or growing treatment was correlated with high PCA loadings. The ANOVA data suggest that only variables with high variance for analytical uncertainty should be deleted. All other variables represent discriminating masses that allow separation of the samples with respect to cultivar and treatment.

  3. Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics.

    Science.gov (United States)

    Ni, Yongnian; Lai, Yanhua; Kokot, Serge

    2012-07-01

    An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

  4. Predictors of experiences of discrimination and positive treatment in people with mental health problems: findings from an Australian national survey.

    Science.gov (United States)

    Reavley, Nicola J; Morgan, Amy J; Jorm, Anthony F

    2017-03-01

    The aim of the study was to assess the factors predicting experiences of avoidance, discrimination and positive treatment in people with mental health problems. In 2014, telephone interviews were carried out with 5220 Australians aged 18+, 1381 of whom reported a mental health problem or scored highly on a symptom screening questionnaire. Questions covered experiences of avoidance, discrimination and positive treatment by friends, spouse, other family, workplace, educational institution and others in the community; as well as disclosure of mental health problems. Avoidance, discrimination and positive treatment scores were calculated by counting the number of domains in which each occurred. Predictors of avoidance, discrimination and positive treatment were modelled with negative binomial regression analyses. After adjusting for the effects of other predictors in multivariate analyses, symptom severity and a diagnosis of 'any other disorder' (most commonly psychotic disorders or eating disorders) predicted experiences of both avoidance and discrimination but not positive treatment. Disclosing a mental health problem in more settings was also associated with higher rates of avoidance and discrimination, but also with positive treatment. Disclosure of mental health problems to others may increases experiences of discrimination, but may also increase experiences of positive treatment. These findings can help to inform decision making by people with mental health problems about disclosure, particularly in the case of more severe or low-prevalence disorders.

  5. Behavioural Analysis of Criminal Law: A Survey

    Directory of Open Access Journals (Sweden)

    Alon Harel

    2014-05-01

    Full Text Available This article is a survey of behavioural analysis of criminal law. Behavioural analysis of criminal law exploits social science methodologies (behavioural economics, psychology and even sociology to explore the effects of criminal law norms and enforcement policy on criminals, judges, juries, lawyers and other decision-makers, to determine the optimal type and size of criminal sanctions, to identify the optimal design of the enforcement system and the rules of evidence. Unlike traditional economic analysis, the behavioural perspective is eclectic rather than unitary; it is composed of various psychological and sociological findings including cognitive biases and their effects, prospect theory, the effects of social norms, findings concerning the ways preferences and beliefs are being shaped and even studies concerning happiness. Behavioural theorists call for the understanding and at times exploitation of various cognitive misperceptions, biases and heuristics to increase the deterrent effect of criminal law prohibitions and sanctions and/or increase their effectiveness. This survey compares this approach to traditional retributive approach and to economic analysis of law. It also provides several examples in which behavioural insights were used and, last it evaluates the prospects that the behavioural approach will influence policy and legislation.

  6. Gender identification of Caspian Terns using external morphology and discriminant function analysis

    Science.gov (United States)

    Ackerman, J.T.; Takekawa, J.Y.; Bluso, J.D.; Yee, J.L.; Eagles-Smith, C. A.

    2008-01-01

    Caspian Tern (Sterna caspia) plumage characteristics are sexually monochromatic and gender cannot easily be distinguished in the field without extensive behavioral observations. We assessed sexual size dimorphism and developed a discriminant function to assign gender in Caspian Terns based on external morphology. We collected and measured Caspian Terns in San Francisco Bay, California, and confirmed their gender based on necropsy and genetic analysis. Of the eight morphological measurements we examined, only bill depth at the gonys and head plus bill length differed between males and females with males being larger than females. A discriminant function using both bill depth at the gonys and head plus bill length accurately assigned gender of 83% of terns for which gender was known. We improved the accuracy of our discriminant function to 90% by excluding individuals that had less than a 75% posterior probability of correctly being assigned to gender. Caspian Terns showed little sexual size dimorphism in many morphometries, but our results indicate they can be reliably assigned to gender in the field using two morphological measurements.

  7. Attractor structure discriminates sleep states: recurrence plot analysis applied to infant breathing patterns.

    Science.gov (United States)

    Terrill, Philip Ian; Wilson, Stephen James; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

    2010-05-01

    Breathing patterns are characteristically different between infant active sleep (AS) and quiet sleep (QS), and statistical quantifications of interbreath interval (IBI) data have previously been used to discriminate between infant sleep states. It has also been identified that breathing patterns are governed by a nonlinear controller. This study aims to investigate whether nonlinear quantifications of infant IBI data are characteristically different between AS and QS, and whether they may be used to discriminate between these infant sleep states. Polysomnograms were obtained from 24 healthy infants at six months of age. Periods of AS and QS were identified, and IBI data extracted. Recurrence quantification analysis (RQA) was applied to each period, and recurrence calculated for a fixed radius in the range of 0-8 in steps of 0.02, and embedding dimensions of 4, 6, 8, and 16. When a threshold classifier was trained, the RQA variable recurrence was able to correctly classify 94.3% of periods in a test dataset. It was concluded that RQA of IBI data is able to accurately discriminate between infant sleep states. This is a promising step toward development of a minimal-channel automatic sleep state classification system.

  8. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

    Science.gov (United States)

    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  9. Using confirmatory factor analysis to manage discriminant validity issues in social pharmacy research.

    Science.gov (United States)

    Carter, Stephen R

    2016-06-01

    Background Confirmatory factory analysis (CFA) and structural equation modelling (SEM) are increasingly used in social pharmacy research. One of the key benefits of CFA is that it allows researchers to provide evidence for the validity of internal factor structure of measurement scales. In particular, CFA can be used to provide evidence for the validity of the assertion that a hypothesized multi-dimensional scale discriminates between sub-scales. Aim This manuscript aims to provide guidance for researchers who wish to use CFA to provide evidence for the internal factor structure of measurement scales. Methods The manuscript places discriminant validity in the context of providing overall validity evidence for measurement scales. Four examples from the recent social pharmacy literature are used to critically examine the various methods which are used to establish discriminant validity. Using a hypothetical scenario, the manuscript demonstrates how commonly used output from CFA computer programs can be used to provide evidence for separateness of sub-scales within a multi-dimensional scale. Conclusion The manuscript concludes with recommendations for the conduct and reporting of studies which use CFA to provide evidence of internal factor structure of measurement scales.

  10. Morphological Discrimination of Greek Honey Bee Populations Based on Geometric Morphometrics Analysis of Wing Shape

    Directory of Open Access Journals (Sweden)

    Charistos Leonidas

    2014-06-01

    Full Text Available Honey bees collected from 32 different localities in Greece were studied based on the geometric morphometrics approach using the coordinates of 19 landmarks located at wing vein intersections. Procrustes analysis, principal component analysis, and Canonical variate analysis (CVA detected population variability among the studied samples. According to the Principal component analysis (PCA of pooled data from each locality, the most differentiated populations were the populations from the Aegean island localities Astypalaia, Chios, and Kythira. However, the populations with the most distant according to the canonical variate analysis performed on all measurements were the populations from Heraklion and Chania (both from Crete island. These results can be used as a starting point for the use of geometric morphometrics in the discrimination of honey bee populations in Greece and the establishment of conservation areas for local honey bee populations.

  11. Cross-linked survey analysis is an approach for separating cause and effect in survey research.

    Science.gov (United States)

    Redelmeier, Donald A; Thiruchelvam, Deva; Lustig, Andrew J

    2015-01-01

    We developed a new research approach, called cross-linked survey analysis, to explore how an acute exposure might lead to changes in survey responses. The goal was to identify associations between exposures and outcomes while reducing some ambiguities related to interpreting cause and effect in survey responses from a population-based community questionnaire. Cross-linked survey analysis differs from a cross-sectional, longitudinal, and panel survey analysis by individualizing the timeline to the unique history of each respondent. Cross-linked survey analysis, unlike a repeated-measures self-matching design, does not track changes in a repeated survey question given to the same respondent at multiple time points. Pilot data from three analyses (n = 1,177 respondents) illustrate how a cross-linked survey analysis can control for population shifts, temporal trends, and reverse causality. Accompanying graphs provide an intuitive display to readers, summarize results, and show differences in response distributions. Population-based individual-level linkages also reduce selection bias and increase statistical power compared with a single-center cross-sectional survey. Cross-linked survey analysis has limitations related to unmeasured confounding, pragmatics, survivor bias, statistical models, and the underlying artifacts in survey responses. We suggest that a cross-linked survey analysis may help in epidemiology science using survey data. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

    Science.gov (United States)

    Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N; Chebib, Hanna; Ducruet, Violette

    2014-11-01

    Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied

  13. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

    Science.gov (United States)

    Singh, Sartajvir; Talwar, Rajneesh

    2016-12-01

    Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

  14. Mental health impacts of racial discrimination in Australian culturally and linguistically diverse communities: a cross-sectional survey.

    Science.gov (United States)

    Ferdinand, Angeline S; Paradies, Yin; Kelaher, Margaret

    2015-04-18

    Racial discrimination denies those from racial and ethnic minority backgrounds access to rights such as the ability to participate equally and freely in community and public life, equitable service provision and freedom from violence. Our study was designed to examine how people from racial and ethnic minority backgrounds in four Australian localities experience and respond to racial discrimination, as well as associated health impacts. Data were collected from 1,139 Australians regarding types of racial discrimination experienced, settings for these incidents, response mechanisms and psychological distress as measured by the Kessler 6 (K6) Psychological Distress Scale. Age, education, religion, gender, visibility and rurality were all significantly associated with differences in the frequency of experiencing racial discrimination. Experiencing racial discrimination was associated with worse mental health. Mental health impacts were not associated with the type of discriminatory experience, but experiencing racial discrimination in shops and in employment and government settings was associated with being above the threshold for high or very high psychological distress. One out of twelve response mechanisms was found to be associated with lower stress following a discriminatory incident. Study results indicate that poorer mental health was associated with the volume of discrimination experienced, rather than the type of experience. However, the impact of experiencing discrimination in some settings was shown to be particularly associated with high or very high psychological distress. Our findings suggest that interventions designed to prevent the occurrence of racism have more potential to increase mental health in racial and ethnic minority communities than interventions that work with individuals in response to experiencing racism.

  15. Mental health impacts of racial discrimination in Australian culturally and linguistically diverse communities: a cross-sectional survey

    OpenAIRE

    Ferdinand, Angeline S; Paradies, Yin; Kelaher, Margaret

    2015-01-01

    Background Racial discrimination denies those from racial and ethnic minority backgrounds access to rights such as the ability to participate equally and freely in community and public life, equitable service provision and freedom from violence. Our study was designed to examine how people from racial and ethnic minority backgrounds in four Australian localities experience and respond to racial discrimination, as well as associated health impacts. Methods Data were collected from 1,139 Austra...

  16. Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis.

    Science.gov (United States)

    Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni

    2016-09-01

    A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS-MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre-separation by creating ions outside the instrument. The present study reports a PS-MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non-volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS-MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS-MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross-validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd.

  17. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  18. Discourse analysis of gender equality and non-discrimination laws and strategies

    Directory of Open Access Journals (Sweden)

    Antonijević Zorana

    2017-01-01

    Full Text Available Based on the contemporary research on gender and language, using the method of discourse analysis applied to the laws and policies, this article explains how certain linguistic practice, in the context of the administrative discourse, produces meaning that may or may not contribute to its better understanding and more efficient implementation. Through discourse analysis of gender equality and non-discrimination laws and strategies in Serbia, it has been shown how and with what consequences the socio-political and academic elites affect defining and promoting certain concepts (gender, sex, gender equality, discrimination in one social and historical moment. The paper is placed in the theoretical framework of three visions of gender equality: perspective of equal treatment, women‘s perspectives and gender perspective (Booth, Bennett 2002, that are corresponding to the three strategies for achieving gender equality: equal treatment, specific policy of gender equality and gender mainstreaming (Verloo, 2001. The discourse analysis of the Law on Gender Equality (2009, the National Strategy for the Improvement of the Position of Women and Advancement of Gender Equality (2009, the Law on Prohibition of Discrimination (2009 and the Strategy for Prevention and Protection against Discrimination (2013, has shown the context of use and meaning of terms gender and sex, as well as implications it has on their potential to change the existing paradigms and understanding of gender equality, and the implementation of policies in Serbia. Analysis of the use of terms sex and gender in the most important legal and strategic documents for achieving gender equality, showed that the choice of certain categories and terms is always a political choice. The authors show how these documents are written in the key of two gender perspectives and strategies: equal treatment and the specific policy of gender equality, while the third - introduction of a gender perspective

  19. Discriminant Analysis of a Spatially Extensive Landsliding Inventory for the Haida Gwaii, British Columbia, Canada

    Science.gov (United States)

    Sjogren, D.; Martin, Y. E.; Jagielko, L.

    2010-12-01

    Gimbarzevsky (1988) collected an exceptional landsliding inventory for the Haida Gwaii, British Columbia (formerly called the Queen Charlotte Islands). This data base includes more than 8 000 landsliding vectors, with an areal coverage of about 10 000 km2. Unfortunately, this landsliding inventory was never published in the referred literature, despite its regional significance. The data collection occurred prior to widespread use of GIS technologies in landsliding analysis, thus restricting the types of analyses that were undertaken at the time relative to what is possible today. Gimbarzevsky identified the landsliding events from 1:50 000 aerial photographs, and then transferred the landslide vectors to NTS map sheets. In this study, we digitized the landslide vectors from these original map sheets and connected each vector to a digital elevation model. Lengths of landslide vectors were then compared to results of Rood (1984), whose landsliding inventory for the Haida Gwaii relied on larger-scale aerial photographs (~ 1:13 000). A comparison of the two data bases shows that Rood’s inventory contains a more complete record of smaller landslides, whereas Gimbarzevsky’s inventory provides a much better statistical representation of less frequently occurring, medium to large landslide events. We then apply discriminant analysis to the Gimbarzevsky data base to assess which of a set of ten predictor variables, selected on the basis of mechanical theory, best predict failed vs. unfailed locations in the landscape (referred to as the grouping variable in discriminant analysis). Certain predictor variables may be cross-correlated, and any one particular variable may be related to several aspects of mechanical theory (for example, a particular variable may affect various components of shear stress and/or shear strength); it is important to recognize that the significance of particular groupings may reflect this information. Eight of the original variables were found

  20. Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

    Science.gov (United States)

    Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

    2014-01-01

    Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

  1. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

    Energy Technology Data Exchange (ETDEWEB)

    Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Maalouly, Jacqueline, E-mail: j_maalouly@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Rutledge, Douglas N., E-mail: douglas.rutledge@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Chebib, Hanna, E-mail: hchebib@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Ducruet, Violette, E-mail: violette.ducruet@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France)

    2014-11-15

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  2. Discriminant analysis of pulmonary function parameters. Healthy adults versus mild asthmatics and moderate asthmatics.

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1982-08-01

    Full Text Available Volume-time (V-T and flow-volume (F-V curves were measured in all the subjects of nonsmoking young males (mean value 26.3 yrs. of age, healthy and asthmatics. Eleven parameters of pulmonary function tests composed of two V-T, six F-V, and three mean time constant (MTC parameters, were calculated from the curves. These parameters were used in the two analyses through the all possible selection procedure (APSP discriminating between healthy adults and mild asthmatics and also between healthy and moderate. Flow rate at 75% of FVC (V75 proved to be the most useful parameter and V50 the next best in both analyses. The probability of misclassification using all eleven parameters was 19.64% in the analysis of healthy adults and mild asthmatics, and 4.29% in the analysis of healthy adults and moderate asthmatics. There was a little difference in the parameters selected at every step. The discriminant analysis proved that the flow-volume patterns were different according to the severity of bronchial asthma. Thus flow-volume recognition was considered to be important in analyzing the severity of bronchial asthma.

  3. Predicting groundwater redox status on a regional scale using linear discriminant analysis

    Science.gov (United States)

    Close, M. E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S.

    2016-08-01

    Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, 100 m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification.

  4. Classification tree analysis for the discrimination of pleural exudates and transudates.

    Science.gov (United States)

    Esquerda, Aureli; Trujillano, Javier; López de Ullibarri, Ignacio; Bielsa, Silvia; Madroñero, Ana B; Porcel, José M

    2007-01-01

    Classification and regression tree (CART) analysis is a non-parametric technique suitable for the generation of clinical decision rules. We have studied the performance of CART analysis in the separation of pleural exudates and transudates. Basic demographic, radiologic and laboratory data were retrospectively evaluated in 1257 pleural effusions (204 transudates and 1053 exudates, according to standard clinical criteria) and submitted for CART analysis. The model's discriminative ability was compared with that of Light's criteria, in both the original formulation and an abbreviated version, i.e., deleting the pleural fluid (PF)/serum lactate dehydrogenase (LDH) ratio from the triad. A first CART model built starting from all available data identified PF/serum protein ratio and PF LDH ratios as the two best discriminatory parameters. This algorithm achieved a sensitivity of 96.8%, slightly lower than that of classical Light's criteria (98.5%) and comparable to that of the abbreviated Light's criteria (97.0%), and significantly better specificity (85.3%) compared to both classical (74.0%) and abbreviated (79.4%) Light's criteria. A second CART model developed after excluding serum measurements selected PF protein and PF LDH as the most discriminatory variables, and correctly classified 97.2% of exudates and 77.0% of transudates. CART-based algorithms can efficiently discriminate between pleural exudates and transudates.

  5. Rapid discrimination of the geographical origins of an oolong tea (anxi-tieguanyin) by near-infrared spectroscopy and partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Si-Min; Liu, Jun-Ping; Xu, Lu; Fu, Xian-Shu; Cui, Hai-Feng; Yun, Zhen-Yu; Yu, Xiao-Ping; Ye, Zi-Hong

    2014-01-01

    This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  6. Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Si-Min Yan

    2014-01-01

    Full Text Available This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV transformation, taking second-order derivatives (D2 spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  7. Complex Surveys A Guide to Analysis Using R

    CERN Document Server

    Lumley, Thomas

    2010-01-01

    A complete guide to carrying out complex survey analysis using R. As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while al

  8. "Does anger regulation mediate the discrimination-mental health link among Mexican-origin adolescents? A longitudinal mediation analysis using multilevel modeling": Correction to Park et al. (2016).

    Science.gov (United States)

    2017-02-01

    Reports an error in "Does Anger Regulation Mediate the Discrimination-Mental Health Link Among Mexican-Origin Adolescents? A Longitudinal Mediation Analysis Using Multilevel Modeling" by Irene J. K. Park, Lijuan Wang, David R. Williams and Margarita Alegría (Developmental Psychology, Advanced Online Publication, Nov 28, 2016, np). In the article, there were several typographical errors in the Recruitment and Procedures section. The percentage of mothers who responded to survey items should have been 99.3%. Additionally, the youths surveyed at T2 and T3 should have been n=246 . Accordingly, the percentage of youths surveyed in T2 and T3 should have been 91.4% and the percentage of mothers surveyed at T2 and T3 should have been 90.7%. Finally, the youths missing at T2 should have been n= 23, and therefore the attrition rate for youth participants should have been 8.6. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-57671-001.) Although prior research has consistently documented the association between racial/ethnic discrimination and poor mental health outcomes, the mechanisms that underlie this link are still unclear. The present 3-wave longitudinal study tested the mediating role of anger regulation in the discrimination-mental health link among 269 Mexican-origin adolescents (Mage = 14.1 years, SD = 1.6; 57% girls), 12 to 17 years old. Three competing anger regulation variables were tested as potential mediators: outward anger expression, anger suppression, and anger control. Longitudinal mediation analyses were conducted using multilevel modeling that disaggregated within-person effects from between-person effects. Results indicated that outward anger expression was a significant mediator; anger suppression and anger control were not significant mediators. Within a given individual, greater racial/ethnic discrimination was associated with more frequent outward anger expression. In turn

  9. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-01

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  10. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-15

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  11. About Statistical Analysis of Qualitative Survey Data

    Directory of Open Access Journals (Sweden)

    Stefan Loehnert

    2010-01-01

    Full Text Available Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods. In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is shown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling matrices. For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process defining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such kind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of the chosen model specification is outlined.

  12. Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

    Science.gov (United States)

    Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

    2013-05-01

    Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

  13. Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

    Science.gov (United States)

    Bevilacqua, Marta; Marini, Federico

    2014-08-01

    The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).

  14. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...... in the soluble and exchangeable phase, these elements being associated primarily with amorphous-crystalline Fe-oxides, organic matter and/or resistant phases. The results obtained with sequential extraction were the prerequisite to the attempt to identify the Cr and As distribution in the solid phase. If high...... concentrations of contaminants are indicated by chemical wet analysis, these contaminants must occur directly in the solid phase. Thin sections of soil aggregates were scanned for Cu, Cr and As using an electron microprobe, and qualitative analysis was made on selected areas. Microphotographs of thin sections...

  15. Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina

    Directory of Open Access Journals (Sweden)

    Deni Memić

    2015-01-01

    Full Text Available This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska. Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit and multiple discriminant analysis (MDA and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.

  16. Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies.

    Science.gov (United States)

    Oswald, Frederick L; Mitchell, Gregory; Blanton, Hart; Jaccard, James; Tetlock, Philip E

    2013-08-01

    This article reports a meta-analysis of studies examining the predictive validity of the Implicit Association Test (IAT) and explicit measures of bias for a wide range of criterion measures of discrimination. The meta-analysis estimates the heterogeneity of effects within and across 2 domains of intergroup bias (interracial and interethnic), 6 criterion categories (interpersonal behavior, person perception, policy preference, microbehavior, response time, and brain activity), 2 versions of the IAT (stereotype and attitude IATs), 3 strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and 4 criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings, and majority-only ratings). IATs were poor predictors of every criterion category other than brain activity, and the IATs performed no better than simple explicit measures. These results have important implications for the construct validity of IATs, for competing theories of prejudice and attitude-behavior relations, and for measuring and modeling prejudice and discrimination.

  17. Correlation analysis of proprioceptive acuity in ipsilateral position-matching and velocity-discrimination.

    Science.gov (United States)

    Djupsjöbacka, Mats; Domkin, Dmitry

    2005-01-01

    In order to plan and control movements the central nervous system (CNS) needs to continuously keep track of the state of the musculoskeletal system. Therefore the CNS constantly uses sensory input from mechanoreceptors in muscles, joints and skin to update information about body configuration on different levels of the CNS. On the conscious level, such representations constitute proprioception. Different tests for assessment of proprioceptive acuity have been described. However, it is unclear if the proprioceptive acuity measurements in these tests correlate within subjects. By using both uni- and multivariate analysis we compared proprioceptive acuity in different variants of ipsilateral active and passive limb position-matching and ipsilateral passive limb movement velocity-discrimination in a group of healthy subjects. The analysis of the position-matching data revealed a higher acuity of matching for active movements in comparison to passive ones. The acuity of matching was negatively correlated to movement extent. There was a lack of correlation between proprioceptive acuity measurements in position-matching and velocity-discrimination.

  18. Prostate lesion detection and localization based on locality alignment discriminant analysis

    Science.gov (United States)

    Lin, Mingquan; Chen, Weifu; Zhao, Mingbo; Gibson, Eli; Bastian-Jordan, Matthew; Cool, Derek W.; Kassam, Zahra; Chow, Tommy W. S.; Ward, Aaron; Chiu, Bernard

    2017-03-01

    Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.

  19. An item factor analysis and item response theory-based revision of the Everyday Discrimination Scale.

    Science.gov (United States)

    Stucky, Brian D; Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter R; Wightman, Linda F

    2011-04-01

    The Everyday Discrimination Scale (EDS), a widely used measure of daily perceived discrimination, is purported to be unidimensional, to function well among African Americans, and to have adequate construct validity. Two separate studies and data sources were used to examine and cross-validate the psychometric properties of the EDS. In Study 1, an exploratory factor analysis was conducted on a sample of African American law students (N = 589), providing strong evidence of local dependence, or nuisance multidimensionality within the EDS. In Study 2, a separate nationally representative community sample (N = 3,527) was used to model the identified local dependence in an item factor analysis (i.e., bifactor model). Next, item response theory (IRT) calibrations were conducted to obtain item parameters. A five-item, revised-EDS was then tested for gender differential item functioning (in an IRT framework). Based on these analyses, a summed score to IRT-scaled score translation table is provided for the revised-EDS. Our results indicate that the revised-EDS is unidimensional, with minimal differential item functioning, and retains predictive validity consistent with the original scale.

  20. Multiple Subject Barycentric Discriminant Analysis (MUSUBADA: How to Assign Scans to Categories without Using Spatial Normalization

    Directory of Open Access Journals (Sweden)

    Hervé Abdi

    2012-01-01

    Full Text Available We present a new discriminant analysis (DA method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels that are themselves grouped into regions of interest (ROIs. Like DA, MUSUBADA (1 assigns observations to predefined categories, (2 gives factorial maps displaying observations and categories, and (3 optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap, its performance is evaluated with confusion matrices (for fixed and random models and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces, of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject and with more variables than observations.

  1. Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

    Science.gov (United States)

    Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

    2012-01-01

    Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

  2. Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers

    Science.gov (United States)

    Ghiasi-Freez, Javad; Soleimanpour, Iman; Kadkhodaie-Ilkhchi, Ali; Ziaii, Mansur; Sedighi, Mahdi; Hatampour, Amir

    2012-08-01

    Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of sixteen largest pores of each image are extracted using image analysis techniques. The extracted parameters and their corresponding pore types of 294 pores are used for training two intelligent discriminant classifiers, namely linear and quadratic discriminant analysis. The trained classifiers take the geometrical features of the pores to identify the type and percentage of five types of porosity, including interparticle, intraparticle, oomoldic, biomoldic, and vuggy in each image. The accuracy of classifiers is determined from two standpoints. Firstly, the predicted and measured percentages of each type of porosity are compared with each other. The results indicate reliable performance for predicting percentage of each type of porosity. In the second step, the precisions of classifiers for categorizing the pore spaces are analyzed. The classifiers also took a high acceptance score when used for individual recognition of pore spaces. The proposed methodology is a further promising application for petroleum geologists allowing statistical study of pore types in a rapid and accurate way.

  3. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis.

    Science.gov (United States)

    Puthenveettil, Saumya; Fluet, Gerard; Qiu, Qinyin; Adamovich, Sergei

    2012-01-01

    This study describes the analysis of hand preshaping using Linear Discriminant Analysis (LDA) to predict hand formation during reaching and grasping tasks of the hemiparetic hand, following a series of upper extremity motor intervention treatments. The purpose of this study is to use classification of hand posture as an additional tool for evaluating the effectiveness of therapies for upper extremity rehabilitation such as virtual reality (VR) therapy and conventional physical therapy. Classification error for discriminating between two objects during hand preshaping is obtained for the hemiparetic and unimpaired hands pre and post training. Eight subjects post stroke participated in a two-week training session consisting of upper extremity motor training. Four subjects trained with interactive VR computer games and four subjects trained with clinical physical therapy procedures of similar intensity. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove® and arm joint angles were measured using the trackSTAR™ system prior to training and after training. The unimpaired hand of subjects preshape into the target object with greater accuracy than the hemiparetic hand as indicated by lower classification errors. Hemiparetic hand improved in preshaping accuracy and time to reach minimum error. Classification of hand preshaping may provide insight into improvements in motor performance elicited by robotically facilitated virtually simulated training sessions or conventional physical therapy.

  4. Shape-based discriminative analysis of combined bilateral hippocampi using multiple object alignment

    Science.gov (United States)

    Shen, Li; Makedon, Fillia; Saykin, Andrew

    2004-05-01

    Shape analysis of hippocampi in schizophrenia has been preformed previously using the spherical harmonic SPHARM description. In these studies, the left and right hippocampi are aligned independently and the spatial relation between them is not explored. This paper presents a new SPHARM-based technique which examines not only the individual shape information of the two hippocampi but also the spatial relation between them. The left and right hippocampi are treated as a single shape configuration. A ploy-shape alignment algorithm is developed for aligning configurations of multiple SPHARM surfaces as follows: (1) the total volume is normalized; (2) the parameter space is aligned for creating the surface correspondence; (3) landmarks are created by a uniform sampling of multiple surfaces for each configuration; (4) a quaternion-based algorithm is employed to align each landmark representation to the mean configuration through the least square rotation and translation iteratively until the mean converges. After applying the poly-shape alignment algorithm, a point distribution model is applied to aligned landmarks for feature extraction. Classification is performed using Fisher's linear discriminant with an effective feature selection scheme. Applying the above procedure to our hippocampal data (14 controls versus 25 schizophrenics, all right-handed males), we achieve the best cross-validation accuracy of 92%, supporting the idea that the whole shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. The results of an ROC analysis and a visualization of discriminative patterns are also included.

  5. The differentiation of camel breeds based on meat measurements using discriminant analysis.

    Science.gov (United States)

    Al-Atiyat, Raed Mahmoud; Suliman, Gamal; AlSuhaibani, Entissar; El-Waziry, Ahmad; Al-Owaimer, Abdullah; Basmaeil, Saeid

    2016-06-01

    The meat productivity of camel in the tropics is still under investigation for identification of better meat breed or type. Therefore, four one-humped Saudi Arabian (SA) camel breeds, Majaheem, Maghateer, Hamrah, and Safrah were experimented in order to differentiate them from each other based on meat measurements. The measurements were biometrical meat traits measured on six intact males from each breed. The results showed higher values of the Majaheem breed than that obtained for the other breeds except few cases such dressing percentage and rib-eye area. In differentiation analysis, the most discriminating meat variables were myofibrillar protein index, meat color components (L* and a*, b*), and cooking loss. Consequently, the Safrah and the Majaheem breeds presented the largest dissimilarity as evidenced by their multivariate means. The canonical discriminant analysis allowed an additional understanding of the differentiation between breeds. Furthermore, two large clusters, one formed by Hamrah and Maghateer in one group along with Safrah. These classifications may assign each breed into one cluster considering they are better as meat producers. The Majaheem was clustered alone in another cluster that might be a result of being better as milk producers. Nevertheless, the productivity type of the camel breeds of SA needs further morphology and genetic descriptions.

  6. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  7. Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Andrade Adriano O

    2009-11-01

    Full Text Available Abstract Background The human body adopts a number of strategies to maintain an upright position. The analysis of the human balance allows for the understanding and identification of such strategies. The displacement of the centre of pressure (COP is a measure that has been successfully employed in studies regarding the postural control. Most of these investigations are related to the analysis of individuals suffering from neuromuscular disorders. Recent studies have shown that the elderly population is growing very fast in many countries all over the world, and therefore, researches that try to understand changes in this group are required. In this context, this study proposes the analysis of the postural control, measured by the displacement of the COP, in groups of young and elderly adults. Methods In total 59 subjects participated of this study. They were divided into seven groups according to their age. The displacement of the COP was collected for each subject standing on a force plate. Two experimental conditions, of 30 seconds each, were investigated: opened eyes and closed eyes. Traditional and recent digital signal processing tools were employed for feature computation from the displacement of the COP. Statistical analyses were carried out in order to identify significant differences between the features computed from the distinct groups that could allow for their discrimination. Results Our results showed that Linear Discrimination Analysis (LDA, which is one of the most popular feature extraction and classifier design techniques, could be successfully employed as a linear transformation, based on the linear combination of standard features for COP analysis, capable of estimating a unique feature, so-called LDA-value, from which it was possible to discriminate the investigated groups and show a high correlation between this feature and age. Conclusion These results show that the analysis of features computed from the displacement of

  8. Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

    Science.gov (United States)

    Melucci, Dora; Bendini, Alessandra; Tesini, Federica; Barbieri, Sara; Zappi, Alessandro; Vichi, Stefania; Conte, Lanfranco; Gallina Toschi, Tullia

    2016-08-01

    At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.

  9. Is it really organic?--multi-isotopic analysis as a tool to discriminate between organic and conventional plants.

    Science.gov (United States)

    Laursen, K H; Mihailova, A; Kelly, S D; Epov, V N; Bérail, S; Schjoerring, J K; Donard, O F X; Larsen, E H; Pedentchouk, N; Marca-Bell, A D; Halekoh, U; Olesen, J E; Husted, S

    2013-12-01

    Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional wheat and barley grains. It is concluded, that multi-isotopic analysis has the potential to disclose fraudulent substitutions of organic with conventionally cultivated plants.

  10. U.S. Geological Survey Gap Analysis Program

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The Gap Analysis Program (GAP) is an element of the U.S. Geological Survey (USGS). GAP helps to implement the Department of Interior?s goals of inventory,...

  11. Discrimination Against State and Local Government LGBT Employees: An Analysis of Administrative Complaints

    OpenAIRE

    Mallory, Christy; Sears, Brad

    2014-01-01

    This article documents evidence of recent discrimination against lesbian, gay, bisexual, and transgender (LGBT) public sector workers by analyzing employment discrimination complaints filed with state and local administrative agencies. We present information about 589 complaints of sexual orientation and gender identity discrimination filed by public sector workers in 123 jurisdictions. We find that discrimination against LGBT people in the public sector is pervasive and occurs nearly as freq...

  12. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-07-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution

  13. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  14. Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

    Science.gov (United States)

    Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

  15. Survey Response Rates and Survey Administration in Counseling and Clinical Psychology: A Meta-Analysis

    Science.gov (United States)

    Van Horn, Pamela S.; Green, Kathy E.; Martinussen, Monica

    2009-01-01

    This article reports results of a meta-analysis of survey response rates in published research in counseling and clinical psychology over a 20-year span and describes reported survey administration procedures in those fields. Results of 308 survey administrations showed a weighted average response rate of 49.6%. Among possible moderators, response…

  16. Survey Response Rates and Survey Administration in Counseling and Clinical Psychology: A Meta-Analysis

    Science.gov (United States)

    Van Horn, Pamela S.; Green, Kathy E.; Martinussen, Monica

    2009-01-01

    This article reports results of a meta-analysis of survey response rates in published research in counseling and clinical psychology over a 20-year span and describes reported survey administration procedures in those fields. Results of 308 survey administrations showed a weighted average response rate of 49.6%. Among possible moderators, response…

  17. Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2013-01-01

    This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.

  18. Colored inks analysis and differentiation: A first step in artistic contemporary prints discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Vila, Anna [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: avila@sct.ub.es; Ferrer, Nuria [Serveis Cientificotecnics, Universitat de Barcelona, C/Lluis Sole i Sabaris 1, 08028 Barcelona (Spain)]. E-mail: nferrer@sctub.es; Garcia, Jose F. [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: ifgarcia@ub.edu

    2007-04-04

    Prints are the most popular artistic technique. Due to their manufacturing procedure, they are also one of the most frequently falsified types of artwork. In terms of their economic and historic value, the chemical analysis and characterisation of coloured inks and their principal constituent materials (pigments), together with the historical and aesthetic information available in the Catalogues Raisonees, are important tools in distinguishing originals from non-original prints. The chemical characterisation and discrimination of coloured inks has test in this study. Analysis using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and X-ray diffraction (XRD) has been done on blue pigments and inks, due to this colour is one of the most representative for the presence of organic and inorganic materials in their composition. Conclusion obtained for this colour would demonstrate the capability of the approach when it is applied to any other coloured set of inks.

  19. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  20. Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation

    DEFF Research Database (Denmark)

    Askjær, Sune; Langgård, Morten

    2008-01-01

    fingerprints proved superior to the TGT and TGD fingerprints. Examples of SAR elucidation based on PLS-DA model interpretation of model coefficients using a reversible pharmacophore fingerprint are given. In addition, we tested the hypothesis that feature combinations coming from the analysis of two...... the lead optimization toward a final drug candidate. This paper presents a combined approach to solving these two problems of ligand-based virtual screening and elucidation of SAR based on interplay between pharmacophore fingerprints and interpretation of PLS-discriminant analysis (PLS-DA) models....... The virtual screening capability of the PLS-DA method is compared to group fusion maximum similarity searching in a test using four graph-based pharmacophore fingerprints over a range of 10 diverse targets. The PLS-DA method was generally found to do better than the Smax method. The GpiDAPH3 and PCH...

  1. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

    Directory of Open Access Journals (Sweden)

    Ningbo Hao

    2016-01-01

    Full Text Available Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA and Locality Preserving Projection (LPP algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA and LPP based on factor analysis (FA-LPP are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.

  2. Mass discrimination in elastic recoil detection analysis and its application to Al2O3 on MoS2

    Science.gov (United States)

    Laricchiuta, G.; Vandervorst, W.; Meersschaut, J.

    2017-09-01

    A time of flight-energy (TOF-E) telescope is often used to detect the scattered and recoiled atoms in elastic recoil detection analysis. The experimental two-dimensional TOF-E histogram may be numerically transformed into a time of flight-mass (TOF-M) histogram. The limited mass resolution in the TOF-M histogram, which results from the limited energy resolution of the energy detector, makes it sometimes difficult to discriminate elements with a small difference in atomic mass. We describe a mass discrimination procedure to numerically discriminate the elements in the TOF-M histogram. The procedure is illustrated on a sample consisting of an Al and a Si layer deposited on a MgO substrate. Besides, we apply the procedure to discriminate Al and Si in a sample consisting of Al2O3 deposited on MoS2/SiO2/Si.

  3. Is it really organic? – Multi-isotopic analysis as a tool to discriminate between organic and conventional plants

    DEFF Research Database (Denmark)

    Laursen, K.H.; Mihailova, A.; Kelly, S.D.

    2013-01-01

    for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between......Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate...... plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional...

  4. Discrimination of Semen cassiae from two related species based on the multivariate analysis of high-performance liquid chromatography fingerprints.

    Science.gov (United States)

    Tang, Liying; Wu, Hongwei; Zhou, Xidan; Xu, Yilong; Zhou, Guohong; Wang, Ting; Kou, Zhenzhen; Wang, Zhuju

    2015-07-01

    A simple and efficient high-performance liquid chromatography fingerprint method was developed to discriminate Semen cassiae from two related species: Cassia obtusifolia L. (CO) and Cassia tora L. (CT), the seeds of which are abbreviated as COS and CTS, respectively. 22 major bioactive ingredients in 42 samples (20 COS and 22 CTS) collected from different provinces of China were identified. The statistical methods included similarity analysis and partial least-squares discriminant analysis. The pattern analysis method was specific and could be readily used for the comprehensive evaluation of Semen cassiae samples. Therefore, high-performance liquid chromatography fingerprint in combination with pattern analysis provided a simple and reliable method for discriminating between COS and CTS. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Genetic Discrimination

    Science.gov (United States)

    ... in Genetics Archive Regulation of Genetic Tests Genetic Discrimination Overview Many Americans fear that participating in research ... I) and employment (Title II). Read more Genetic Discrimination and Other Laws Genetic Discrimination and Other Laws ...

  6. Promises of silent salesman to the FMCG industry: an investigation using linear discriminant analysis approach

    Directory of Open Access Journals (Sweden)

    Shekhar Suraj Kushe

    2015-12-01

    Full Text Available Packaging which is often called as the ‘silent salesman’ is an important component of marketing. Today the importance of packaging has risen to such an extent that product packaging is rightly called as the fifth ‘P’ of marketing mix. FMCG are products which are utilized by large number of people. The present study examined the discriminating power of five selected FMCG packaging variables namely ‘picture’, ‘colour’, ‘size’, ‘shape’ and ‘material’ amidst those who purchased FMCG based on these packaging variables and for those who purchased FMCG not based on these packaging variables. Descriptive research was carried out in the study. Respondents (students were asked to rate four packaging variable on a five point Likert’s scale. Discriminant analysis showed that only two variables namely ‘Colour’ (.706 and ‘Shape’ (–.527 were good predictors. Variables ‘Picture’, ‘size’ and ‘material’ were considered as poor predictors as far as the student communities were considered. The cross validated classification showed that out of the 240 samples drawn, 91.8% of the cases were correctly classified.

  7. Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-07-01

    The determination of sex is an important part of building the biological profile for unknown human remains. Many of the bones traditionally used for the determination of sex are often found fragmented or incomplete in forensic and archaeological cases. The goal of the present research was to derive discriminant function equations from the talus, a preservationally favoured bone, for sexing skeletons from a contemporary Greek population. Nine parameters were measured on 182 individuals (96 males and 86 females) from the University of Athens Human Skeletal Reference Collection. The individuals ranged in age from 20 to 99 years old. The statistical analyses showed that all measured parameters were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 65.2% to 93.4% for the univariate analysis, 90%-96.5% for the direct method and 86.7% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 65.5% to 83.2% and males were most often correctly identified. The talus was shown to be useful for sex determination in the modern Greek population.

  8. Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-12-01

    The skull and post-cranium have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the calcaneus may be used for the determination of sex as it is a preservationally favored bone. The goal of the present research was to derive discriminant function equations from the calcaneus for estimation of sex from a contemporary Greek population. Nine parameters were measured on 198 individuals (103 males and 95 females), ranging in age from 20 to 99 years old, from the University of Athens Human Skeletal Reference Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 70% to 90% for the univariate analysis, 82.9% to 87.5% for the direct method, and 86.2% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 48.6% to 56.1% with males most often identified correctly and females most often misidentified. The calcaneus was shown to be useful for sex determination in the twentieth century Greek population.

  9. Wave Mode Discrimination of Coded Ultrasonic Guided Waves Using Two-Dimensional Compressed Pulse Analysis.

    Science.gov (United States)

    Malo, Sergio; Fateri, Sina; Livadas, Makis; Mares, Cristinel; Gan, Tat-Hean

    2017-07-01

    Ultrasonic guided waves testing is a technique successfully used in many industrial scenarios worldwide. For many complex applications, the dispersive nature and multimode behavior of the technique still poses a challenge for correct defect detection capabilities. In order to improve the performance of the guided waves, a 2-D compressed pulse analysis is presented in this paper. This novel technique combines the use of pulse compression and dispersion compensation in order to improve the signal-to-noise ratio (SNR) and temporal-spatial resolution of the signals. The ability of the technique to discriminate different wave modes is also highlighted. In addition, an iterative algorithm is developed to identify the wave modes of interest using adaptive peak detection to enable automatic wave mode discrimination. The employed algorithm is developed in order to pave the way for further in situ applications. The performance of Barker-coded and chirp waveforms is studied in a multimodal scenario where longitudinal and flexural wave packets are superposed. The technique is tested in both synthetic and experimental conditions. The enhancements in SNR and temporal resolution are quantified as well as their ability to accurately calculate the propagation distance for different wave modes.

  10. A New Method for Improving the Discrimination Power and Weights Dispersion in the Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    S. Kordrostami

    2013-06-01

    Full Text Available The appropriate choice of input-output weights is necessary to have a successful DEA model. Generally, if the number of DMUs i.e., n, is less than number of inputs and outputs i.e., m+s, then many of DMUs are introduced as efficient then the discrimination between DMUs is not possible. Besides, DEA models are free to choose the best weights. For resolving the problems that are resulted from freedom of weights, some constraints are set on the input-output weights. Symmetric weight constraints are a kind of weight constrains. In this paper, we represent a new model based on a multi-criterion data envelopment analysis (MCDEA are developed to moderate the homogeneity of weights distribution by using symmetric weight constrains.Consequently, we show that the improvement of the dispersal of unrealistic input-output weights and the increasing discrimination power for our suggested models. Finally, as an application of the new model, we use this model to evaluate and ranking guilan selected hospitals.

  11. Assessing Reclamation Levels of Coastal Saline Lands with Integrated Stepwise Discriminant Analysis and Laboratory Hyperspectral Data

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content.These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488,530, 670, 880, 1 400, and 1 900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%.Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.

  12. Image analysis of food particles can discriminate deficient mastication of mixed foodstuffs simulating daily meal.

    Science.gov (United States)

    Sugimoto, K; Hashimoto, Y; Fukuike, C; Kodama, N; Minagi, S

    2014-03-01

    Because food texture is regarded as an important factor for smooth deglutition, identification of objective parameters that could provide a basis for food texture selection for elderly or dysphagic patients is of great importance. We aimed to develop an objective evaluation method of mastication using a mixed test food comprising foodstuffs, simulating daily dietary life. The particle size distribution (>2 mm in diameter) in a bolus was analysed using a digital image under dark-field illumination. Ten female participants (mean age ± s.d., 27·6 ± 2·6 years) masticated a mixed test food comprising prescribed amounts of rice, sausage, hard omelette, raw cabbage and raw cucumber with 100%, 75%, 50% and 25% of the number of their masticatory strokes. A single set of coefficient thresholds of 0·10 for the homogeneity index and 1·62 for the particle size index showed excellent discrimination of deficient masticatory conditions with high sensitivity (0·90) and specificity (0·77). Based on the results of this study, normal mastication was discriminated from deficient masticatory conditions using a large particle analysis of mixed foodstuffs, thus showing the possibility of future application of this method for objective decision-making regarding the properties of meals served to dysphagic patients.

  13. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification.

    Science.gov (United States)

    Sun, Yu; Lim, Julian; Meng, Jianjun; Kwok, Kenneth; Thakor, Nitish; Bezerianos, Anastasios

    2014-10-01

    Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

  14. [Discriminant analysis of raw milk adulterated with botanical filling material using near infrared spectroscopy].

    Science.gov (United States)

    Li, Liang; Ding, Wu

    2010-05-01

    In order to find out a fast measure method of adulterated milk based on near infrared spectroscopy, milk adulterated with plant butter, vegetable protein and starch was collected respectively. Using Fourier transform near infrared spectroscopy to scan the samples, the spectrum data were obtained. The samples were scanned in the spectral region between 4 000 and 12 000 cm(-1) by FT-NIR spectrometer with an optic fiber of 2 mm path-length and an InGaAs detector. Then all data were analyzed by principal component analysis combined with Fisher line discriminant analysis (FLDA) and partial least squares (PLS). Results show that the accumulative reliabilities of the first six components were more than 99%, so the first six components were applied as FLDA inputs and the values of the type of milk were applied as the outputs. An adulterated milk qualitative discriminant model based on Fisher line discriminant analysis was developed finally. The result indicated that the accuracy of detection of calibration samples is 97.78%. The unknown test samples were tested by this model and the correct identification rate is 94.44%. Partial least square models for detecting the content of material added to raw milk were set up with good veracity. The predictive correlation coefficient (R2) of calibration sets of milk adulterated with plant butter, vegetable protein and starch are 99.08%, 99.96% and 99.39%, respectively, while the root mean square errors of cross validation (RMSECV) of the three calibration sets are 0.304%, 0.013 5% and 0.060%, respectively. The R2 of validation sets of the three kinds of adulterated milk are 98.50%, 99.94% and 98.50%, respectively, while the root mean square errors of prediction (RMSEP) of the three validation sets are 0.323%, 0.028 8% and 0.068%, respectively. All of these suggested that near infrared spectroscopy has good potential for rapid qualitative and quantitative detection of milk adulterated with botanical filling material.

  15. Combined cluster and discriminant analysis: An efficient chemometric approach in diesel fuel characterization.

    Science.gov (United States)

    Novák, Márton; Palya, Dóra; Bodai, Zsolt; Nyiri, Zoltán; Magyar, Norbert; Kovács, József; Eke, Zsuzsanna

    2017-01-01

    Combined cluster and discriminant analysis (CCDA) as a chemometric tool in compound specific isotope analysis of diesel fuels was studied. The stable carbon isotope ratios (δ(13)C) of n-alkanes in diesel fuel can be used to characterize or differentiate diesels originating from different sources. We investigated 25 diesel fuel samples representing 20 different brands. The samples were collected from 25 different service stations in 11 European countries over a 2 year period. The n-alkane fraction of diesel fuels was separated using solid-state urea clathrate formation combined with silica gel fractionation. The stable carbon isotope ratios of C10-C24 n-alkanes were measured with gas chromatography-isotope ratio mass spectrometry (GC-IRMS) using perdeuterated n-alkanes as internal standards. Beside the 25 samples one additional diesel fuel was prepared and measured three times to get totally homogenous samples in order to test the performance of our analytical and statistical routine. Stable isotope ratio data were evaluated with hierarchical cluster analysis (HCA), principal component analysis (PCA) and CCDA. CCDA combines two multivariate data analysis methods hierarchical cluster analysis with linear discriminant analysis (LDA). The main idea behind CCDA is to compare the goodness of preconceived (based on the sample origins) and random groupings. In CCDA all the samples were compared pairwise. The results for the parallel sample preparations showed that the analytical procedure does not have any significant effect on the δ(13)C values of n-alkanes. The three parallels proved to be totally homogenous with CCDA. HCA and PCA can be useful tools when the examining of the relationship among several samples is in question. However, these two techniques cannot be always decisive on the origin of similar samples. The initial hypothesis that all diesel fuel samples are considered chemically unique was verified by CCDA. The main advantage of CCDA is that it gives an

  16. Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications.

    Science.gov (United States)

    Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E

    2010-03-01

    Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.

  17. Content Analysis of Survey Feedback Meetings: An Evaluation Tool

    Science.gov (United States)

    1975-05-01

    AD-AO10 210 CONTENT ANALYSIS OF SURVEY FEEDBACK MEETINGS: AN EVALUATION TOOL Patricia A. Pecorella Michigan University Prepared for: Office of Naval...RECIPIENIT’S CATALOG NUMSEA 4, TITLE (#wtd$4bIII*) 5.&TYJ F REPORT 6PEFlIOg COVERlEO Content Analysis of Survey Feedback Meetings: Technical Report An...Ratings Coder Re1liability Evaluation Supervisory Leadership Consultant Roles Problem-Identification Survey Feedback Content Analysis Problem-Solving

  18. A longitudinal analysis of Hispanic youth acculturation and cigarette smoking: the roles of gender, culture, family, and discrimination.

    Science.gov (United States)

    Lorenzo-Blanco, Elma I; Unger, Jennifer B; Ritt-Olson, Anamara; Soto, Daniel; Baezconde-Garbanati, Lourdes

    2013-05-01

    Risk for smoking initiation increases as Hispanic youth acculturate to U.S. society, and this association seems to be stronger for Hispanic girls than boys. To better understand the influence of culture, family, and everyday discrimination on cigarette smoking, we tested a process-oriented model of acculturation and cigarette smoking. Data came from Project RED (Reteniendo y Entendiendo Diversidad para Salud), which included 1,436 Hispanic students (54% girls) from Southern California. We used data from 9th to 11th grade (85% were 14 years old, and 86% were U.S. born) to test the influence of acculturation-related experiences on smoking over time. Multigroup structural equation analysis suggested that acculturation was associated with increased familismo and lower traditional gender roles, and enculturation was linked more with familismo and respeto. Familismo, respeto, and traditional gender roles were linked with lower family conflict and increased family cohesion, and these links were stronger for girls. Familismo and respeto were further associated with lower discrimination. Conversely, fatalismo was linked with worse family functioning (especially for boys) and increased discrimination in both the groups. Discrimination was the only predictor of smoking for boys and girls. In all, the results of the current study indicate that reducing discrimination and helping youth cope with discrimination may prevent or reduce smoking in Hispanic boys and girls. This may be achieved by promoting familismo and respeto and by discouraging fatalistic beliefs.

  19. Design Effects and the Analysis of Survey Data.

    Science.gov (United States)

    Folsom, Ralph E.; Williams, Rick L.

    The National Assessment of Educational Progress (NAEP), like most large national surveys, employs a complex stratified multistage unequal probability sample. The design provides a rigorous justification for extending survey results to the entire U.S. target population. Developments in the analysis of data from complex surveys which provide a…

  20. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Science.gov (United States)

    Pagliaro, Antonio; D'Alí Staiti, G.; D'Anna, F.

    2011-03-01

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  1. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Pagliaro, Antonio, E-mail: pagliaro@ifc.inaf.it [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Ali Staiti, G. [Universita degli Studi di Palermo, Dipartimento di Fisica e Tecnologie Relative, Viale delle Scienze, Edificio 18, 90128 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Anna, F. [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy)

    2011-03-15

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  2. GSK-3β polymorphism discriminates bipolar disorder and schizophrenia: a systematic meta-analysis.

    Science.gov (United States)

    Tang, Hui; Shen, Na; Jin, Huijuan; Liu, Dan; Miao, Xiaoping; Zhu, Ling-Qiang

    2013-12-01

    Glycogen synthase kinase 3 (GSK-3) is a well-known conserved and ubiquitous protein kinase and playing a pivotal role in neurodevelopment, neurogenesis, learning/memory, and neuronal cell death. Dysfunction of GSK-3 had been seen in multiple neurodegenerative and psychiatric diseases. Bipolar disorder and schizophrenia are two common psychiatric diseases first occur in adolescence or young adulthood. They share similar risk genes as well as clinical symptoms, which make it is difficult to be discriminated from each other. Here, by using meta-analysis we reported that glycogen synthase kinase 3β promoter inactive mutant rs334558 may contribute to the development of schizophrenia not bipolar disorder. This might be used to distinguish these two diseases.

  3. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    CERN Document Server

    Khan, Aamir; Khurshid, Aasim; Akram, Adeel

    2012-01-01

    Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  4. External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers

    Directory of Open Access Journals (Sweden)

    K.Vijayarekha

    2012-12-01

    Full Text Available Linear Discriminant Analysis (LDA is one technique for transforming raw data into a new feature space in which classification can be carried out more robustly. It is useful where the within-class frequencies are unequal. This method maximizes the ratio of between-class variance to the within-class variance in any particular data set and the maximal separability is guaranteed. LDA clustering models are used to classify object into different category. This study makes use of LDA for clustering the features obtained for the citrus fruit images taken in five different domains. Sub-windows of size 40x40 are cropped from the citrus fruit images having defects such as pitting, splitting and stem end rot. Features are extracted in four domains such as statistical features, fourier transform based features, discrete wavelet transform based features and stationary wavelet transform based features. The results of clustering and classification using LDA and ANN classifiers are reported

  5. Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images

    Directory of Open Access Journals (Sweden)

    R. Rajalakshmi

    2015-03-01

    Full Text Available Face recognition is the process of categorizing a person in an image by evaluating with a known face image library. The pose and illumination variations are two main practical confronts for an automatic face recognition system. This study proposes a novel face recognition algorithm known as Efficient Discriminant Component Analysis (EDCA for face recognition under varying poses and illumination conditions. This EDCA algorithm overcomes the high dimensionality problem in the feature space by extracting features from the low dimensional frequency band of the image. It combines the features of both LDA and PCA algorithms and these features are used in the training set and is classified using Support Vector Machine classifier. The experiments were performed on the CMU-PIE datasets. The experimental results show that the proposed algorithm produces a higher recognition rate than the existing LDA and PCA based face recognition techniques.

  6. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    . Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3......Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods...

  7. On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

    KAUST Repository

    Zollanvari, Amin

    2013-05-24

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  8. On Kolmogorov Asymptotics of Estimators of the Misclassification Error Rate in Linear Discriminant Analysis.

    Science.gov (United States)

    Zollanvari, Amin; Genton, Marc G

    2013-08-01

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  9. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  10. Study of major volatiles in wines and discriminant analysis applied to classification according to region.

    Science.gov (United States)

    Huerta-Díaz-Regañon, M D; Salinas Fernández, M R; Masoud, T

    1997-12-01

    The major volatiles of eighty eight wines (white, rosè and red) from Madrid were studied. The samples came from the three districts forming the "Vinos de Madrid" DO (Denominación de Origen) region: Arganda, Navalcarnero and San Martín, and were analyzed by gas chromatography. The resulting data were treated by Stepwise Discriminant Analysis (SDA) in order to ascertain the efficacity of these compounds in classifying the wines according to their geographical origin. The results confirm that the above components were of little use in classifying the red and white wines and, although a correct classification percentage of 90.91% was obtained for the rosés when all the variables were used, this too was considered unsatisfactory.

  11. Sex determination using discriminant analysis of the medial and lateral condyles of the femur in Koreans.

    Science.gov (United States)

    Kim, Deog-Im; Kwak, Dai-Soon; Han, Seung-Ho

    2013-12-10

    The proximal and distal parts of the femur show the differences between the sexes. Head diameter and the breadth of the epicondyle of the femur are known to distinguish males from females. The proximal end of the femur is studied to determine sex using discriminant analysis but; its distal end is not done. This study aims to develop an equation specific to Koreans by using the medial and lateral condyles of the femur, and to demonstrate the usefulness of equations for specific population groups. We used three-dimensional images from 202 Korean femurs. Twelve variables were measured with a computer program after the femurs were in alignment. Eleven variables showed a statistically significant difference between the sexes (Psex determination in situations where the skull and pelvis are missing and part of the femur is available. The study also demonstrates the need for different equations for different population groups.

  12. The Importance of the Discriminant Analysis for the Evolution of the Equity Prices

    Directory of Open Access Journals (Sweden)

    Dinca G.

    2014-12-01

    Full Text Available This paper aims to show the correlation between the results obtained using the discriminant analysis and the evolution of stock prices of listed Romanian companies. For this purpose, we have carried out the research on a sample of 32 issuers from categories I and II of the Bucharest Stock Exchange, pertaining to nine economic sectors, for the period 2010- 2012. Our study is based on the Anghel prediction model of bankruptcy, using the stock prices of the 32 listed companies from the first and the last day of trading for each year examined. The results obtained by applying the prediction model allow the classification of issuers into potential bankrupt and non-bankrupt firms and help investors take appropriate decisions on the stock market.

  13. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods....... Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3...

  14. Quantitative Classification of Quartz by Laser Induced Breakdown Spectroscopy in Conjunction with Discriminant Function Analysis

    Directory of Open Access Journals (Sweden)

    A. Ali

    2016-01-01

    Full Text Available A responsive laser induced breakdown spectroscopic system was developed and improved for utilizing it as a sensor for the classification of quartz samples on the basis of trace elements present in the acquired samples. Laser induced breakdown spectroscopy (LIBS in conjunction with discriminant function analysis (DFA was applied for the classification of five different types of quartz samples. The quartz plasmas were produced at ambient pressure using Nd:YAG laser at fundamental harmonic mode (1064 nm. We optimized the detection system by finding the suitable delay time of the laser excitation. This is the first study, where the developed technique (LIBS+DFA was successfully employed to probe and confirm the elemental composition of quartz samples.

  15. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Science.gov (United States)

    Portillo-Portillo, Jose; Leyva, Roberto; Sanchez, Victor; Sanchez-Perez, Gabriel; Perez-Meana, Hector; Olivares-Mercado, Jesus; Toscano-Medina, Karina; Nakano-Miyatake, Mariko

    2016-01-01

    This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy. PMID:28025484

  16. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Jose Portillo-Portillo

    2016-12-01

    Full Text Available This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA. The framework, which employs gait energy images (GEIs, creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

  17. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    Directory of Open Access Journals (Sweden)

    Aamir Khan

    2011-11-01

    Full Text Available Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  18. Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis.

    Science.gov (United States)

    Hargrove, Levi J; Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

    2010-02-01

    This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error ( p myoelectric control system ( p myoelectric control system which is robust, easily configured, and highly usable.

  19. Discrimination between landmine and mine-like targets using wavelets and spectral analysis

    Science.gov (United States)

    Mohana, Mahmoud A.; Abbas, Abbas M.; Gomaa, Mohamed L.; Ebrahim, Shereen M.

    2013-06-01

    Landmine is an explosive apparatus hidden in or on the ground, which blows up when a person or vehicle passes over it. Egypt is one of the countries suffering due to the unexploded ordnance (UXO). Around 2 million UXO are present in the Egyptian soil especially at Al-Alameen province, north of the western desert. Detection of buried landmines is a problem of military and humanitarian importance. Ground penetrating radar (GPR) is a powerful and non-destructive geophysical approach with a wide range of advantages in the field of landmine inspection. In the present paper, we apply different simulation models with Vivaldi antenna and mine-like targets by using the CST Microwave studio program. The field work is carried out by using a GPR device of model SIR 2000 from GSSI (Geophysical Survey Systems Incorporation) connected to 900 MHz antenna where the targets were buried in sand soil. Depending on the fact that the receiving powers (reflected, refracted and scattered) from the different materials are different, we study the spectral power densities for the received power from the different targets. The techniques used in this study are: direct fast Fourier transform, short time Fourier transform (spectrogram), wavelets transform and denoising techniques. Our results ought to be considered as finger prints for different scanned targets during this work. So we can discriminate between landmines and mine-like targets.

  20. Discrimination between landmine and mine-like targets using wavelets and spectral analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud A. Mohana

    2013-06-01

    Ground penetrating radar (GPR is a powerful and non-destructive geophysical approach with a wide range of advantages in the field of landmine inspection. In the present paper, we apply different simulation models with Vivaldi antenna and mine-like targets by using the CST Microwave studio program. The field work is carried out by using a GPR device of model SIR 2000 from GSSI (Geophysical Survey Systems Incorporation connected to 900 MHz antenna where the targets were buried in sand soil. Depending on the fact that the receiving powers (reflected, refracted and scattered from the different materials are different, we study the spectral power densities for the received power from the different targets. The techniques used in this study are: direct fast Fourier transform, short time Fourier transform (spectrogram, wavelets transform and denoising techniques. Our results ought to be considered as finger prints for different scanned targets during this work. So we can discriminate between landmines and mine-like targets.

  1. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

    Science.gov (United States)

    Acquah, Gifty E; Via, Brian K; Billor, Nedret; Fasina, Oladiran O; Eckhardt, Lori G

    2016-08-27

    As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest

  2. Neighbor Class Linear Discriminate Analysis%近邻类鉴别分析方法

    Institute of Scientific and Technical Information of China (English)

    王言伟; 丁晓青; 刘长松

    2012-01-01

    提出一种近邻类鉴别分析方法,线性鉴别分析是该方法的一个特例.线性鉴别分析通过最大化类间散度同时最小化类内散度寻找最佳投影,其中类间散度是所有类之间散度的总体平均;而近邻类鉴别分析中类间散度定义为各个类与其k个近邻类之间的平均散度.该方法通过选取适当的近邻类数,能够缓解线性鉴别降维后造成的部分类的重叠.实验结果表明近邻类鉴别分析方法性能稳定且优于传统的线性鉴别分析.%A method of neighbor class linear discriminant analysis (NCLDA) is proposed. Linear discriminant analysis ( LDA) is a special case of this method. LDA finds the optimal projections by maximum between-class scatter while by minimum within-class scatter. The between-class scatter is an average over divergences among all classes. In NCLDA, between-class scatter is defined as average divergences between one class and its k nearest neighbor classes. By selecting proper numbers of neighbor class, NCLDA alleviates overlaps among classes caused by LDA. The experimental results show that the proposed NCLDA is robust and outperforms LDA.

  3. Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

    OpenAIRE

    Karoui, Romdhane; Hammami, Moncef; Rouissi, Hamadi; Blecker, Christophe

    2011-01-01

    Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000-2800, 1700-1500 and 1500-900 cm(-1)), the cl...

  4. Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

    Science.gov (United States)

    Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

    1984-01-01

    An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

  5. Bibliometric Analysis of Current Web Survey Research in China

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qian; SHAO Peiji; FANG Jiaming

    2008-01-01

    In recent years, with the advancement of information technology and its application in survey activities, web surveys have not only greatly developed, but have also encountered many problems in China. An analysis of domestic research is important for better understanding of web surveys, to guide further research and application. This paper gives a bibliometric analysis of 120 domestic articles on web surveys from 1998 to 2006, on publication growth, author and organization distribution, journal distribution, and research subjects. Research on web surveys in China should make progress comparable with research abroad in comparative studies, specific studies, and technical application studies.

  6. Discrimination and support from friends and family members experienced by people with mental health problems: findings from an Australian national survey.

    Science.gov (United States)

    Morgan, Amy J; Reavley, N J; Jorm, A F; Beatson, R

    2017-05-05

    To investigate the scope and nature of discrimination and positive treatment experienced by adults with mental health problems from their friends and family in a population-based survey. An Australian telephone-survey of 5220 adults included 1381 individuals who reported a mental health problem or scored high on a screening questionnaire. Respondents were interviewed about their experience of discrimination and positive treatment from their friends, spouse and other family members. Descriptions of experiences were content-analysed to identify key characteristics. Mental health diagnoses were primarily depression or anxiety disorders, and just over half had received treatment in the last 12 months. Positive treatment from family and friends was far more common than discrimination, reported by 74.1% of respondents. This was primarily characterised by providing emotional support and maintaining contact, as well as checking on their mental health and being a good listener. Nevertheless, discriminatory behaviours from friends and family were reported by 25.8% of respondents, with reducing or cutting contact being by far the most common. Friends and family also commonly dismissed that mental illness was real or caused suffering and showed a lack of understanding about mental health problems or treatments and how they can impact behaviour and functioning. This nationally representative study of real life experiences highlights the potential for harm or benefit from a person's social support network. Despite positive experiences being common, there is an ongoing need to reduce mental illness stigma and improve understanding of how to support a loved one with a mental health problem.

  7. Whole-genome single-nucleotide-polymorphism analysis for discrimination of Clostridium botulinum group I strains.

    Science.gov (United States)

    Gonzalez-Escalona, Narjol; Timme, Ruth; Raphael, Brian H; Zink, Donald; Sharma, Shashi K

    2014-04-01

    Clostridium botulinum is a genetically diverse Gram-positive bacterium producing extremely potent neurotoxins (botulinum neurotoxins A through G [BoNT/A-G]). The complete genome sequences of three strains harboring only the BoNT/A1 nucleotide sequence are publicly available. Although these strains contain a toxin cluster (HA(+) OrfX(-)) associated with hemagglutinin genes, little is known about the genomes of subtype A1 strains (termed HA(-) OrfX(+)) that lack hemagglutinin genes in the toxin gene cluster. We sequenced the genomes of three BoNT/A1-producing C. botulinum strains: two strains with the HA(+) OrfX(-) cluster (69A and 32A) and one strain with the HA(-) OrfX(+) cluster (CDC297). Whole-genome phylogenic single-nucleotide-polymorphism (SNP) analysis of these strains along with other publicly available C. botulinum group I strains revealed five distinct lineages. Strains 69A and 32A clustered with the C. botulinum type A1 Hall group, and strain CDC297 clustered with the C. botulinum type Ba4 strain 657. This study reports the use of whole-genome SNP sequence analysis for discrimination of C. botulinum group I strains and demonstrates the utility of this analysis in quickly differentiating C. botulinum strains harboring identical toxin gene subtypes. This analysis further supports previous work showing that strains CDC297 and 657 likely evolved from a common ancestor and independently acquired separate BoNT/A1 toxin gene clusters at distinct genomic locations.

  8. Rapid discrimination of Salmonella isolates by single-strand conformation polymorphism analysis.

    Science.gov (United States)

    Al-Adhami, Batol H; Huby-Chilton, Florence; Blais, Burton W; Martinez-Perez, Amalia; Chilton, Neil B; Gajadhar, Alvin A

    2008-10-01

    A molecular typing technique was developed for the differentiation of Salmonella isolates based on single-strand conformation polymorphism (SSCP) analysis of amplicons generated by PCR. Amplicons from parts of the fimA (both the 5' and 3' ends), mdh, invA, and atpD genes were generated separately from a panel of Salmonella strains representing Salmonella bongori, and four subspecies and 17 serovars of Salmonella enterica. These amplicons were subjected to SSCP analysis for differentiation of the salmonellae on the basis of different conformational forms arising due to nucleotide sequence variations in the target genes. Several distinct SSCP banding patterns (a maximum of 14 each for atpD and fimA 3' end) were observed with this panel of Salmonella strains for amplicons generated from each target gene. The best discrimination of Salmonella subspecies and serovar was achieved from the SSCP analysis of a combination of at least three gene targets: atpD, invA, and either mdh or fimA 3' end. This demonstrates the applicability of SSCP analysis as an important additional method to classical typing approaches for the differentiation of foodborne Salmonella isolates. SSCP is simple to perform and should be readily transferable to food microbiology laboratories with basic PCR capability.

  9. The Analysis of the Ethnical Discrimination on the Manpower’s Market under the Economical Crisis

    Directory of Open Access Journals (Sweden)

    Mihaela Hrisanta DOBRE

    2012-06-01

    Full Text Available Discrimination means any difference, exclusion, restriction, preference or different treatment that brings forth disadvantages for a person or a group as compared to other ones that are in similar situations. The reasons on which discrimination is based can be various, such as race, nationality, ethnics, religion, gender, sexual orientation, language, age, disabilities etc. and in this case we talk about multiple discrimination. In Romania the main forms of discrimination are linked to ethnics and to sexual appurtenance. Within this column we analysed the discrimination amongst the Romany ethnics people, according to a statistical investigation (Access onto the Labour Market – A Chance for You, the research goal being to identify the answer to the following questions: Is there any discrimination inside the Romany ethnic group? What is the correlation between their level of education and their income? What is the correlation between the level of education of the parents and the respondent’s?

  10. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

    Science.gov (United States)

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2013-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311

  11. Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options

    OpenAIRE

    Quanbao, Jiang; Shuzhuo, Li; Marcus W., Feldman

    2011-01-01

    The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female chil...

  12. A Brief Analysis of Discrimination in Language Classroom--from the Perspecti ve of Soci oli ngui sti cs

    Institute of Scientific and Technical Information of China (English)

    曹志勇

    2014-01-01

    It is widely acknowledged that sociolinguistics is the study of language in certain context concerned with our soci-ety. Sociolinguistics and linguistics are intrinsically related to each other, but there has been difference as well. Linguistics research deals with language system itself, which belongs to the micro lev-el on the one hand; many phenomena reflect discrimination in language classroom, these discrimination are caused by social fac-tors to a certain degree. This paper makes a brief analysis of dis-crimination in language classroom from the perspective of socio-linguistics, which deals with many issues such as depiction of lan-guage discrimination、analysis of phenomenon and accordingly-solved measures.

  13. SPORTS ORGANIZATIONS MANAGEMENT IMPROVEMENT: A SURVEY ANALYSIS

    Directory of Open Access Journals (Sweden)

    Alin Molcut

    2015-07-01

    Full Text Available Sport organizations exist to perform tasks that can only be executed through cooperative effort, and sport management is responsible for the performance and success of these organizations. The main of the paper is to analyze several issues of management sports organizations in order to asses their quality management. In this respect a questionnaire has been desingned for performing a survey analysis through a statistical approach. Investigation was conducted over a period of 3 months, and have been questioned a number of managers and coaches of football, all while pursuing an activity in football clubs in the counties of Timis and Arad, the level of training for children and juniors. The results suggest that there is a significant interest for the improvement of management across teams of children and under 21 clubs, emphasis on players' participation and rewarding performance. Furthermore, we can state that in the sports clubs there is established a vision and a mission as well as the objectives of the club's general refers to both sporting performance, and financial performance.

  14. Classification of the long-QT syndrome based on discriminant analysis of T-wave morphology

    DEFF Research Database (Denmark)

    Struijk, Johannes; Kanters, J.K.; Andersen, Mads Peter

    2006-01-01

    been shown to be useful discriminators, but no single ECG parameter has been sufficient to solve the diagnostic problem. In this study we present a method for discrimination among persons with a normal genotype and those with mutations in the KCNQ1 (KvLQT1 or LQT1) and KCNH2 (HERG or LQT2) genes...... on the basis of parameters describing T-wave morphology in terms of duration, asymmetry, flatness and amplitude. Discriminant analyses based on 4 or 5 parameters both resulted in perfect discrimination in a learning set of 36 subjects. In both cases cross-validation of the resulting classifiers showed...

  15. Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers

    Energy Technology Data Exchange (ETDEWEB)

    Awasthi, Rishi [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Radiodiagnosis, Lucknow (India); Rathore, Ram K.S.; Sahoo, Prativa [Indian Institute of Technology, Department of Mathematics and Statistics, Kanpur (India); Soni, Priyanka; Husain, Nuzhat [Chhatrapati Sahuji Maharj Medical University, Department of Pathology, Lucknow (India); Awasthi, Ashish; Pandey, Chandra M. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Biostatistics, Lucknow (India); Behari, Sanjay; Singh, Rohit K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Neurosurgery, Lucknow (India); Gupta, Rakesh K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, MR Section, Department of Radiodiagnosis, Lucknow, UP (India)

    2012-03-15

    The purpose of the present study was to look for the possible predictors which might discriminate between high- and low-grade gliomas by pooling dynamic contrast-enhanced (DCE)-perfusion derived indices and immunohistochemical markers. DCE-MRI was performed in 76 patients with different grades of gliomas. Perfusion indices, i.e., relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), permeability (k{sup trans} and k{sub ep}), and leakage (v{sub e}) were quantified. MMP-9-, PRL-3-, HIF-1{alpha}-, and VEGF-expressing cells were quantified from the excised tumor tissues. Discriminant function analysis using these markers was used to identify discriminatory variables using a stepwise procedure. To look for correlations between immunohistochemical parameters and DCE metrics, Pearson's correlation coefficient was also used. A discriminant function for differentiating between high- and low-grade tumors was constructed using DCE-MRI-derived rCBV, k{sub ep}, and v{sub e}. The form of the functions estimated are ''D{sub 1} = 0.642 x rCBV + 0.591 x k{sub ep} - 1.501 x v{sub e} - 1.550'' and ''D{sub 2} = 1.608 x rCBV + 3.033 x k{sub ep} + 5.508 x v{sub e} - 8.784'' for low- and high-grade tumors, respectively. This function classified overall 92.1% of the cases correctly (89.1% high-grade tumors and 100% low-grade tumors). In addition, VEGF expression correlated with rCBV and rCBF, whereas MMP-9 expression correlated with k{sub ep}. A significant positive correlation of HIF-1{alpha} with rCBV and VEGF expression was also found. DCE-MRI may be used to differentiate between high-grade and low-grade brain tumors non-invasively, which may be helpful in appropriate treatment planning and management of these patients. The correlation of its indices with immunohistochemical markers suggests that this imaging technique is useful in tissue characterization of gliomas. (orig.)

  16. Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

  17. Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

    Directory of Open Access Journals (Sweden)

    Salim Aijaz Bhat

    2014-01-01

    Full Text Available Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA. The first spatial discriminant function (DF accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

  18. An Improved Method for Discriminating ECG Signals using Typical Nonlinear Dynamic Parameters and Recurrence Quantification Analysis in Cardiac Disease Therapy.

    Science.gov (United States)

    Tang, M; Chang, C Q; Fung, P C W; Chau, K T; Chan, F H Y

    2005-01-01

    The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.

  19. Einstein Slew Survey: Data analysis innovations

    Science.gov (United States)

    Elvis, Martin S.; Plummer, David; Schachter, Jonathan F.; Fabbiano, G.

    1992-01-01

    Several new methods were needed in order to make the Einstein Slew X-ray Sky Survey. The innovations which enabled the Slew Survey to be done are summarized. These methods included experimental approach to large projects, parallel processing on a LAN, percolation source detection, minimum action identifications, and rapid dissemination of the whole data base.

  20. Multivariate analysis of dermatoglyphics of severe mental retardates: an application of the constellation graphical method for discriminant analysis.

    Directory of Open Access Journals (Sweden)

    Wakita,Yoshiharu

    1988-06-01

    Full Text Available We studied the dermatoglyphics of 353 severe mental retardates (excluding those with chromosomal abnormalities and major limb malformations, using multivariate analysis, to determine how early intrauterine factors are related to the etiology of mental retardation. First, dermatoglyphics were compared between 140 individuals with undefined prenatal factors and 700 normal controls. After 6 and 9 dermatoglyphic traits were chosen as discriminative variables for males and females, respectively, the data were subjected separately for each sex to the constellation graphical method for discriminant analysis. The same formula as obtained in the idiopathic group was subsequently applied to data from cases in other etiological categories. When the misclassification rate was 0.03, the rates of correct classification of the male patients into the etiological categories of undefined prenatal, defined prenatal, perinatal, postnatal and unknown (no anamnestic data available categories were 19.7% (13/66, 20.0% (3/15, 8.8% (5/57, 5.0% (1/20 and 7.7% (2/26, while the correct classification rates of females were 24.3% (18/74, 42.1% (8/19, 18.9% (7/37, 5.1% (1/16 and 13.0% (3/23, respectively. The results suggest that early intrauterine factors such as those producing dermatoglyphic deviations may contribute to the pathogenesis of severe mental retardation not only in patients with undefined prenatal etiological factors but also in those with perinatal factors, especially those of the female sex.

  1. A Differential Item Functional Analysis by Age of Perceived Interpersonal Discrimination in a Multi-racial/ethnic Sample of Adults.

    Science.gov (United States)

    Owens, Sherry; Kristjansson, Alfgeir L; Hunte, Haslyn E R

    2015-11-05

    We investigated whether individual items on the nine item William's Perceived Everyday Discrimination Scale (EDS) functioned differently by age (racial groups in the United States: Asians (n=2,017); Hispanics (n=2,688); Black Caribbeans (n=1,377); African Americans (n=3,434); and Whites (n=854). We used data from the 2001-2003 National Survey of American Lives and the 2001-2003 National Latino and Asian Studies. Multiple-indicator, multiple-cause models (MIMIC) were used to examine differential item functioning (DIF) on the EDS by age within each racial/ethnic group. Overall, Asian and Hispanic respondents reported less discrimination than Whites; on the other hand, African Americans and Black Caribbeans reported more discrimination than Whites. Regardless of race/ethnicity, the younger respondents (aged discrimination than the older respondents (aged ≥ 45 years). In terms of age by race/ethnicity, the results were mixed for 19 out of 45 tests of DIF (40%). No differences in item function were observed among Black Caribbeans. "Being called names or insulted" and others acting as "if they are afraid" of the respondents were the only two items that did not exhibit differential item functioning by age across all racial/ethnic groups. Overall, our findings suggest that the EDS scale should be used with caution in multi-age multi-racial/ethnic samples.

  2. Discrimination versus specialization: a survey of economic studies on sexual orientation, gender and earnings in the United States.

    Science.gov (United States)

    Schmitt, Elizabeth Dunne

    2008-01-01

    Several studies examine the link between sexual orientation and earnings using large data sets that distinguish sexual orientation through questions about sexual behavior and/or by allowing respondents to self-identify as part of a same-sex cohabitating couple. After controlling for other earnings-related characteristics these studies generally show an earnings penalty for gay/bisexual men relative to heterosexual men and an earnings premium for lesbian/bisexual women relative to heterosexual women. Explanations for this gender disparity include gender differences in sexual orientation discrimination, greater labor force attachment for lesbian/bisexual women, and the effects of the overall gender earnings gap.

  3. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  4. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  5. A nutritional risk screening model for patients with liver cirrhosis established using discriminant analysis

    Directory of Open Access Journals (Sweden)

    ZHU Binghua

    2017-06-01

    Full Text Available ObjectiveTo establish a nutritional risk screening model for patients with liver cirrhosis using discriminant analysis. MethodsThe clinical data of 273 patients with liver cirrhosis who were admitted to Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from August 2015 to March 2016 were collected. Body height, body weight, upper arm circumference, triceps skinfold thickness, subscapular skinfold thickness, and hand grip strength were measured and recorded, and then body mass index (BMI and upper arm muscle circumference were calculated. Laboratory markers including liver function parameters, renal function parameters, and vitamins were measured. The patients were asked to complete Nutritional Risk Screening 2002 and Malnutrition Universal Screening Tool (MUST, and a self-developed nutritional risk screening pathway was used for nutritional risk classification. Observation scales of the four diagnostic methods in traditional Chinese medicine were used to collect patients′ symptoms and signs. Continuous data were expressed as mean±SD (x±s; an analysis of variance was used for comparison between multiple groups, and the least significant difference t-test was used for further comparison between two groups. Discriminant analysis was used for model establishment, and cross validation was used for model verification. ResultsThe nutritional risk screening pathway for patients with liver cirrhosis was used for the screening of respondents, and there were 49 patients (17.95% in non-risk group, 49 (17.95% in possible-risk group, and 175 (64.10% in risk group. The distance criterion function was used to establish the nutritional risk screening model for patients with liver cirrhosis: D1=-11.885+0.310×BMI+0150×MAC+0.005×P-Alb-0.001×Vit B12+0.103×Vit D-0.89×ascites-0.404×weakness-0.560×hypochondriac pain+0035×dysphoria with feverish sensation (note: if a patient has ascites, weakness, hypochondriac pain

  6. Lacunarity analysis of spaceborne radar image texture for rock unit discrimination

    Science.gov (United States)

    Dong, Pinliang

    Fractal geometry has led to new understanding of many natural objects and phenomena. As a scale-dependent measure, lacunarity can be used to discriminate different textures that may not be differentiated by fractal dimension. Based on a differential box counting method and a gliding-box algorithm, a new lacunarity estimation method is developed for texture analysis of digital images, and a "Lacunarity Analysis" extension built for ArcView (ESRI) geographical information system software. To reveal the directional properties of textures, the directionality of lacunarity is also defined. The new lacunarity measure is evaluated through quantitative comparison with the Voss lacunarity, the binary lacunarity, the grey level cooccurrence matrix (GLCM) based texture measures (homogeneity, contrast, dissimilarity, entropy), the fractal dimension, and the min-max operator using Brodatz textures. The results from Brodatz textures suggest that the new lacunarity estimation method for grey-scale images provides more accurate texture measurements than the above-mentioned fractal-based and statistical texture measures. In comparison with the Voss lacunarity, the fractal dimension, and the GLCM-based texture measures, the new lacunarity measure is then applied to dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR-C), and C-band HH polarization Radarsat images of two imaging modes for rock unit discrimination in a study area between California and Arizona, USA. Using textural analysis of 36 SIR-C and Radarsat sub-images and classification accuracy assessment of the combined Landsat Thematic Mapper (TM) images and spaceborne radar textural feature images, it has been demonstrated that the new lacunarity measure outperformed other texture measures in comparison, and the L-band HH polarization SIR-C image provides more textural information of the rock units compared with the Radarsat and other SIR-C radar images used in this study. The study shows that

  7. Regularized discriminate analysis for breast mass detection on full field digital mammograms

    Science.gov (United States)

    Wei, Jun; Sahiner, Berkman; Zhang, Yiheng; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Zhou, Chuan; Ge, Jun; Wu, Yi-Ta

    2006-03-01

    In computer-aided detection (CAD) applications, an important step is to design a classifier for the differentiation of the abnormal from the normal structures. We have previously developed a stepwise linear discriminant analysis (LDA) method with simplex optimization for this purpose. In this study, our goal was to investigate the performance of a regularized discriminant analysis (RDA) classifier in combination with a feature selection method for classification of the masses and normal tissues detected on full field digital mammograms (FFDM). The feature selection scheme combined a forward stepwise feature selection process and a backward stepwise feature elimination process to obtain the best feature subset. An RDA classifier and an LDA classifier in combination with this new feature selection method were compared to an LDA classifier with stepwise feature selection. A data set of 130 patients containing 260 mammograms with 130 biopsy-proven masses was used. All cases had two mammographic views. The true locations of the masses were identified by experienced radiologists. To evaluate the performance of the classifiers, we randomly divided the data set into two independent sets of approximately equal size for training and testing. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained by averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 80% and 70% on the test set, our RDA classifier with the new feature selection scheme achieved an FP rate of 1.8, 1.1, and 0.6 FPs/image, respectively, compared to 2.1, 1.4, and 0.8 FPs/image with stepwise LDA with simplex optimization. Our results indicate that RDA in combination with the sequential forward inclusion

  8. Differential diagnosis of posterior fossa brain tumors: Multiple discriminant analysis of Tl-SPECT and FDG-PET.

    Science.gov (United States)

    Yamauchi, Moritaka; Okada, Tomohisa; Okada, Tsutomu; Yamamoto, Akira; Fushimi, Yasutaka; Arakawa, Yoshiki; Miyamoto, Susumu; Togashi, Kaori

    2017-08-01

    This study investigated the combined capability of thallium-201 (Tl)-SPECT and fluorine-18-fluoro-deoxy-glucose (FDG)-PET for differential diagnosis of posterior fossa brain tumors using multiple discriminant analysis.This retrospective study was conducted under approval of the institutional review board. In the hospital information system, 27 patients with posterior fossa intra-axial tumor between January 2009 and June 2015 were enrolled and grouped as the following 7 entities: low grade glioma (LGG) 6, anaplastic astrocytoma (AA) 2, glioblastoma (GBM) 3, medulloblastoma (MB) 3, hemangioblastoma (HB) 6, metastatic tumor (Mets) 3, and malignant lymphoma (ML) 4. Tl and FDG uptakes were measured at the tumors and control areas, and several indexes were derived. Using indexes selected by the stepwise method, discriminant analysis was conducted with leave-one-out cross-validation.The predicted accuracy for tumor classification was 70.4% at initial analysis and 55.6% at cross-validation to differentiate 7 tumor entities. HB, LGG, and ML were well-discriminated, but AA was located next to LGG. GBM, MB, and Mets largely overlapped and could not be well distinguished even applying multiple discriminant analysis. Correct classification in the original and cross-validation analyses was 44.4% and 33.3% for Tl-SPECT and 55.6% and 48.1% for FDG-PET.

  9. Improvement Distance Discriminant Analysis Method%改进的距离判别分析法

    Institute of Scientific and Technical Information of China (English)

    黄利文

    2011-01-01

    在不对判别变量进行处理的条件下,对传统的距离判别方法进行改进,提出一种新的判别方法,试图解决复杂球形数据的判别问题,以提高判别的正确率.通过实例表明,该方法的判别效果良好,能较好地处理复杂球形数据的判别问题.%This paper presents a new discriminant method through improving the traditional distance discriminant method and tries to improve the accuracy of discriminant problems of complex spherical data in condition of not reducing variables. The example shows that this method has good discriminant effect, and can effectively deal with the discriminant problems of complex spherical data.

  10. The Effectiveness of the Discriminant Analysis Models. Study Based on the Selected Polish Companies Quoted on the Warsaw Stock Exchange

    Directory of Open Access Journals (Sweden)

    Grzegorz Gołębiowski

    2008-07-01

    Full Text Available The article presents the result of research on en effectiveness of discriminant models on the example of selected Polish joint-stock companies which declared bankruptcy. Aside from general results describing an effectiveness of discriminant models on the base of the own research of the authors, a comparison of the received findings with others economists results of research was made. Moreover, an analysis how the relation of an enterprise with the economic situation impacts on an effectiveness of considered models concerning the forecast of a risk of bankruptcy was carried out.

  11. DISCRIMINATIVE ANALYSIS OF MORPHOLOGIC AND MOTORIC PARAMETER TO JUDO AND KARATE SPORTIEST BOYS

    Directory of Open Access Journals (Sweden)

    Lulzim Ibri

    2013-07-01

    Full Text Available In sample from 160 boys from secondary schools of Prizren 16-17 age, separated in two groups were implicated 18 tests, from them 10 test for valuation morphologic characteristic and 8 test, for valuation motoric abilities. Group (A is component from 80 judo athletes’ boys and group (B from 80 karate athletes’ boys. Purpose of this investigation is to verify changes between judo and karate athletes’ boys in morphologic characteristic and motoric abilities. The problem of investigation was to investigate if there are changes between judo and karate athletes’ boys in morphologic characteristic that represent longitudinal dimensionality, body measure and adipose tissue, and in motoric abilities (used is eurofit battery tests. For global analysis of dimension to some changes and variable system (which contribute in changes between judo and karate athletes’ boys were implicated t-test for small independent sample and, canonic discriminative analysis. The results of this study show that judo and karate athletes significantly differ among themselves in motoric abilities, judo athletes are better in the tests: long jump from place (LOJU, squeeze palm (SQPA and support the knuckle (SUKN, while the karate athletes are better in the tests: taping for hands (TAHE, reach sitting down position (RSDP and run there-hire 10x5 meters (R10x5M, but these changes were not noticed and morphological variables.

  12. Discriminant Context Information Analysis for Post-Ranking Person Re-Identification.

    Science.gov (United States)

    Garcia, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christian

    2017-01-16

    Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark datasets support our believes. For every dataset, the proposed method remarkably improves the first ranks results and outperforms state-of-the-art approaches.

  13. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

    Science.gov (United States)

    Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

    2017-01-01

    Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

  14. Ultrasonic analysis to discriminate bread dough of different types of flour

    Science.gov (United States)

    García-Álvarez, J.; Rosell, C. M.; García-Hernández, M. J.; Chávez, J. A.; Turó, A.; Salazar, J.

    2012-12-01

    Many varieties of bread are prepared using flour coming from wheat. However, there are other types of flours milled from rice, legumes and some fruits and vegetables that are also suitable for baking purposes, used alone or in combination with wheat flour. The type of flour employed strongly influences the dough consistency, which is a relevant property for determining the dough potential for breadmaking purposes. Traditional methods for dough testing are relatively expensive, time-consuming, off-line and often require skilled operators. In this work, ultrasonic analysis are performed in order to obtain acoustic properties of bread dough samples prepared using two different types of flour, wheat flour and rice flour. The dough acoustic properties can be related to its viscoelastic characteristics, which in turn determine the dough feasibility for baking. The main advantages of the ultrasonic dough testing can be, among others, its low cost, fast, hygienic and on-line performance. The obtained results point out the potential of the ultrasonic analysis to discriminate doughs of different types of flour.

  15. Lateral cephalometric analysis of mandibular morphology: discrimination among subjects with and without temporomandibular joint disk displacement and osteoarthrosis.

    Science.gov (United States)

    Bertram, S; Moriggl, A; Neunteufel, N; Rudisch, A; Emshoff, R

    2012-02-01

    To assess whether in patients with temporomandibular joint (TMJ) arthralgia cephalometric variables of mandibular morphology may discriminate among the magnetic resonance (MR) imaging-based TMJ groups of 'bilateral presence of disk displacement without reduction (DDwoR) and osteoarthrosis (OA)' and 'bilateral absence of bilateral DDwoR and OA'. Bilateral MR imaging of the TMJ was performed in 45 consecutive TMJ arthralgia patients to identify individuals with the specific structural characteristics of bilateral TMJ DDwoR associated with OA. Linear and angular cephalometric measurements were taken from lateral cephalograms to apply selected criteria of mandibular morphology. A discriminant function analysis was used to investigate how cephalometric parameters discriminate among the TMJ groups of 'bilateral presence of DDwoR with OA' and 'bilateral absence of DDwoR and OA'. Ramus height (Ar-Go) and effective mandibular length (Ar-Pog) produced a significant discriminant function that predicted TMJ group membership (P < 0·001). This function correctly classified 80·2% of original and cross-validated grouped cases. This study supports the concept that cephalometric variables of mandibular morphology discriminate among subjects with and without bilateral TMJ DDwoR and OA.

  16. Instrument and Survey Analysis Technical Report: Program Implementation Survey. Technical Report #1112

    Science.gov (United States)

    Alonzo, Julie; Tindal, Gerald

    2011-01-01

    This technical document provides guidance to educators on the creation and interpretation of survey instruments, particularly as they relate to an analysis of program implementation. Illustrative examples are drawn from a survey of educators related to the use of the easyCBM learning system. This document includes specific sections on…

  17. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four

  18. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four diffe

  19. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gifty E. Acquah

    2016-08-01

    Full Text Available As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS and Fourier transform infrared spectroscopy (FTIRS together with linear discriminant analysis (LDA. Forest logging residue harvested from several Pinus taeda (loblolly pine plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage. Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability

  20. San Francisco Estuary Midwinter Waterfowl Survey: 2012 Survey Results and Trend Analysis (1981-2012)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This report provides results of the 2012 San Francisco Estuary Midwinter Waterfowl Survey and presents an analysis of trends in waterfowl counts from 1981-2012. The...

  1. The systematic spectral analysis of radio surveys

    CERN Document Server

    Harwood, Jeremy J

    2016-01-01

    Current and future continuum surveys being undertaken by the new generation of radio telescopes are now poised to address many important science questions, ranging from the earliest galaxies, to the physics of nearby AGN, as well as potentially providing new and unexpected discoveries. However, how to efficiently analyse the large quantities of data collected by these studies in order to maximise their scientific output remains an open question. In these proceedings we present details of the surveys module for the Broadband Radio Astronomy Tools (BRATS) software package which will combine new observations with existing multi-frequency data in order to automatically analyse and select sources based on their spectrum. We show how these methods can been applied to investigate objects observed on a variety of spatial scales, and suggest a pathway for how this can be used in the wider context of surveys and large samples.

  2. Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG.

    Science.gov (United States)

    Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli

    2013-12-01

    Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.

  3. On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xu; SHAO Hui-he

    2006-01-01

    A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis (FDA) was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance,the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the results were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

  4. PENDETEKSIAN KANKER PARU–PARU DENGAN MENGGUNAKAN TRANSFORMASI WAVELET DAN METODE LINEAR DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Hanung Tyas Saksono

    2010-07-01

    Full Text Available Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA. Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.

  5. Discriminating lava flows of different age within Nyamuragira's volcanic field using spectral mixture analysis

    Science.gov (United States)

    Li, Long; Canters, Frank; Solana, Carmen; Ma, Weiwei; Chen, Longqian; Kervyn, Matthieu

    2015-08-01

    In this study, linear spectral mixture analysis (LSMA) is used to characterize the spectral heterogeneity of lava flows from Nyamuragira volcano, Democratic Republic of Congo, where vegetation and lava are the two main land covers. In order to estimate fractions of vegetation and lava through satellite remote sensing, we made use of 30 m resolution Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Imager (ALI) imagery. 2 m Pleiades data was used for validation. From the results, we conclude that (1) LSMA is capable of characterizing volcanic fields and discriminating between different types of lava surfaces; (2) three lava endmembers can be identified as lava of old, intermediate and young age, corresponding to different stages in lichen growth and chemical weathering; (3) a strong relationship is observed between vegetation fraction and lava age, where vegetation at Nyamuragira starts to significantly colonize lava flows ∼15 years after eruption and occupies over 50% of the lava surfaces ∼40 years after eruption. Our study demonstrates the capability of spectral unmixing to characterize lava surfaces and vegetation colonization over time, which is particularly useful for poorly known volcanoes or those not accessible for physical or political reasons.

  6. Discrimination of Anemonefish Species by PCR-RFLP Analysis of Mitochondrial Gene Fragments

    Directory of Open Access Journals (Sweden)

    Chuta Boonphakdee

    2008-01-01

    Full Text Available A means of discriminating among species of clown anemonefishes, based on restriction enzyme analysis of partial mitochondrial DNA sequences, was investigated. Mitochondrial 16S rRNA and cytochrome b genes from 6 species (7 strains of anemonefish (Premnas biculeatus, Amphiprion polymnus, A. sandaracinos, A. perideraion, A. ocellaris, A. ocellaris var. and A. percula were PCR-amplified. A 623-bp portion of 16S rRNA gene was obtained from different fishes using the same pair of primers. Further investigation of this 16S rRNA fragment, by restriction endonuclease digestion with BfuCI and RsaI, was not able to distinguish all fishes studied, but did yield 3 different digestion patterns. The first was specific to P. biculaetus, the sole member of the genus Premnas, while the remaining two separated the Amphiprion species into 2 groups: 1 A. polymnas, A. sandaracinos and A. perideraion, and 2 A. ocellaris, A. ocellaris var. and A. percula. In contrast to this, restriction endonuclease digestion of a 786-bp fragment of the cytochrome b gene with HinfI and RsaI, was able to differentiate different 7 anemonefishes. This utility marker is valuable for unambiguous species/strain identification of juvenile anemonefishes.

  7. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

  8. Comparing discriminant analysis and neural network for the determination of sex using femur head measurements.

    Science.gov (United States)

    Alunni, Véronique; Jardin, Philippe du; Nogueira, Luisa; Buchet, Luc; Quatrehomme, Gérald

    2015-08-01

    The measurement of the femoral head is usually considered an interesting variable for the sex determination of skeletal remains. To date, there are few published reference measurements of the femoral head in a modern European population for the purpose of sex determination. In this study, 116 femurs from 58 individuals of the South of France (Nice Bone Collection, Nice, France) were studied. Three measurements of the femoral head were taken: the vertical head diameter (VHD), the transversal head diameter (THD) and the head circumference (HC). The results show that: (i) there is no statistical difference between the right and left femurs for each of the three measurements (VHD, THD and HC). Therefore we arbitrarily chose to use the measures from the right femurs (N=58) to pursue our experiments; (ii) the measurements of the femoral head are similar to those of contemporary American populations; (iii) the dimensions of the femoral head place the measurements of the French population somewhere between Germany or Croatia, and Spain; (iv) there is no significant secular trend (in contrast with the femoral neck diameter); (v) the femoral head measurement as a single variable is useful for sex determination: a 96.5% rate of accuracy was obtained using THD and HC measurements with the artificial neural network; and a 94.8% rate of accuracy using VHD, both with the discriminant analysis and the neural network.

  9. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

    Directory of Open Access Journals (Sweden)

    Pang Herbert

    2010-10-01

    Full Text Available Abstract Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  10. Partial Least Square Discriminant Analysis Discovered a Dietary Pattern Inversely Associated with Nasopharyngeal Carcinoma Risk.

    Science.gov (United States)

    Lo, Yen-Li; Pan, Wen-Harn; Hsu, Wan-Lun; Chien, Yin-Chu; Chen, Jen-Yang; Hsu, Mow-Ming; Lou, Pei-Jen; Chen, I-How; Hildesheim, Allan; Chen, Chien-Jen

    2016-01-01

    Evidence on the association between dietary component, dietary pattern and nasopharyngeal carcinoma (NPC) is scarce. A major challenge is the high degree of correlation among dietary constituents. We aimed to identify dietary pattern associated with NPC and to illustrate the dose-response relationship between the identified dietary pattern scores and the risk of NPC. Taking advantage of a matched NPC case-control study, data from a total of 319 incident cases and 319 matched controls were analyzed. Dietary pattern was derived employing partial least square discriminant analysis (PLS-DA) performed on energy-adjusted food frequencies derived from a 66-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated with multiple conditional logistic regression models, linking pattern scores and NPC risk. A high score of the PLS-DA derived pattern was characterized by high intakes of fruits, milk, fresh fish, vegetables, tea, and eggs ordered by loading values. We observed that one unit increase in the scores was associated with a significantly lower risk of NPC (ORadj = 0.73, 95% CI = 0.60-0.88) after controlling for potential confounders. Similar results were observed among Epstein-Barr virus seropositive subjects. An NPC protective diet is indicated with more phytonutrient-rich plant foods (fruits, vegetables), milk, other protein-rich foods (in particular fresh fish and eggs), and tea. This information may be used to design potential dietary regimen for NPC prevention.

  11. Differentiation of Pueraria lobata and Pueraria thomsonii using partial least square discriminant analysis (PLS-DA).

    Science.gov (United States)

    Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin

    2013-10-01

    The aims of the study were to differentiate Pueraria lobata from its related species Pueraria thomsonii and to examine the raw herbal material used in manufacturing kudzu root granules using partial least square discriminant analysis (PLS-DA). Sixty-four raw materials of P. lobata and P. thomsonii and kudzu root-labelled granules were analysed by ultra performance liquid chromatography. To differentiate P. lobata from P. thomsonii, PLS-DA models using the variables selected from the entire chromatograms, genetic algorithm (GA), successive projection algorithm (SPA), puerarin alone and six selected peaks were employed. The models constructed by GA and SPA demonstrated superior classification ability and lower model's complexity as compared to the model based on the entire chromatographic matrix, whilst the model constructed by the six selected peaks was comparable to the entire chromatographic model. The model established by puerarin alone showed inferior classification ability. In addition, the PLS-DA models constructed by the entire chromatographic matrix, GA, SPA and the six selected peaks showed that four brands out of seventeen granules were mislabelled as P. lobata. In conclusion, PLS-DA is a promising procedure for differentiating Pueraria species and determining raw material used in commercial products.

  12. Material decomposition with the multi-energy attenuation coefficient ratio by using a multiple discriminant analysis

    Science.gov (United States)

    Lee, Woo-Jin; Kang, Se-Ryong; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Yi, Won-Jin

    2016-07-01

    The objective of this study was to develop a spectral CT system using a photon counting detector and to decompose materials by applying a multiple discriminant analysis (MDA) to the energy-dependent attenuation coefficient ratios. We imaged cylindrical phantoms of Polymethyl methacrylate (PMMA) with four holes filled with calcium chloride, iodine, and gold nanoparticle contrast agents. The attenuation coefficients were measured via reconstructed multi-energy images, and the linear attenuation ratio was used for material identification. The MDA projection matrix, determined from training phantoms, was used to identify the four materials in the testing phantoms. For quantification purposes, the relationships between the attenuation coefficients at multiple energy bins and the concentrations were characterized by using the least-squares method for each material. The mean identification accuracy for each of the three materials were 0.94 ± 0.09 for iodine, 0.96 ± 0.07 for gold nanoparticles, and 0.92 ± 0.05 for calcium chloride. The mean quantification errors were 1.90 ± 1.58% for iodine, 3.85 ± 3.13% for gold nanoparticle, and 3.40 ± 2.62% for calcium chloride. The developed multi-energy CT system based on the photon-counting detector with MDA can precisely decompose the four materials.

  13. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LIANG GuiZhao; LIAO ChunYang; WU ShiRong; LI GenRong; HE Liu; GAO JianKun; Gan MengYu; LI DeJing; CHEN GuoPing; WANG GuiXue; LONG Sha; CHEN ZeCong; JING JuHua; ZHENG XiaoLin; ZENG Hui; ZHANG QiaoXia; ZHANG MengJun; YANG Qi; TIAN FeiFei; TONG JianBo; WANG JiaoNa; LIU YongHong; YANG ShanBin; LI Bo; QIU LiangJia; CAI ShaoXi; ZHAO Na; YANG Yan; SU XiaLi; SONG Jian; CHEN MeiXia; ZHANG XueJiao; SUN JiaYing; MEI Hu; LI JingWei; CHEN GuoHua; CHEN Gang; DENG Jie; PENG ChuanYou; ZHU WanPing; XU LuoNan; WU YuQuan; LIAO LiMin; LI Zhi; ZHOU Yuan; LI Jun; LU DaJun; SU QinLiang; HUANG ZhengHu; ZHOU Ping; LI ZhiLiang; YANG Li; ZHOU Peng; YANG ShengXi; SHU Mao

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples.Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA).The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%.Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively.External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model.The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

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

    Science.gov (United States)

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

    2016-08-01

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

  15. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.

    Science.gov (United States)

    Treder, Matthias S; Porbadnigk, Anne K; Shahbazi Avarvand, Forooz; Müller, Klaus-Robert; Blankertz, Benjamin

    2016-04-01

    We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.

  16. UV-visible microscope spectrophotometric polarization and dichroism with increased discrimination power in forensic analysis

    Science.gov (United States)

    Purcell, Dale Kevin

    Microanalysis of transfer (Trace) evidence is the application of a microscope and microscopical techniques for the collection, observation, documentation, examination, identification, and discrimination of micrometer sized particles or domains. Microscope spectrophotometry is the union of microscopy and spectroscopy for microanalysis. Analytical microspectroscopy is the science of studying the emission, reflection, transmission, and absorption of electromagnetic radiation to determine the structure or chemical composition of microscopic-size materials. Microscope spectrophotometry instrument designs have evolved from monochromatic illumination which transmitted through the microscope and sample and then is detected by a photometer detector (photomultiplier tube) to systems in which broad-band (white light) illumination falls incident upon a sample followed by a non-scanning grating spectrometer equipped with a solid-state multi-element detector. Most of these small modern spectrometers are configured with either silicon based charged-couple device detectors (200-950 nm) or InGaAs based diode array detectors (850-2300 nm) with computerized data acquisition and signal processing being common. A focus of this research was to evaluate the performance characteristics of various modern forensic (UV-Vis) microscope photometer systems as well as review early model instrumental designs. An important focus of this research was to efficiently measure ultraviolet-visible spectra of microscopically small specimens for classification, differentiation, and possibly individualization. The first stage of the project consisted of the preparation of microscope slides containing neutral density filter reference materials, molecular fluorescence reference materials, and dichroic reference materials. Upon completion of these standard slide preparations analysis began with measurements in order to evaluate figures of merit for comparison of the instruments investigated. The figures of

  17. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    Directory of Open Access Journals (Sweden)

    Shengwen Guo

    2017-05-01

    Full Text Available Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI. Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI, the converted MCI (cMCI, and the normal control (NC groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM. An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and

  18. Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

    Science.gov (United States)

    Luo, Congpei; He, Tao; Chun, Ze

    2013-04-01

    Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

  19. Discriminant analysis for characterization of hydrochemistry of two mountain river basins of contrasting climates in the southern Western Ghats, India.

    Science.gov (United States)

    Thomas, Jobin; Joseph, Sabu; Thrivikramji, K P

    2015-06-01

    Discriminant analysis (DA) was performed on river hydrochemistry data for three seasons (i.e., monsoon (MON), post-monsoon (POM), and pre-monsoon (PRM)) to examine the spatio-temporal hydrochemical variability of two mountain river basins (Muthirapuzha River Basin (MRB) and Pambar River Basin (PRB)) of the southern Western Ghats, India. Although the river basins drain tropical mountainous terrain, climate and degree of anthropogenic disturbances show significant differences (i.e., humid, more disturbed MRB vs semiarid, less disturbed PRB). In MRB, TDS, Na(+), pH, Mg(2+), and K(+) are the attributes responsible for significant hydrochemical variations between the seasons, while Cl(-), TH, and Na(+) are the predictors in PRB. The temporal discriminant models imply the importance of rainfall pattern, relative contribution of groundwater toward stream discharge and farming activities in hydrochemistry between the seasons. Inclusion of hydrochemical attributes (in the temporal discriminant functions) that can be derived from both natural and anthropogenic sources suggests that ionic enrichment strongly depends on the seasons, and is mainly due to the variability in the intensity of anthropogenic activities as well as fluctuations in river discharge. In spatial discriminant models, Cl(-) is the only variable responsible for hydrochemical variations between the basins (during MON), whereas Si discriminates during POM and PRM, implying the role of atmospheric supply, anthropogenic modifications as well as intensity of weathering. In the spatial discrimination models, misclassification of hydrochemistry data between MRB and PRB can be attributed to the overlapping effect of humid climate of MRB extending toward the upstream of (semiarid) PRB. This study underscores the versatility of DA in deciphering the significance of climatic controls on hydrochemical composition of tropical mountain rivers.

  20. Patterns of Discrimination, Grievances and Political Activity Among Europe's Roma: A Cross-Sectional Analysis

    Directory of Open Access Journals (Sweden)

    Jonathan Fox

    2001-01-01

    Full Text Available The purpose of this study is to analyse in a large-n cross-sectional format the patterns of discrimination, grievances and political activity among European Roma (Gypsies using data from the Minority at Risk project. The model tested here is a two-step model positing that discrimination leads to grievance formation which in turn leads to protest and rebellion. The results show that the Roma, in general, conform to this model but differ in some important specifics.

  1. Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2013-01-01

    Full Text Available This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.

  2. Survey of SNMP performance analysis studies

    NARCIS (Netherlands)

    Andrey, Laurent; Festor, Olivier; Lahmadi, Abdelkader; Pras, Aiko; Schönwälder, Jürgen

    2009-01-01

    This paper provides a survey of Simple Network Management Protocol (SNMP)-related performance studies. Over the last 10 years, a variety of such studies have been published. Performance benchmarking of SNMP, like all benchmarking studies, is a non-trivial task that requires substantial effort to be

  3. Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

    Directory of Open Access Journals (Sweden)

    Gilberto Rodrigues Liska

    2015-12-01

    Full Text Available Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA and discriminant analysis via boosting algorithm (AdaBoost. The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

  4. In Vitro Cell Death Discrimination and Screening Method by Simple and Cost-Effective Viability Analysis.

    Science.gov (United States)

    Helm, Katharina; Beyreis, Marlena; Mayr, Christian; Ritter, Markus; Jakab, Martin; Kiesslich, Tobias; Plaetzer, Kristjan

    2017-01-01

    For in vitro cytotoxicity testing, discrimination of apoptosis and necrosis represents valuable information. Viability analysis performed at two different time points post treatment could serve such a purpose because the dynamics of metabolic activity of apoptotic and necrotic cells is different, i.e. a more rapid decline of cellular metabolism during necrosis whereas cellular metabolism is maintained during the entire execution phase of apoptosis. This study describes a straightforward approach to distinguish apoptosis and necrosis. A431 human epidermoid carcinoma cells were treated with different concentrations/doses of actinomycin D (Act-D), 4,5,6,7-tetrabromo-2-azabenzimidazole (TBB), Ro 31-8220, H2O2 and photodynamic treatment (PDT). The resazurin viability signal was recorded at 2 and 24 hrs post treatment. Apoptosis and necrosis were verified by measuring caspase 3/7 and membrane integrity. Calculation of the difference curve between the 2 and 24 hrs resazurin signals yields the following information: a positive difference signal indicates apoptosis (i.e. high metabolic activity at early time points and low signal at 24 hrs post treatment) while an early reduction of the viability signal indicates necrosis. For all treatments, this dose-dependent sequence of cellular responses could be confirmed by independent assays. Simple and cost-effective viability analysis provides reliable information about the dose ranges of a cytotoxic agent where apoptosis or necrosis occurs. This may serve as a starting point for further in-depth characterisation of cytotoxic treatments. © 2017 The Author(s)Published by S. Karger AG, Basel.

  5. Methylation Linear Discriminant Analysis (MLDA for identifying differentially methylated CpG islands

    Directory of Open Access Journals (Sweden)

    Vass J Keith

    2008-08-01

    Full Text Available Abstract Background Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA. Results MLDA was programmed in R (version 2.7.0 and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing. Conclusion MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of

  6. Structural Discrimination

    DEFF Research Database (Denmark)

    Thorsen, Mira Skadegård

    In this article, I discuss structural discrimination, an underrepresented area of study in Danish discrimination and intercultural research. It is defined here as discursive and constitutive, and presented as a central element of my analytical approach. This notion is employed in the with which...... to understand and identify aspects of power and asymmetry in communication and interactions. With this as a defining term, I address how exclusion and discrimination exist, while also being indiscernible, within widely accepted societal norms. I introduce the concepts of microdiscrimination and benevolent...... discrimination as two ways of articulating particular, opaque forms of racial discrimination that occur in everyday Danish (and other) contexts, and have therefore become normalized. I present and discuss discrimination as it surfaces in data from my empirical studies of discrimination in Danish contexts...

  7. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Directory of Open Access Journals (Sweden)

    Zvi N Roth

    2016-03-01

    Full Text Available Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e. discriminative information may be less relevant than information regarding the relative location of two objects (i.e. relative information. For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action.We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in early visual cortex, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS, but not from EVC, where we find the spatial representation to be warped.We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model’s receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC.These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representation into relative spatial representation along the visual stream.

  8. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

    Science.gov (United States)

    Roth, Zvi N

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

  9. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Science.gov (United States)

    Roth, Zvi N.

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

  10. Application of Discriminant Analysis for Studying the Source Rock Potential of Probable Formations in the Lorestan Basin, Iran

    Directory of Open Access Journals (Sweden)

    Amir Negahdari

    2014-06-01

    Full Text Available Understanding the performance and role of each formation in a petroleum play is crucial for the efficient and precise exploration and exploitation of trapped hydrocarbons in a sedimentary basin. The Lorestan basin is one of the most important hydrocarbon basins of Iran, and it includes various oil-prone potential source rocks and reservoir rocks. Previous geochemical studies of the basin were not accurate and there remain various uncertainties about the potential of the probable source rocks of the basin. In the present research, the geochemical characteristics of four probable source rocks of the Lorestan basin are studied using Rock-Eval pyrolysis and discriminant analysis. In achieving this goal, several discriminant functions are defined to evaluate the discriminant factor for the division of samples into two groups. The function with the highest discriminant factor was selected for the classification of probable source rocks into two groups: weak and strong. Among the studied formations, Garau and Pabdeh had the richest and poorest source rocks of the Lorestan basin, respectively. The comparison of the obtained results with the previous literature shows that the proposed model is more reliable for the recognition of the richness of source rock in the area.

  11. Racial/ethnic differences in responses to the everyday discrimination scale: a differential item functioning analysis.

    Science.gov (United States)

    Lewis, Tené T; Yang, Frances M; Jacobs, Elizabeth A; Fitchett, George

    2012-03-01

    The authors examined the impact of race/ethnicity on responses to the Everyday Discrimination Scale, one of the most widely used discrimination scales in epidemiologic and public health research. Participants were 3,295 middle-aged US women (African-American, Caucasian, Chinese, Hispanic, and Japanese) from the Study of Women's Health Across the Nation (SWAN) baseline examination (1996-1997). Multiple-indicator, multiple-cause models were used to examine differential item functioning (DIF) on the Everyday Discrimination Scale by race/ethnicity. After adjustment for age, education, and language of interview, meaningful DIF was observed for 3 (out of 10) items: "receiving poorer service in restaurants or stores," "being treated as if you are dishonest," and "being treated with less courtesy than other people" (all P's discrimination differed slightly for women of different racial/ethnic groups, with certain "public" experiences appearing to have more salience for African-American and Chinese women and "dishonesty" having more salience for racial/ethnic minority women overall. "Courtesy" appeared to have more salience for Hispanic women only in comparison with African-American women. Findings suggest that the Everyday Discrimination Scale could potentially be used across racial/ethnic groups as originally intended. However, researchers should use caution with items that demonstrated DIF.

  12. Racial discrimination mediates race differences in sleep problems: A longitudinal analysis.

    Science.gov (United States)

    Fuller-Rowell, Thomas E; Curtis, David S; El-Sheikh, Mona; Duke, Adrienne M; Ryff, Carol D; Zgierska, Aleksandra E

    2017-04-01

    To examine changes in sleep problems over a 1.5-year period among Black or African American (AA) and White or European American (EA) college students and to consider the role of racial discrimination as a mediator of race differences in sleep problems over time. Students attending a large, predominantly White university (N = 133, 41% AA, 57% female, mean age = 18.8, SD = .90) reported on habitual sleep characteristics and experiences of racial discrimination at baseline and follow-up assessments. A latent variable for sleep problems was assessed from reports of sleep latency, duration, efficiency, and quality. Longitudinal models were used to examine race differences in sleep problems over time and the mediating role of perceived discrimination. Covariates included age, gender, parent education, parent income, body mass index, self-rated physical health, and depressive symptoms. Each of the individual sleep measures was also examined separately, and sensitivity analyses were conducted using alternative formulations of the sleep problems measure. AAs had greater increases in sleep problems than EAs. Perceived discrimination was also associated with increases in sleep problems over time and mediated racial disparities in sleep. This pattern of findings was similar when each of the sleep indicators was considered separately and held with alternative sleep problems measures. The findings highlight the importance of racial disparities in sleep across the college years and suggest that experiences of discrimination contribute to group disparities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  13. EVOLUTION OF NEUROENDOCRINE CELL POPULATION AND PEPTIDERGIC INNERVATION, ASSESSED BY DISCRIMINANT ANALYSIS, DURING POSTNATAL DEVELOPMENT OF THE RAT PROSTATE

    Directory of Open Access Journals (Sweden)

    Rosario Rodríguez

    2011-05-01

    Full Text Available Serotonin immunoreactive neuroendocrine cells and peptidergic nerves (NPY and VIP could have a role in prostate growth and function. In the present study, rats grouped by stages of postnatal development (prepubertal, pubertal, young and aged adults were employed in order to ascertain whether age causes changes in the number of serotoninergic neuroendocrine cells and the length of NPY and VIP fibres. Discriminant analysis was performed in order to ascertain the classificatory power of stereologic variables (absolute and relative measurements of cell number and fibre length on age groups. The following conclusions were drawn: a discriminant analysis confirms the androgen-dependence of both neuroendocrine cells and NPYVIP innervation during the postnatal development of the rat prostate; b periglandular innervation has more relevance than interglandular innervation in classifying the rats in age groups; and c peptidergic nerves from ventral, ampullar and periductal regions were more age-dependent than nerves from the dorso-lateral region.

  14. Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

    Science.gov (United States)

    Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan

    2016-12-01

    Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

  15. Towards Maximum Spanning Tree Model in Web 3.0 Design and Development for Students using Discriminant Analysis

    CERN Document Server

    Padma, S

    2012-01-01

    Web 3.0 is an evolving extension of the web 2.0 scenario. The perceptions regarding web 3.0 is different from person to person . Web 3.0 Architecture supports ubiquitous connectivity, network computing, open identity, intelligent web, distributed databases and intelligent applications. Some of the technologies which lead to the design and development of web 3.0 applications are Artificial intelligence, Automated reasoning, Cognitive architecture, Semantic web . An attempt is made to capture the requirements of Students inline with web 3.0 so as to bridge the gap between the design and development of web 3.0 applications and requirements among Students. Maximum Spanning Tree modeling of the requirements facilitate the identification of key areas and key attributes in the design and development of software products for Students in Web 3.0 using Discriminant analysis. Keywords : Web 3.0, Discriminant analysis, Design and Development, Model, Maximum Spanning Tree 1.

  16. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Directory of Open Access Journals (Sweden)

    Guan Yu

    Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and

  17. The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

    Science.gov (United States)

    Wen, Gezheng; Markey, Mia K.

    2015-03-01

    It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

  18. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Science.gov (United States)

    Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

    2014-01-01

    Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images

  19. Studies in genetic discrimination. Final progress report

    Energy Technology Data Exchange (ETDEWEB)

    1994-06-01

    We have screened 1006 respondents in a study of genetic discrimination. Analysis of these responses has produced evidence of the range of institutions engaged in genetic discrimination and demonstrates the impact of this discrimination on the respondents to the study. We have found that both ignorance and policy underlie genetic discrimination and that anti-discrimination laws are being violated.

  20. Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

    Science.gov (United States)

    Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

    2014-03-01

    It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law.

  1. Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study.

    Directory of Open Access Journals (Sweden)

    Susanne Hartmann

    Full Text Available BACKGROUND: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas. From a theoretical point of view, such distributed networks elude categorical analysis because subtraction methods are univariate. Thus, the aim of this study was to identify the neural networks involved in somatosensory object discrimination using a voxel-based principal component analysis (PCA of event-related functional magnetic resonance images. METHODOLOGY/PRINCIPAL FINDINGS: Seven healthy, right-handed subjects aged between 22 and 44 years were required to discriminate with their dominant hand the length differences between otherwise identical parallelepipeds in a two-alternative forced-choice paradigm. Of the 34 principal components retained for analysis according to the 'bootstrapped' Kaiser-Guttman criterion, t-tests applied to the subject-condition expression coefficients showed significant mean differences between the object presentation and inter-stimulus phases in PC 1, 3, 26 and 32. Specifically, PC 1 reflected object exploration or manipulation, PC 3 somatosensory and short-term memory processes. PC 26 evinced the perception that certain parallelepipeds could not be distinguished, while PC 32 emerged in those choices when they could be. Among the cerebral regions evident in the PCs are the left posterior parietal lobe and premotor cortex in PC 1, the left superior parietal lobule (SPL and the right cuneus in PC 3, the medial frontal and orbitofrontal cortex bilaterally in PC 26, and the right intraparietal sulcus, anterior SPL and dorsolateral prefrontal cortex in PC 32. CONCLUSIONS/SIGNIFICANCE: The analysis provides evidence for the concerted action of large-scale cortico-subcortical networks mediating tactile object discrimination. Parallel to activity in nodes processing object-related impulses we found activity in key cerebral regions

  2. [Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

    Science.gov (United States)

    Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

    2015-01-01

    The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

  3. Characterization of Fatty Acid Profile of Argan Oil and Other Edible Vegetable Oils by Gas Chromatography and Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Ascensión Rueda

    2014-01-01

    Full Text Available Virgin argan oil is an emergent oil that is being introduced into specialized international markets as a healthy and luxury food. In order to compare the fatty acid composition of argan oil with that of the eleven other vegetable edible oils, a combination of gas chromatography as analytical technique and multivariate discriminant analysis was applied. This analysis takes into account the conjoint effect of all the variables analyzed in the discrimination between oils and also shows the contribution of each variable to oils characterization. The model correctly classified 100% oil samples. According to the fatty acid composition, argan oil showed closest similarity firstly with sesame oil and secondly with high oleic sunflower oil. Olive oil was close to avocado oil and almond oil, followed by argan oil. Thus, similarities and differences between vegetable oils based on their fatty acid profile were established by the application of multivariate discriminant analysis. This method was proven to be a useful tool to study the relationships between oils according to the fat composition and to determine the importance of the fatty acid variables on the oils classification.

  4. [Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal].

    Science.gov (United States)

    Li, Qiong-Fei; Yang, Zeng-Ling; Han, Lu-Jia

    2008-03-01

    In order to study the feasibility of using near infrared (NIR) diffuse reflectance spectroscopy to discriminate adultera tion of non-ruminant meat and bone meal (MBM) with ruminant MBM, a total of 39 MBM samples made up of 15 from pig, 15 from poultry, 5 from cattle and 4 from sheep produced in different areas in China were chosen. The MBM samples were ground with 0. 5 mm sieve. 252 specimens were prepared by non-ruminant MBM deliberately adulterated with different proportion of ruminant MBM. The specimens were scanned by FOSS NIRSystem 6500. A calibration set of 180 specimens and an independent validation set of 72 specimens were randomly selected by the WINISI software. Discriminant analysis model was developed by partial least squares (PLS) on the calibration set and validated with independent validation set. The best discriminant model was obtained using standard normal variate and detrend (SNVD) and second derivative for spectrum pretreatment; this model had a coefficient of determination (R2(CV)) of 0.83 and a standard error of cross-validation (SECV) of 0. 147 1. For the independent validation set, the correct classification rate is 90%. There were a false negative specimen (0.5%) and two uncertain specimens (1%, 1.5%) in validation set. Results showed that it is feasible to use NIR diffuse reflectance spectroscopy to discriminate adulteration of non-ruminant MBM with ruminant MBM, but for specimens adulterated with ruminant MBM at less than 2%, the accuracy of calibration model needs to be improved. NIR was a rapid and non-destructive approach to discriminating adulteration of non-ruminant MBM with ruminant MIBM.

  5. Sentiment Analysis Using Hybrid Approach: A Survey

    Directory of Open Access Journals (Sweden)

    Chauhan Ashish P

    2015-01-01

    Full Text Available Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressionsthat are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing

  6. Automatic Facial Expression Analysis A Survey

    Directory of Open Access Journals (Sweden)

    C.P. Sumathi

    2013-01-01

    Full Text Available The Automatic Facial Expression Recognition has been one of the latest research topic since1990’s.There have been recent advances in detecting face, facial expression recognition andclassification. There are multiple methods devised for facial feature extraction which helps in identifyingface and facial expressions. This paper surveys some of the published work since 2003 till date. Variousmethods are analysed to identify the Facial expression. The Paper also discusses about the facialparameterization using Facial Action Coding System(FACS action units and the methods whichrecognizes the action units parameters using facial expression data that are extracted. Various kinds offacial expressions are present in human face which can be identified based on their geometric features,appearance features and hybrid features . The two basic concepts of extracting features are based onfacial deformation and facial motion. This article also identifies the techniques based on thecharacteristics of expressions and classifies the suitable methods that can be implemented.

  7. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  8. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  9. Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity

    NARCIS (Netherlands)

    J.P. Svensson; L.J.A. Stalpers; R.E.E. Esveldt-van Lange; N.A.P. Franken; J. Haveman; B. Klein; I. Turesson; H. Vrieling; M. Giphart-Gassler

    2006-01-01

    Background Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%-10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late

  10. The discriminative power of the EuroQol visual analog scale is sensitive to survey language in Singapore

    Directory of Open Access Journals (Sweden)

    Luo Nan

    2012-03-01

    Full Text Available Abstract Background Existing evidence for validity of the visual analog scale of the EQ-5D-3L questionnaire (EQ-VAS is weak in Chinese-speaking respondents in Singapore. We therefore investigated the validity of the Chinese (Singapore version of EQ-VAS in patients with diabetes. Methods In a cross-sectional survey, patients with type 2 diabetes seen in a primary care facility completed an identical Chinese or English questionnaire containing the EQ-5D-3L and questions assessing other health and disease-related characteristics. Convergent and known-groups validity of the EQ-VAS was examined for Chinese- and English-speaking respondents separately. Results The EQ-VAS was correlated with the EQ-5D-3L health index and a 5-point Likert-type scale for assessing global health in both Chinese-speaking (N = 335 and English-speaking respondents (N = 298, suggesting convergent validity. The mean EQ-VAS scores differed between English-speaking patients with differing duration of diabetes ( Conclusions Chinese- and English-speaking Singaporeans respond differently to the EQ-VAS. The Chinese version of EQ-VAS appears less sensitive than its English version for measuring global health in patient populations in Singapore.

  11. Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

    Science.gov (United States)

    Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

    2017-08-01

    The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.

  12. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis

    Science.gov (United States)

    de Siqueira e Oliveira, Fernanda SantAna; Giana, Hector Enrique; Silveira, Landulfo

    2012-10-01

    A method, based on Raman spectroscopy, for identification of different microorganisms involved in bacterial urinary tract infections has been proposed. Spectra were collected from different bacterial colonies (Gram-negative: Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa and Enterobacter cloacae, and Gram-positive: Staphylococcus aureus and Enterococcus spp.), grown on culture medium (agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from the agar surface and placed on an aluminum foil for Raman measurements. After preprocessing, spectra were submitted to a principal component analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. We found that the mean Raman spectra of different bacterial species show similar bands, and S. aureus was well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram-positive bacteria with sensitivity and specificity of 100% and Gram-negative bacteria with sensitivity ranging from 58 to 88% and specificity ranging from 87% to 99%.

  13. Sex determination using discriminant function analysis in Indigenous (Kurubas) children and adolescents of Coorg, Karnataka, India: A lateral cephalometric study.

    Science.gov (United States)

    Devang Divakar, Darshan; John, Jacob; Al Kheraif, Abdulaziz Abdullah; Mavinapalla, Seema; Ramakrishnaiah, Ravikumar; Vellappally, Sajith; Hashem, Mohamed Ibrahim; Dalati, M H N; Durgesh, B H; Safadi, Rima A; Anil, Sukumaran

    2016-11-01

    Aim: To test the validity of sex discrimination using lateral cephalometric radiograph and discriminant function analysis in Indigenous (Kuruba) children and adolescents of Coorg, Karnataka, India. Methods and materials: Six hundred and sixteen lateral cephalograms of 380 male and 236 females of age ranging from 6.5 to 18 years of Indigenous population of Coorg, Karnataka, India called Kurubas having a normal occlusion were included in the study. Lateral cephalograms were obtained in a standard position with teeth in centric occlusion and lips relaxed. Each radiograph was traced and cephalometric landmarks were measured using digital calliper. Calculations of 24 cephalometric measurements were performed. Results: Males exhibited significantly greater mean angular and linear cephalometric measurements as compared to females (p determine other landmarks that can help in sex determination and norms for Indigenous (Kuruba) population and also other Indigenous population of Coorg, Karnataka, India.

  14. Formal concept analysis in knowledge processing: a survey on applications

    NARCIS (Netherlands)

    Poelmans, J.; Ignatov, D.I.; Kuznetsov, S.O.; Dedene, G.

    2013-01-01

    This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract a

  15. Formal concept analysis in knowledge processing: a survey on applications

    NARCIS (Netherlands)

    Poelmans, J.; Ignatov, D.I.; Kuznetsov, S.O.; Dedene, G.

    2013-01-01

    This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract

  16. The Curriculum in Quantitative Analysis: Results of a Survey.

    Science.gov (United States)

    Locke, David C.; Grossman, William E. L.

    1987-01-01

    Reports on the results of a survey of college level instructors of quantitative analysis courses. Discusses what topics are taught in such courses, how much weight is given to these topics, and which experiments are used in the laboratory. Poses some basic questions about the curriculum in quantitative analysis. (TW)

  17. Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

    Directory of Open Access Journals (Sweden)

    Ping Wan

    2016-08-01

    Full Text Available Driving anger, called “road rage”, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β% and relative energy spectrum of θ band (θ% for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC curve analysis, the optimal thresholds (best cut-off points of β% and θ% for identifying none anger state (i.e., neutral were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

  18. Does international trade impact wage discrimination?

    OpenAIRE

    Jongsung Kim; Edinaldo Tebaldi

    2011-01-01

    This paper uses microdata from the 2006 Current Population Survey (CPS) combined with data from the U.S. International Trade Commission (USITC) and the Bureau of Economic Analysis (BEA) to evaluate the degree to which international trade affects wage discrimination. The paper's findings contribute to the literature in two fronts. First, it shows that empirical analyses that fail to properly account for gender or race differences might produce unreliable results regarding the impact of interna...

  19. Conclusions from the image analysis of the VSOP Survey

    OpenAIRE

    Dodson, R.; Fomalont, E.; Wiik, K.

    2009-01-01

    In February 1997, the Japanese radio astronomy satellite HALCA was launched to provide the space-bourne element for the VLBI Space Observatory Programme (VSOP) mission. A significant fraction of the mission time was to be dedicated to the VSOP Survey of bright compact Active Galactic Nuclei (AGN) at 5 GHz, which was lead by ISAS. The VSOP Survey Sources are an unbiased dataset of 294 targets, of which 82% were successfully observed. These are now undergoing statistical analysis to tease out t...

  20. Spectral analysis of the Chandra comet survey

    NARCIS (Netherlands)

    Bodewits, D.; Christian, D. J.; Torney, M.; Dryer, M.; Lisse, C. M.; Dennerl, K.; Zurbuchen, T. H.; Wolk, S. J.; Tielens, A. G. G. M.; Hoekstra, R.

    2007-01-01

    Aims. We present results of the analysis of cometary X-ray spectra with an extended version of our charge exchange emission model (Bodewits et al. 2006). We have applied this model to the sample of 8 comets thus far observed with the Chandra X-ray observatory and acis spectrometer in the 300 - 1000

  1. Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features

    National Research Council Canada - National Science Library

    McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

    2016-01-01

    .... In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images...

  2. Stepwise discriminant function analysis for rapid identification of acute promyelocytic leukemia from acute myeloid leukemia with multiparameter flow cytometry.

    Science.gov (United States)

    Chen, Zhanguo; Li, Yan; Tong, Yongqing; Gao, Qingping; Mao, Xiaolu; Zhang, Wenjing; Xia, Zunen; Fu, Chaohong

    2016-03-01

    Diagnosis of acute promyelocytic leukemia (APL) has been accelerated by multiparameter flow cytometry (MFC). However, diagnostic interpretation of MFC readouts for APL depends on individual experience and knowledge, which inevitably increases the risk of arbitrariness. We appraised the feasibility of using stepwise discriminant function analysis (SDFA) based on MFC to optimize the minimal variables needed to distinguish APL from other acute myeloid leukemia (AML) without complicated data interpretation. Samples from 327 patients with APL (n = 51) and non-APL AML (n = 276) were randomly allocated into training (243 AML) and test sets (84 AML) for SDFA. The discriminant functions from SDFA were examined by correct classification, and the final variables were validated by differential expression. Finally, additional 20 samples from patients with atypical APL and AML confusable with APL were also identified by SDFA method and morphological analysis. The weighed discriminant function reveals seven differentially expressed variables (CD2/CD9/CD11b/CD13/CD34/HLA-DR/CD117), which predict a molecular result for APL characterization with an accuracy that approaches 99% (99.6 and 98.8% for AML samples in training and test sets, respectively). Furthermore, the SDFA outperformed either single variable analysis or the more limited 3-component analysis (CD34/CD117/HLA-DR) via separate SDFA, and was also superior to morphological analysis in terms of diagnostic efficacy. The established SDFA based on MFC with seven variables can precisely and rapidly differentiate APL and non-APL AML, which may contribute to the urgent initiation of all-trans-retinoic acid-based APL therapy.

  3. Morphometric analysis to discriminate between species: The case of the Megalobulimus leucostoma complex

    Directory of Open Access Journals (Sweden)

    Victor Borda

    2014-10-01

    Full Text Available Plasticity of conchological characters had led to erroneous descriptions and the accumulation of synonyms making difficult the discrimination among species. The land snail genus Megalobulimus is an example of this problem. Megalobulimus leucostoma (Sowerby, 1835 has three subspecies which are difficult to differentiate by using the original descriptions. The aim of this paper is to discriminate among the subspecies of M. leucostoma by using morphometric and distribution analyses. Both provide substantial differences between M. l. leucostoma and M. l lacunosus that would not support the subspecies status of the former. Megalobulimus leucostoma weyrauchi fits into the great conchological variability of M. l .leucostoma; also the sympatric status between these two subspecies would not support the subspecies status of the former, and M. l. weyrauchi should be considered as part of M. l. leucostoma.

  4. Relationship between acculturation, discrimination, and suicidal ideation and attempts among US Hispanics in the National Epidemiologic Survey of Alcohol and Related Conditions.

    Science.gov (United States)

    Perez-Rodriguez, M Mercedes; Baca-Garcia, Enrique; Oquendo, Maria A; Wang, Shuai; Wall, Melanie M; Liu, Shang-Min; Blanco, Carlos

    2014-04-01

    Acculturation is the process by which immigrants acquire the culture of the dominant society. Little is known about the relationship between acculturation and suicidal ideation and attempts among US Hispanics. Our aim was to examine the impact of 5 acculturation measures (age at migration, time in the United States, social network composition, language, race/ethnic orientation) on suicidal ideation and attempts in the largest available nationally representative sample of US Hispanics. Study participants were US Hispanics (N = 6,359) from Wave 2 of the 2004-2005 National Epidemiologic Survey of Alcohol and Related Conditions (N = 34,653). We used linear χ(2) tests and logistic regression models to analyze the association between acculturation and risk of suicidal ideation and attempts. Factors associated with a linear increase in lifetime risk for suicidal ideation and attempts were (1) younger age at migration (linear χ(2)(1) = 57.15; P social network (linear χ(2)(1) = 36.34; P culture, such as high social support, coping strategies, and moral objections to suicide, which are modifiable factors and potential targets for public health interventions aimed at decreasing suicide risk. Culturally sensitive mental health resources need to be made more available to decrease discrimination and stigma. © Copyright 2014 Physicians Postgraduate Press, Inc.

  5. Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

    Science.gov (United States)

    Han, Bangxing; Peng, Huasheng; Yan, Hui

    2016-01-01

    Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

  6. Scientists Admitting to Plagiarism: A Meta-analysis of Surveys.

    Science.gov (United States)

    Pupovac, Vanja; Fanelli, Daniele

    2015-10-01

    We conducted a systematic review and meta-analysis of anonymous surveys asking scientists whether they ever committed various forms of plagiarism. From May to December 2011 we searched 35 bibliographic databases, five grey literature databases and hand searched nine journals for potentially relevant studies. We included surveys that asked scientists if, in a given recall period, they had committed or knew of a colleague who committed plagiarism, and from each survey extracted the proportion of those who reported at least one case. Studies that focused on academic (i.e. student) plagiarism were excluded. Literature searches returned 12,460 titles from which 17 relevant survey studies were identified. Meta-analysis of studies reporting committed (N = 7) and witnessed (N = 11) plagiarism yielded a pooled estimate of, respectively, 1.7% (95% CI 1.2-2.4) and 30% (95% CI 17-46). Basic methodological factors, including sample size, year of survey, delivery method and whether survey questions were explicit rather than indirect made a significant difference on survey results. Even after controlling for these methodological factors, between-study differences in admission rates were significantly above those expected by sampling error alone and remained largely unexplained. Despite several limitations of the data and of this meta-analysis, we draw three robust conclusions: (1) The rate at which scientists report knowing a colleague who committed plagiarism is higher than for data fabrication and falsification; (2) The rate at which scientists report knowing a colleague who committed plagiarism is correlated to that of fabrication and falsification; (3) The rate at which scientists admit having committed either form of misconduct (i.e. fabrication, falsification and plagiarism) in surveys has declined over time.

  7. Automated satellite cloud analysis: a multispectral approach to the problem of snow/cloud discrimination

    OpenAIRE

    Allen, Robert C. Jr.

    1987-01-01

    Approved for public release; distribution is unlimited An algorithm is developed and evaluated for discriminating among clouds, snow cover and clear land. The multispectral technique uses daytime images of AVHRR channels 1 (0.63^m). 3 (3.7jim) and 4 (11.0[im). Reflectance is derived for channel 3 by using the channel 4 emission temperature to estimate and remove the channel 3 thermal emission. Separation of clouds from snow and land is based primarily on this derived channel...

  8. [Gender determination based on osteometric characteristics of the upper and lower extremities by discriminant analysis].

    Science.gov (United States)

    Zviagin, V N; Sineva, I M

    2007-01-01

    The authors studied the osteological collection of the Chair of Antropology of the Moscow State University. The results of measurement of length of long tubular bones and articular parts of scapula and pelvis were statistically treated. The complex of discriminant models calculated by the Fisher's method is recommended for the sex identification. The diagnostic accuracy is 74 - 83.5% (separated bones) and 85.7 - 95.2% (complex of bones of upper and lower extremities).

  9. Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines.

    Science.gov (United States)

    van Tulder, Gijs; de Bruijne, Marleen

    2016-05-01

    The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn filters that are optimized for classification.

  10. Analysis of discriminants for experimental 3D SAR imagery of human targets

    Science.gov (United States)

    Chan, Brigitte; Sévigny, Pascale; DiFilippo, David D. J.

    2014-10-01

    Development of a prototype 3-D through-wall synthetic aperture radar (SAR) system is currently underway at Defence Research and Development Canada. The intent is to map out building wall layouts and to detect targets of interest and their location behind walls such as humans, arms caches, and furniture. This situational awareness capability can be invaluable to the military working in an urban environment. Tools and algorithms are being developed to exploit the resulting 3-D imagery. Current work involves analyzing signatures of targets behind a wall and understanding the clutter and multipath signals in a room of interest. In this paper, a comprehensive study of 3-D human target signature metrics in free space is presented. The aim is to identify features for discrimination of the human target from other targets. Targets used in this investigation include a human standing, a human standing with arms stretched out, a chair, a table, and a metallic plate. Several features were investigated as potential discriminants and five which were identified as good candidates are presented in this paper. Based on this study, no single feature could be used to fully discriminate the human targets from all others. A combination of at least two different features is required to achieve this.

  11. Formal concept analysis in knowledge processing: a survey on applications.

    OpenAIRE

    Poelmans, Jonas; Ignatov, D.; Kuznetsov, S.; Dedene, Guido

    2013-01-01

    This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and concep...

  12. SURVEY AND ANALYSIS OF 3D STEGANOGRAPHY

    Directory of Open Access Journals (Sweden)

    K .LAKSHMI

    2011-01-01

    Full Text Available Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, eg., images, audio, and video files. The remarkable growth in computational power, increase in current security approaches and techniques are often used together to ensures security of the secret message. Steganography’s ultimate objectives, which are capacity and invisibility, are the main factors that separate it from related techniques. In this paper we focus on 3D models of steganography and conclude with some review analysis of high capacity data hiding and low-distortion 3D models.

  13. Cluster analysis of the hot subdwarfs in the PG survey

    Science.gov (United States)

    Thejll, Peter; Charache, Darryl; Shipman, Harry L.

    1989-01-01

    Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

  14. Spatial discrimination and visual discrimination

    DEFF Research Database (Denmark)

    Haagensen, Annika M. J.; Grand, Nanna; Klastrup, Signe

    2013-01-01

    Two methods investigating learning and memory in juvenile Gottingen minipigs were evaluated for potential use in preclinical toxicity testing. Twelve minipigs were tested using a spatial hole-board discrimination test including a learning phase and two memory phases. Five minipigs were tested...... in a visual discrimination test. The juvenile minipigs were able to learn the spatial hole-board discrimination test and showed improved working and reference memory during the learning phase. Performance in the memory phases was affected by the retention intervals, but the minipigs were able to remember...... the concept of the test in both memory phases. Working memory and reference memory were significantly improved in the last trials of the memory phases. In the visual discrimination test, the minipigs learned to discriminate between the three figures presented to them within 9-14 sessions. For the memory test...

  15. Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis.

    Science.gov (United States)

    Buratti, Susanna; Sinelli, Nicoletta; Bertone, Elisa; Venturello, Alberto; Casiraghi, Ernestina; Geobaldo, Francesco

    2015-08-30

    The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica. Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively. NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples. © 2014 Society of Chemical Industry.

  16. Discrimination of gastric cancer from normal by serum RNA based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis

    Science.gov (United States)

    Chen, Yanping; Chen, Gang; Zheng, Xiongwei; He, Cheng; Feng, Shangyuan; Chen, Yan; Lin, Xiaoqian; Chen, Rong; Zeng, Haisan

    2012-01-01

    Purpose: Here, the authors explore the feasibility of discriminating cancer patients from healthy controls by serum RNA detection based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis. Methods: MgSO4-aggregated silver nanoparticles (Ag NP) as the SERS-active substrate presented strong SERS signals to RNA. SERS measurements were performed on two groups of serum RNA samples: one group from patients (n = 31) with gastric cancer and the other group from healthy volunteers (n = 34). Results: Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of circulating RNA between the gastric cancer group and the control group. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to differentiate gastric cancer from normal and achieved sensitivity of 100% and specificity of 94.1%. Conclusions: This exploratory study demonstrated potential for developing serum RNA SERS analysis into a useful clinical tool for noninvasive screening and detection of cancer. PMID:22957632

  17. Chemical discrimination of lubricant marketing types using direct analysis in real time time-of-flight mass spectrometry.

    Science.gov (United States)

    Maric, Mark; Harvey, Lauren; Tomcsak, Maren; Solano, Angelique; Bridge, Candice

    2017-06-30

    In comparison to other violent crimes, sexual assaults suffer from very low prosecution and conviction rates especially in the absence of DNA evidence. As a result, the forensic community needs to utilize other forms of trace contact evidence, like lubricant evidence, in order to provide a link between the victim and the assailant. In this study, 90 personal bottled and condom lubricants from the three main marketing types, silicone-based, water-based and condoms, were characterized by direct analysis in real time time of flight mass spectrometry (DART-TOFMS). The instrumental data was analyzed by multivariate statistics including hierarchal cluster analysis, principal component analysis, and linear discriminant analysis. By interpreting the mass spectral data with multivariate statistics, 12 discrete groupings were identified, indicating inherent chemical diversity not only between but within the three main marketing groups. A number of unique chemical markers, both major and minor, were identified, other than the three main chemical components (i.e. PEG, PDMS and nonoxynol-9) currently used for lubricant classification. The data was validated by a stratified 20% withheld cross-validation which demonstrated that there was minimal overlap between the groupings. Based on the groupings identified and unique features of each group, a highly discriminating statistical model was then developed that aims to provide the foundation for the development of a forensic lubricant database that may eventually be applied to casework. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  18. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Yanli Zhao

    Full Text Available Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR spectroscopy and high performance liquid chromatography (HPLC combined with multivariate analysis. The NIR parameter settings were scanning (64 times, resolution (4 cm(-1, scanning range (10,000 cm(-1∼4000 cm(-1 and parallel collection (3 times. NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm(-1 and 5973∼4007 cm(-1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA. From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R(2X and Q(2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence.

  19. Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

    Institute of Scientific and Technical Information of China (English)

    CHEN Xiaona; SURongguo; BAIYing; SHI Xiaoyong; YANG Rujun

    2014-01-01

    An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis (PARAFAC) and CHEMTAX. The PARAFAC model was applied to fluo-rescence excitation-emission matrix (EEM) of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis (BDA). Then the reference fluo-rescent component ratio matrix was constructed for CHEMTAX, and the EEM–PARAFAC–CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios (CDRs) when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacil-lariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.

  20. Discrimination of Wild Paris Based on Near Infrared Spectroscopy and High Performance Liquid Chromatography Combined with Multivariate Analysis

    Science.gov (United States)

    Zhao, Yanli; Zhang, Ji; Yuan, Tianjun; Shen, Tao; Li, Wei; Yang, Shihua; Hou, Ying; Wang, Yuanzhong; Jin, Hang

    2014-01-01

    Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR) spectroscopy and high performance liquid chromatography (HPLC) combined with multivariate analysis. The NIR parameter settings were scanning (64 times), resolution (4 cm−1), scanning range (10000 cm−1∼4000 cm−1) and parallel collection (3 times). NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm−1 and 5973∼4007 cm−1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA). From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R2X and Q2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence. PMID:24558477

  1. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Science.gov (United States)

    Zhao, Yanli; Zhang, Ji; Yuan, Tianjun; Shen, Tao; Li, Wei; Yang, Shihua; Hou, Ying; Wang, Yuanzhong; Jin, Hang

    2014-01-01

    Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR) spectroscopy and high performance liquid chromatography (HPLC) combined with multivariate analysis. The NIR parameter settings were scanning (64 times), resolution (4 cm(-1)), scanning range (10,000 cm(-1)∼4000 cm(-1)) and parallel collection (3 times). NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm(-1) and 5973∼4007 cm(-1) were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA). From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R(2)X and Q(2)Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence.

  2. Distinguishing between CAT and non-CAT areas by use of discriminant function analysis. [clear air turbulence

    Science.gov (United States)

    Clark, T. L.; Scoggins, J. R.; Cox, R. E.

    1974-01-01

    The investigation considered is concerned with a method in which a statistical approach is employed to determine algebraic functions involving selected synoptic-scale parameters which would indicate areas and altitudes of CAT in the stratosphere over the western U.S. The statistical approach selected is based on discriminant function analysis. The functions are determined from combinations of synoptic-scale parameters and stratospheric turbulence data. It was found in the investigation that there is a relationship between selected combinations of synoptic-scale parameters of the upper troposphere and lower stratosphere and stratospheric clear-air turbulence.

  3. Detection of Helicobacter pylori carriers by discriminant analysis of urea and pH levels in gastric juices.

    Science.gov (United States)

    Ameglio, F; Abbolito, M R; Giannarelli, D; Citarda, F; Grassi, A; Gandolfo, G M; Casale, V

    1991-08-01

    An alternative approach to the problems inherent in current methods for detecting Helicobacter pylori carriers--that of being generally time-consuming, expensive, and not sufficiently sensitive--was devised by using the urea concentration and pH levels of gastric juices. A linear discriminant analysis of these variables, measured in 54 patients submitted to digestive endoscopy for gastritis, provided a mathematical formula for assigning the subjects (previously classified by other standard methods) to groups of either positive or negative H pylori carriers. The results obtained showed a correct classification in 52 out of 54 cases with only one false negative and one false positive case.

  4. Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Alia Colniță

    2017-09-01

    Full Text Available Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS, are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei and Listeria monocytogenes (L. monocytogenes were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA to their specific spectral data.

  5. Contributions to sensitivity analysis and generalized discriminant analysis; Contributions a l'analyse de sensibilite et a l'analyse discriminante generalisee

    Energy Technology Data Exchange (ETDEWEB)

    Jacques, J

    2005-12-15

    Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

  6. Analysis on discriminants based on Lg wave%基于Lg波识别判据的特性分析

    Institute of Scientific and Technical Information of China (English)

    何永锋; 赵克常; 姚国正; 张献兵; 马裕然

    2011-01-01

    Lg波是检测、识别地下核爆炸的重要震相.通常情况下,地下核爆炸的低频段Lg波能量相对较强,基于Lg波的识别判据在高频段表现较好.然而在低频范围内却表现出难以用球对称爆炸源模式解释的现象,如Lg/Pg判据在1 Hz附近失效等现象.本文利用理论地震图方法,分析了伴随层裂过程产生的重要辅助源——CLVD源对Lg波低频成分的调制作用,给出了基于Lg波的识别判据成功及失效的原因,并将层裂过程视为输入扰动,利用振型叠加原理,进一步解释了基于Lg波识别判据特性的物理机制.这对于更好地利用Lg波识别地下核爆炸,具有较好的参考意义.%Lg wave characteristic is widely used to discriminate underground nuclear explosions from earthquakes. In contrast, the high-frequency Lg/Pg discriminant appears to perform very well, but fails around 1 Hz. This phenomenon can not be explained by pure explosion source model, but the CLVD source appears to take a key role to explain it. In this paper, the effect of CLVD source on the performance of the discriminants based on Lg wave is studied by means of theoretical seismogram analysis. Considering the spall as an input of disturbance with special frequency, we proposed a physical mechanism of the performance of Lg/Pg based on the normal mode method. An understanding of the effect on Lg caused by CLVD source can be useful not only for improving the performance of existing regional discriminants, but also for exploring new discriminants.

  7. [Discriminant analysis of graphic elements of the EEG. Application to the detection of episodes of diffuse spike-waves].

    Science.gov (United States)

    Pinon, J M; Rubel, P; Maraval, G; Mauguiere, F; Revol, M

    1982-05-01

    A database of EEG information was collected from EEG recordings performed in epileptic patients with diffuse spike-wave complex discharges. Normal activity, spike-waves, slow waves and artefacts were mixed up in these recordings. The analysis of EEGs stored in the database was performed, channel by channel, through a 2.56 s moving window. For each so defined EEG sequence, a set of 22 variables chosen for their discriminatory power was computed. A subset of 8 highly discriminating variables was selected by the means of a stepwise discriminant analysis. Each class of the learning set contained 40 up to 100 EEG sequences. A classifying algorithm that takes into account zones of uncertainty is proposed. It has been evaluated on a test set which was composed of 1981 EEG sequences issued from 15 different patients. The results have been checked by two neurologists. The agreement rate between each of them and the proposed algorithm was more than 92%; this result is comparable to the agreement rate between the two neurologists (94%). A contextual analysis algorithm, using bi-dimensional smoothing techniques, allowed to improve the agreement rates which exceeded 94%.

  8. Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols and partial least square-discriminant analysis.

    Science.gov (United States)

    Ruiz-Samblás, Cristina; Arrebola-Pascual, Cristina; Tres, Alba; van Ruth, Saskia; Cuadros-Rodríguez, Luis

    2013-11-15

    Main goals of the present work were to develop authentication models based on liquid and gas chromatographic fingerprinting of triacylglycerols (TAGs) from palm oil of different geographical origins in order to compare them. For this purpose, a set of palm oil samples were collected from different continents: South eastern Asia, Africa and South America. For the analysis of the information in these fingerprint profiles, a pattern recognition technique such as partial least square discriminant analysis (PLS-DA) was applied to discriminate the geographical origin of these oils, at continent level. The liquid chromatography, coupled to a charged aerosol detector, (HPLC-CAD) TAGs separation was optimized in terms of mobile phase composition and by means of a solid silica core column. The gas chromatographic method with a mass spectrometer was applied under high temperature (HTGC-MS) in order to analyze the intact TAGs. Satisfactory chromatographic resolution within a short total analysis time was achieved with both chromatographic approaches and without any prior sample treatment. The rates of successful in prediction of the geographical origin of the 85 samples varied between 70% and 100%.

  9. Discrimination and characterization of breath from smokers and non-smokers via electronic nose and GC/MS analysis.

    Science.gov (United States)

    Witt, Katharina; Reulecke, Sina; Voss, Andreas

    2011-01-01

    The objective of this study was to prove the general applicability of an electronic nose for analyzing exhaled breath considering the dependency on smoking. At first, odor compounds from spices (n=6) were detected via the electronic nose and further characterized and classified with gas chromatography/ mass spectrometry to demonstrate the principle ability of the electronic nose. Then, the exhaled breath from smokers and non-smokers were analyzed to prove the influence of smoking on breath analyses with the electronic nose. The exhaled breath was sampled from 11 smokers and 11 non-smokers in a special sampling bag with the mounted sensor chip of the electronic nose. Additionally, solid phase micro-extraction (SPME) technique was established for detection of the specific chemical compounds with gas chromatography and mass spectrometry (GC/MS). For analyses of the sensor signals the principle component analysis (PCA) was applied and the groups were differentiated by linear discriminant function analysis. In accordance to the discrimination between the different spices and between smokers and non-smokers the PCA analysis leads to an optimum accuracy of 100%. The results of this study show that an electronic nose has the ability to detect different changes of odor components and provides separation of smoking side effects in smelling different diseases.

  10. Child Custody Decisions: Content Analysis of a Judicial Survey.

    Science.gov (United States)

    Settle, Shirley A; Lowery, Carol R.

    1982-01-01

    Surveyed judges and trial commissioners (N=80) regarding child custody decisions in divorce. The content analysis described the responents' comments which clarified their reasons for attaching greater or lesser importance to a particular consideration or the method using in assessing a particular consideration during a court proceeding. (JAC)

  11. Experienced discrimination amongst European old citizens

    NARCIS (Netherlands)

    van den Heuvel, Wim J. A.; van Santvoort, Marc M.

    2011-01-01

    This study analyses the experienced age discrimination of old European citizens and the factors related to this discrimination. Differences in experienced discrimination between old citizens of different European countries are explored. Data from the 2008 ESS survey are used. Old age is defined as b

  12. Experienced discrimination amongst European old citizens

    NARCIS (Netherlands)

    van den Heuvel, Wim J. A.; van Santvoort, Marc M.

    2011-01-01

    This study analyses the experienced age discrimination of old European citizens and the factors related to this discrimination. Differences in experienced discrimination between old citizens of different European countries are explored. Data from the 2008 ESS survey are used. Old age is defined as b

  13. Sex Discrimination and Women's Labor Market Outcomes.

    Science.gov (United States)

    Neumark, David; McLennan, Michele

    1995-01-01

    Using self-reported sex discrimination data from the National Longitudinal Survey of Young Women, a study found that working women who report discrimination are more likely to change employers or interrupt their labor force participation. However, women who report discrimination do not accrue less experience or have lower wage growth. (SK)

  14. Experienced discrimination amongst European old citizens

    NARCIS (Netherlands)

    van den Heuvel, Wim J. A.; van Santvoort, Marc M.

    2011-01-01

    This study analyses the experienced age discrimination of old European citizens and the factors related to this discrimination. Differences in experienced discrimination between old citizens of different European countries are explored. Data from the 2008 ESS survey are used. Old age is defined as

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

    Directory of Open Access Journals (Sweden)

    Grahić Jasmin

    2013-01-01

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

  16. Estimating the mental health costs of racial discrimination.

    Science.gov (United States)

    Elias, Amanuel; Paradies, Yin

    2016-11-29

    Racial discrimination is a pervasive social problem in several advanced countries such as the U.S., U.K., and Australia. Public health research also indicates a range of associations between exposure to racial discrimination and negative health, particularly, mental health including depression, anxiety, and post-traumatic stress disorder (PTSD). However, the direct negative health impact of racial discrimination has not been costed so far although economists have previously estimated indirect non-health related productivity costs. In this study, we estimate the burden of disease due to exposure to racial discrimination and measure the cost of this exposure. Using prevalence surveys and data on the association of racial discrimination with health outcomes from a global meta-analysis, we apply a cost of illness method to measure the impact of racial discrimination. This estimate indicates the direct health cost attributable to racial discrimination and we convert the estimates to monetary values based on conventional parameters. Racial discrimination costs the Australian economy 235,452 in disability adjusted life years lost, equivalent to $37.9 billion per annum, roughly 3.02% of annual gross domestic product (GDP) over 2001-11, indicating a sizeable loss for the economy. Substantial cost is incurred due to increased prevalence of racial discrimination as a result of its association with negative health outcomes (e.g. depression, anxiety and PTSD). This implies that potentially significant cost savings can be made through measures that target racial discrimination. Our research contributes to the debate on the social impact of racial discrimination, with implications for policies and efforts addressing it.

  17. Sensitivity analysis and related analysis : A survey of statistical techniques

    NARCIS (Netherlands)

    Kleijnen, J.P.C.

    1995-01-01

    This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical

  18. Conclusions from the Image Analysis of the VSOP Survey

    Science.gov (United States)

    Dodson, R.; Fomalont, E.; Wiik, K.

    2009-08-01

    In February 1997, the Japanese radio astronomy satellite HALCA was launched to provide the space-bourne element for the VLBI Space Observatory Programme (VSOP) mission. A significant fraction of the mission time was to be dedicated to the VSOP Survey of bright compact Active Galactic Nuclei (AGN) at 5 GHz, which was lead by ISAS. The VSOP Survey Sources are an unbiased dataset of 294 targets, of which 82% were successfully observed. These are now undergoing statistical analysis to tease out the characteristics of typical AGN sources. We present here the summary of the imaging and conclusions we have reached.

  19. Accuracy Analysis of a Dam Model from Drone Surveys

    OpenAIRE

    Elena Ridolfi; Giulia Buffi; Sara Venturi; Piergiorgio Manciola

    2017-01-01

    This paper investigates the accuracy of models obtained by drone surveys. To this end, this work analyzes how the placement of ground control points (GCPs) used to georeference the dense point cloud of a dam affects the resulting three-dimensional (3D) model. Images of a double arch masonry dam upstream face are acquired from drone survey and used to build the 3D model of the dam for vulnerability analysis purposes. However, there still remained the issue of understanding the real impact of a...

  20. A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph.

    Science.gov (United States)

    Zu, Baokai; Xia, Kewen; Pan, Yongke; Niu, Wenjia

    2017-01-01

    Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines.

  1. A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

    Directory of Open Access Journals (Sweden)

    Baokai Zu

    2017-01-01

    Full Text Available Semisupervised Discriminant Analysis (SDA is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines.

  2. Rapid discrimination of the causal agents of urinary tract infection using ToF-SIMS with chemometric cluster analysis

    Science.gov (United States)

    Fletcher, John S.; Henderson, Alexander; Jarvis, Roger M.; Lockyer, Nicholas P.; Vickerman, John C.; Goodacre, Royston

    2006-07-01

    Advances in time of flight secondary ion mass spectrometry (ToF-SIMS) have enabled this technique to become a powerful tool for the analysis of biological samples. Such samples are often very complex and as a result full interpretation of the acquired data can be extremely difficult. To simplify the interpretation of these information rich data, the use of chemometric techniques is becoming widespread in the ToF-SIMS community. Here we discuss the application of principal components-discriminant function analysis (PC-DFA) to the separation and classification of a number of bacterial samples that are known to be major causal agents of urinary tract infection. A large data set has been generated using three biological replicates of each isolate and three machine replicates were acquired from each biological replicate. Ordination plots generated using the PC-DFA are presented demonstrating strain level discrimination of the bacteria. The results are discussed in terms of biological differences between certain species and with reference to FT-IR, Raman spectroscopy and pyrolysis mass spectrometric studies of similar samples.

  3. Application of microcalorimetry of Escherichia coli growth and discriminant analysis to the quality assessment of a Chinese herbal injection (Yinzhihuang

    Directory of Open Access Journals (Sweden)

    Yongshen Ren

    2012-06-01

    Full Text Available This paper describes a novel approach to the quality control of a Chinese herbal injection based on microcalorimetric determination of its effect on the biothermal active fingerprint (BTAF of Escherichia coli (E. coli growth. Yinzhihuang Injection (YZHI was selected for this proof of concept study. Reference samples of YZHI were collected and compared with test (stressed samples prepared under different stress conditions. The BTAF of E. coli growth was found to be affected by YZHI and the changes were analyzed on the basis of eleven biothermokinetic parameters. Similarity and multivariate statistical analysis were used to investigate the differences between reference and test samples and discriminant analysis was used to delineate the altered samples. Reference samples were found to have coincident BTAFs with similarity index >0.99. Stressed samples showed differences in the BTAF which increased in line with decreased quality. Discriminant formulae were developed based on a sensitivity parameter which could identify all altered samples. In conclusion, BTAF can be used to assess the quality of YZHI both qualitatively and quantitatively and has the potential to provide a sensitive method for quality control of Chinese herbal injections.

  4. The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals.

    Science.gov (United States)

    Güven, Ayşegül; Polat, Kemal; Kara, Sadik; Güneş, Salih

    2008-01-01

    In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals.

  5. Like/dislike analysis using EEG: determination of most discriminative channels and frequencies.

    Science.gov (United States)

    Yılmaz, Bülent; Korkmaz, Sümeyye; Arslan, Dilek Betül; Güngör, Evrim; Asyalı, Musa H

    2014-02-01

    In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4-19 Hz and high frequency (HF) 20-40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential

  6. Male-female discrimination: an analysis of gender gap and its determinants

    Directory of Open Access Journals (Sweden)

    Claudio Quintano

    2013-05-01

    Full Text Available In recent years, the occupational dynamics have brought in significant innovations in Italy, as the increased participation of women in the labour market, that have stimulated studies about the gender wage gap, concerning the different remuneration reserved to male and female workers. In this work the Authors, following Oaxaca and Blinder approach, estimate the gap for Italian employers and proceed to its decomposition, one part due to differences in individual characteristics (endowment effect and another part due to the different returns on the same characteristics (coefficient effect, related to discrimination. Then, the gender wage gap and its decomposition is analyzed with reference to Italian macro-areas considered separately with the aim to highlight the different fundamental dynamics. The model has also been modified using the Heckmann correction to eliminate the bias due to self-selection; i.e. the different propensity to work for men and women.

  7. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2016-01-01

    unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both......The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...

  8. The Green Bank Northern Celestial Cap Pulsar Survey - I: Survey Description, Data Analysis, and Initial Results

    CERN Document Server

    Stovall, K; Ransom, S M; Archibald, A M; Banaszak, S; Biwer, C M; Boyles, J; Dartez, L P; Day, D; Ford, A J; Flanigan, J; Garcia, A; Hessels, J W T; Hinojosa, J; Jenet, F A; Kaplan, D L; Karako-Argaman, C; Kaspi, V M; Kondratiev, V I; Leake, S; Lorimer, D R; Lunsford, G; Martinez, J G; Mata, A; McLaughlin, M A; Roberts, M S E; Rohr, M D; Siemens, X; Stairs, I H; van Leeuwen, J; Walker, A N; Wells, B L

    2014-01-01

    We describe an ongoing search for pulsars and dispersed pulses of radio emission, such as those from rotating radio transients (RRATs) and fast radio bursts (FRBs), at 350 MHz using the Green Bank Telescope. With the Green Bank Ultimate Pulsar Processing Instrument, we record 100 MHz of bandwidth divided into 4,096 channels every 81.92 $\\mu s$. This survey will cover the entire sky visible to the Green Bank Telescope ($\\delta > -40^\\circ$, or 82% of the sky) and outside of the Galactic Plane will be sensitive enough to detect slow pulsars and low dispersion measure ($<$30 $\\mathrm{pc\\,cm^{-3}}$) millisecond pulsars (MSPs) with a 0.08 duty cycle down to 1.1 mJy. For pulsars with a spectral index of $-$1.6, we will be 2.5 times more sensitive than previous and ongoing surveys over much of our survey region. Here we describe the survey, the data analysis pipeline, initial discovery parameters for 62 pulsars, and timing solutions for 5 new pulsars. PSR J0214$+$5222 is an MSP in a long-period (512 days) orbit a...

  9. Latent class analysis of reading, decoding, and writing performance using the Academic Performance Test: concurrent and discriminating validity.

    Science.gov (United States)

    Cogo-Moreira, Hugo; Carvalho, Carolina Alves Ferreira; de Souza Batista Kida, Adriana; de Avila, Clara Regina Brandão; Salum, Giovanni Abrahão; Moriyama, Tais Silveira; Gadelha, Ary; Rohde, Luis Augusto; de Moura, Luciana Monteiro; Jackowski, Andrea Parolin; de Jesus Mari, Jair

    2013-01-01

    To explore and validate the best returned latent class solution for reading and writing subtests from the Academic Performance Test (TDE). A total of 1,945 children (6-14 years of age), who answered the TDE, the Development and Well-Being Assessment (DAWBA), and had an estimated intelligence quotient (IQ) higher than 70, came from public schools in São Paulo (35 schools) and Porto Alegre (22 schools) that participated in the 'High Risk Cohort Study for Childhood Psychiatric Disorders' project. They were on average 9.52 years old (standard deviation = 1.856), from the 1st to 9th grades, and 53.3% male. The mean estimated IQ was 102.70 (standard deviation = 16.44). Via Item Response Theory (IRT), the highest discriminating items ('a'>1.7) were selected from the TDE subtests of reading and writing. A latent class analysis was run based on these subtests. The statistically and empirically best latent class solutions were validated through concurrent (IQ and combined attention deficit hyperactivity disorder [ADHD] diagnoses) and discriminant (major depression diagnoses) measures. A three-class solution was found to be the best model solution, revealing classes of children with good, not-so-good, or poor performance on TDE reading and writing tasks. The three-class solution has been shown to be correlated with estimated IQ and to ADHD diagnosis. No association was observed between the latent class and major depression. The three-class solution showed both concurrent and discriminant validity. This work provides initial evidence of validity for an empirically derived categorical classification of reading, decoding, and writing performance using the TDE. A valid classification encourages further research investing correlates of reading and writing performance using the TDE.

  10. Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

    Science.gov (United States)

    Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.

    2016-06-01

    Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

  11. UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    F. Mohammadimanesh

    2016-06-01

    Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

  12. Latent class analysis of reading, decoding, and writing performance using the Academic Performance Test: concurrent and discriminating validity

    Science.gov (United States)

    Cogo-Moreira, Hugo; Carvalho, Carolina Alves Ferreira; de Souza Batista Kida, Adriana; de Avila, Clara Regina Brandão; Salum, Giovanni Abrahão; Moriyama, Tais Silveira; Gadelha, Ary; Rohde, Luis Augusto; de Moura, Luciana Monteiro; Jackowski, Andrea Parolin; de Jesus Mari, Jair

    2013-01-01

    Aim To explore and validate the best returned latent class solution for reading and writing subtests from the Academic Performance Test (TDE). Sample A total of 1,945 children (6–14 years of age), who answered the TDE, the Development and Well-Being Assessment (DAWBA), and had an estimated intelligence quotient (IQ) higher than 70, came from public schools in São Paulo (35 schools) and Porto Alegre (22 schools) that participated in the ‘High Risk Cohort Study for Childhood Psychiatric Disorders’ project. They were on average 9.52 years old (standard deviation = 1.856), from the 1st to 9th grades, and 53.3% male. The mean estimated IQ was 102.70 (standard deviation = 16.44). Methods Via Item Response Theory (IRT), the highest discriminating items (‘a’>1.7) were selected from the TDE subtests of reading and writing. A latent class analysis was run based on these subtests. The statistically and empirically best latent class solutions were validated through concurrent (IQ and combined attention deficit hyperactivity disorder [ADHD] diagnoses) and discriminant (major depression diagnoses) measures. Results A three-class solution was found to be the best model solution, revealing classes of children with good, not-so-good, or poor performance on TDE reading and writing tasks. The three-class solution has been shown to be correlated with estimated IQ and to ADHD diagnosis. No association was observed between the latent class and major depression. Conclusion The three-class solution showed both concurrent and discriminant validity. This work provides initial evidence of validity for an empirically derived categorical classification of reading, decoding, and writing performance using the TDE. A valid classification encourages further research investing correlates of reading and writing performance using the TDE. PMID:23983466

  13. Sex determination by discriminant function analysis of the tibia for contemporary Croats.

    Science.gov (United States)

    Slaus, Mario; Bedić, Zeljka; Strinović, Davor; Petrovečki, Vedrana

    2013-03-10

    Previous studies have demonstrated that populations differ from each other in size and proportion, and that these differences can affect metric assessment of sex. This paper establishes standards for determining sex from fragmentary and complete tibiae in the modern Croatian population. Measurements were taken on 180 tibiae (109 male and 71 female) from positively identified victims of the 1991-1995 War in Croatia. Six standard dimensions: length of the tibia (CML), maximum epiphyseal breadth of the proximal tibia (MPEB), maximum epiphyseal breadth of the distal tibia (MDEB), maximum diameter of the tibia at the nutrient foramen (MDNF), transverse diameter of the tibia at the nutrient foramen (TDNF), and circumference of the tibia at the nutrient foramen (CNF), were taken and subjected to different discriminant function analyses. The highest level of accuracy (91.1%) in the analyzed data set was achieved employing the variables: maximum epiphyseal breadth of the proximal tibia, maximum epiphyseal breadth of the distal tibia, maximum diameter of the tibia at the nutrient foramen, transverse diameter of the tibia at the nutrient foramen, and circumference of the tibia at the nutrient foramen. The second highest level of accuracy (90.6%) was achieved using a combination of only three variables: maximum epiphyseal breadth of the proximal tibia, maximum diameter of the tibia at the nutrient foramen, and circumference of the tibia at the nutrient foramen. The lowest accuracy (84.4%) was obtained when only one variable (maximum diameter of the tibia at the nutrient foramen) was employed. The results of this study show that the modern Croatian tibia is a good skeletal component for determining sex. Standardized coefficients of the discriminant functions generated in this study support the results of previous studies that found that breadth dimensions provide better separation of the sexes than length.

  14. Discriminating poststroke depression from stroke by nuclear magnetic resonance spectroscopy-based metabonomic analysis

    Directory of Open Access Journals (Sweden)

    Xiao J

    2016-08-01

    Full Text Available Jianqi Xiao,1,* Jie Zhang,2,* Dan Sun,3,* Lin Wang,4,* Lijun Yu,5 Hongjing Wu,5 Dan Wang,5 Xuerong Qiu5 1Department of Neurosurgery, The First Hospital of Qiqihar City, Qiqihar, 2Department of Internal Medicine, Central Hospital of Jiamusi City, Jiamusi, 3Department of Geriatrics, General Hospital of Daqing Oil Field, Daqing, 4Department of Nursing, 5Department of Neurology, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang, People’s Republic of China *These authors contributed equally to this work Abstract: Poststroke depression (PSD, the most common psychiatric disease that stroke survivors face, is estimated to affect ~30% of poststroke patients. However, there are still no objective methods to diagnose PSD. In this study, to explore the differential metabolites in the urine of PSD subjects and to identify a potential biomarker panel for PSD diagnosis, the nuclear magnetic resonance-based metabonomic method was applied. Ten differential metabolites responsible for discriminating PSD subjects from healthy control (HC and stroke subjects were found, and five of these metabolites were identified as potential biomarkers (lactate, α-hydroxybutyrate, phenylalanine, formate, and arabinitol. The panel consisting of these five metabolites provided excellent performance in discriminating PSD subjects from HC and stroke subjects, achieving an area under the receiver operating characteristic curve of 0.946 in the training set (43 HC, 45 stroke, and 62 PSD subjects. Moreover, this panel could classify the blinded samples from the test set (31 HC, 33 stroke, and 32 PSD subjects with an area under the curve of 0.946. These results laid a foundation for the future development of urine-based objective methods for PSD diagnosis and investigation of PSD pathogenesis. Keywords: poststroke depression, PSD, stroke, nuclear magnetic resonance, NMR, metabonomic

  15. Inverse spatial principal component analysis for geophysical survey data interpolation

    Science.gov (United States)

    Li, Qingmou; Dehler, Sonya A.

    2015-04-01

    The starting point for data processing, visualization, and overlay with other data sources in geological applications often involves building a regular grid by interpolation of geophysical measurements. Typically, the sampling interval along survey lines is much higher than the spacing between survey lines because the geophysical recording system is able to operate with a high sampling rate, while the costs and slower speeds associated with operational platforms limit line spacing. However, currently available interpolating methods often smooth data observed with higher sampling rate along a survey line to accommodate the lower spacing across lines, and much of the higher resolution information is not captured in the interpolation process. In this approach, a method termed as the inverse spatial principal component analysis (isPCA) is developed to address this problem. In the isPCA method, a whole profile observation as well as its line position is handled as an entity and a survey collection of line entities is analyzed for interpolation. To test its performance, the developed isPCA method is used to process a simulated airborne magnetic survey from an existing magnetic grid offshore the Atlantic coast of Canada. The interpolation results using the isPCA method and other methods are compared with the original survey grid. It is demonstrated that the isPCA method outperforms the Inverse Distance Weighting (IDW), Kriging (Geostatistical), and MINimum Curvature (MINC) interpolation methods in retaining detailed anomaly structures and restoring original values. In a second test, a high resolution magnetic survey offshore Cape Breton, Nova Scotia, Canada, was processed and the results are compared with other geological information. This example demonstrates the effective performance of the isPCA method in basin structure identification.

  16. Accuracy Analysis of a Dam Model from Drone Surveys.

    Science.gov (United States)

    Ridolfi, Elena; Buffi, Giulia; Venturi, Sara; Manciola, Piergiorgio

    2017-08-03

    This paper investigates the accuracy of models obtained by drone surveys. To this end, this work analyzes how the placement of ground control points (GCPs) used to georeference the dense point cloud of a dam affects the resulting three-dimensional (3D) model. Images of a double arch masonry dam upstream face are acquired from drone survey and used to build the 3D model of the dam for vulnerability analysis purposes. However, there still remained the issue of understanding the real impact of a correct GCPs location choice to properly georeference the images and thus, the model. To this end, a high number of GCPs configurations were investigated, building a series of dense point clouds. The accuracy of these resulting dense clouds was estimated comparing the coordinates of check points extracted from the model and their true coordinates measured via traditional topography. The paper aims at providing information about the optimal choice of GCPs placement not only for dams but also for all surveys of high-rise structures. The knowledge a priori of the effect of the GCPs number and location on the model accuracy can increase survey reliability and accuracy and speed up the survey set-up operations.

  17. Accuracy Analysis of a Dam Model from Drone Surveys

    Directory of Open Access Journals (Sweden)

    Elena Ridolfi

    2017-08-01

    Full Text Available This paper investigates the accuracy of models obtained by drone surveys. To this end, this work analyzes how the placement of ground control points (GCPs used to georeference the dense point cloud of a dam affects the resulting three-dimensional (3D model. Images of a double arch masonry dam upstream face are acquired from drone survey and used to build the 3D model of the dam for vulnerability analysis purposes. However, there still remained the issue of understanding the real impact of a correct GCPs location choice to properly georeference the images and thus, the model. To this end, a high number of GCPs configurations were investigated, building a series of dense point clouds. The accuracy of these resulting dense clouds was estimated comparing the coordinates of check points extracted from the model and their true coordinates measured via traditional topography. The paper aims at providing information about the optimal choice of GCPs placement not only for dams but also for all surveys of high-rise structures. The knowledge a priori of the effect of the GCPs number and location on the model accuracy can increase survey reliability and accuracy and speed up the survey set-up operations.

  18. An Analysis of the Association between Perceived Discrimination and Self-Reported Health among University Students in Southwest Florida

    Science.gov (United States)

    McFarland, Renee L.

    2013-01-01

    The experience of discrimination is a complex phenomenon. At present, there are few studies that have captured the experience of discrimination on a predominately white university campus. This study was designed to investigate the association between perceived discrimination and self-reported health outcomes among university students in Southwest…

  19. Discrimination between Bacillus and Alicyclobacillus isolates in apple juice by Fourier transform infrared spectroscopy and multivariate analysis.

    Science.gov (United States)

    Al-Holy, Murad A; Lin, Mengshi; Alhaj, Omar A; Abu-Goush, Mahmoud H

    2015-02-01

    Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera.

  20. SIRTF Focal Plane Survey: A Pre-flight Error Analysis

    Science.gov (United States)

    Bayard, David S.; Brugarolas, Paul B.; Boussalis, Dhemetrios; Kang, Bryan H.

    2003-01-01

    This report contains a pre-flight error analysis of the calibration accuracies expected from implementing the currently planned SIRTF focal plane survey strategy. The main purpose of this study is to verify that the planned strategy will meet focal plane survey calibration requirements (as put forth in the SIRTF IOC-SV Mission Plan [4]), and to quantify the actual accuracies expected. The error analysis was performed by running the Instrument Pointing Frame (IPF) Kalman filter on a complete set of simulated IOC-SV survey data, and studying the resulting propagated covariances. The main conclusion of this study is that the all focal plane calibration requirements can be met with the currently planned survey strategy. The associated margins range from 3 to 95 percent, and tend to be smallest for frames having a 0.14" requirement, and largest for frames having a more generous 0.28" (or larger) requirement. The smallest margin of 3 percent is associated with the IRAC 3.6 and 5.8 micron array centers (frames 068 and 069), and the largest margin of 95 percent is associated with the MIPS 160 micron array center (frame 087). For pointing purposes, the most critical calibrations are for the IRS Peakup sweet spots and short wavelength slit centers (frames 019, 023, 052, 028, 034). Results show that these frames are meeting their 0.14" requirements with an expected accuracy of approximately 0.1", which corresponds to a 28 percent margin.

  1. A Clinical Analysis of 293 FUO Patients, A Diagnostic Model Discriminating infectious Diseases from Non-infectious Diseases

    Institute of Scientific and Technical Information of China (English)

    2014-01-01

    Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin (FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The ifrst group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases (αin= 0.05, αout= 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis. Results The diagnostic rate of 143 patients in the ifrst group was 87.4%, the diagnosis included infectious disease (52.4%), connective tissue diseases (16.8%), neoplastic disease (16.1%) and miscellaneous (2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the ifrst group. Logistic regression analysis showed that decreased white blood cell count (WBC 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11 (P≤ 0.01) , respectively. In ROC analysis, the sensitivity and speciifcity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively (AUC = 0.76,P = 0.00). Conclusions The combination of WBC 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.

  2. Technical, perceptual and motor skills in novice-expert water polo players: an individual discriminant analysis for talent development.

    Science.gov (United States)

    DʼErcole, Alessandro A; DʼErcole, Cristina; Gobbi, Massimo; Gobbi, Fabio

    2013-12-01

    The 4 tasks (A, B, C, and Y) have the characteristic of containing one more element than the task performed before it. In fact, task B introduces the slalom which is not present in task A. Task C introduces the ball control that are not present in tasks A and B, whereas task Y introduces the slalom and ball control in a visual dual task situation developed in horizontal swimming over a distance of 20 m at maximum speed. This exercise not included in task C. These tasks were performed by a group of pre-adolescent players and national under 18 water polo players. The novice players showed that tasks B and C are predictors of task Y. Such characteristics were not present in the expert players. The novice players also had difficulty in performing task Y because of the visual-attention overload, a difficulty that was not present in the expert players. To improve the 4 skills, the coach of the novice players developed a technical-didactic program, which was checked 6 months after the pretest. The posttest was not significantly different from the pretest while the individual discriminant analysis identified the improvements in some novice players, which on elaboration proved significant, enabling us to distinguish 2 subgroups, one with higher learning rates and the other with lower learning rates. In the practical applications, we describe the didactic tools (task analysis) and the different levels of development of technical skills in water polo. Improvements in these skills are explained through computational models like the HMOSAIC (Hierarchical, Modular, Selection and Identification for Control) while the individual discriminant analysis enables us to do a longitudinal analysis that is not possible with cross-sectional models.

  3. Revealing discriminating power of the elements in edible sea salts: Line-intensity correlation analysis from laser-induced plasma emission spectra

    Science.gov (United States)

    Lee, Yonghoon; Ham, Kyung-Sik; Han, Song-Hee; Yoo, Jonghyun; Jeong, Sungho

    2014-11-01

    We have investigated the discriminating power of the elements in edible sea salts using Laser-Induced Breakdown Spectroscopy (LIBS). For the ten different sea salts from South Korea, China, Japan, France, Mexico and New Zealand, LIBS spectra were recorded in the spectral range between 190 and 1040 nm, identifying the presence of Na, Cl, K, Ca, Mg, Li, Sr, Al, Si, Ti, Fe, C, O, N, and H. Intensity correlation analysis of the observed emission lines provided a valuable insight into the discriminating power of the different elements in the sea salts. The correlation analysis suggests that the elements with independent discrimination power can be categorized into three groups; those that represent dissolved ions in seawater (K, Li, and Mg), those that are associated with calcified particles (Ca and Sr), and those that are present in soils contained in the sea salts (Al, Si, Ti, and Fe). Classification models using a few emission lines selected based on the results from intensity correlation analysis and full broadband LIBS spectra were developed based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) and their performances were compared. Our results indicate that effective combination of a few emission lines can provide a dependable model for discriminating the edible sea salts and the performance is not much degraded from that based on the full broadband spectra. This can be rationalized by the intensity correlation results.

  4. Discrimination of surface wear on obsidian tools using LSCM and RelA: pilot study results (area-scale analysis of obsidian tool surfaces).

    Science.gov (United States)

    Stemp, W James; Chung, Steven

    2011-01-01

    This pilot study tests the reliability of laser scanning confocal microscopy (LSCM) to quantitatively measure wear on experimental obsidian tools. To our knowledge, this is the first use of confocal microscopy to study wear on stone flakes made from an amorphous silicate like obsidian. Three-dimensional surface roughness or texture area scans on three obsidian flakes used on different contact materials (hide, shell, wood) were documented using the LSCM to determine whether the worn surfaces could be discriminated using area-scale analysis, specifically relative area (RelA). When coupled with the F-test, this scale-sensitive fractal analysis could not only discriminate the used from unused surfaces on individual tools, but was also capable of discriminating the wear histories of tools used on different contact materials. Results indicate that such discriminations occur at different scales. Confidence levels for the discriminations at different scales were established using the F-test (mean square ratios or MSRs). In instances where discrimination of surface roughness or texture was not possible above the established confidence level based on MSRs, photomicrographs and RelA assisted in hypothesizing why this was so. Copyright © 2011 Wiley Periodicals, Inc.

  5. Medical students' perception of lesbian, gay, bisexual, and transgender (LGBT) discrimination in their learning environment and their self-reported comfort level for caring for LGBT patients: a survey study.

    Science.gov (United States)

    Nama, Nassr; MacPherson, Paul; Sampson, Margaret; McMillan, Hugh J

    2017-01-01

    Historically, medical students who are lesbian, gay, bisexual or transgendered (LGBT) report higher rates of social stress, depression, and anxiety, while LGBT patients have reported discrimination and poorer access to healthcare. The objectives of this study were: (1) to assess if medical students have perceived discrimination in their learning environment and; (2) to determine self-reported comfort level for caring for LGBT patients. Medical students at the University of Ottawa (N = 671) were contacted via email and invited to complete a confidential web-based survey. Response rate was 15.4% (103/671). This included 66 cis-gender heterosexuals (64.1%) and 37 LGBT students (35.9%). Anti-LGBT discrimination had been witnessed by 14.6% and heterosexism by 31.1% of respondents. Anti-LGBT discrimination most often originated from fellow medical students. Respondents who self-identified as LGBT were more likely to have perceived heterosexism (favoring opposite-sex relationships) (OR = 8.2, p LGBT discrimination (OR = 6.6, p = 0.002). While half of LGBT students shared their status with all classmates (51.4%), they were more likely to conceal this from staff physicians (OR = 27.2, p = 0.002). Almost half of medical students (41.7%) reported anti-LGBT jokes, rumors, and/or bullying by fellow medical students and/or other members of the healthcare team. Still, most respondents indicated that they felt comfortable with and capable of providing medical care to LGBT patients (≥83.5%), and were interested in further education around LGBT health issues (84.5%). Anti-LGBT discrimination and heterosexism are noted by medical students, indicating a suboptimal learning environment for LGBT students. Nonetheless, students report a high level of comfort and confidence providing health care to LGBT patients.

  6. Use of linear discriminant analysis to characterise three dairy cattle breeds on the basis of several milk characteristics

    Directory of Open Access Journals (Sweden)

    Roberto Leotta

    2010-01-01

    Full Text Available To characterise individuals of differents breeds on the basis of milk composition and to identify the best set of variablesa linear discriminant analysis (LDA, on 14 milk production traits, was performed on milk samples from 199 cows of differentbreeds (respectively, 127 subjects were Italian Friesians (IF, 62 were German Friesians (GF, and 10 were Jerseys(J and all came from the same breeding farm in Tuscany. The variables were: test day milk yield (kg milk, % Fat, %Protein,% Lactose, % solid non fat (SNF, % total solid (TS, pH and titratable acidity (TA; five rheological variables: r,k20, a30, a45, and somatic cell counts /ml (SCC; and one hygiene-related variable: total bacterial count (TBC. The analysisperformed on the 14 variables, with regard to the three breeds, allowed us to identify 10 of these as variables usefulfor discrimination (leaving out kg milk, pH, a45, and TBC. The most important variables were the percentage of Fat andTS for the first canonical variate and SNF, Lactose and Protein for the second. Fat and TS play an important role sincethey present significant values (even if opposite sign in the two variates. The resulting classification of subjects was satisfactory:79% of the Italian Friesians, 73% of German Friesians and 100% of the Jersey cows were classified correctly.

  7. High Resolution Melting Analysis Targeting hsp70 as a Fast and Efficient Method for the Discrimination of Leishmania Species.

    Directory of Open Access Journals (Sweden)

    Ricardo Andrade Zampieri

    2016-02-01

    Full Text Available Protozoan parasites of the genus Leishmania cause a large spectrum of clinical manifestations known as Leishmaniases. These diseases are increasingly important public health problems in many countries both within and outside endemic regions. Thus, an accurate differential diagnosis is extremely relevant for understanding epidemiological profiles and for the administration of the best therapeutic protocol.Exploring the High Resolution Melting (HRM dissociation profiles of two amplicons using real time polymerase chain reaction (real-time PCR targeting heat-shock protein 70 coding gene (hsp70 revealed differences that allowed the discrimination of genomic DNA samples of eight Leishmania species found in the Americas, including Leishmania (Leishmania infantum chagasi, L. (L. amazonensis, L. (L. mexicana, L. (Viannia lainsoni, L. (V. braziliensis, L. (V. guyanensis, L. (V. naiffi and L. (V. shawi, and three species found in Eurasia and Africa, including L. (L. tropica, L. (L. donovani and L. (L. major. In addition, we tested DNA samples obtained from standard promastigote culture, naturally infected phlebotomines, experimentally infected mice and clinical human samples to validate the proposed protocol.HRM analysis of hsp70 amplicons is a fast and robust strategy that allowed for the detection and discrimination of all Leishmania species responsible for the Leishmaniases in Brazil and Eurasia/Africa with high sensitivity and accuracy. This method could detect less than one parasite per reaction, even in the presence of host DNA.

  8. Advanced signal processing analysis of laser-induced breakdown spectroscopy data for the discrimination of obsidian sources.

    Science.gov (United States)

    Remus, Jeremiah J; Harmon, Russell S; Hark, Richard R; Haverstock, Gregory; Baron, Dirk; Potter, Ian K; Bristol, Samantha K; East, Lucille J

    2012-03-01

    Obsidian is a natural glass of volcanic origin and a primary resource used by indigenous peoples across North America for making tools. Geochemical studies of obsidian enhance understanding of artifact production and procurement and remain a priority activity within the archaeological community. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique being examined as a means for identifying obsidian from different sources on the basis of its 'geochemical fingerprint'. This study tested whether two major California obsidian centers could be distinguished from other obsidian localities and the extent to which subsources could be recognized within each of these centers. LIBS data sets were collected in two different spectral bands (350±130 nm and 690±115 nm) using a Nd:YAG 1064 nm laser operated at ~23 mJ, a Czerny-Turner spectrograph with 0.2-0.3 nm spectral resolution and a high performance imaging charge couple device (ICCD) detector. Classification of the samples was performed using partial least-squares discriminant analysis (PLSDA), a common chemometric technique for performing statistical regression on high-dimensional data. Discrimination of samples from the Coso Volcanic Field, Bodie Hills, and other major obsidian areas in north-central California was possible with an accuracy of greater than 90% using either spectral band. © 2012 Optical Society of America

  9. Validity of Rorschach Inkblot scores for discriminating psychopaths from non-psychopaths in forensic populations: a meta-analysis.

    Science.gov (United States)

    Wood, James M; Lilienfeld, Scott O; Nezworski, M Teresa; Garb, Howard N; Allen, Keli Holloway; Wildermuth, Jessica L

    2010-06-01

    Gacono and Meloy (2009) have concluded that the Rorschach Inkblot Test is a sensitive instrument with which to discriminate psychopaths from nonpsychopaths. We examined the association of psychopathy with 37 Rorschach variables in a meta-analytic review of 173 validity coefficients derived from 22 studies comprising 780 forensic participants. All studies included the Hare Psychopathy Checklist or one of its versions (Hare, 1980, 1991, 2003) and Exner's (2003) Comprehensive System for the Rorschach. Mean validity coefficients of Rorschach variables in the meta-analysis ranged from -.113 to .239, with a median validity of .070 and a mean validity of .062. Psychopathy displayed a significant and medium-sized association with the number of Aggressive Potential responses (weighted mean validity coefficient = .232) and small but significant associations with the Sum of Texture responses, Cooperative Movement = 0, the number of Personal responses, and the Egocentricity Index (weighted mean validity coefficients = .097 to .159). The remaining 32 Rorschach variables were not significantly related to psychopathy. The present findings contradict the view that the Rorschach is a clinically sensitive instrument for discriminating psychopaths from nonpsychopaths.

  10. Discriminant analysis in career studying "decision/indecision": the Career Factors Inventory (CFI) as a diagnostic measure.

    Science.gov (United States)

    Ferreira, Ana Sousa; Lima, Rosário

    2010-11-01

    Literature has shown that, nowadays, a multidimensional approach to decision-making has become prioritized. The Careers Factor Inventory (CFI) is, in fact, a multidimensional measurement instrument for evaluating career indecision, which may be useful in the diagnosis of adaptation behaviors in terms of career decision versus indecision. This study emerges as a follow-up to a previous study which used the CFI on a sample of university students in which this measurement instrument was found to be capable of distinguishing Low decided vs. Highly decided groups and to evaluate the discriminatory capacity of the CFI scales. It is the aim, here, to further analyse the results obtained in such study with a view to grounding the importance of the use of this Inventory as an instrument for distinguishing people who present different decision levels in relation to their careers. In this study, 494 university students from a number of higher education establishments and courses are part of the afore mentioned Low decided and Highly decided groups. The collected data were analysed by means of Discrete Discriminant Analysis models and corroborate the discriminant power of the Inventory and its use as a diagnostic instrument in the psychological intervention of career counseling and development.

  11. Ego Network Analysis of Upper Division Physics Student Survey

    Science.gov (United States)

    Brewe, Eric

    2017-01-01

    We present the analysis of student networks derived from a survey of upper division physics students. Ego networks focus on the connections that center on one person (the ego). The ego networks in this talk come from a survey that is part of an overall project focused on understanding student retention and persistence. The theory underlying this work is that social and academic integration are essential components to supporting students continued enrollment and ultimately graduation. This work uses network analysis as a way to investigate the role of social and academic interactions in retention and persistence decisions. We focus on student interactions with peers, on mentoring interactions with physics department faculty, and on engagement in physics groups and how they influence persistence. Our results, which are preliminary, will help frame the ongoing research project and identify ways in which departments can support students. This work supported by NSF grant #PHY 1344247.

  12. Optimal Moments for the Analysis of Peculiar Velocity Surveys

    CERN Document Server

    Watkins, R; Chambers, S W; Gorman, P; Melott, A L; Watkins, Richard; Feldman, Hume A.; Chambers, Scott W.; Gorman, Patrick; Melott, Adrian L.

    2001-01-01

    We present a new method for the analysis of peculiar velocity surveys which removes contributions to velocities from small scale, nonlinear velocity modes while retaining information about large scale motions. Our method utilizes Karhunen--Lo\\`eve methods of data compression to construct a set of moments out of the velocities which are minimally sensitive to small scale power. The set of moments are then used in a likelihood analysis. We develop criteria for the selection of moments, as well as a statistic to quantify the overall sensitivity of a set of moments to small scale power. Although we discuss our method in the context of peculiar velocity surveys, it may also prove useful in other situations where data filtering is required.

  13. Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

    Science.gov (United States)

    Friedel, M. J.; Asch, T. H.; Oden, C.

    2012-08-01

    The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot-Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

  14. Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

    Science.gov (United States)

    Friedel, M.J.; Asch, T.H.; Oden, C.

    2012-01-01

    The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

  15. Country Risk Analysis: A Survey of the Quantitative Methods

    OpenAIRE

    Hiranya K Nath

    2008-01-01

    With globalization and financial integration, there has been rapid growth of international lending and foreign direct investment (FDI). In view of this emerging trend, country risk analysis has become extremely important for the international creditors and investors. This paper briefly discusses the concepts and definitions, and presents a survey of the quantitative methods that are used to address various issues related to country risk. It also gives a summary review of selected empirical st...

  16. Analysis and Verification of Service Interaction Protocols - A Brief Survey

    CERN Document Server

    Salaün, Gwen

    2010-01-01

    Modeling and analysis of interactions among services is a crucial issue in Service-Oriented Computing. Composing Web services is a complicated task which requires techniques and tools to verify that the new system will behave correctly. In this paper, we first overview some formal models proposed in the literature to describe services. Second, we give a brief survey of verification techniques that can be used to analyse services and their interaction. Last, we focus on the realizability and conformance of choreographies.

  17. Nuclear power and the public: analysis of collected survey research

    Energy Technology Data Exchange (ETDEWEB)

    Melber, B.D.; Nealey, S.M.; Hammersla, J.; Rankin, W.L.

    1977-11-01

    This executive summary highlights the major findings of a comprehensive synthesis and analysis of over 100 existing surveys dealing with public attitudes toward nuclear power issues. Questions of immediate policy relevance to the nuclear debate are posed and answered on the basis of these major findings. For each issue area, those sections of the report in which more-detailed discussion and presentation of relevant data may be found are indicated.

  18. Adaptive integrated global and local discriminant analysis%自适应全局-局部集成判别分析

    Institute of Scientific and Technical Information of China (English)

    魏莱

    2014-01-01

    将数据集进行合理的维数约简,对于提高一些机器学习算法的效率起着至关重要的影响.本文提出了一种自适应全局-局部集成判别分析算法(Adaptive integrated global and local discriminant analysis,AIGLD).AILGD利用数据集的全局判别结构和局部判别结构,将线性判别算法(Linear Discriminant Analysis,LDA)与提出的局部判别算法自适应的相结合.在 UCI数据库及标准人脸数据库上的识别实验证明,相比于现有算法,AIGLD具有更高的识别准确率及更强的鲁棒性.%In computer vision and information retrieval fields,many applications,such as appearance-based image recognition, often confront high-dimensional data samples.The curse of high dimensionality is usually a maj or cause of limitations of many machine learning algorithms.Hence,it is desired to consider methods of feature extraction (or dimensionality reduction)which are able to find the low-dimensional and compact representations for the high-dimensional data points.The subspace learning algorithm is one of the most popular feature extraction methods.Supervised subspace learning algorithms usually achieve better performances than unsupervised ones.And supervised subspace learning algorithms can be divided into two categories,the global structures based discriminator,such as linear discriminative analysis (LDA),and the local structures based methods,such as marginal Fisher analysis(MFA).From the experiments on image recognition,we can find that the global structures based discriminator and the local structures based discriminator are suitable for different feature extraction tasks.Hence,we hope to seek a discriminative analysis method which can combine the global structures and local-structures of data sets together.In this paper,a new supervised extraction method,called adaptive integrated global and local discriminant analysis (AIGLD),is proposed.The AIGLD algorithm combines the global structure based discriminator

  19. Power Spectrum Analysis of Three-Dimensional Redshift Surveys

    CERN Document Server

    Feldman, H A; Peacock, J A; Feldman, Hume A.; Kaiser, Nick; Peacock, John A.

    1994-01-01

    We develop a general method for power spectrum analysis of three dimensional redshift surveys. We present rigorous analytical estimates for the statistical uncertainty in the power and we are able to derive a rigorous optimal weighting scheme under the reasonable (and largely empirically verified) assumption that the long wavelength Fourier components are Gaussian distributed. We apply the formalism to the updated 1-in-6 QDOT IRAS redshift survey, and compare our results to data from other probes: APM angular correlations; the CfA and the Berkeley 1.2Jy IRAS redshift surveys. Our results bear out and further quantify the impression from e.g.\\ counts-in-cells analysis that there is extra power on large scales as compared to the standard CDM model with $\\Omega h\\simeq 0.5$. We apply likelihood analysis using the CDM spectrum with $\\Omega h$ as a free parameter as a phenomenological family of models; we find the best fitting parameters in redshift space and transform the results to real space. Finally, we calcul...

  20. Application of otolith shape analysis for stock discrimination and species identification of five goby species (Perciformes: Gobiidae) in the northern Chinese coastal waters

    Science.gov (United States)

    Yu, Xin; Cao, Liang; Liu, Jinhu; Zhao, Bo; Shan, Xiujuan; Dou, Shuozeng

    2014-09-01

    We tested the use of otolith shape analysis to discriminate between species and stocks of five goby species ( Ctenotrypauchen chinensis, Odontamblyopus lacepedii, Amblychaeturichthys hexanema, Chaeturichthys stigmatias, and Acanthogobius hasta) found in northern Chinese coastal waters. The five species were well differentiated with high overall classification success using shape indices (83.7%), elliptic Fourier coefficients (98.6%), or the combination of both methods (94.9%). However, shape analysis alone was only moderately successful at discriminating among the four stocks (Liaodong Bay, LD; Bohai Bay, BH; Huanghe (Yellow) River estuary HRE, and Jiaozhou Bay, JZ stocks) of A. hasta (50%-54%) and C. stigmatias (65.7%-75.8%). For these two species, shape analysis was moderately successful at discriminating the HRE or JZ stocks from other stocks, but failed to effectively identify the LD and BH stocks. A large number of otoliths were misclassified between the HRE and JZ stocks, which are geographically well separated. The classification success for stock discrimination was higher using elliptic Fourier coefficients alone (70.2%) or in combination with shape indices (75.8%) than using only shape indices (65.7%) in C. stigmatias whereas there was little difference among the three methods for A. hasta. Our results supported the common belief that otolith shape analysis is generally more effective for interspecific identification than intraspecific discrimination. Moreover, compared with shape indices analysis, Fourier analysis improves classification success during inter- and intra-species discrimination by otolith shape analysis, although this did not necessarily always occur in all fish species.