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Sample records for discriminant analysis lda

  1. Micro analysis of fringe field formed inside LDA measuring volume

    International Nuclear Information System (INIS)

    Ghosh, Abhijit; Nirala, A K

    2016-01-01

    In the present study we propose a technique for micro analysis of fringe field formed inside laser Doppler anemometry (LDA) measuring volume. Detailed knowledge of the fringe field obtained by this technique allows beam quality, alignment and fringe uniformity to be evaluated with greater precision and may be helpful for selection of an appropriate optical element for LDA system operation. A complete characterization of fringes formed at the measurement volume using conventional, as well as holographic optical elements, is presented. Results indicate the qualitative, as well as quantitative, improvement of fringes formed at the measurement volume by holographic optical elements. Hence, use of holographic optical elements in LDA systems may be advantageous for improving accuracy in the measurement. (paper)

  2. Orthogonal sparse linear discriminant analysis

    Science.gov (United States)

    Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

    2018-03-01

    Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

  3. Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

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    Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

    2018-05-01

    Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

  4. LDA measurements and turbulence spectral analysis in an agitated vessel

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    Chára Zdeněk

    2013-04-01

    Full Text Available During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique, methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

  5. LDA measurements and turbulence spectral analysis in an agitated vessel

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    Kysela, Bohuš; Konfršt, Jiří; Chára, Zdeněk

    2013-04-01

    During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA) has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique), methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number) were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

  6. Linear discriminant analysis for welding fault detection

    International Nuclear Information System (INIS)

    Li, X.; Simpson, S.W.

    2010-01-01

    This work presents a new method for real time welding fault detection in industry based on Linear Discriminant Analysis (LDA). A set of parameters was calculated from one second blocks of electrical data recorded during welding and based on control data from reference welds under good conditions, as well as faulty welds. Optimised linear combinations of the parameters were determined with LDA and tested with independent data. Short arc welds in overlap joints were studied with various power sources, shielding gases, wire diameters, and process geometries. Out-of-position faults were investigated. Application of LDA fault detection to a broad range of welding procedures was investigated using a similarity measure based on Principal Component Analysis. The measure determines which reference data are most similar to a given industrial procedure and the appropriate LDA weights are then employed. Overall, results show that Linear Discriminant Analysis gives an effective and consistent performance in real-time welding fault detection.

  7. Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

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    Qiu, Shanshan; Wang, Jun; Gao, Liping

    2014-07-09

    An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

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

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    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.

  9. The application of sparse estimation of covariance matrix to quadratic discriminant analysis

    OpenAIRE

    Sun, Jiehuan; Zhao, Hongyu

    2015-01-01

    Background Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) acros...

  10. Analysis of Fringe Field Formed Inside LDA Measurement Volume Using Compact Two Hololens Imaging Systems

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    Ghosh, Abhijit; Nirala, A. K.; Yadav, H. L.

    2018-03-01

    We have designed and fabricated four LDA optical setups consisting of aberration compensated four different compact two hololens imaging systems. We have experimentally investigated and realized a hololens recording geometry which is interferogram of converging spherical wavefront with mutually coherent planar wavefront. Proposed real time monitoring and actual fringe field analysis techniques allow complete characterizations of fringes formed at measurement volume and permit to evaluate beam quality, alignment and fringe uniformity with greater precision. After experimentally analyzing the fringes formed at measurement volume by all four imaging systems, it is found that fringes obtained using compact two hololens imaging systems get improved both qualitatively and quantitatively compared to that obtained using conventional imaging system. Results indicate qualitative improvement of non-uniformity in fringe thickness and micro intensity variations perpendicular to the fringes, and quantitative improvement of 39.25% in overall average normalized standard deviations of fringe width formed by compact two hololens imaging systems compare to that of conventional imaging system.

  11. Robust linear discriminant analysis with distance based estimators

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    Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

    2017-11-01

    Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

  12. An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

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    Jun Huang

    2014-01-01

    Full Text Available We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA and linear discriminant analysis (LDA. Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while the K nearest neighbor (KNN is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

  13. Latent Dirichlet Allocation (LDA) for Sentiment Analysis Toward Tourism Review in Indonesia

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    Putri, IR; Kusumaningrum, R.

    2017-01-01

    The tourism industry is one of foreign exchange sector, which has considerable potential development in Indonesia. Compared to other Southeast Asia countries such as Malaysia with 18 million tourists and Singapore 20 million tourists, Indonesia which is the largest Southeast Asia’s country have failed to attract higher tourist numbers compared to its regional peers. Indonesia only managed to attract 8,8 million foreign tourists in 2013, with the value of foreign tourists each year which is likely to decrease. Apart from the infrastructure problems, marketing and managing also form of obstacles for tourism growth. An evaluation and self-analysis should be done by the stakeholder to respond toward this problem and capture opportunities that related to tourism satisfaction from tourists review. Recently, one of technology to answer this problem only relying on the subjective of statistical data which collected by voting or grading from user randomly. So the result is still not to be accountable. Thus, we proposed sentiment analysis with probabilistic topic model using Latent Dirichlet Allocation (LDA) method to be applied for reading general tendency from tourist review into certain topics that can be classified toward positive and negative sentiment.

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

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    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.

  15. General tensor discriminant analysis and gabor features for gait recognition.

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    Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

    2007-10-01

    The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine

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

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    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.

  17. The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

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    Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

    2018-08-15

    Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Comparison of Principal Component Analysis and Linear Discriminant Analysis applied to classification of excitation-emission matrices of the selected biological material

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    Maciej Leśkiewicz

    2016-03-01

    Full Text Available Quality of two linear methods (PCA and LDA applied to reduce dimensionality of feature analysis is compared and efficiency of their algorithms in classification of the selected biological materials according to their excitation-emission fluorescence matrices is examined. It has been found that LDA method reduces the dimensions (or a number of significant variables more effectively than PCA method. A relatively good discrimination within the examined biological material has been obtained with the use of LDA algorithm.[b]Keywords[/b]: Feature Analysis, Fluorescence Spectroscopy, Biological Material Classification

  19. Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

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    He, Xin; Frey, Eric C.

    2007-03-01

    Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

  20. Two-Stage Regularized Linear Discriminant Analysis for 2-D Data.

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    Zhao, Jianhua; Shi, Lei; Zhu, Ji

    2015-08-01

    Fisher linear discriminant analysis (LDA) involves within-class and between-class covariance matrices. For 2-D data such as images, regularized LDA (RLDA) can improve LDA due to the regularized eigenvalues of the estimated within-class matrix. However, it fails to consider the eigenvectors and the estimated between-class matrix. To improve these two matrices simultaneously, we propose in this paper a new two-stage method for 2-D data, namely a bidirectional LDA (BLDA) in the first stage and the RLDA in the second stage, where both BLDA and RLDA are based on the Fisher criterion that tackles correlation. BLDA performs the LDA under special separable covariance constraints that incorporate the row and column correlations inherent in 2-D data. The main novelty is that we propose a simple but effective statistical test to determine the subspace dimensionality in the first stage. As a result, the first stage reduces the dimensionality substantially while keeping the significant discriminant information in the data. This enables the second stage to perform RLDA in a much lower dimensional subspace, and thus improves the two estimated matrices simultaneously. Experiments on a number of 2-D synthetic and real-world data sets show that BLDA+RLDA outperforms several closely related competitors.

  1. LDA merging and splitting with applications to multiagent cooperative learning and system alteration.

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    Pang, Shaoning; Ban, Tao; Kadobayashi, Youki; Kasabov, Nikola K

    2012-04-01

    To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.

  2. L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

    Science.gov (United States)

    Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

    2014-04-01

    A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

  3. Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)

    2014-11-15

    The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

  4. Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

    International Nuclear Information System (INIS)

    Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

    2014-01-01

    The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

  5. An example of multidimensional analysis: Discriminant analysis

    International Nuclear Information System (INIS)

    Lutz, P.

    1990-01-01

    Among the approaches on the data multi-dimensional analysis, lectures on the discriminant analysis including theoretical and practical aspects are presented. The discrimination problem, the analysis steps and the discrimination categories are stressed. Examples on the descriptive historical analysis, the discrimination for decision making, the demonstration and separation of the top quark are given. In the linear discriminant analysis the following subjects are discussed: Huyghens theorem, projection, discriminant variable, geometrical interpretation, case for g=2, classification method, separation of the top events. Criteria allowing the obtention of relevant results are included [fr

  6. The application of sparse estimation of covariance matrix to quadratic discriminant analysis.

    Science.gov (United States)

    Sun, Jiehuan; Zhao, Hongyu

    2015-02-18

    Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.

  7. Hierarchical Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Di Lu

    2018-01-01

    Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

  8. Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

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    Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

    2007-02-01

    Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

  9. News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM

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    M’hamed Bilal Abidine

    2015-08-01

    Full Text Available Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA, Independent Component Analysis (ICA, and Linear Discriminant Analysis (LDA. The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF, standard Support Vector Machines (SVM, Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition.

  10. Fast Solution in Sparse LDA for Binary Classification

    Science.gov (United States)

    Moghaddam, Baback

    2010-01-01

    An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

  11. Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis

    Science.gov (United States)

    Tkaczyk, J. Eric; Langan, David; Wu, Xiaoye; Xu, Daniel; Benson, Thomas; Pack, Jed D.; Schmitz, Andrea; Hara, Amy; Palicek, William; Licato, Paul; Leverentz, Jaynne

    2009-02-01

    Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.

  12. MANOVA, LDA, and FA criteria in clusters parameter estimation

    Directory of Open Access Journals (Sweden)

    Stan Lipovetsky

    2015-12-01

    Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.

  13. Feature extraction with deep neural networks by a generalized discriminant analysis.

    Science.gov (United States)

    Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

    2012-04-01

    We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

  14. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

    2011-01-01

    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  15. Statistics that learn: can logistic discriminant analysis improve diagnosis in brain SPECT?

    International Nuclear Information System (INIS)

    Behin-Ain, S.; Barnden, L.; Kwiatek, R.; Del Fante, P.; Casse, R.; Burnet, R.; Chew, G.; Kitchener, M.; Boundy, K.; Unger, S.

    2002-01-01

    Full text: Logistic discriminant analysis (LDA) is a statistical technique capable of discriminating individuals within a diseased group against normals. It also enables classification of various diseases within a group of patients. This technique provides a quantitative, automated and non-subjective clinical diagnostic tool. Based on a population known to have the disease and a normal control group, an algorithm was developed and trained to identify regions in the human brain responsible for the disease in question. The algorithm outputs a statistical map representing diseased or normal probability on a voxel or cluster basis from which an index is generated for each subject. The algorithm also generates a set of coefficients which is used to generate an index for the purpose of classification of new subjects. The results are comparable and complement those of Statistical Parametric Mapping (SPM) which employs a more common linear discriminant technique. The results are presented for brain SPECT studies of two diseases: chronic fatigue syndrome (CFS) and fibromyalgia (FM). A 100% specificity and 94% sensitivity is achieved for the CFS study (similar to SPM results) and for the FM study 82% specificity and 94% sensitivity is achieved with corresponding SPM results showing 90% specificity and 82% sensitivity. The results encourages application of LDA for discrimination of new single subjects as well as of diseased and normal groups. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  16. Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

    Science.gov (United States)

    Wihardi, Y.; Setiawan, W.; Nugraha, E.

    2018-01-01

    On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

  17. 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.

  18. Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

    Science.gov (United States)

    Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

    2011-06-01

    Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

  19. Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

    International Nuclear Information System (INIS)

    YangDai, Tianyi; Zhang, Li

    2016-01-01

    Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

  20. Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

    Energy Technology Data Exchange (ETDEWEB)

    YangDai, Tianyi [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China); Zhang, Li, E-mail: zhangli@nuctech.com [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China)

    2016-02-01

    Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

  1. Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

    Science.gov (United States)

    YangDai, Tianyi; Zhang, Li

    2016-02-01

    Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

  2. Discriminant analysis of plasma fusion data

    International Nuclear Information System (INIS)

    Kardaun, O.J.W.F.; Kardaun, J.W.P.F.; Itoh, S.; Itoh, K.

    1992-06-01

    Several discriminant analysis methods has been applied and compared to predict the type of ELM's in H-mode discharges: (a) quadratic discriminant analysis (linear discriminant analysis being a special case), (b) discrimination by non-parametric (kernel-) density estimates, and (c) discrimination by a product multinomial model on a discretised scale. Practical evaluation was performed using SAS in the first two cases, and INDEP, a standard FORTRAN program, initially developed for medical applications, in the last case. We give here a flavour of the approach and its results. In summary, discriminant analysis can be used as a useful descriptive method of specifying regions where particular types of plasma discharges can be produced. Parametric methods have the advantage of a rather compact mathematical formulation . Pertinent graphical representations are useful to make the theory and the results more palatable to the experimental physicists. (J.P.N.)

  3. 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.

  4. Tracking topic birth and death in LDA.

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, Andrew T.; Robinson, David Gerald

    2011-09-01

    Most topic modeling algorithms that address the evolution of documents over time use the same number of topics at all times. This obscures the common occurrence in the data where new subjects arise and old ones diminish or disappear entirely. We propose an algorithm to model the birth and death of topics within an LDA-like framework. The user selects an initial number of topics, after which new topics are created and retired without further supervision. Our approach also accommodates many of the acceleration and parallelization schemes developed in recent years for standard LDA. In recent years, topic modeling algorithms such as latent semantic analysis (LSA)[17], latent Dirichlet allocation (LDA)[10] and their descendants have offered a powerful way to explore and interrogate corpora far too large for any human to grasp without assistance. Using such algorithms we are able to search for similar documents, model and track the volume of topics over time, search for correlated topics or model them with a hierarchy. Most of these algorithms are intended for use with static corpora where the number of documents and the size of the vocabulary are known in advance. Moreover, almost all current topic modeling algorithms fix the number of topics as one of the input parameters and keep it fixed across the entire corpus. While this is appropriate for static corpora, it becomes a serious handicap when analyzing time-varying data sets where topics come and go as a matter of course. This is doubly true for online algorithms that may not have the option of revising earlier results in light of new data. To be sure, these algorithms will account for changing data one way or another, but without the ability to adapt to structural changes such as entirely new topics they may do so in counterintuitive ways.

  5. Boosting Discriminant Learners for Gait Recognition Using MPCA Features

    Directory of Open Access Journals (Sweden)

    Haiping Lu

    2009-01-01

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

  6. LDA measurements under plasma conditions

    International Nuclear Information System (INIS)

    Lesinski, J.; Mizera-Lesinska, B.; Fanton, J.C.; Boulos, M.I.

    1979-01-01

    A study was made of the application of Laser Doppler Anemometry (LDA) for the measurement of the fluid and particle velocities under plasma conditions. The flow configuration, is that of a dc plasma jet called the principal jet, in which an alumina powder of a mean particle diameter of 115 μm and a standard deviation of 11.3 μm was injected using a secondary jet. The plasma jet immerged from a 7.1 mm ID nozzle while that of the secondary jet was 2 nm in diameter. The secondary jet was introduced at the nozzle level of the plasma jet directed 90 0 to its axis. Details of the nozzle and the gas flow system are shown in Figure 2

  7. Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

    Science.gov (United States)

    Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

    2016-09-01

    In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

  8. Robust linear discriminant models to solve financial crisis in banking sectors

    Science.gov (United States)

    Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

    2014-12-01

    Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

  9. Discriminant Analysis of Student Loan Applications

    Science.gov (United States)

    Dyl, Edward A.; McGann, Anthony F.

    1977-01-01

    The use of discriminant analysis in identifying potentially "good" versus potentially "bad" student loans is explained. The technique is applied to a sample of 200 student loan applications at the University of Wyoming. (LBH)

  10. On the use of LDA performance as a metric of feature extraction methods for a P300 BCI classification task

    International Nuclear Information System (INIS)

    Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Bañuelos, Verónica Medina; Rufiner, Leonardo

    2011-01-01

    Brain computer interfaces (BCIs) translate brain activity into computer commands. To enhance the performance of a BCI, it is necessary to improve the feature extraction techniques being applied to decode the users' intentions. Objective comparison methods are needed to analyze different feature extraction techniques. One possibility is to use the classifier performance as a comparative measure. In this work the effect of several variables that affect the behaviour of linear discriminant analysis (LDA) has been studied when used to distinguish between electroencephalographic signals with and without the presence of event related potentials (ERPs). The error rate (ER) and the area under the receiver operating characteristic curve (AUC) were used as performance estimators of LDA. The results show that the number of characteristics, the degree of balance of the training patterns set and the number of averaged trials affect the classifier's performance and therefore, must be considered in the design of the integrated system.

  11. USING DISCRIMINANT ANALYSIS IN RELATIONSHIP MARKETING

    OpenAIRE

    Iacob Catoiu; Mihai Èšichindelean; Simona Vinerean

    2013-01-01

    The purpose of the present paper is to describe and apply discriminant analysis withina relationship marketing context. The paper is structured into two parts; the first part contains aliterature review regarding the value chain concept and the dimensions it is built on, while thesecond part includes the results of applying discriminant analysis on several value chaindimensions. The authors have considered the client-company relationships of the gas-station marketas proper for studying the di...

  12. Credit scoring analysis using kernel discriminant

    Science.gov (United States)

    Widiharih, T.; Mukid, M. A.; Mustafid

    2018-05-01

    Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

  13. Classification of Surface and Deep Soil Samples Using Linear Discriminant Analysis

    International Nuclear Information System (INIS)

    Wasim, M.; Ali, M.; Daud, M.

    2015-01-01

    A statistical analysis was made of the activity concentrations measured in surface and deep soil samples for natural and anthropogenic gamma-emitting radionuclides. Soil samples were obtained from 48 different locations in Gilgit, Pakistan covering about 50 km/sup 2/ areas at an average altitude of 1550 m above sea level. From each location two samples were collected: one from the top soil (2-6 cm) and another from a depth of 6-10 cm. Four radionuclides including /sup 226/Ra, /sup 232/Th, /sup 40/K and /sup 137/Cs were quantified. The data was analyzed using t-test to find out activity concentration difference between the surface and depth samples. At the surface, the median activity concentrations were 23.7, 29.1, 4.6 and 115 Bq kg/sup -1/ for 226Ra, 232Th, 137Cs and 40K respectively. For the same radionuclides, the activity concentrations were respectively 25.5, 26.2, 2.9 and 191 Bq kg/sup -1/ for the depth samples. Principal component analysis (PCA) was applied to explore patterns within the data. A positive significant correlation was observed between the radionuclides /sup 226/Ra and /sup 232/Th. The data from PCA was further utilized in linear discriminant analysis (LDA) for the classification of surface and depth samples. LDA classified surface and depth samples with good predictability. (author)

  14. Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

    Science.gov (United States)

    Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

    2016-09-01

    The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

  15. Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?

    Science.gov (United States)

    Xue, Jing-Hao; Hall, Peter

    2015-05-01

    Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then use the rebalanced data to train the classifiers. This leads to interesting empirical patterns. In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. The AUC is a widely-used quantitative measure of classification performance, but the property that it increases with rebalancing has, as yet, no theoretical explanation. In this note, using Gaussian-based linear discriminant analysis (LDA) as the classifier, we demonstrate that, at least for LDA, there is an intrinsic, positive relationship between the rebalancing of class sizes and the improvement of AUC. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.

  16. Thyroid nodule classification using ultrasound elastography via linear discriminant analysis.

    Science.gov (United States)

    Luo, Si; Kim, Eung-Hun; Dighe, Manjiri; Kim, Yongmin

    2011-05-01

    The non-surgical diagnosis of thyroid nodules is currently made via a fine needle aspiration (FNA) biopsy. It is estimated that somewhere between 250,000 and 300,000 thyroid FNA biopsies are performed in the United States annually. However, a large percentage (approximately 70%) of these biopsies turn out to be benign. Since the aggressive FNA management of thyroid nodules is costly, quantitative risk assessment and stratification of a nodule's malignancy is of value in triage and more appropriate healthcare resources utilization. In this paper, we introduce a new method for classifying the thyroid nodules based on the ultrasound (US) elastography features. Unlike approaches to assess the stiffness of a thyroid nodule by visually inspecting the pseudo-color pattern in the strain image, we use a classification algorithm to stratify the nodule by using the power spectrum of strain rate waveform extracted from the US elastography image sequence. Pulsation from the carotid artery was used to compress the thyroid nodules. Ultrasound data previously acquired from 98 thyroid nodules were used in this retrospective study to evaluate our classification algorithm. A classifier was developed based on the linear discriminant analysis (LDA) and used to differentiate the thyroid nodules into two types: (I) no FNA (observation-only) and (II) FNA. Using our method, 62 nodules were classified as type I, all of which were benign, while 36 nodules were classified as Type-II, 16 malignant and 20 benign, resulting in a sensitivity of 100% and specificity of 75.6% in detecting malignant thyroid nodules. This indicates that our triage method based on US elastography has the potential to substantially reduce the number of FNA biopsies (63.3%) by detecting benign nodules and managing them via follow-up observations rather than an FNA biopsy. Published by Elsevier B.V.

  17. Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

    Science.gov (United States)

    Santoso, Noviyanti; Wibowo, Wahyu

    2018-03-01

    A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

  18. Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

    Directory of Open Access Journals (Sweden)

    Lijun Yao

    2018-03-01

    Full Text Available The moving-window bis-correlation coefficients (MW-BiCC was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and β-thalassemia with visible and near-infrared (Vis–NIR spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA was also conducted for comparison. A total of 306 transgenic (positive and 150 nontransgenic (negative leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG smoothing with first-order derivative (d=1, third-degree polynomial (p=3 and 25 smoothing points (m=25. The selected waveband was 736–1054nm with MW-BiCC, and the positive and negative validation recognition rates (V_REC+, V_REC− were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 β-thalassemia (positive and 148 nonthalassemia (negative of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with d=1, p=3 and m=53. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342nm. The V_REC+ and V_REC− were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing β-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

  19. 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.

  20. Discriminant forest classification method and system

    Science.gov (United States)

    Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

    2012-11-06

    A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

  1. Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

    Directory of Open Access Journals (Sweden)

    Shunfang Wang

    2015-12-01

    Full Text Available An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC, pseudo-amino acid composition (PseAAC and position specific scoring matrix (PSSM, are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

  2. Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

    Science.gov (United States)

    Wang, Shunfang; Liu, Shuhui

    2015-12-19

    An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

  3. Discrimination of wine from grape cultivated in Japan, imported wine, and others by multi-elemental analysis.

    Science.gov (United States)

    Shimizu, Hideaki; Akamatsu, Fumikazu; Kamada, Aya; Koyama, Kazuya; Okuda, Masaki; Fukuda, Hisashi; Iwashita, Kazuhiro; Goto-Yamamoto, Nami

    2018-04-01

    Differences in mineral concentrations were examined among three types of wine in the Japanese market place: Japan wine, imported wine, and domestically produced wine mainly from foreign ingredients (DWF), where Japan wine has been recently defined by the National Tax Agency as domestically produced wine from grapes cultivated in Japan. The main objective of this study was to examine the possibility of controlling the authenticity of Japan wine. The concentrations of 18 minerals (Li, B, Na, Mg, Si, P, S, K, Ca, Mn, Co, Ni, Ga, Rb, Sr, Mo, Ba, and Pb) in 214 wine samples were determined by inductively coupled-plasma mass spectrometry (ICP-MS) and ICP-atomic emission spectrometry (ICP-AES). In general, Japan wine had a higher concentration of potassium and lower concentrations of eight elements (Li, B, Na, Si, S, Co, Sr, and Pb) as compared with the other two groups of wine. Linear discriminant analysis (LDA) models based on concentrations of the 18 minerals facilitated the identification of three wine groups: Japan wine, imported wine, and DWF with a 91.1% classification score and 87.9% prediction score. In addition, an LDA model for discrimination of wine from four domestic geographic origins (Yamanashi, Nagano, Hokkaido, and Yamagata Prefectures) using 18 elements gave a classification score of 93.1% and a prediction score of 76.4%. In summary, we have shown that an LDA model based on mineral concentrations is useful for distinguishing Japan wine from other wine groups, and can contribute to classification of the four main domestic wine-producing regions of Japan. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  4. Supervised chemical pattern recognition in almond ( Prunus dulcis ) Portuguese PDO cultivars: PCA- and LDA-based triennial study.

    Science.gov (United States)

    Barreira, João C M; Casal, Susana; Ferreira, Isabel C F R; Peres, António M; Pereira, José Alberto; Oliveira, M Beatriz P P

    2012-09-26

    Almonds harvested in three years in Trás-os-Montes (Portugal) were characterized to find differences among Protected Designation of Origin (PDO) Amêndoa Douro and commercial non-PDO cultivars. Nutritional parameters, fiber (neutral and acid detergent fibers, acid detergent lignin, and cellulose), fatty acids, triacylglycerols (TAG), and tocopherols were evaluated. Fat was the major component, followed by carbohydrates, protein, and moisture. Fatty acids were mostly detected as monounsaturated and polyunsaturated forms, with relevance of oleic and linoleic acids. Accordingly, 1,2,3-trioleoylglycerol and 1,2-dioleoyl-3-linoleoylglycerol were the major TAG. α-Tocopherol was the leading tocopherol. To verify statistical differences among PDO and non-PDO cultivars independent of the harvest year, data were analyzed through an analysis of variance, a principal component analysis, and a linear discriminant analysis (LDA). These differences identified classification parameters, providing an important tool for authenticity purposes. The best results were achieved with TAG analysis coupled with LDA, which proved its effectiveness to discriminate almond cultivars.

  5. Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

    Science.gov (United States)

    Wilson, S. R.; Close, M. E.; Abraham, P.

    2018-01-01

    Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological bypassing that may occur via artificial drainage systems.

  6. Regularized Discriminant Analysis: A Large Dimensional Study

    KAUST Repository

    Yang, Xiaoke

    2018-04-28

    In this thesis, we focus on studying the performance of general regularized discriminant analysis (RDA) classifiers. The data used for analysis is assumed to follow Gaussian mixture model with different means and covariances. RDA offers a rich class of regularization options, covering as special cases the regularized linear discriminant analysis (RLDA) and the regularized quadratic discriminant analysis (RQDA) classi ers. We analyze RDA under the double asymptotic regime where the data dimension and the training size both increase in a proportional way. This double asymptotic regime allows for application of fundamental results from random matrix theory. Under the double asymptotic regime and some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that only depends on the data statistical parameters and dimensions. This result not only implicates some mathematical relations between the misclassification error and the class statistics, but also can be leveraged to select the optimal parameters that minimize the classification error, thus yielding the optimal classifier. Validation results on the synthetic data show a good accuracy of our theoretical findings. We also construct a general consistent estimator to approximate the true classification error in consideration of the unknown previous statistics. We benchmark the performance of our proposed consistent estimator against classical estimator on synthetic data. The observations demonstrate that the general estimator outperforms others in terms of mean squared error (MSE).

  7. Determination of the stagnation point in pulverized coal swirl flames by detailed analysis of laser velocity measurements; Staupunktbestimmung in Kohlenstaub-Drallflammen mittels detaillierter Analyse von LDA-Daten

    Energy Technology Data Exchange (ETDEWEB)

    Ohliger, A.; Stadler, H.; Foerster, M.; Kneer, R. [RWTH Aachen University (Germany). Lehrstuhl fuer Waerme- und Stoffuebertragung

    2009-07-01

    When Laser Doppler Anemometry (LDA) is used for experimental investigation of flow fields in pulverised coal flames, the measured coal particle velocities are usually averaged in order to determine the gas velocity. This paper shows that this approach can lead to a misinterpretation of the data. In the burner vicinity of the investigated flame, where high accelerations in the gas phase occur, a discrepancy appears between the measured velocity distribution and the expected normal distribution. Thus, a detailed analysis of the measured particle data is conducted and compared to conventional averaging. The difference can be attributed to large particles from the inner recirculation zone of the flame, which do not follow the gas flow properly. (orig.)

  8. 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.

  9. Weighted piecewise LDA for solving the small sample size problem in face verification.

    Science.gov (United States)

    Kyperountas, Marios; Tefas, Anastasios; Pitas, Ioannis

    2007-03-01

    A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance.

  10. Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance

    Directory of Open Access Journals (Sweden)

    Septa Cahyani

    2018-04-01

    Full Text Available The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting using Linear Discriminant Analysis (LDA and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing. The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.

  11. Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM

    International Nuclear Information System (INIS)

    Liu, T; Chen, J; Zhou, X N; Xiao, W B

    2012-01-01

    Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.

  12. 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.

  13. Application of PCA-LDA method to determine the geographical origin of tea based on determination of stable isotopes and multi-elements

    International Nuclear Information System (INIS)

    Yuan Yuwei; Zhang Yongzhi; Yang Guiling; Zhang Zhiheng; Fu Haiyan; Han Wenyan; Li Shufang

    2013-01-01

    The ratio of stable isotope and concentration of multi-element in tea was determinated with isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition techniques with principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the geographical origins of tea from Fujian, Shandong and Zhejiang province, and Yuyao, Jinhua and Xihu region of Zhejiang. The results showed the values of δ"1"5N, δ"1"3C, δD, δ"1"8O and the ratios of "2"0"6Pb/"2"0"7Pb, "2"0"8Pb/"2"0"6Pb and "8"7Sr/"8"6Sr in tea samples were different from different origins. There was also large variable for the concentrations of 27 mineral elements, such as Li, Be, Na and so on, with a specific character of origin. The method of PCA could be used to classify the geographical origin of tea from different origins but with a cross in the scatter plot. However, PCA combining with LDA could gave correct assignation percentages of 99% for the tea samples among Fujian, Shandong and Zhejiang provinces, and 87% for the tea samples among Yuyao, Jinhua and Xihu region of Zhejiang. These results revealed that it was possible and feasible to classify the geographical origin of tea by the method of PCA-LDA based on the determination of isotopes and multi-elements. (authors)

  14. Characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger (Zingiber officinale) according to geographical origin using HPLC-DAD combined with chemometrics.

    Science.gov (United States)

    Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

    2014-09-01

    Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. 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.

  16. Application of Discriminant Analysis on Romanian Insurance Market

    OpenAIRE

    Constantin Anghelache; Dan Armeanu

    2008-01-01

    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 descrip...

  17. 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

  18. Using discriminant analysis for credit decision

    Directory of Open Access Journals (Sweden)

    Gheorghiţa DINCĂ

    2015-12-01

    Full Text Available This paper follows to highlight the link between the results obtained applying discriminant analysis and lending decision. For this purpose, we have carried out the research on a sample of 24 Romanian private companies, pertaining to 12 different economic sectors, from I and II categories of Bucharest Stock Exchange, for the period 2010-2012. Our study works with two popular bankruptcy risk’s prediction models, the Altman model and the Anghel model. We have double-checked and confirmed the results of our research by comparing the results from applying the two fore-mentioned models as well as by checking existing debt commitments of each analyzed company to credit institutions during the 2010-2012 period. The aim of this paper was the classification of studied companies into potential bankrupt and non-bankrupt, to assist credit institutions in their decision to grant credit, understanding the approval or rejection algorithm of loan applications and even help potential investors in these ompanies.

  19. Pulse shape discrimination and classification methods for continuous depth of interaction encoding PET detectors

    International Nuclear Information System (INIS)

    Roncali, Emilie; Phipps, Jennifer E; Marcu, Laura; Cherry, Simon R

    2012-01-01

    In previous work we demonstrated the potential of positron emission tomography (PET) detectors with depth-of-interaction (DOI) encoding capability based on phosphor-coated crystals. A DOI resolution of 8 mm full-width at half-maximum was obtained for 20 mm long scintillator crystals using a delayed charge integration linear regression method (DCI-LR). Phosphor-coated crystals modify the pulse shape to allow continuous DOI information determination, but the relationship between pulse shape and DOI is complex. We are therefore interested in developing a sensitive and robust method to estimate the DOI. Here, linear discriminant analysis (LDA) was implemented to classify the events based on information extracted from the pulse shape. Pulses were acquired with 2×2×20 mm 3 phosphor-coated crystals at five irradiation depths and characterized by their DCI values or Laguerre coefficients. These coefficients were obtained by expanding the pulses on a Laguerre basis set and constituted a unique signature for each pulse. The DOI of individual events was predicted using LDA based on Laguerre coefficients (Laguerre-LDA) or DCI values (DCI-LDA) as discriminant features. Predicted DOIs were compared to true irradiation depths. Laguerre-LDA showed higher sensitivity and accuracy than DCI-LDA and DCI-LR and was also more robust to predict the DOI of pulses with higher statistical noise due to low light levels (interaction depths further from the photodetector face). This indicates that Laguerre-LDA may be more suitable to DOI estimation in smaller crystals where lower collected light levels are expected. This novel approach is promising for calculating DOI using pulse shape discrimination in single-ended readout depth-encoding PET detectors. (paper)

  20. Contributions to sensitivity analysis and generalized discriminant analysis

    International Nuclear Information System (INIS)

    Jacques, J.

    2005-12-01

    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)

  1. LDA measurements in the Francis-99 draft tube cone

    International Nuclear Information System (INIS)

    Sundstrom, L R J; Amiri, K; Cervantes, M J; Bergan, C; Dahlhaug, O G

    2014-01-01

    Velocity measurements were performed in the draft tube cone of a 1:5.1 scaled model of the Tokke hydropower plant, Norway; also known as the Francis-99 model. Results from the laser Doppler anemometry measurements undertaken at three operating points will be used as validation data for an upcoming workshop on the state of the art of Francis turbine numerical simulation. With the turbine operating at the best efficiency point, a sensitivity analysis of the flow parameters head, flow rate and runner rotational speed shows that the effects on the dimensionless velocity profiles are small as long as n ED and Q ED are held constant. The results indicate a well-functioning turbine at the best efficiency point and high load. At the part load operating point, a vortex breakdown occurs which distorts the velocity profiles and significantly lowers the turbine's hydraulic efficiency. Frequency spectrums of each LDA signal at part load reveals a peak which is asynchronous to that of the runner angular speed. The peaks might be related to the precession of a rotating vortex rope but the characteristics of the LDA signals are different compared to previous studies involving rotating vortex ropes

  2. Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Abbas Gorji-Chakespari

    2016-05-01

    Full Text Available Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP and linear discriminant analysis (LDA algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world using an electronic nose (EN system based on seven metal oxide semiconductor (MOS sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response. The extracted features were ordered in relevance by the intra/inter variance criterion (Vr, also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

  3. 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.

  4. On the peculiarities of LDA method in two-phase flows with high concentrations of particles

    Science.gov (United States)

    Poplavski, S. V.; Boiko, V. M.; Nesterov, A. U.

    2016-10-01

    Popular applications of laser Doppler anemometry (LDA) in gas dynamics are reviewed. It is shown that the most popular method cannot be used in supersonic flows and two-phase flows with high concentrations of particles. A new approach to implementation of the known LDA method based on direct spectral analysis, which offers better prospects for such problems, is presented. It is demonstrated that the method is suitable for gas-liquid jets. Owing to the progress in laser engineering, digital recording of spectra, and computer processing of data, the method is implemented at a higher technical level and provides new prospects of diagnostics of high-velocity dense two-phase flows.

  5. Use of linear discriminant function analysis in seed morphotype ...

    African Journals Online (AJOL)

    Use of linear discriminant function analysis in seed morphotype relationship study in 31 ... Data were collected on 100-seed weight, seed length and seed width. ... to the Mesoamerican gene pool, comprising the cultigroups Sieva-Big Lima, ...

  6. Dimensional Analysis with space discrimination applied to Fickian difussion phenomena

    International Nuclear Information System (INIS)

    Diaz Sanchidrian, C.; Castans, M.

    1989-01-01

    Dimensional Analysis with space discrimination is applied to Fickian difussion phenomena in order to transform its partial differen-tial equations into ordinary ones, and also to obtain in a dimensionl-ess fom the Ficks second law. (Author)

  7. Discrimination between smiling faces: Human observers vs. automated face analysis.

    Science.gov (United States)

    Del Líbano, Mario; Calvo, Manuel G; Fernández-Martín, Andrés; Recio, Guillermo

    2018-05-11

    This study investigated (a) how prototypical happy faces (with happy eyes and a smile) can be discriminated from blended expressions with a smile but non-happy eyes, depending on type and intensity of the eye expression; and (b) how smile discrimination differs for human perceivers versus automated face analysis, depending on affective valence and morphological facial features. Human observers categorized faces as happy or non-happy, or rated their valence. Automated analysis (FACET software) computed seven expressions (including joy/happiness) and 20 facial action units (AUs). Physical properties (low-level image statistics and visual saliency) of the face stimuli were controlled. Results revealed, first, that some blended expressions (especially, with angry eyes) had lower discrimination thresholds (i.e., they were identified as "non-happy" at lower non-happy eye intensities) than others (especially, with neutral eyes). Second, discrimination sensitivity was better for human perceivers than for automated FACET analysis. As an additional finding, affective valence predicted human discrimination performance, whereas morphological AUs predicted FACET discrimination. FACET can be a valid tool for categorizing prototypical expressions, but is currently more limited than human observers for discrimination of blended expressions. Configural processing facilitates detection of in/congruence(s) across regions, and thus detection of non-genuine smiling faces (due to non-happy eyes). Copyright © 2018 Elsevier B.V. All rights reserved.

  8. The use of the discriminant analysis method for e π μ separation in BES

    International Nuclear Information System (INIS)

    Jiang Zhijin; Wang Taijie; Xie Yigang; Huang Tao

    1994-01-01

    We use the discriminant analysis method in multivariate statistical theory to handle the e π μ separation in BES, describing the principle of the discriminant analysis method, deriving the unstandardized discriminant functions (responsible for particle separation), giving the discriminant efficiency for e π μ and comparing the results from the discriminant analysis method with those obtained in a conventional way. ((orig.))

  9. 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.......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...

  10. A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-11-01

    This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under mild assumptions, we show that the asymptotic classification error approaches a deterministic quantity that depends only on the means and covariances associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized discriminant analsysis, in practical large but finite dimensions, and can be used to determine and pre-estimate the optimal regularization parameter that minimizes the misclassification error probability. Despite being theoretically valid only for Gaussian data, our findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from the popular USPS data base, thereby making an interesting connection between theory and practice.

  11. LDA boost classification: boosting by topics

    Science.gov (United States)

    Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

    2012-12-01

    AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

  12. e-IQ and IQ knowledge mining for generalized LDA

    Science.gov (United States)

    Jenkins, Jeffrey; van Bergem, Rutger; Sweet, Charles; Vietsch, Eveline; Szu, Harold

    2015-05-01

    How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in

  13. 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.

  14. Linear Discriminant Analysis achieves high classification accuracy for the BOLD fMRI response to naturalistic movie stimuli.

    Directory of Open Access Journals (Sweden)

    Hendrik eMandelkow

    2016-03-01

    Full Text Available Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI. However, conventional fMRI analysis based on statistical parametric mapping (SPM and the general linear model (GLM is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA, have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbour (NN, Gaussian Naïve Bayes (GNB, and (regularised Linear Discriminant Analysis (LDA in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie.Results show that LDA regularised by principal component analysis (PCA achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2s apart during a 300s movie (chance level 0.7% = 2s/300s. The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

  15. 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.

  16. Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

    Science.gov (United States)

    McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

    2016-01-01

    Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. 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. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

  17. Discriminant Function Analysis as a Proof for Sexual Dimorphism ...

    African Journals Online (AJOL)

    Background: Forensic scientists study human skeleton in legal setting. Discriminant function analysis has become important in forensic anthropology. The aim of this study was to determine the sex of adolescent Yoruba ethnic group of Nigeria using iscriminant function analysis. Methodology: One thousand (500 males and ...

  18. 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…

  19. Pharmacokinetic-Pharmacodynamic (PKPD) Analysis with Drug Discrimination.

    Science.gov (United States)

    Negus, S Stevens; Banks, Matthew L

    2016-08-30

    Discriminative stimulus and other drug effects are determined by the concentration of drug at its target receptor and by the pharmacodynamic consequences of drug-receptor interaction. For in vivo procedures such as drug discrimination, drug concentration at receptors in a given anatomical location (e.g., the brain) is determined both by the dose of drug administered and by pharmacokinetic processes of absorption, distribution, metabolism, and excretion that deliver drug to and from that anatomical location. Drug discrimination data are often analyzed by strategies of dose-effect analysis to determine parameters such as potency and efficacy. Pharmacokinetic-Pharmacodynamic (PKPD) analysis is an alternative to conventional dose-effect analysis, and it relates drug effects to a measure of drug concentration in a body compartment (e.g., venous blood) rather than to drug dose. PKPD analysis can yield insights on pharmacokinetic and pharmacodynamic determinants of drug action. PKPD analysis can also facilitate translational research by identifying species differences in pharmacokinetics and providing a basis for integrating these differences into interpretation of drug effects. Examples are discussed here to illustrate the application of PKPD analysis to the evaluation of drug effects in rhesus monkeys trained to discriminate cocaine from saline.

  20. Enamel surface topography analysis for diet discrimination. A methodology to enhance and select discriminative parameters

    Science.gov (United States)

    Francisco, Arthur; Blondel, Cécile; Brunetière, Noël; Ramdarshan, Anusha; Merceron, Gildas

    2018-03-01

    Tooth wear and, more specifically, dental microwear texture is a dietary proxy that has been used for years in vertebrate paleoecology and ecology. DMTA, dental microwear texture analysis, relies on a few parameters related to the surface complexity, anisotropy and heterogeneity of the enamel facets at the micrometric scale. Working with few but physically meaningful parameters helps in comparing published results and in defining levels for classification purposes. Other dental microwear approaches are based on ISO parameters and coupled with statistical tests to find the more relevant ones. The present study roughly utilizes most of the aforementioned parameters in their more or less modified form. But more than parameters, we here propose a new approach: instead of a single parameter characterizing the whole surface, we sample the surface and thus generate 9 derived parameters in order to broaden the parameter set. The identification of the most discriminative parameters is performed with an automated procedure which is an extended and refined version of the workflows encountered in some studies. The procedure in its initial form includes the most common tools, like the ANOVA and the correlation analysis, along with the required mathematical tests. The discrimination results show that a simplified form of the procedure is able to more efficiently identify the desired number of discriminative parameters. Also highlighted are some trends like the relevance of working with both height and spatial parameters, as well as the potential benefits of dimensionless surfaces. On a set of 45 surfaces issued from 45 specimens of three modern ruminants with differences in feeding preferences (grazing, leaf-browsing and fruit-eating), it is clearly shown that the level of wear discrimination is improved with the new methodology compared to the other ones.

  1. Discrimination analysis of ononis repens and ononis spinosa of the ...

    African Journals Online (AJOL)

    Discrimination analysis of ononis repens and ononis spinosa of the British Isles. CE Stephens. Abstract. No Abstract. Journal of the Ghana Association Vol. 2 (3) 1999: pp.88-94. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/jgsa.v2i3.17997.

  2. Discriminant analysis of functional optical topography for schizophrenia diagnosis

    Science.gov (United States)

    Chuang, Ching-Cheng; Nakagome, Kazuyuki; Pu, Shenghong; Lan, Tsuo-Hung; Lee, Chia-Yen; Sun, Chia-Wei

    2014-01-01

    Abnormal prefrontal function plays a central role in the cognition deficits of schizophrenic patients; however, the character of the relationship between discriminant analysis and prefrontal activation remains undetermined. Recently, evidence of low prefrontal cortex (PFC) activation in individuals with schizophrenia has also been found during verbal fluency tests (VFT) and other cognitive tests with several neuroimaging methods. The purpose of this study is to assess the hemodynamic changes of the PFC and discriminant analysis between schizophrenia patients and healthy controls during VFT task by utilizing functional optical topography. A total of 99 subjects including 53 schizophrenic patients and 46 age- and gender-matched healthy controls were studied. The results showed that the healthy group had larger activation in the right and left PFC than in the middle PFC. Besides, the schizophrenic group showed weaker task performance and lower activation in the whole PFC than the healthy group. The result of the discriminant analysis showed a significant difference with P value <0.001 in six channels (CH 23, 29, 31, 40, 42, 52) between the schizophrenic and healthy groups. Finally, 68.69% and 71.72% of subjects are correctly classified as being schizophrenic or healthy with all 52 channels and six significantly different channels, respectively. Our findings suggest that the left PFC can be a feature region for discriminant analysis of schizophrenic diagnosis.

  3. Linear discriminant analysis of structure within African eggplant 'Shum'

    African Journals Online (AJOL)

    A MANOVA preceded linear discriminant analysis, to model each of 61 variables, as predicted by clusters and experiment to filter out non-significant traits. Four distinct clusters emerged, with a cophenetic relation coefficient of 0.87 (P<0.01). Canonical variates that best predicted the observed clusters include petiole length, ...

  4. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

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

    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......In the present study, fresh beef fillets were purchased from a local butcher shop and stored aerobically and in modified atmosphere packaging (MAP, CO2 40%/O2 30%/N2 30%) at six different temperatures (0, 4, 8, 12, 16 and 20°C). Microbiological analysis in terms of total viable counts (TVC......) 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...

  5. Fish otoliths analysis by PIXE: application to stock discrimination

    International Nuclear Information System (INIS)

    Arai, Nobuaki; Takai, Noriyuki; Sakamoto, Wataru; Yoshida, Koji; Maeda, Kuniko.

    1996-01-01

    Fish otoliths are continuously deposited from fish birth to its death along with encoding environmental information. In order to decode the information, PIXE was adopted as trace elemental analysis of the otoliths. Strontium to calcium concentration ratios of red sea bream otoliths varied among rearing stations. The Sr/Ca ratios of Lake Biwa catfishes also varied between male and female and among fishing grounds. The PIXE analysis was applied to the fish stock discrimination. (author)

  6. Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Vítková, Gabriela [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); Prokeš, Lubomír [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); Department of Physical Electronics, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); CEPLANT, R and D Center for Low-Cost Plasma and Nanotechnology Surface Modifications, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); Novotný, Karel [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); CEITEC — Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno (Czech Republic); Pořízka, Pavel [Institute of Physical Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 616 69 Brno (Czech Republic); Novotný, Jan [CEITEC — Central European Institute of Technology, Brno University of Technology, Technická 3058/10, 616 00 Brno (Czech Republic); Všianský, Dalibor [Department of Geological Sciences, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); Čelko, Ladislav [CEITEC — Central European Institute of Technology, Brno University of Technology, Technická 3058/10, 616 00 Brno (Czech Republic); and others

    2014-11-01

    Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups — stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements. - Highlights: • Comparison of two potentially field-deployable LIBS setups is introduced. • The aim is classification of LIBS spectra of brick samples into several localities. • LDA filled with PCA scores is proposed as an effective classification method. • An influence of brick firing temperature on LIBS spectra is discovered and examined.

  7. Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

    Science.gov (United States)

    Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

    2015-05-01

    In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

  8. STARTING BLOCK PERFORMANCE IN SPRINTERS: A STATISTICAL METHOD FOR IDENTIFYING DISCRIMINATIVE PARAMETERS OF THE PERFORMANCE AND AN ANALYSIS OF THE EFFECT OF PROVIDING FEEDBACK OVER A 6-WEEK PERIOD

    Directory of Open Access Journals (Sweden)

    Sylvie Fortier

    2005-06-01

    Full Text Available The purpose of this study was twofold: (a to examine if kinetic and kinematic parameters of the sprint start could differentiate elite from sub-elite sprinters and, (b to investigate whether providing feedback (FB about selected parameters could improve starting block performance of intermediate sprinters over a 6-week training period. Twelve male sprinters, assigned to an elite or a sub-elite group, participated in Experiment 1. Eight intermediate sprinters participated in Experiment 2. All athletes were required to perform three sprint starts at maximum intensity followed by a 10-m run. To detect differences between elite and sub-elite groups, comparisons were made using t-tests for independent samples. Parameters reaching a significant group difference were retained for the linear discriminant analysis (LDA. The LDA yielded four discriminative kinetic parameters. Feedback about these selected parameters was given to sprinters in Experiment 2. For this experiment, data acquisition was divided into three periods. The first six sessions were without specific FB, whereas the following six sessions were enriched by kinetic FB. Finally, athletes underwent a retention session (without FB 4 weeks after the twelfth session. Even though differences were found in the time to front peak force, the time to rear peak force, and the front peak force in the retention session, the results of the present study showed that providing FB about selected kinetic parameters differentiating elite from sub-elite sprinters did not improve the starting block performance of intermediate sprinters

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

    CERN Multimedia

    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. 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.

  11. Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis.

    Science.gov (United States)

    Noh, Yung-Kyun; Hamm, Jihun; Park, Frank Chongwoo; Zhang, Byoung-Tak; Lee, Daniel D

    2018-01-01

    Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.

  12. Phylogenetic comparative methods complement discriminant function analysis in ecomorphology.

    Science.gov (United States)

    Barr, W Andrew; Scott, Robert S

    2014-04-01

    In ecomorphology, Discriminant Function Analysis (DFA) has been used as evidence for the presence of functional links between morphometric variables and ecological categories. Here we conduct simulations of characters containing phylogenetic signal to explore the performance of DFA under a variety of conditions. Characters were simulated using a phylogeny of extant antelope species from known habitats. Characters were modeled with no biomechanical relationship to the habitat category; the only sources of variation were body mass, phylogenetic signal, or random "noise." DFA on the discriminability of habitat categories was performed using subsets of the simulated characters, and Phylogenetic Generalized Least Squares (PGLS) was performed for each character. Analyses were repeated with randomized habitat assignments. When simulated characters lacked phylogenetic signal and/or habitat assignments were random, ecomorphology. Copyright © 2013 Wiley Periodicals, Inc.

  13. Discriminant analysis in Polish manufacturing sector performance assessment

    Directory of Open Access Journals (Sweden)

    Józef Dziechciarz

    2004-01-01

    Full Text Available This is a presentation of the preliminary results of a larger project on the determination of the attractiveness of manufacturing branches. Results of the performance assessment of Polish manufacturing branches in 2000 (section D „Manufacturing” – based on NACE – Nomenclatures des Activites de Communite Europeene are shown. In the research, the classical (Fisher’s linear discriminant analysis technique was used for the analysis of the profit generation ability by the firms belonging to a certain production branch. For estimation, the data describing group level was used – for cross-validation, the classes data.

  14. 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.

  15. 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.

  16. A new kernel discriminant analysis framework for electronic nose recognition

    International Nuclear Information System (INIS)

    Zhang, Lei; Tian, Feng-Chun

    2014-01-01

    Graphical abstract: - Highlights: • This paper proposes a new discriminant analysis framework for feature extraction and recognition. • The principle of the proposed NDA is derived mathematically. • The NDA framework is coupled with kernel PCA for classification. • The proposed KNDA is compared with state of the art e-Nose recognition methods. • The proposed KNDA shows the best performance in e-Nose experiments. - Abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose

  17. [State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

    Science.gov (United States)

    Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

    2016-01-01

    In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct

  18. Using discriminant analysis as a nucleation event classification method

    Directory of Open Access Journals (Sweden)

    S. Mikkonen

    2006-01-01

    Full Text Available More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteorological and trace gas variables effect on the emergence of nucleation events. As the analysis method, we used discriminant analysis with non-parametric Epanechnikov kernel, included in non-parametric density estimation method. The best classification result in our data was reached with the combination of relative humidity, ozone concentration and a third degree polynomial of radiation. RH appeared to have a preventing effect on the new particle formation whereas the effects of O3 and radiation were more conductive. The concentration of SO2 and NO2 also appeared to have significant effect on the emergence of nucleation events but because of the great amount of missing observations, we had to exclude them from the final analysis.

  19. Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

    Science.gov (United States)

    Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

    2017-10-12

    Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

  20. Sustainable Production and Trade Discrimination: An Analysis of the WTO

    Directory of Open Access Journals (Sweden)

    María Alejandra Calle Saldarriaga

    2018-02-01

    Full Text Available This article aims to examine the legality of trade measures addressing environmental conditions of production (PPMs in the context of non-discrimination provisions under the General Agreement on Tariffs and Trade (GATT  and the Agreement on Technical Barriers to Trade (TBT Agreement.  It shows that the notion of de facto discrimination is still a sensitive subject in the analysis of origin-neutral measures, including those based on environmental PPMs. Much of the discussion regarding PPMs focuses on the issue of ‘like products’. The interpretation of ‘likeness’ has also served to classify PPMs into the two categories of product related and non-product related. Such distinction rests on how the PPM affects the final product. However, it is important to analyse to what extent these measures can accord less favourable treatment to like products. The author argues that this requires a competition analysis. This article also elucidates how depending upon the applicable law (the TBT Agreement or the GATT PPMs are likely to face different legal challenges, particularly in terms of less favourable treatment. The author also assesses the possibility of transposing concepts such as ‘legitimate regulatory distinctions’ stemming from the TBT jurisprudence into GATT cases involving PPMs, and whether there will be an additional ‘test’ for PPMs characterised as TBT measures. This article is based on an extensive literature review and doctrinal legal research

  1. Frontotemporal Dysfunction in Amyotrophic Lateral Sclerosis: A Discriminant Function Analysis.

    Science.gov (United States)

    Nidos, Andreas; Kasselimis, Dimitrios S; Simos, Panagiotis G; Rentzos, Michael; Alexakis, Theodoros; Zalonis, Ioannis; Zouvelou, Vassiliki; Potagas, Constantin; Evdokimidis, Ioannis; Woolley, Susan C

    2016-01-01

    There is growing evidence for extramotor dysfunction (EMd) in amyotrophic lateral sclerosis (ALS), with a reported prevalence of up to 52%. In the present study, we explore the clinical utility of a brief neuropsychological battery for the investigation of cognitive, behavioral, and language deficits in patients with ALS. Thirty-four consecutive ALS patients aged 44-89 years were tested with a brief neuropsychological battery, including executive, behavioral, and language measures. Patients were initially classified as EMd or non-EMd based on their scores on the frontal assessment battery (FAB). Between-group comparisons revealed significant differences in all measures (p < 0.01). Discriminant analysis resulted in a single canonical function, with all tests serving as significant predictors. This function agreed with the FAB in 13 of 17 patients screened as EMd and identified extramotor deficits in 2 additional patients. Overall sensitivity and specificity estimates against FAB were 88.2%. We stress the importance of discriminant function analysis in clinical neuropsychological assessment and argue that the proposed neuropsychological battery may be of clinical value, especially when the option of extensive and comprehensive neuropsychological testing is limited. The psychometric validity of an ALS-frontotemporal dementia diagnosis using neuropsychological tests is also discussed. © 2015 S. Karger AG, Basel.

  2. Anti-discrimination Analysis Using Privacy Attack Strategies

    KAUST Repository

    Ruggieri, Salvatore; Hajian, Sara; Kamiran, Faisal; Zhang, Xiangliang

    2014-01-01

    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

  3. [Development of Tianma HPLC fingerprint and discriminant analysis].

    Science.gov (United States)

    Xiao, Jia-Jia; Huang, Hong; Lei, You-Cheng; Lin, Ting-Wen; Ma, Yue; Zhang, Jing; Zhang, Xing-Guo; Zhang, Da-Quan; Lv, Guang-Hua

    2017-07-01

    Tianma(the tuber of Gastrodia eleta) is a widely used and pricy Chinese herb. Its counterfeits are often found in herbal markets, which are the plant materials with similar macroscopic characteristics of Tianma. Moreover, the prices of Winter Tianma(cultivated Tianma) and Spring Tianma(mostly wild Tianma) have significant difference. However, it is difficult to identify the true or false, good or bad quality of Tianma samples. Thus, a total of 48 Tianma samples with different characteristics(including Winter Tianma, Spring Tianma, slice, powder, etc.) and 9 plant species 10 samples of Tianma counterfeits were collected and analyzed by HPLC-DAD-MS techniques. After optimizing the procedure of sample preparation, chromatographic and mass-spectral conditions, the HPLC chromatograms of all those samples were collected and compared. The similarities and Fisher discriminant analysis were further conducted between the HPLC chromatograms of Tianma and counterfeit, Winter Tianma and Spring Tianma. The results showed the HPLC chromatograms of 48 Tianma samples were similar at the correlation coefficient more than 0.848(n=48). Their mean chromatogram was simulated and used as Tianma HPLC fingerprint. There were 11 common peaks on the HPLC chromatograms of Tianma, in which 6 main peaks were chosen as characteristic peaks and identified as gastrodin, p-hydroxybenzyl alcohol, parishin A, parishin B, parishin C, parishin E, respectively by comparison of the retention time, UV and MS data with those of standard chemical compounds. All the six chemical compounds are bioactive in Tianma. However, the HPLC chromatograms of the 10 counterfeit samples were significantly different from Tianma fingerprint. The correlation coefficients between HPLC fingerprints of Tianma with the HPLC chromatograms of counterfeits were less than 0.042 and the characteristic peaks were not observed on the HPLC chromatograms of these counterfeit samples. It indicated the true or false Tianma can be

  4. 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.

  5. 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…

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

    KAUST Repository

    Dutta, Subhajit; Chaudhuri, Probal; Ghosh, Anil

    2014-01-01

    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.

  7. 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.

  8. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples

    Science.gov (United States)

    Chen, Xue; Li, Xiaohui; Yang, Sibo; Yu, Xin; Liu, Aichun

    2018-01-01

    Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies. PMID:29541503

  10. 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.

  11. 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...... the methods in two: Those who assume independence between the variables and thus use a diagonal estimate of the within-class covariance matrix, and those who assume dependence between the variables and thus use an estimate of the within-class covariance matrix, which also estimates the correlations between...... variables. The two groups of methods are compared and the pros and cons are exemplied using dierent cases of simulated data. The results illustrate that the estimate of the covariance matrix is an important factor with respect to choice of method, and the choice of method should thus be driven by the nature...

  12. A novel method for qualitative analysis of edible oil oxidation using an electronic nose.

    Science.gov (United States)

    Xu, Lirong; Yu, Xiuzhu; Liu, Lei; Zhang, Rui

    2016-07-01

    An electronic nose (E-nose) was used for rapid assessment of the degree of oxidation in edible oils. Peroxide and acid values of edible oil samples were analyzed using data obtained by the American Oil Chemists' Society (AOCS) Official Method for reference. Qualitative discrimination between non-oxidized and oxidized oils was conducted using the E-nose technique developed in combination with cluster analysis (CA), principal component analysis (PCA), and linear discriminant analysis (LDA). The results from CA, PCA and LDA indicated that the E-nose technique could be used for differentiation of non-oxidized and oxidized oils. LDA produced slightly better results than CA and PCA. The proposed approach can be used as an alternative to AOCS Official Method as an innovative tool for rapid detection of edible oil oxidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. A study on native defects and magnetic properties in undoped rutile TiO2 using LDA and LDA+UO p+UTi d methods

    Science.gov (United States)

    Shi, Li-Bin; Wang, Yong Ping

    2016-05-01

    The native defects and magnetic properties in undoped rutile TiO2 are studied using local density approximation (LDA) and LDA adding Hubbard parameters (U) schemes. The band gap is adjusted to experimental value of 3.0 eV by combination of UTi d=4.2 eV and UO p=4.8 eV. This LDA+U methodology overcomes the band-gap problem and renders the approach more predictive. The formation energies of oxygen vacancy (VO), oxygen interstitial (Oi), titanium vacancy (VTi), titanium interstitial (Tii), oxygen anti-sites (OTi), and titanium anti-sites (TiO) are investigated by the LDA and LDA+U methods. In addition, some ground state configurations can be obtained by optimization of total spin. It is found that native defects can induce spin polarization and produce magnetic moment.

  14. Estimation of burst-mode LDA power spectra

    DEFF Research Database (Denmark)

    Velte, Clara Marika; George, William K.; Buchhave, Preben

    2014-01-01

    . The signal can be interpreted correctly by applying residence time weighting to all statistics and using the residence time-weighted discrete Fourier transform to compute the Fourier transform. A new spectral algorithm using the latter is applied to two experiments: a cylinder wake and an axisymmetric......The estimation of power spectra from LDA data provides signal processing challenges for fluid dynamicists for several reasons: acquisition is dictated by randomly arriving particles, the registered particle velocities tend to be biased toward higher values, and the signal is highly intermittent...

  15. Asymptotic performance of regularized quadratic discriminant analysis based classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-12-13

    This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.

  16. 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…

  17. Discriminant analysis of maintaining a vertical position in the water

    Directory of Open Access Journals (Sweden)

    Bratuša Zoran

    2015-01-01

    Full Text Available Water polo is the only sports game that takes place in the water. During the outplay, a vertical body position with the two basic mechanisms of the leg work - a breaststroke leg kick and an eggbeater leg kick, prevails. Starting from the significance of a vertical position during the game play, the methods of assessing physical preparedness of the athletes of all the categories also include the evaluation of maintaining a vertical position and consequently the load of the leg muscles. The measurements are performed during the maintenance of a vertical position (swimming in place through one of the specified mechanisms of leg work, i.e. a vertical position technique. The aim of this paper was to determine the application of different mechanisms of the leg kicks in maintaining a vertical position with young water polo players in relation to their position. The study included 29 selected junior water polo players (age_15.8 ± 0.8 years; BH_185.2 ± 5.3cm and BW_81.7 ± 7.7kg. The measurements were performed during the tests of swimming in place at the maximum intensity lasting 10 seconds, by the breaststroke and eggbeater leg kicks. The isometric tensiometry tests were used for the measurements. The results were analysed by the application of descriptive statistics, and the kinetic selection characteristic was defined by the application of discriminant analysis. Higher average values were achieved with the breaststroke leg kick technique Fmax, ImpF and RFD (avgFmaxLEGGBK =157.46±19.93N; avgImpF_LEGGBK =45.43±10.64Ns; avgRFD_LEGGBK=337.85±80.73N/s; avgFmaxLBKICK=227.18±49.17N; avgImpF_LBKICK=55.99±14.59Ns; avgRFD_LBKICK=545.47±159.15N/s. After discriminant analysis, the results have shown that the eggbeater leg kick is a selection technique, whereas the force - Fmax is a kinetic selection variable. Based on the obtained results and the analyses performed it may be concluded that a training factor dominant for maintaining a vertical position by

  18. 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.

  19. Discrimination of ginseng cultivation regions using light stable isotope analysis.

    Science.gov (United States)

    Kim, Kiwook; Song, Joo-Hyun; Heo, Sang-Cheol; Lee, Jin-Hee; Jung, In-Woo; Min, Ji-Sook

    2015-10-01

    Korean ginseng is considered to be a precious health food in Asia. Today, thieves frequently compromise ginseng farms by pervasive theft. Thus, studies regarding the characteristics of ginseng according to growth region are required in order to deter ginseng thieves and prevent theft. In this study, 6 regions were selected on the basis of Korea regional criteria (si, gun, gu), and two ginseng-farms were randomly selected from each of the 6 regions. Then 4-6 samples of ginseng were acquired from each ginseng farm. The stable isotopic compositions of H, O, C, and N of the collected ginseng samples were analyzed. As a result, differences in the hydrogen isotope ratios could be used to distinguish regional differences, and differences in the nitrogen isotope ratios yielded characteristic information regarding the farms from which the samples were obtained. Thus, stable isotope values could be used to differentiate samples according to regional differences. Therefore, stable isotope analysis serves as a powerful tool to discriminate the regional origin of Korean ginseng samples from across Korea. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  20. ANALYSIS ON WOMEN DISCRIMINATION IN THE LABOUR MARKET IN ROMANIA

    OpenAIRE

    Victoria-Mihaela Brînzea

    2011-01-01

    Eliminating gender-based discrimination is one of the important prerequisite for building a fair society; this can be achieved only through the active involvement of the authorities and of each person. Although during recent years there have been positive changes in the relationships between men and women, improving women's situation to some extent, it can be said that discrimination based on social gender was reduced but not eliminated entirely, equality of chances having not been achieved e...

  1. Measurement of mist cooling of PWR during LOCA by LDA

    International Nuclear Information System (INIS)

    Lee, S.L.; Sheen, H.J.; Issapour, I.

    1985-01-01

    The prediction of temperature distribution and heat transfer within rod bundles during the refill and reflood phase of a LOCA (loss of coolant accident) is of critical importance for determining the location and size of blockages due to clad deformation in a pressurized water reactor (PWR). Mist cooling by small droplets generated from large droplets on hitting grid spacers has been suggested as one of the most important heat transfer mechanisms which are responsible for the development of this temperature transient. The questions to be asked are whether such small droplets indeed exist and, if so, how are they related to the cooling of the fuel rods. Hereby reported is the result of a direct experimental investigation on these questions by a special laser-Doppler anemometry (LDA) particle sizing technique together with temperature measurements of the rod claddings and flow in the subchannel

  2. WOMEN RESISTANCE TOWARD DISCRIMINATIONS: A MODERN LITERARY WORK ANALYSIS ON FEMINISM REVIEW IN BEKISAR MERAH

    Directory of Open Access Journals (Sweden)

    Mujiono .

    2016-02-01

    Full Text Available This study was conducted to discover the discriminations against women in the Bekisar Merah novel and how they formulate resistance to those discriminations. To address the above objective, this study used descriptive qualitative research design with a feminism approach. Source of the data in this study was the second edition of Bekisar Merah novel written by Ahmad Tohari. The data included were words, phrases, sentences, and paragraphs on Bekisar Merah which portray womens discrimination toward Lasi, the women figure in the novel, and power types formulated by her who resisted the discrimination. To analyze the data, content analysis was applied. Triangulation was used to ensure the trustworthiness of the data. The result of the study showed eight forms of discriminations and three resistances. The discriminations were domestic abuse, molestation, gender harassment, seduction behavior, imposition, coercion, bribery, and subordination. The resistances were physically, mentally, and verbally.

  3. Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration

    International Nuclear Information System (INIS)

    Kwanhoong Ng; Senghuat Ong; Bradley, D.A.; Laimeng Looi

    1997-01-01

    Discriminant analysis of six trace element concentrations measured by instrumental neutron activation analysis (INAA) in 26 paired-samples of malignant and histologically normal human breast tissues shows the technique to be a potentially valuable clinical tool for making malignant-normal classification. Nonparametric discriminant analysis is performed for the data obtained. Linear and quadratic discriminant analyses are also carried out for comparison. For this data set a formal analysis shows that the elements which may be useful in distinguishing between malignant and normal tissues are Ca, Rb and Br, providing correct classification for 24 out of 26 normal samples and 22 out of 26 malignant samples. (Author)

  4. Non-Discrimination à la Cour: the ECJ’s (lack of) Comparability Analysis in Direct Tax Cases

    NARCIS (Netherlands)

    Wattel, P.

    2015-01-01

    The ECJ’s discrimination analysis in direct tax cases is inconsistent. It sometimes creates discrimination, condemns non-existent discrimination or fails to address discrimination. Only one comparability standard makes sense: to be (subject to tax) or not to be (subject to tax). The ECJ is not

  5. Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    ZHANG Long

    2015-09-01

    Full Text Available Near infrared reflectance spectroscopy (NIRS, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA to discriminate the transgenic (TCTP and mi166 and wild type (Zhonghua 11 rice. Furthermore, rice lines transformed with protein gene (OsTCTP and regulation gene (Osmi166 were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000–8 000 cm-1 and 4 000–10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

  6. 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

    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, and it is for...

  7. 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.

  8. Statistical analysis of agarwood oil compounds in discriminating the ...

    African Journals Online (AJOL)

    Enhancing and improving the discrimination technique is the main aim to determine or grade the good quality of agarwood oil. In this paper, all statistical works were performed via SPSS software. Two parameters involved are abundance of compound (%) and quality of t agarwood oil either low or high quality. The result ...

  9. Logistic discriminant analysis of breast cancer using ultrasound measurement

    International Nuclear Information System (INIS)

    Abdolmaleki, P.; Mokhtari Dizaji, M.; Vahead, M.R.; Gity, M.

    2004-01-01

    Background: Logistic discriminant method was applied to differentiate malignant from benign in a group of patients with proved breast lesions of the basis of ultrasonic parameters. Materials and methods: Our database include 273 patients' ultrasonographic pictures consisting of 14 quantitative variables. The measured variables were ultrasound propagation velocity, acoustic impedance and attenuation coefficient at 10 MHz in breast lesions at 20, 25, 30 and 35 d ig c temperature, physical density and age. This database was randomly divided into the estimation of 201 and validation of 72 samples. The estimation samples were used to build the logistic discriminant model, and validation samples were used to validate the performance. Finally important criteria such as sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were evaluated. Results: Our results showed that the logistic discriminant method was able to classify correctly 67 out of 72 cases presented in the validation sample. The results indicate a remarkable diagnostic accuracy of 93%. Conclusion: A logistic discriminator approach is capable of predicting the probability of malignancy of breast cancer. Features from ultrasonic measurement on ultrasound imaging is used in this approach

  10. Linear Discriminant Analysis for the in Silico Discovery of Mechanism-Based Reversible Covalent Inhibitors of a Serine Protease: Application of Hydration Thermodynamics Analysis and Semi-empirical Molecular Orbital Calculation.

    Science.gov (United States)

    Masuda, Yosuke; Yoshida, Tomoki; Yamaotsu, Noriyuki; Hirono, Shuichi

    2018-01-01

    We recently reported that the Gibbs free energy of hydrolytic water molecules (ΔG wat ) in acyl-trypsin intermediates calculated by hydration thermodynamics analysis could be a useful metric for estimating the catalytic rate constants (k cat ) of mechanism-based reversible covalent inhibitors. For thorough evaluation, the proposed method was tested with an increased number of covalent ligands that have no corresponding crystal structures. After modeling acyl-trypsin intermediate structures using flexible molecular superposition, ΔG wat values were calculated according to the proposed method. The orbital energies of antibonding π* molecular orbitals (MOs) of carbonyl C=O in covalently modified catalytic serine (E orb ) were also calculated by semi-empirical MO calculations. Then, linear discriminant analysis (LDA) was performed to build a model that can discriminate covalent inhibitor candidates from substrate-like ligands using ΔG wat and E orb . The model was built using a training set (10 compounds) and then validated by a test set (4 compounds). As a result, the training set and test set ligands were perfectly discriminated by the model. Hydrolysis was slower when (1) the hydrolytic water molecule has lower ΔG wat ; (2) the covalent ligand presents higher E orb (higher reaction barrier). Results also showed that the entropic term of hydrolytic water molecule (-TΔS wat ) could be used for estimating k cat and for covalent inhibitor optimization; when the rotational freedom of the hydrolytic water molecule is limited, the chance for favorable interaction with the electrophilic acyl group would also be limited. The method proposed in this study would be useful for screening and optimizing the mechanism-based reversible covalent inhibitors.

  11. An Application of Monte-Carlo-Based Sensitivity Analysis on the Overlap in Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    S. Razmyan

    2012-01-01

    Full Text Available Discriminant analysis (DA is used for the measurement of estimates of a discriminant function by minimizing their group misclassifications to predict group membership of newly sampled data. A major source of misclassification in DA is due to the overlapping of groups. The uncertainty in the input variables and model parameters needs to be properly characterized in decision making. This study combines DEA-DA with a sensitivity analysis approach to an assessment of the influence of banks’ variables on the overall variance in overlap in a DA in order to determine which variables are most significant. A Monte-Carlo-based sensitivity analysis is considered for computing the set of first-order sensitivity indices of the variables to estimate the contribution of each uncertain variable. The results show that the uncertainties in the loans granted and different deposit variables are more significant than uncertainties in other banks’ variables in decision making.

  12. Classification of astrocyto-mas and meningiomas using statistical discriminant analysis on MRI data

    International Nuclear Information System (INIS)

    Siromoney, Anna; Prasad, G.N.S.; Raghuram, Lakshminarayan; Korah, Ipeson; Siromoney, Arul; Chandrasekaran, R.

    2001-01-01

    The objective of this study was to investigate the usefulness of Multivariate Discriminant Analysis for classifying two groups of primary brain tumours, astrocytomas and meningiomas, from Magnetic Resonance Images. Discriminant analysis is a multivariate technique concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Allocation or classification rules are usually developed from learning examples in a supervised learning environment. Data from signal intensity measurements in the multiple scan performed on each patient in routine clinical scanning was analysed using Fisher's Classification, which is one method of discriminant analysis

  13. Discrimination based on HIV/AIDS status: A comparative analysis of ...

    African Journals Online (AJOL)

    Discrimination based on HIV/AIDS status: A comparative analysis of the Nigerian court's decision in Festus Odaife & Ors v Attorney General of the Federation & Ors with other Commonwealth jurisdictions.

  14. 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

  15. Research on n-γ discrimination method based on spectrum gradient analysis of signals

    International Nuclear Information System (INIS)

    Luo Xiaoliang; Liu Guofu; Yang Jun; Wang Yueke

    2013-01-01

    Having discovered that there are distinct differences between the spectrum gradient of the output neutron and γ-ray signal from liquid scintillator detectors, this paper presented a n-γ discrimination method called spectrum gradient analysis (SGA) based on frequency-domain features of the pulse signals. The basic principle and feasibility of SGA method were discussed and the validity of n-γ discrimination results of SGA was verified by the associated particle neutron flight experiment. The discrimination performance of SGA was evaluated under different conditions of sampling rates ranging from 5 G/s to 250 M/s. The results show that SGA method exhibits insensitivity to noise, strong anti-interference ability, stable discrimination performance and lower amount of calculation in contrast with time-domain n-γ discrimination methods. (authors)

  16. On the Use of Shortwave Infrared for Tree Species Discrimination in Tropical Semideciduous Forest

    Science.gov (United States)

    Ferreira, M. P.; Zortea, M.; Zanotta, D. C.; Féret, J. B.; Shimabukuro, Y. E.; Souza Filho, C. R.

    2015-08-01

    Tree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands.

  17. Application of discriminant analysis and generalized distance measures to uranium exploration

    International Nuclear Information System (INIS)

    Beauchamp, J.J.; Begovich, C.L.; Kane, V.E.; Wolf, D.A.

    1980-01-01

    The National Uranium Resource Evaluation (NURE) Program has as its goal the estimation of the nation's uranium resources. It is possile to use discriminant analysis methods on hydrogeochemical data collected in the NURE Program to aid in fomulating geochemical models that can be used to identify the anomalous areas used in resource estimation. Discriminant' analysis methods have been applied to data from the Plainview, Texas Quadrangle which has approximately 850 groundwater samples with more than 40 quantitative measurements per sample. Discriminant analysis topics involving estimation of misclassification probabilities, variable selection, and robust discrimination are applied. A method using generalized distance measures is given which enables the assignment of samples to a background population or a mineralized population whose parameters were estimated from separate studies. Each topic is related to its relevance in identifying areas of possible interest to uranium exploration. However, the methodology presented here is applicable to the identification of regions associated with other types of resources. 8 figures, 3 tables

  18. Application of discriminant analysis and generalized distance measures to uranium exploration

    International Nuclear Information System (INIS)

    Beauchamp, J.J.; Begovich, C.L.; Kane, V.E.; Wolf, D.A.

    1979-10-01

    The National Uranium Resource Evaluation (NURE) Project has as its goal estimation of the nation's uranium resources. It is possible to use discriminant analysis methods on hydrogeochemical data collected in the NURE Program to aid in formulating geochemical models which can be used to identify the anomalous regions necessary for resource estimation. Discriminant analysis methods have been applied to data from the Plainview, Texas Quadrangle which has approximately 850 groundwater samples with more than 40 quantitative measurements per sample. Discriminant analysis topics involving estimation of misclassification probabilities, variable selection, and robust discrimination are applied. A method using generalized distance measures is given which enables assigning samples to a background population or a mineralized population whose parameters were estimated from separate studies. Each topic is related to its relevance in identifying areas of possible interest to uranium exploration

  19. MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

    Science.gov (United States)

    Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

    2013-01-01

    Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

  20. PIXE analysis of fish otoliths. Application to fish stock discrimination

    International Nuclear Information System (INIS)

    Arai, Nobuaki; Sakamoto, Wataru; Tateno, Koji; Yoshida, Koji.

    1996-01-01

    PIXE was adopted to analyze trace elements in otoliths of Japanese flounder to discriminate among several local fish stocks. The otoliths were removed from samples caught at five different sea areas along with the coast of the Sea of Japan: Akita, Ishikawa, Kyoto (2 stations), and Fukuoka. Besides calcium as main component, strontium, manganese, and zinc were detected. Especially Sr concentrations were different among 4 areas except between 2 stations in Kyoto. It suggested that the fish in the 2 stations in Kyoto were the same stock differed to the others. (author)

  1. 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.

  2. 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.

  3. Women ministers' experiences of gender discrimination in the Lutheran Church : a discourse analysis

    OpenAIRE

    2011-01-01

    M.A. The aim of this psychological study was to uncover women minister’s experiences of gender discrimination in the Lutheran Church by using a discourse analysis. Three female participants, who are involved in ministry in the Lutheran Church, were interviewed about their experiences and perceptions of gender discrimination. The resultant texts were analysed using Parker’s (2005) steps to discourse analytic reading. The discourses that were discovered indicate that power struggles are prev...

  4. Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

    Directory of Open Access Journals (Sweden)

    Joachim Goschnick

    2004-05-01

    Full Text Available Abstract: The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 oC/mm and 6.7 oC/mm, applied across the sensor elements (segments of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

  5. Theoretical remarks on the statistics of three discriminants in Piety's automated signature analysis of PSD [Power Spectral Density] data

    International Nuclear Information System (INIS)

    Behringer, K.; Spiekerman, G.

    1984-01-01

    Piety (1977) proposed an automated signature analysis of power spectral density data. Eight statistical decision discriminants are introduced. For nearly all the discriminants, improved confidence statements can be made. The statistical characteristics of the last three discriminants, which are applications of non-parametric tests, are considered. (author)

  6. [Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

    Science.gov (United States)

    Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

    2017-08-15

    Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

  7. Factors that Affect Poverty Areas in North Sumatera Using Discriminant Analysis

    Science.gov (United States)

    Nasution, D. H.; Bangun, P.; Sitepu, H. R.

    2018-04-01

    In Indonesia, especially North Sumatera, the problem of poverty is one of the fundamental problems that become the focus of government both central and local government. Although the poverty rate decreased but the fact is there are many people who are poor. Poverty happens covers several aspects such as education, health, demographics, and also structural and cultural. This research will discuss about several factors such as population density, Unemployment Rate, GDP per capita ADHK, ADHB GDP per capita, economic growth and life expectancy that affect poverty in Indonesia. To determine the factors that most influence and differentiate the level of poverty of the Regency/City North Sumatra used discriminant analysis method. Discriminant analysis is one multivariate analysis technique are used to classify the data into a group based on the dependent variable and independent variable. Using discriminant analysis, it is evident that the factor affecting poverty is Unemployment Rate.

  8. Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

    Science.gov (United States)

    Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

    2001-04-01

    Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

  9. Optical selection of trace elements for discriminant analysis

    International Nuclear Information System (INIS)

    Rasmussen, S.E.; Erasmus, C.S.; Watterson, J.I.W.; Sellschop, J.P.F.

    This report describes different methods of element selection; a combination of stepwise multivariate analysis of variance for primary element selection, and principle component analysis regression for the element interrelationship analysis. These offer a satisfactory solution to the problem of element selection

  10. Meta-analysis of field experiments shows no change in racial discrimination in hiring over time.

    Science.gov (United States)

    Quillian, Lincoln; Pager, Devah; Hexel, Ole; Midtbøen, Arnfinn H

    2017-10-10

    This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos ( n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 ( n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callbacks than African Americans, and 24% more callbacks than Latinos. We observe no change in the level of hiring discrimination against African Americans over the past 25 years, although we find modest evidence of a decline in discrimination against Latinos. Accounting for applicant education, applicant gender, study method, occupational groups, and local labor market conditions does little to alter this result. Contrary to claims of declining discrimination in American society, our estimates suggest that levels of discrimination remain largely unchanged, at least at the point of hire.

  11. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.

    Science.gov (United States)

    Cao, Longlong; Guo, Shuixia; Xue, Zhimin; Hu, Yong; Liu, Haihong; Mwansisya, Tumbwene E; Pu, Weidan; Yang, Bo; Liu, Chang; Feng, Jianfeng; Chen, Eric Y H; Liu, Zhening

    2014-02-01

    Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis. Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated. After subset selection of high-dimension features, the support vector machine classifier reached up to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module. The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

  12. 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.

  13. Identification of roselle varieties through simple discriminating physicochemical characteristics using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Alé KANE

    2018-01-01

    Full Text Available Abstract The objective of this work is to study the feasibility of a more objective and rigorous classification of the calices of Hibiscus sabdariffa based on their physicochemical profile. To do so, 19 analyses were carried out on 4 varieties of calices cultivated in Senegal: Vimto, Koor, Thaï and CLT92. Principal component analysis results showed that 15 physicochemical and biochemical parameters could be potentially used to discriminate the varieties of calices. Polyphenolic and anthocyanin contents were anti-correlated to protein content and could be used to differentiate the Vimto/CLT92 and the Koor/Thaï varieties. Within these two clusters, pH and lipid content could discriminate each variety. Finally, factorial discriminant analysis showed that total anthocyanin content, lipid content and chromaticity C* were the 3 parameters enabling the most efficient classification of calices according to variety and led to 100% classification accuracy.

  14. Sub-pattern based multi-manifold discriminant analysis for face recognition

    Science.gov (United States)

    Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

    2018-04-01

    In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

  15. 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

  16. Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

    Science.gov (United States)

    De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano

    2016-07-01

    Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. linear discriminant analysis of structure within african eggplant 'shum'

    African Journals Online (AJOL)

    ACSS

    observed clusters include petiole length, sepal length (or seed color), fruit calyx length, seeds per fruit, leaf fresh .... obtain means. A table of means per trait for each accession was then imported into R statistical software for UPGMA reordered hierarchical cluster analysis. ..... Mwale, S.E., Ssemakula, M.O., Sadik, K.,.

  18. Use of linear discriminant function analysis in seed morphotype ...

    African Journals Online (AJOL)

    Variation in seed morphology of the Lima bean in 31 accessions was studied. Data were collected on 100-seed weight, seed length and seed width. The differences among the accessions were significant, based on the three seed characteristics. K-means cluster analysis grouped the 31 accessions into four distinct groups, ...

  19. Use of Linear Discriminant Function Analysis in Five Yield Sub ...

    African Journals Online (AJOL)

    K-means cluster analysis grouped the 134 accessions into four distinct groups. Pairwise Mahalanobis 2 distance (D) among some of the groups was highly significant. From the study the yield sub-characters pod length, pod width, peduncle length and 100-seed weight contributed most to group separation in the cowpea ...

  20. Harassment and discrimination in medical training: a systematic review and meta-analysis.

    Science.gov (United States)

    Fnais, Naif; Soobiah, Charlene; Chen, Maggie Hong; Lillie, Erin; Perrier, Laure; Tashkhandi, Mariam; Straus, Sharon E; Mamdani, Muhammad; Al-Omran, Mohammed; Tricco, Andrea C

    2014-05-01

    Harassment and discrimination include a wide range of behaviors that medical trainees perceive as being humiliating, hostile, or abusive. To understand the significance of such mistreatment and to explore potential preventive strategies, the authors conducted a systematic review and meta-analysis to examine the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. In 2011, the authors identified relevant studies by searching MEDLINE and EMBASE, scanning reference lists of relevant studies, and contacting experts. They included studies that reported the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. Two reviewers independently screened all articles and abstracted study and participant characteristics and study results. The authors assessed the methodological quality in individual studies using the Newcastle-Ottawa Scale. They also conducted a meta-analysis. The authors included 57 cross-sectional and 2 cohort studies in their review. The meta-analysis of 51 studies demonstrated that 59.4% of medical trainees had experienced at least one form of harassment or discrimination during their training (95% confidence interval [CI]: 52.0%-66.7%). Verbal harassment was the most commonly cited form of harassment (prevalence: 63.0%; 95% CI: 54.8%-71.2%). Consultants were the most commonly cited source of harassment and discrimination, followed by patients or patients' families (34.4% and 21.9%, respectively). This review demonstrates the surprisingly high prevalence of harassment and discrimination among medical trainees that has not declined over time. The authors recommend both drafting policies and promoting cultural change within academic institutions to prevent future abuse.

  1. Use of discriminant analysis to determine black shales of the Lesser Carpathian crystal field

    Energy Technology Data Exchange (ETDEWEB)

    Khun, M.

    1980-01-01

    Discriminant analysis of results from geochemical testing was used to separate black shales of the ore level from the nonproductive deposits. Based on a large number of experiments, the accuracy of isolating the black shales according to content of vandium, copper and nickel reached 78%. These elements have basic importance for separation of productive shales from nonproductive.

  2. Comparison of cranial sex determination by discriminant analysis and logistic regression.

    Science.gov (United States)

    Amores-Ampuero, Anabel; Alemán, Inmaculada

    2016-04-05

    Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

  3. 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...

  4. Development and Validation of Discriminant Analysis Models for Student Loan Defaultees and Non-Defaultees.

    Science.gov (United States)

    Myers, Greeley; Siera, Steven

    1980-01-01

    Default on guaranteed student loans has been increasing. The use of discriminant analysis as a technique to identify "good" v "bad" student loans based on information available from the loan application is discussed. Research to test the ability of models to such predictions is reported. (Author/MLW)

  5. 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...

  6. Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Hujun He

    2017-01-01

    Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

  7. A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia

    OpenAIRE

    Pena-Boquete, Yolanda

    2006-01-01

    The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women's average earnings are lower than men's. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

  8. A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia.

    OpenAIRE

    Yolanda Pena-Boquete

    2006-01-01

    The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women’s average earnings are lower than men’s. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

  9. Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry.

    Science.gov (United States)

    Wandy, Joe; Zhu, Yunfeng; van der Hooft, Justin J J; Daly, Rónán; Barrett, Michael P; Rogers, Simon

    2017-09-14

    We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations. Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a dataset onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. The website can be found at http://ms2lda.org , while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  10. Local self-energies for V and Pd emergent from a nonlocal LDA+FLEX implementation

    Science.gov (United States)

    Savrasov, Sergey Y.; Resta, Giacomo; Wan, Xiangang

    2018-04-01

    In the spirit of recently developed LDA+U and LDA+DMFT methods, we implement a combination of density functional theory in its local density approximation (LDA) with a k - and ω -dependent self-energy found from diagrammatic fluctuational exchange (FLEX) approximation. The active Hilbert space here is described by the correlated subset of electrons which allows one to tremendously reduce the sizes of the matrices needed to represent charge and spin susceptibilities. The method is perturbative in nature but accounts for both bubble and ladder diagrams and accumulates the physics of momentum-resolved spin fluctuations missing in such popular approach as GW. As an application, we study correlation effects on band structures in V and Pd. The d -electron self-energies emergent from this calculation are found to be remarkably k independent. However, when we compare our calculated electronic mass enhancements against LDA+DMFT, we find that for the longstanding problem of spin fluctuations in Pd, LDA+FLEX delivers a better agreement with experiment, although this conclusion depends on a particular value of the Hubbard U used in the simulation. We also discuss outcomes of a recently proposed combination of k -dependent FLEX with dynamical mean-field theory (DMFT).

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

    International Nuclear Information System (INIS)

    Liu Guofu; Luo Xiaoliang; Yang Jun; Lin Cunbao; Hu Qingqing; Peng Jinxian

    2013-01-01

    Frequency gradient analysis (FGA) effectively discriminates neutrons and γ 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 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 γ 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 GSample/s 8-bit oscilloscope. The theoretical and experimental results both show that the estimation of the baseline shift is necessary, and the removal of baseline drift from the pulse shapes can improve the discrimination performance of FGA. (authors)

  12. Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber.

    Science.gov (United States)

    Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P

    2018-04-12

    Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).

  13. Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber

    Directory of Open Access Journals (Sweden)

    Mohamed A. S. Zaghloul

    2018-04-01

    Full Text Available Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz, which is 2.63 (3.67 times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%.

  14. Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data.

    Science.gov (United States)

    Sedghipour, Mohammad Reza; Sadeghi-Bazargani, Homayoun

    2012-01-01

    In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods. The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single) photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed. The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables. Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.

  15. Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Jongguk Lim

    2017-09-01

    Full Text Available The purpose of this study is to use near-infrared reflectance (NIR spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.

  16. Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica

    Directory of Open Access Journals (Sweden)

    María Calviño-Cancela

    2016-10-01

    Full Text Available Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and spatial resolutions needed owing to the patchiness and similarity between vegetation elements. We analyze the reflectance spectra of the most representative vegetation elements in ice-free areas of Antarctica to assess the potential for discrimination. This research is aimed as a basis for future aircraft/satellite research for long-term vegetation monitoring. The study was conducted in the Barton Peninsula, King George Island. The reflectance of ground patches of different types of vegetation or bare ground (c. 0.25 m 2 , n = 30 patches per class was recorded with a spectrophotometer measuring between 340 nm to 1025 nm at a resolution of 0.38 n m . We used Linear Discriminant Analysis (LDA to classify the cover classes according to reflectance spectra, after reduction of the number of bands using Principal Component Analysis (PCA. The first five principal components explained an accumulated 99.4% of the total variance and were added to the discriminant function. The LDA classification resulted in c. 92% of cases correctly classified (a hit ratio 11.9 times greater than chance. The most important region for discrimination was the visible and near ultraviolet (UV, with the relative importance of spectral bands steeply decreasing in the Near Infra-Red (NIR region. Our study shows the feasibility of discriminating among representative taxa of Antarctic vegetation using their spectral patterns in the near UV, visible and NIR. The results are encouraging for hyperspectral vegetation mapping in Antarctica, which could greatly facilitate monitoring vegetation changes in response to a changing environment, reducing the costs and environmental impacts of

  17. Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data

    Directory of Open Access Journals (Sweden)

    Sedghipour MR

    2012-09-01

    Full Text Available Mohammad Reza Sedghipour,1 Homayoun Sadeghi-Bazargani2,31Nikoukari Ophthalmology University Hospital, Tabriz, Iran; 2Department of Statistics and Epidemiology, Neuroscience Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; 3Department of Public Health Sciences, Karolinska Institute, Stockholm, SwedenBackground: In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods.Methods: The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed.Results: The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables.Conclusion: Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.Keywords: astigmatism, regression, partial least squares regression

  18. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. 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.

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

    OpenAIRE

    Geroukis, Asterios; Brorson, Erik

    2014-01-01

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

  1. First-principles modeling of localized d states with the GW@LDA+U approach

    Science.gov (United States)

    Jiang, Hong; Gomez-Abal, Ricardo I.; Rinke, Patrick; Scheffler, Matthias

    2010-07-01

    First-principles modeling of systems with localized d states is currently a great challenge in condensed-matter physics. Density-functional theory in the standard local-density approximation (LDA) proves to be problematic. This can be partly overcome by including local Hubbard U corrections (LDA+U) but itinerant states are still treated on the LDA level. Many-body perturbation theory in the GW approach offers both a quasiparticle perspective (appropriate for itinerant states) and an exact treatment of exchange (appropriate for localized states), and is therefore promising for these systems. LDA+U has previously been viewed as an approximate GW scheme. We present here a derivation that is simpler and more general, starting from the static Coulomb-hole and screened exchange approximation to the GW self-energy. Following our previous work for f -electron systems [H. Jiang, R. I. Gomez-Abal, P. Rinke, and M. Scheffler, Phys. Rev. Lett. 102, 126403 (2009)10.1103/PhysRevLett.102.126403] we conduct a systematic investigation of the GW method based on LDA+U(GW@LDA+U) , as implemented in our recently developed all-electron GW code FHI-gap (Green’s function with augmented plane waves) for a series of prototypical d -electron systems: (1) ScN with empty d states, (2) ZnS with semicore d states, and (3) late transition-metal oxides (MnO, FeO, CoO, and NiO) with partially occupied d states. We show that for ZnS and ScN, the GW band gaps only weakly depend on U but for the other transition-metal oxides the dependence on U is as strong as in LDA+U . These different trends can be understood in terms of changes in the hybridization and screening. Our work demonstrates that GW@LDA+U with “physical” values of U provides a balanced and accurate description of both localized and itinerant states.

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

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-03

    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.

  3. Discriminant analysis method to determine the power of the boys 11-12 year

    Directory of Open Access Journals (Sweden)

    Mirosława Cieślicka

    2016-10-01

    Full Text Available Purpose: To determine the model of power in boys 11-12 years old. Material and methods: To achieve the objectives, the following methods: analysis of scientific literature, statistical methods for analysis of results. The study involved 35 boys 11 year (n = 35 and 32 boys 12 year (n = 32. Results: Analysis of the results shows that the statistical significance of differences in the test results of boys 11 and 12 years there has been research jump from the place of execution and the amount of squats (the amount of execution time (p <0.001, p <0. Conclusions: Structural factors discriminant function suggest that more attention is paid to training of speed and endurance, the more likely to increase the force to prepare the boys. The canonical discriminant function can  be used to assess and forecast the development of motor skills in boys.

  4. Analysis of pulse-shape discrimination techniques for BC501A using GHz digital signal processing

    International Nuclear Information System (INIS)

    Rooney, B.D.; Dinwiddie, D.R.; Nelson, M.A.; Rawool-Sullivan, Mohini W.

    2001-01-01

    A comparison study of pulse-shape analysis techniques was conducted for a BC501A scintillator using digital signal processing (DSP). In this study, output signals from a preamplifier were input directly into a 1 GHz analog-to-digital converter. The digitized data obtained with this method was post-processed for both pulse-height and pulse-shape information. Several different analysis techniques were evaluated for neutron and gamma-ray pulse-shape discrimination. It was surprising that one of the simplest and fastest techniques resulted in some of the best pulse-shape discrimination results. This technique, referred to here as the Integral Ratio technique, was able to effectively process several thousand detector pulses per second. This paper presents the results and findings of this study for various pulse-shape analysis techniques with digitized detector signals.

  5. Financial consumer protection and customer satisfaction. A relationship study by using factor analysis and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Marimuthu SELVAKUMAR

    2015-11-01

    Full Text Available This paper tries to make an attempt to study the relationship between the financial consumer protection and customer satisfaction by using factor analysis and discriminant analysis. The main objectives of the study are to analyze the financial consumer protection in commercial banks, to examine the customer satisfaction of commercial banks and to identify the factors of financial consumer protection lead customer satisfaction. There are many research work carried out on financial consumer protection in financial literacy, but the identification of factors which lead the financial consumer protection and the relationship between financial consumer protection and the customer satisfaction is very important, Particularly for banks to improve its quality and increase the customer satisfaction. Therefore this study is carried out with the aim of identifying the factors of financial consumer protection and its influence on customer satisfaction. This study is both descriptive and analytical in nature. It covers both primary and secondary data. The primary data has been collected from the customers of commercial banks using pre-tested interview schedule and the secondary data has been collected from standard books, journals, magazines, websites and so on.

  6. A novel electroencephalographic analysis method discriminates alcohol effects from those of other sedative/hypnotics.

    Science.gov (United States)

    Steffensen, Scott C; Lee, Rong-Sheng; Henriksen, Steven J; Packer, Thomas L; Cook, Daniel R

    2002-04-15

    Here we describe a mathematical and statistical signal processing strategy termed event resolution imaging (ERI). Our principal objective was to determine if the acute intoxicating effects of ethanol on spontaneous EEG activity could be discriminated from those of other sedative/hypnotics. We employed ERI to combine and integrate standard analysis methods to learn multiple signal features of time-varying EEG signals. We recorded cortical EEG, electromyographic activity, and motor activity during intravenous administration of saline, ethanol (1.0 g/kg), chlordiazepoxide (10 mg/kg), pentobarbital (6 mg/kg), heroin (0.3 mg/kg), and methamphetamine (2 mg/kg) administered on separate days in six rats. A blind treatment of one of the drugs was readministered to validate the efficacy of ERI analysis. Significant changes in spontaneous EEG activity produced by all five drugs were detected by ERI analysis with a time resolution of 5-10 s. ERI analysis of spontaneous EEG activity also discriminated, with 90-95% accuracy, an ataxic dose of ethanol versus equivalent ataxic doses of chlordiazepoxide or pentobarbital, as well as the effects of saline, a reinforcing dose of heroin, or a locomotor activating dose of methamphetamine. ERI correctly matched the 'blind drug' as ethanol. These findings indicate that ERI analysis can detect the central nervous system effects of various psychoactive drugs and accurately discriminate the electrocortical effects of select sedative/hypnotics, with similar behavioral endpoints, but with dissimilar mechanisms of action.

  7. Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

    Science.gov (United States)

    Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

    2012-03-01

    Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

  8. Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field

    Directory of Open Access Journals (Sweden)

    Hong Sun

    2013-05-01

    Full Text Available This paper proposes a multi-level max-margin discriminative analysis (M3DA framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines and topic-level features (generated by MedLDA via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information.

  9. Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants

    Energy Technology Data Exchange (ETDEWEB)

    Manera, Maurizio, E-mail: mmanera@unite.it [Faculty of Biosciences, Food and Environmental Technologies, University of Teramo, Piano d’Accio, I-64100 Teramo (Italy); Giari, Luisa [Department of Life Sciences and Biotechnology, University of Ferrara, St. Borsari 46, I-44121 Ferrara (Italy); De Pasquale, Joseph A. [Morphogenyx Inc., PO Box 717, East Northport, NY 11731 (United States); Sayyaf Dezfuli, Bahram [Department of Life Sciences and Biotechnology, University of Ferrara, St. Borsari 46, I-44121 Ferrara (Italy)

    2016-06-15

    Highlights: • An objective, operator unbiased method was developed to evaluate gill pathology. • The method relies on the measure of local connected fractal dimension frequency. • Exposure classes were adequately discriminated by linear discriminant analysis. - Abstract: An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48 h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias

  10. Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants

    International Nuclear Information System (INIS)

    Manera, Maurizio; Giari, Luisa; De Pasquale, Joseph A.; Sayyaf Dezfuli, Bahram

    2016-01-01

    Highlights: • An objective, operator unbiased method was developed to evaluate gill pathology. • The method relies on the measure of local connected fractal dimension frequency. • Exposure classes were adequately discriminated by linear discriminant analysis. - Abstract: An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48 h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias

  11. 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.

  12. Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

    Directory of Open Access Journals (Sweden)

    Fubiao Feng

    2017-03-01

    Full Text Available Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA.

  13. Cross coherence independent component analysis in resting and action states EEG discrimination

    International Nuclear Information System (INIS)

    Almurshedi, A; Ismail, A K

    2014-01-01

    Cross Coherence time frequency transform and independent component analysis (ICA) method were used to analyse the electroencephalogram (EEG) signals in resting and action states during open and close eyes conditions. From the topographical scalp distributions of delta, theta, alpha, and beta power spectrum can clearly discriminate between the signal when the eyes were open or closed, but it was difficult to distinguish between resting and action states when the eyes were closed. In open eyes condition, the frontal area (Fp1, Fp2) was activated (higher power) in delta and theta bands whilst occipital (O1, O2) and partial (P3, P4, Pz) area of brain was activated alpha band in closed eyes condition. The cross coherence method of time frequency analysis is capable of discrimination between rest and action brain signals in closed eyes condition

  14. Derivation of LDA log likelihood ratio one-to-one classifier

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan

    2014-01-01

    The common expression for the Likelihood Ratio classifier using LDA assumes that the reference class mean is available. In biometrics, this is often not the case and only a single sample of the reference class is available. In this paper expressions are derived for biometric comparison between

  15. Discriminant analysis on the treatment results of interstitial radium tongue implants

    International Nuclear Information System (INIS)

    Hoshina, Masao; Shibuya, Hitoshi; Horiuchi, Jun-Ichi; Matsubara, Sho; Suzuki, Soji; Takeda, Masamune

    1989-01-01

    Discriminant analysis was carried out for 48 tongue cancer patients who were treated with radium single-plane implantation. The 48 patients were grouped into 32 successfully cured without complications, five successfully cured with complications, six successfully cured but requiring additional boost therapy and five with local recurrence. To evaluate the relation between the dose distribution and the local treatment results, the analysis was based on a volume-dose relationship. The functions introduced by this discriminant analysis were linear, and the parameters used were modal dose, average dose and shape factors of histograms. Each group of treatment results had a correction rate of >80%, except for the successfully cured group with ulcers. The discriminant functions were useful as an index to obtain a final clinical treatment result at the early time of implantation, and these functions could be used as a criterion for the optimal treatment of tongue carcinoma. We were also able to recognize the limitation of the actual arrangement of sources in the single-plane implant. (author)

  16. Differentiation of free-ranging chicken using discriminant analysis of phenotypic traits

    Directory of Open Access Journals (Sweden)

    Raed M. Al-Atiyat

    Full Text Available ABSTRACT In this study, we investigated the differentiation of five different chicken ecotypes - Center, North, South, West, and East - of Saudi Arabia using discriminate analysis. The analysis was based on nine important morphological and phenotypic traits: body color, beak color, earlobe color, eye color, shank color, comb color, comb type, comb size, and feather distribution. There was a strong significant relationship between the phenotype and effect of geographic height in terms of comb type and earlobe color in males as well as body, beak, eye, and shank color. In particular, the comb type and earlobe color differentiated the ecotypes of males. Among the females, the beak, earlobe, eye, shank color, and feather distribution had more differentiating power. Moreover, the discriminant analysis revealed that the five ecotypes were grouped into three clusters; the Center and the North in one cluster, the West and the South ecotypes in the second for males, and the East ecotype in the last cluster. The female dendogram branching was similar to the male dendrogram branching, except that the Center ecotype was grouped with the North instead of the South. The East ecotype was highly discriminated from the other ecotypes. Nevertheless, the potential of recent individual migration between ecotypes was also noted. Accordingly, the results of the utilized traits in this study might be effective in characterization and conservation of the genetic resources of the Saudi chicken.

  17. 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.

  18. Using Dynamic Fourier Analysis to Discriminate Between Seismic Signals from Natural Earthquakes and Mining Explosions

    Directory of Open Access Journals (Sweden)

    Maria C. Mariani

    2017-08-01

    Full Text Available A sequence of intraplate earthquakes occurred in Arizona at the same location where miningexplosions were carried out in previous years. The explosions and some of the earthquakes generatedvery similar seismic signals. In this study Dynamic Fourier Analysis is used for discriminating signalsoriginating from natural earthquakes and mining explosions. Frequency analysis of seismogramsrecorded at regional distances shows that compared with the mining explosions the earthquake signalshave larger amplitudes in the frequency interval ~ 6 to 8 Hz and significantly smaller amplitudes inthe frequency interval ~ 2 to 4 Hz. This type of analysis permits identifying characteristics in theseismograms frequency yielding to detect potentially risky seismic events.

  19. [Etiological analysis and establishment of a discriminant model for lower respiratory tract infections in hospitalized patients].

    Science.gov (United States)

    Chen, Y S; Lin, X H; Li, H R; Hua, Z D; Lin, M Q; Huang, W S; Yu, T; Lyu, H Y; Mao, W P; Liang, Y Q; Peng, X R; Chen, S J; Zheng, H; Lian, S Q; Hu, X L; Yao, X Q

    2017-12-12

    Objective: To analyze the pathogens of lower respiratory tract infection(LRTI) including bacterial, viral and mixed infection, and to establish a discriminant model based on clinical features in order to predict the pathogens. Methods: A total of 243 hospitalized patients with lower respiratory tract infections were enrolled in Fujian Provincial Hospital from April 2012 to September 2015. The clinical data and airway (sputum and/or bronchoalveolar lavage) samples were collected. Microbes were identified by traditional culture (for bacteria), loop-mediated isothermal amplification(LAMP) and gene sequencing (for bacteria and atypical pathogen), or Real-time quantitative polymerase chain reaction (Real-time PCR)for viruses. Finally, a discriminant model was established by using the discriminant analysis methods to help to predict bacterial, viral and mixed infections. Results: Pathogens were detected in 53.9% (131/243) of the 243 cases.Bacteria accounted for 23.5%(57/243, of which 17 cases with the virus, 1 case with Mycoplasma pneumoniae and virus), mainly Pseudomonas Aeruginosa and Klebsiella Pneumonia. Atypical pathogens for 4.9% (12/243, of which 3 cases with the virus, 1 case of bacteria and viruses), all were mycoplasma pneumonia. Viruses for 34.6% (84/243, of which 17 cases of bacteria, 3 cases with Mycoplasma pneumoniae, 1 case with Mycoplasma pneumoniae and bacteria) of the cases, mainly Influenza A virus and Human Cytomegalovirus, and other virus like adenovirus, human parainfluenza virus, respiratory syncytial virus, human metapneumovirus, human boca virus were also detected fewly. Seven parameters including mental status, using antibiotics prior to admission, complications, abnormal breath sounds, neutrophil alkaline phosphatase (NAP) score, pneumonia severity index (PSI) score and CRUB-65 score were enrolled after univariate analysis, and discriminant analysis was used to establish the discriminant model by applying the identified pathogens as the

  20. Using otolith shape for intraspecific discrimination: the case of gurnards (Scorpaeniformes, Triglidae

    Directory of Open Access Journals (Sweden)

    Stefano Montanini

    2015-11-01

    Full Text Available The sagittal otoliths are sound transducers and play an important role in fish hearing. Triglidae (Teleostei, Scorpaeniformes are known for sound producing ability in agonistic contexts related to territorial defence, reproduction and competitive feeding (Amorim et al., 2004. Chelidonichthys cuculus and C. lucerna show a significant body size-depth relationship and specie-specific feeding strategies with growth. Both juveniles and adults of C. cuculus prey necto-benthic invertebrates while C. lucerna specimens change diet from crustaceans to teleost during growth (Stagioni et al., 2012; Vallisneri et al., 2014; Montanini et al., 2015. The goal of this study was to analyze intraspecific shape variations in sagitta of model species of gurnards. 217 specimens were collected during bottom trawl surveys in Adriatic sea (northeastern Mediterranean. Each left sagitta was removed, cleaned in ultrasounds bath and kept dry. The otolith digital images were processed to calculate five shape indices (aspect ratio, roundness, rectangularity, ellipticity and circularity. Indices were normalised to avoid allometric effects according to Lleonart et al. (2000, than processed by linear discriminant analysis (LDA. The SHAPE program was used to extract the outline and to assess the variability of shapes (EFA method and estimated it through the study of principal component analysis (PCA. Considering the first two discriminant functions, LDA plot showed a clearly separation between juvenile and adults for both species. About EFA, the first 4 principal component discriminated over 80% of variance and significant differences were found at critical size between juveniles and adults for all the components analysed. The allometric trends corresponded to a relative elongation of the sulcus acusticus and an increase of excisura ostii. The combined use of the two external outlines methods should be highly informative for intraspecific discrimination and might be related to

  1. Dynamical spin susceptibility in the TD-LDA and QSGW approximations

    Energy Technology Data Exchange (ETDEWEB)

    Schilfgaarde, Mark Van [Arizona State Univ., Mesa, AZ (United States); Kotani, Takao [Arizona State Univ., Mesa, AZ (United States)

    2012-10-15

    Abstract. This project was aimed at building the transverse dynamical spin susceptibility with the TD-LDA and the recently-developed Quasparticle Self-Consisent Approximations, which determines an optimum quasiparticle picture in a self-consistent manner within the GW approximation. Our main results were published into two papers, (J. Phys. Cond. Matt. 20, 95214 (2008), and Phys. Rev. B83, 060404(R) (2011). In the first paper we present spin wave dispersions for MnO, NiO, and -MnAs based on quasiparticle self-consistent GW approximation (QSGW). For MnO and NiO, QSGW results are in rather good agreement with experiments, in contrast to the LDA and LDA+U descriptions. For -MnAs, we find a collinear ferromagnetic ground state in QSGW, while this phase is unstable in the LDA. In the second, we apply TD-LDA to the CaFeAs2 the first attempt the first ab initio calculation of dynamical susceptibililty in a system with complex electronic structure Magnetic excitations in the striped phase of CaFe2As2 are studied as a function of local moment amplitude. We find a new kind of excitation: sharp resonances of Stoner-like (itinerant) excitations at energies comparable to the ´eel temperature, originating largely from a narrow band of Fe d states near the Fermi level, and coexisting with more conventional (localized) spin waves. Both kinds of excitations can show multiple branches, highlighting the inadequacy of a description based on a localized spin model.

  2. The contribution of cluster and discriminant analysis to the classification of complex aquifer systems.

    Science.gov (United States)

    Panagopoulos, G P; Angelopoulou, D; Tzirtzilakis, E E; Giannoulopoulos, P

    2016-10-01

    This paper presents an innovated method for the discrimination of groundwater samples in common groups representing the hydrogeological units from where they have been pumped. This method proved very efficient even in areas with complex hydrogeological regimes. The proposed method requires chemical analyses of water samples only for major ions, meaning that it is applicable to most of cases worldwide. Another benefit of the method is that it gives a further insight of the aquifer hydrogeochemistry as it provides the ions that are responsible for the discrimination of the group. The procedure begins with cluster analysis of the dataset in order to classify the samples in the corresponding hydrogeological unit. The feasibility of the method is proven from the fact that the samples of volcanic origin were separated into two different clusters, namely the lava units and the pyroclastic-ignimbritic aquifer. The second step is the discriminant analysis of the data which provides the functions that distinguish the groups from each other and the most significant variables that define the hydrochemical composition of the aquifer. The whole procedure was highly successful as the 94.7 % of the samples were classified to the correct aquifer system. Finally, the resulted functions can be safely used to categorize samples of either unknown or doubtful origin improving thus the quality and the size of existing hydrochemical databases.

  3. Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis.

    Science.gov (United States)

    Gutiérrez, R; Vega, S; Díaz, G; Sánchez, J; Coronado, M; Ramírez, A; Pérez, J; González, M; Schettino, B

    2009-05-01

    Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n = 216) and B) ultrapasteurized milk fat from 3 industries (n = 36), as well as pork lard (n = 2), bovine tallow (n = 2), fish oil (n = 2), peanut (n = 2), corn (n = 2), olive (n = 2), and soy (n = 2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.

  4. Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

    Directory of Open Access Journals (Sweden)

    Shi He

    2015-01-01

    Full Text Available A Bayesian hierarchical model is presented to classify very high resolution (VHR images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.

  5. Loss Distribution Approach (LDA: metodología actuarial aplicada al riesgo operacional Loss Distribution Approach (LDA: actuarial methodology apply to operational risk

    Directory of Open Access Journals (Sweden)

    Luis Ceferino Franco Arbeláez

    2008-07-01

    Full Text Available Este artículo es resultado de un proyecto de investigación sobre la gestión integral del riesgo operacional promovido por la Vicerrectoria de Investigaciones de la Universidad de Medellín, y cofinanciado por una firma comisionista. Se presenta una aplicación del modelo LDA, el cual se basa en la recopilación de los datos de pérdidas históricas (frecuencia y severidad, que se registran internamente en las organizaciones. Dichos datos pueden ser complementados con datos externos. Estas pérdidas son clasificadas en una matriz que relaciona las líneas de negocio de la organización y los eventos operacionales de pérdida, a partir de la cual se calcula la carga de capital. La aplicación se desarrolló para una entidad financiera. El artículo está organizado de la siguiente forma: la primera sección es introductoria al tema. En la segunda parte se presenta formalmente el modelo LDA; luego se realiza una aplicación, y en la cuarta sección se presentan algunas conclusiones.This paper is the result of a research project on integrated management of operational risk, promoted by Universidad de Medellin Research Vice-Principal's Office and co-financed by a financial company. It presents an application of the LDA model, which is based on data collection of historical losses (frequency and severity, which are recorded internally in organizations. Such data can be supplemented with external data. These losses are classified in a matrix that relates business lines of the organization and operational loss events, from which capital charge is estimated. The application was developed for a financial institution. The paper is organized as follows: The first section is introductory to the subject. The second part formally presents a LDA model; then an application is made, and in the fourth part some conclusions are presented.

  6. Background reduction and noise discrimination in the proportional counting of tritium using pulse-shape analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hochel, R C; Hayes, D W [Du Pont de Nemours (E.I.) and Co., Aiken, S.C. (USA). Savannah River Lab.

    1975-12-01

    A pulse-shape analysis (PSA) unit of commercial design has been incorporated into a proportional counting system to determine the effectiveness of pulse-shape discrimination in increasing the sensitivity of tritium counting. It was found that a quantitative determination of tritium could be obtained directly from the PSA time spectrum eliminating the need for beta-ray energy selection used in the pulse-shape discrimination (PSD) technique. The performance of the proportional counting system was tested using the PSA unit and anticoincidence shielding, both singly and combined, under several types of background. A background reduction factor of 169 was obtained from the combined PSA-anticoincidence system with only a 2% loss in tritium counting efficiency. The PSA method was also found to offer significant reductions in noise background.

  7. Background reduction and noise discrimination in the proportional counting of tritium using pulse-shape analysis

    International Nuclear Information System (INIS)

    Hochel, R.C.; Hayes, D.W.

    1975-01-01

    A pulse-shape analysis (PSA) unit of commercial design has been incorporated into a proportional counting system to determine the effectiveness of pulse-shape discrimination in increasing the sensitivity of tritium counting. It was found that a quantitative determination of tritium could be obtained directly from the PSA time spectrum eliminating the need for beta-ray energy selection used in the pulse-shape discrimination (PSD) technique. The performance of the proportional counting system was tested using the PSA unit and anticoincidence shielding, both singly and combined, under several types of background. A background reduction factor of 169 was obtained from the combined PSA-anticoincidence system with only a 2% loss in tritium counting efficiency. The PSA method was also found to offer significant reductions in noise background. (Auth.)

  8. INCOME INEQUALITY IN SOME MAJOR EUROPEAN UNION ECONOMIES A DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    JYOTIRMAYEE KAR

    2012-12-01

    Full Text Available This exercise is an attempt to assess the importance of some social, economic, demographic and infrastructural factors which account for the prevailing income inequality across some of the EU countries. Using discriminant analysis the study suggests that crime recorded by police is the most important predictor in discriminating between the group of countries with relatively more equitable distribution of income from those with less. This variable is followed by number of students in the country. Reduction in the level of crime and improvement in the student strength could help in reducing income inequality. Quite intuitively, improvement in all the economic factors like GDP per capita and agricultural index will help to reduce income inequality. Identical is the case of the demographic factors. This calls for implementation of developmental policies towards improvement in these areas.

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

    International Nuclear Information System (INIS)

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

    2001-01-01

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

  10. Sex assessment from carpals bones: discriminant function analysis in a contemporary Mexican sample.

    Science.gov (United States)

    Mastrangelo, Paola; De Luca, Stefano; Sánchez-Mejorada, Gabriela

    2011-06-15

    Sex assessment is one of the first essential steps in human identification, in both medico-legal cases and bio-archaeological contexts. Fragmentary human remains compromised by different types of burial or physical insults may frustrate the use of the traditional sex estimation methods, such as the analysis of the skull and pelvis. Currently, the application of discriminant functions to sex unidentified skeletal remains is steadily increasing. However, several studies have demonstrated that, due to variation in size and patterns of sexual dimorphism, discriminant functions are population-specific. In this study, in order to improve sex assessment from skeletal remains and to establish population-specific discriminant functions, the diagnostic values of the carpal bones were considered. A sample of 136 individuals (78 males, 58 females) of known sex and age was analyzed. They belong to a contemporary identified collection from the Laboratory of Physical Anthropology, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México, Mexico City). The age of the individuals ranged between 25 and 85 years. Between four and nine measurements of each carpal bone were taken. Independent t-tests confirm that all carpals are sexually dimorphic. Univariate measurements produce accuracy levels that range from 61.8% to 90.8%. Classification accuracies ranged between 81.3% and 92.3% in the multivariate stepwise discriminant analysis. In addition, intra- and inter-observer error tests were performed. These indicated that replication of measurements was satisfactory for the same observer over time and between observers. These results suggest that carpal bones can be used for assessing sex in both forensic and bio-archaeological identification procedures and that bone dimensions are population specific. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  11. Study on discriminant analysis by military mental disorder prediction scale for mental disorder of new recruits

    Directory of Open Access Journals (Sweden)

    Li-yi ZHANG

    2011-11-01

    Full Text Available Objective To examine the predictive role of the Military Mental Disorder Prediction Scale on the mental disorder of new recruits.Methods The present study examined 115 new recruits diagnosed with mental disorder and 115 healthy new recruits.The recruits were tested using the Military Mental Disorder Prediction Scale.The discriminant function was built by discriminant analysis method.The current study analyzed the predictive value of 11 factors(family medical record and past medical record(X1,growth experience(X2,introversion(X3,stressor(X4,poor mental defense(X5,social support(X6,psychosis(X7,depression(X8,mania(X9,neurosis(X10,and personality disorder(X11 aside from lie factor on the mental disorder of new recruits.Results The mental disorder group has higher total score and factor score in family medical record and past medical record,introversion,stressor,poor mental defense,social support,psychosis,depression,mania,neurosis,personality disorder,and lie than those of the contrast group(P < 0.01.For the score of growth experience factor,that of the mental disorder group is higher than the score of the contrast group(P < 0.05.All 11 factors except the lie factor in the Mental Disorder Prediction Scale are taken as independent variables by enforced introduction to obtain the Fisher linear discriminant function as follows: The mental disorder group=-7.014-0.278X1+1.556X2+1.563X3+0.878X4+0.183X5-0.845X6-0.562X7-0.353X8+1.246X9-0.505X10+1.029X11.The contrast group=-2.971+0.056X1+2.194X2+0.707X3+0.592X4-0.086X5-0.888X6-0.133X7-0.360X8+0.654X9-0.467X10+0.308X11.The discriminant function has an accuracy rate of 76.5% on the new recruits with mental disorders and 100% on the healthy new recruits.The total accurate discrimination rate is 88.3% and the total inaccurate discrimination rate is 11.7%.Conclusion The Military Mental Disorder Prediction Scale has a high accuracy rate on the prediction of mental disorder of new recruits and is worthy of

  12. Investigation of the distal margins of the LVF/LDA environments in the northern midlatitudes of Mars.

    Science.gov (United States)

    Morgan, G. A.; Head, J. W., III

    2008-09-01

    Introduction The northern dichotomy boundary on Mars consists of an abrupt escarpment between the cratered uplands and the northern plains. The boundary is characterized by fretted valleys which divide the uplands into a series of plateaus and mesas which become progressively smaller to the north and eventually merge with the northern plains [1]. Lineated Valley Fill (LVF) and Lobate Debris Aprons (LDA) deposits are a prominent feature of the dichotomy boundary [2] and have been attributed by some [e.g., 3] to be the remains of debris-covered glaciers that were active during previous climatic regimes when snow was deposited along the dichotomy boundary [4-5]. Recent in depth analysis of LVF/LDA has been made possible by new comprehensive high-resolution data from Mars Odyssey, Mars Express and Mars Reconnaissance Orbiter. and has revealed evidence for multiple LVF/LDA emplacement events [6-8], as well as evidence for plateau glaciation [4] and evidence that glacial conditions extended much further from the margins of the dichotomy boundary than previously thought [4]. However, remaining unexamined, however, are the regions adjacent to the current positions of the LVF/LDA deposits where one can explore the relationship between the distal landforms present there and the main bodies of LVF/LDA. We have therefore investigated an area of the northern plains directly north of a region of the dichotomy boundary where detailed studies have reported largescale integrated LVF systems >10,000 km3 in extent, that are analogous to terrestrial debris covered glacial systems [3,6-8]. Through the utilization of the most recent data sets (CTX and HiRISE) we have categorized the distinct landform assemblages associated with this area and documented the degraded nature of lobate debris aprons and related features present there. Degraded Lobate Debris Aprons All of the LDAs in the study region have troughs cut into their surface which are aligned in the same direction as the flanks of

  13. Textural Maturity Analysis and Sedimentary Environment Discrimination Based on Grain Shape Data

    Science.gov (United States)

    Tunwal, M.; Mulchrone, K. F.; Meere, P. A.

    2017-12-01

    Morphological analysis of clastic sedimentary grains is an important source of information regarding the processes involved in their formation, transportation and deposition. However, a standardised approach for quantitative grain shape analysis is generally lacking. In this contribution we report on a study where fully automated image analysis techniques were applied to loose sediment samples collected from glacial, aeolian, beach and fluvial environments. A range of shape parameters are evaluated for their usefulness in textural characterisation of populations of grains. The utility of grain shape data in ranking textural maturity of samples within a given sedimentary environment is evaluated. Furthermore, discrimination of sedimentary environment on the basis of grain shape information is explored. The data gathered demonstrates a clear progression in textural maturity in terms of roundness, angularity, irregularity, fractal dimension, convexity, solidity and rectangularity. Textural maturity can be readily categorised using automated grain shape parameter analysis. However, absolute discrimination between different depositional environments on the basis of shape parameters alone is less certain. For example, the aeolian environment is quite distinct whereas fluvial, glacial and beach samples are inherently variable and tend to overlap each other in terms of textural maturity. This is most likely due to a collection of similar processes and sources operating within these environments. This study strongly demonstrates the merit of quantitative population-based shape parameter analysis of texture and indicates that it can play a key role in characterising both loose and consolidated sediments. This project is funded by the Irish Petroleum Infrastructure Programme (www.pip.ie)

  14. 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.

  15. Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

    Science.gov (United States)

    Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

    2015-01-01

    Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

  16. 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

  17. First-principles study of SnS electronic properties using LDA, PBE and HSE06 functionals

    Science.gov (United States)

    Ibragimova, R.; Ganchenkova, M.; Karazhanov, S.; Marstein, E. S.

    2018-03-01

    Recently, tin sulphide (SnS) has emerged as a promising alternative to conventional CIGS and CZTC for use in solar cells, possessing such properties as non-toxicity, low cost and production stability. SnS has a high theoretically predicted efficiency above 20%, but the experimentally achieved efficiency so far is as low as 4.36%. The reason for the low achieved efficiency is unclear. One of the powerful tools to get deeper insights about the nature of the problem is first-principles calculation approaches. That is why SnS has become an attractive subject for first-principles calculations recently. Previously calculated data, however, show a widespread of such fundamental value as the bandgap varying from 0.26 to 1.26 eV. In order to understand a reason for that, in this work, we concentrate on a systematic study of calculation parameters effects on the resulting electronic structure, with the particular attention paid to the influence of the exchange-correlation functional chosen for calculations. Several exchange-correlation functionals (LDA, PBE and HSE06) were considered. The systematic analysis has shown that the bandgap variation can result from a tensile/compressive hydrostatic pressure introduced by non-equilibrium lattice parameters used for the calculations. The study of the applicability of three functionals has shown that HSE06 gives the best match to both experimentally obtained bandgap and the XPS valence band spectra. LDA underestimates the bandgap but qualitatively reproduces experimentally measured valence DOS similar to that of HSE06 in contrast to PBE. PBE underestimates the bandgap and does not match to the measured XPS spectra.

  18. Microaggressions, Discrimination, and Phenotype among African Americans: A Latent Class Analysis of the Impact of Skin Tone and BMI.

    Science.gov (United States)

    Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M

    2017-05-01

    Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.

  19. 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. Copyright © 2016 John Wiley & Sons, Ltd.

  20. High-Pressure Behaviour of β-HMX Crystal Studied by DFT-LDA

    International Nuclear Information System (INIS)

    Dan, Lian; Lai-Yu, Lu; Dong-Qing, Wei; Qing-Ming, Zhang; Zi-Zheng, Gong; Yong-Xin, Guo

    2008-01-01

    Density functional theory (DFT) with local density approximation (LDA) is employed to study the structural and electronic properties of the high explosive octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) under high pressure compression up to 40 GPa. Pressure dependences of the cell volume, lattice constants, and molecular geometry of solid β-HMX are presented and discussed. It is found that N-N and N-C bonds are subject to significant change. This may implies that these bonds may be related to the sensitivity. The band gap is calculated and plotted as a function of pressure. Compared the experimental results with other theoretical works we find that LDA gives good results

  1. Flow field measurements using LDA and numerical computation for rod bundle of reactor fuel assembly

    International Nuclear Information System (INIS)

    Hu Jun; Zou Zunyu

    1995-02-01

    Local mean velocity and turbulence intensity measurements were obtained with DANTEC 55 X two-dimensional Laser Dopper Anemometry (LDA) for rod bundle of reactor fuel assembly test model which was a 4 x 4 rod bundle. The data were obtained from different experimental cross-sections both upstream and downstream of the model support plate. Measurements performed at test Reynolds numbers of 1.8 x 10 4 ∼3.6 x 10 4 . The results described the local and gross effects of the support plate on upstream and downstream flow distributions. A numerical computation was also given, the experimental results are in good agreement with the numerical one and the others in references. Finally, a few suggestions were proposed for how to use the LDA system well. (11 figs.)

  2. Double counting in LDA + DMFT-The example of NiO

    Energy Technology Data Exchange (ETDEWEB)

    Karolak, M., E-mail: mkarolak@physnet.uni-hamburg.d [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany); Ulm, G.; Wehling, T. [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany); Mazurenko, V. [Theoretical Physics and Applied Mathematic Department, Urals State Technical University, 620002, Mira street 19, Yekaterinburg (Russian Federation); Poteryaev, A. [Institute of Metal Physics, Russian Academy of Sciences, 620041 Yekaterinburg GSP-170 (Russian Federation); Lichtenstein, A. [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany)

    2010-07-15

    An intrinsic issue of the LDA + DMFT approach is the so called double counting of interaction terms. How to choose the double-counting potential in a manner that is both physically sound and consistent is unknown. We have conducted an extensive study of the charge-transfer system NiO in the LDA + DMFT framework using quantum Monte Carlo and exact diagonalization as impurity solvers. By explicitly treating the double-counting correction as an adjustable parameter we systematically investigated the effects of different choices for the double counting on the spectral function. Different methods for fixing the double counting can drive the result from Mott insulating to almost metallic. We propose a reasonable scheme for the determination of double-counting corrections for insulating systems.

  3. LDA+U and tight-binding electronic structure of InN nanowires

    Science.gov (United States)

    Molina-Sánchez, A.; García-Cristóbal, A.; Cantarero, A.; Terentjevs, A.; Cicero, G.

    2010-10-01

    In this paper we employ a combined ab initio and tight-binding approach to obtain the electronic and optical properties of hydrogenated Indium nitride (InN) nanowires. We first discuss InN band structure for the wurtzite structure calculated at the LDA+U level and use this information to extract the parameters needed for an empirical tight-binging implementation. These parameters are then employed to calculate the electronic and optical properties of InN nanowires in a diameter range that would not be affordable by ab initio techniques. The reliability of the large nanowires results is assessed by explicitly comparing the electronic structure of a small diameter wire studied both at LDA+U and tight-binding level.

  4. First-principle study on bonding mechanism of ZnO by LDA+U method

    International Nuclear Information System (INIS)

    Zhou, G.C.; Sun, L.Z.; Zhong, X.L.; Chen Xiaoshuang; Wei Lu; Wang, J.B.

    2007-01-01

    The electronic structure and the bonding mechanism of ZnO have been studied by using the Full-Potential Linear Augmented Plane Wave (FP-LAPW) method within the density-functional theory (DFT) based on LDA+U exchange correlation potential. The valence and the bonding charge density are calculated and compared with those derived from LDA and GGA to describe the bonding mechanism. The charge transfer along with the bonding process is analyzed by using the theory of Atoms in Molecules (AIM). The bonding, the topological characteristics and the p-d coupling effects on the bonding mechanism of ZnO are shown quantitatively with the critical points (CPs) along the bonding trajectory and the charge in the atomic basins. Meanwhile, the bonding characteristics for wurtzite, zinc blende and rocksalt phase of ZnO are discussed systematically in the present paper

  5. 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.

  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

    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......). 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...

  7. 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 orthogonal...... light scatter. The method was optimized using the human leukemia cell lines HL-60 and K-562. Samples of 10(5) ethanol-fixed cells were treated with pepsin/HCl and stained as a nuclear suspension with anti-BrdUrd antibody, FITC-conjugated secondary antibody, and propidium iodide. Labelled mitoses could...

  8. 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.

  9. 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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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

  11. The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

    Science.gov (United States)

    Leka, K. D.; Barnes, Graham; Wagner, Eric

    2018-04-01

    A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

  12. Discriminant analysis of Social Work’s performance in licensure examination

    Directory of Open Access Journals (Sweden)

    Jonel R. Alonzo

    2017-12-01

    Full Text Available Many research studies have examined academic factors as predictors of success in licensure examination. The purpose of this descriptive discriminant analysis was to explore possible factors in passing social work licensure examination. Data were examined from academic records of 69 (37 passed and 32 failed Social Work graduates of the University of Mindanao who took Social Work Licensure Examination 2014. This can be used as a basis of Social Work program in planning and administering strategies to improve its national passing rates. Discriminant analysis was employed along five academic factors which are Human Behavior and Social Environment (HBSE, Social Work Programs and Policies (SWPP, Social Work Methods (SWM, Field Practice (FP and Grade Point Average (GPA. The analysis generated three significant predictors accounting for 76.22% of between group variability. The function had a hit ratio of 100%. Structure matrix revealed that three cluster subjects were identified as good factors of passing the social work licensure examination: HBSE, SWPP and SWM had a correlation value of 0.713, 0.768 and 0.840, respectively.

  13. NBLDA: negative binomial linear discriminant analysis for RNA-Seq data.

    Science.gov (United States)

    Dong, Kai; Zhao, Hongyu; Tong, Tiejun; Wan, Xiang

    2016-09-13

    RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications. We have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data

  14. Discriminant analysis to predict the occurrence of ELMs in H-mode discharges

    International Nuclear Information System (INIS)

    Kardaun, O.J.W.F.; Itoh, S.; Itoh, K.; Kardaun, J.W.P.F.

    1993-08-01

    After an exposition of its theoretical background, discriminant analysis is applied to the H-mode confinement database to find the region in plasma parameter space in which H-mode with small ELMs (Edge Localized Modes) is likely to occur. The boundary of this region is determined by the condition that the probability of appearance of such a type of H-mode, as a function of the plasma parameters, should be (1) larger than some threshold value and (2) larger than the corresponding probability for other types of H-mode (i.e., H-mode without ELMs or with giant ELMs). In practice, the discrimination has been performed for the ASDEX, JET and JFT-2M tokamaks (a) using four instantaneous plasma parameters (injected power P inj , magnetic field B t , plasma current I p and line averaged electron density (n-bar e ) and (b) taking also memory effects of the plasma and the distance between the plasma and the wall into account, while using variables that are normalised with respect to machine size. Generally speaking, it is found that there is a substantial overlap between the region of H-mode with small ELMs and the region of the two other types of H-mode. However, the ELM-free and the giant ELM H-modes relatively rarely appear in the region, that, according to the analysis, is allocated to small ELMs. A reliable production of H-mode with only small ELMs seems well possible by choosing this regime in parameter space. In the present study, it was not attempted to arrive at a unified discrimination across the machines. So, projection from one machine to another remains difficult, and a reliable determination of the region where small ELMs occur still requires a training sample from the device under consideration. (author) 53 refs

  15. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. 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.

  17. Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

    Science.gov (United States)

    Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

    2015-02-01

    Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides

  18. 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.

  19. Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

    Science.gov (United States)

    Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

    2015-01-01

    Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

  20. 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.

  1. Combined use of correlation dimension and entropy as discriminating measures for time series analysis

    Science.gov (United States)

    Harikrishnan, K. P.; Misra, R.; Ambika, G.

    2009-09-01

    We show that the combined use of correlation dimension (D2) and correlation entropy (K2) as discriminating measures can extract a more accurate information regarding the different types of noise present in a time series data. For this, we make use of an algorithmic approach for computing D2 and K2 proposed by us recently [Harikrishnan KP, Misra R, Ambika G, Kembhavi AK. Physica D 2006;215:137; Harikrishnan KP, Ambika G, Misra R. Mod Phys Lett B 2007;21:129; Harikrishnan KP, Misra R, Ambika G. Pramana - J Phys, in press], which is a modification of the standard Grassberger-Proccacia scheme. While the presence of white noise can be easily identified by computing D2 of data and surrogates, K2 is a better discriminating measure to detect colored noise in the data. Analysis of time series from a real world system involving both white and colored noise is presented as evidence. To our knowledge, this is the first time that such a combined analysis is undertaken on a real world data.

  2. Application of FT-IR spectroscopy on breast cancer serum analysis

    Science.gov (United States)

    Elmi, Fatemeh; Movaghar, Afshin Fayyaz; Elmi, Maryam Mitra; Alinezhad, Heshmatollah; Nikbakhsh, Novin

    2017-12-01

    Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950-1200 cm- 1(sugar), 1190-1350 cm- 1 (collagen), 1475-1710 cm- 1 (protein), 1710-1760 cm- 1 (ester), 2800-3000 cm- 1 (stretching motions of -CH2 & -CH3), and 3090-3700 cm- 1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000-3600 cm- 1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.

  3. Combining features from ERP components in single-trial EEG for discriminating four-category visual objects

    Science.gov (United States)

    Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

    2012-10-01

    Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

  4. 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...

  5. 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. Copyright © 2016. Published by Elsevier Ltd.

  6. 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

  7. Methodological considerations in discriminating olive-orchard management type using olive-canopy arthropod fauna at the level of order

    Energy Technology Data Exchange (ETDEWEB)

    Jerez-Valle, C.; García-López, P.A.; Campos, M.; Pascual, F.

    2015-07-01

    The cultivation of the olive tree (Olea europaea L.) has great importance in the entire Mediterranean basin, so that the implementation of organic practices in their management directly affects the sustainability of the agricultural system. Bioindication with arthropods can help to detect the different agricultural practices. In this work, we analyse the most appropriate methodology for discriminating between management using arthropods at the taxonomic level of order, with the novelty of taking into account the weather conditions to select the sampling dates. Between 12 and 15 sampling stations (depending on the year) were selected from olive orchards belonging to organic, conventional non-tillage, and strict conventional management, being sampled by beating the canopy fortnightly in the spring-summer period of 2007, 2008 and 2009. Organic management was more abundant and richer than the rest for the three years. Most groups with significant differences in terms of relative abundance were more abundant in organic orchard, except Neuroptera. Finally, different discriminant methods were evaluated (Linear Discriminant Analysis, Multiple Discriminant Analysis, and Support Vector Machine) with several different data sets. The discriminant analysis with interannual variability reached 97.9% accuracy in differentiating between organic and non-organic management using the LDA method, considering the taxa with significant differences from the abundance, excluding pests, and using samples with more uniform and stable weather patterns (late summer. (Author)

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

    International Nuclear Information System (INIS)

    Vila, Anna; Ferrer, Nuria; Garcia, Jose F.

    2007-01-01

    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

  9. 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.

  10. 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.

  11. 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...

  12. 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.

  13. 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. © 2014 John Wiley & Sons Ltd.

  14. 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.

  15. Forensic analysis of explosives using isotope ratio mass spectrometry (IRMS)--discrimination of ammonium nitrate sources.

    Science.gov (United States)

    Benson, Sarah J; Lennard, Christopher J; Maynard, Philip; Hill, David M; Andrew, Anita S; Roux, Claude

    2009-06-01

    An evaluation was undertaken to determine if isotope ratio mass spectrometry (IRMS) could assist in the investigation of complex forensic cases by providing a level of discrimination not achievable utilising traditional forensic techniques. The focus of the research was on ammonium nitrate (AN), a common oxidiser used in improvised explosive mixtures. The potential value of IRMS to attribute Australian AN samples to the manufacturing source was demonstrated through the development of a preliminary AN classification scheme based on nitrogen isotopes. Although the discrimination utilising nitrogen isotopes alone was limited and only relevant to samples from the three Australian manufacturers during the evaluated time period, the classification scheme has potential as an investigative aid. Combining oxygen and hydrogen stable isotope values permitted the differentiation of AN prills from three different Australian manufacturers. Samples from five different overseas sources could be differentiated utilising a combination of the nitrogen, oxygen and hydrogen isotope values. Limited differentiation between Australian and overseas prills was achieved for the samples analysed. The comparison of nitrogen isotope values from intact AN prill samples with those from post-blast AN prill residues highlighted that the nitrogen isotopic composition of the prills was not maintained post-blast; hence, limiting the technique to analysis of un-reacted explosive material.

  16. Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

    Science.gov (United States)

    Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto

    2017-09-01

    With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Nathalie André

    2018-01-01

    Full Text Available Background. Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Methods. Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. Results. A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers’ self-efficacy, internal memory, and attentional control strategies of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. Conclusions. This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control, to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging.

  18. 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...

  19. Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

    Directory of Open Access Journals (Sweden)

    Masatoshi Yamada

    2016-01-01

    Full Text Available Background: Intraductal proliferative lesions (IDPLs of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH and low-grade ductal carcinoma in situ (LG-DCIS. In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH, ADH, LG-DCIS, intermediate-grade (IM-DCIS, and high-grade (HG-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA and a support vector machine. Results: High diagnostic accuracy (81.8–99.3% was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.

  20. Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

    Science.gov (United States)

    Yamada, Masatoshi; Saito, Akira; Yamamoto, Yoichiro; Cosatto, Eric; Kurata, Atsushi; Nagao, Toshitaka; Tateishi, Ayako; Kuroda, Masahiko

    2016-01-01

    Background: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. Results: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists’ diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer. PMID:26955499

  1. 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.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

    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.

  3. Detection of feigned mental disorders on the personality assessment inventory: a discriminant analysis.

    Science.gov (United States)

    Rogers, R; Sewell, K W; Morey, L C; Ustad, K L

    1996-12-01

    Psychological assessment with multiscale inventories is largely dependent on the honesty and forthrightness of those persons evaluated. We investigated the effectiveness of the Personality Assessment Inventory (PAI) in detecting participants feigning three specific disorders: schizophrenia, major depression, and generalized anxiety disorder. With a simulation design, we tested the PAI validity scales on 166 naive (undergraduates with minimal preparation) and 80 sophisticated (doctoral psychology students with 1 week preparation) participants. We compared their results to persons with the designated disorders: schizophrenia (n = 45), major depression (n = 136), and generalized anxiety disorder (n = 40). Although moderately effective with naive simulators, the validity scales evidenced only modest positive predictive power with their sophisticated counterparts. Therefore, we performed a two-stage discriminant analysis that yielded a moderately high hit rate (> 80%) that was maintained in the cross-validation sample, irrespective of the feigned disorder or the sophistication of the simulators.

  4. 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.

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

    KAUST Repository

    Zollanvari, Amin; Genton, Marc G.

    2013-01-01

    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.

  6. Non-destructive Testing of Wood Defects Based on Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Wenshu LIN

    2015-09-01

    Full Text Available The defects of wood samples were tested by the technique of stress wave and ultrasonic technology, and the testing results were comparatively analyzed by using the Fisher discriminant analysis in the statistic software of SPSS. The differences of defect detection sensitivity and accuracy for stress wave and ultrasonic under different wood properties and defects were concluded. Therefore, in practical applications, according to different situations the corresponding wood non- destructive testing method should be used, or the two detection methods are applied at the same time in order to compensate for its shortcomings with each other to improve the ability to distinguish the timber defects. The results can provide a reference for further improvement of the reliability of timber defects detection.

  7. Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. II. Discriminant Analysis

    Science.gov (United States)

    Leka, K. D.; Barnes, G.

    2003-10-01

    We apply statistical tests based on discriminant analysis to the wide range of photospheric magnetic parameters described in a companion paper by Leka & Barnes, with the goal of identifying those properties that are important for the production of energetic events such as solar flares. The photospheric vector magnetic field data from the University of Hawai'i Imaging Vector Magnetograph are well sampled both temporally and spatially, and we include here data covering 24 flare-event and flare-quiet epochs taken from seven active regions. The mean value and rate of change of each magnetic parameter are treated as separate variables, thus evaluating both the parameter's state and its evolution, to determine which properties are associated with flaring. Considering single variables first, Hotelling's T2-tests show small statistical differences between flare-producing and flare-quiet epochs. Even pairs of variables considered simultaneously, which do show a statistical difference for a number of properties, have high error rates, implying a large degree of overlap of the samples. To better distinguish between flare-producing and flare-quiet populations, larger numbers of variables are simultaneously considered; lower error rates result, but no unique combination of variables is clearly the best discriminator. The sample size is too small to directly compare the predictive power of large numbers of variables simultaneously. Instead, we rank all possible four-variable permutations based on Hotelling's T2-test and look for the most frequently appearing variables in the best permutations, with the interpretation that they are most likely to be associated with flaring. These variables include an increasing kurtosis of the twist parameter and a larger standard deviation of the twist parameter, but a smaller standard deviation of the distribution of the horizontal shear angle and a horizontal field that has a smaller standard deviation but a larger kurtosis. To support the

  8. 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.

  9. A Longitudinal Analysis of Hispanic Youth Acculturation and Cigarette Smoking: The Roles of Gender, Culture, Family, and Discrimination

    Science.gov (United States)

    2013-01-01

    Introduction: 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. Methods: 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. Results: 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. Conclusions: 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. PMID:23109671

  10. 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?

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

    Science.gov (United States)

    Quanbao, Jiang; Marcus W., Feldman

    2013-01-01

    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 child mortality, and the effect of gender discrimination on China's population development. We find that gender discrimination will decrease China's population size, number of births, and working age population, accelerate population aging and exacerbate the male marriage squeeze. These results provide theoretical support for suggesting that the government enact and implement public policies aimed at eliminating gender discrimination. PMID:24363477

  12. Phonological experience modulates voice discrimination: Evidence from functional brain networks analysis.

    Science.gov (United States)

    Hu, Xueping; Wang, Xiangpeng; Gu, Yan; Luo, Pei; Yin, Shouhang; Wang, Lijun; Fu, Chao; Qiao, Lei; Du, Yi; Chen, Antao

    2017-10-01

    Numerous behavioral studies have found a modulation effect of phonological experience on voice discrimination. However, the neural substrates underpinning this phenomenon are poorly understood. Here we manipulated language familiarity to test the hypothesis that phonological experience affects voice discrimination via mediating the engagement of multiple perceptual and cognitive resources. The results showed that during voice discrimination, the activation of several prefrontal regions was modulated by language familiarity. More importantly, the same effect was observed concerning the functional connectivity from the fronto-parietal network to the voice-identity network (VIN), and from the default mode network to the VIN. Our findings indicate that phonological experience could bias the recruitment of cognitive control and information retrieval/comparison processes during voice discrimination. Therefore, the study unravels the neural substrates subserving the modulation effect of phonological experience on voice discrimination, and provides new insights into studying voice discrimination from the perspective of network interactions. Copyright © 2017. Published by Elsevier Inc.

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

    Science.gov (United States)

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

    2011-08-01

    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 child mortality, and the effect of gender discrimination on China's population development. We find that gender discrimination will decrease China's population size, number of births, and working age population, accelerate population aging and exacerbate the male marriage squeeze. These results provide theoretical support for suggesting that the government enact and implement public policies aimed at eliminating gender discrimination.

  14. 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

  15. 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.

  16. Statistical analysis for discrimination of prompt gamma ray peak induced by high energy neutron: Monte Carlo simulation study

    International Nuclear Information System (INIS)

    Do-Kun Yoon; Joo-Young Jung; Tae Suk Suh; Seong-Min Han

    2015-01-01

    The purpose of this research is a statistical analysis for discrimination of prompt gamma ray peak induced by the 14.1 MeV neutron particles from spectra using Monte Carlo simulation. For the simulation, the information of 18 detector materials was used to simulate spectra by the neutron capture reaction. The discrimination of nine prompt gamma ray peaks from the simulation of each detector material was performed. We presented the several comparison indexes of energy resolution performance depending on the detector material using the simulation and statistics for the prompt gamma activation analysis. (author)

  17. 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...

  18. Laws' masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis

    International Nuclear Information System (INIS)

    Rachidi, M.; Marchadier, A.; Gadois, C.; Lespessailles, E.; Chappard, C.; Benhamou, C.L.

    2008-01-01

    The objective of this study was to explore Laws' masks analysis to describe structural variations of trabecular bone due to osteoporosis on high-resolution digital radiographs and to check its dependence on the spatial resolution. Laws' masks are well established as one of the best methods for texture analysis in image processing and are used in various applications, but not in bone tissue characterisation. This method is based on masks that aim to filter the images. From each mask, five classical statistical parameters can be calculated. The study was performed on 182 healthy postmenopausal women with no fractures and 114 age-matched women with fractures [26 hip fractures (HFs), 29 vertebrae fractures (VFs), 29 wrist fractures (WFs) and 30 other fractures (OFs)]. For all subjects radiographs were obtained of the calcaneus with a new high-resolution X-ray device with direct digitisation (BMA, D3A, France). The lumbar spine, femoral neck, and total hip bone mineral density (BMD) were assessed by dual-energy X-ray absorptiometry. In terms of reproducibility, the best results were obtained with the TR E5E5 mask, especially for three parameters: ''mean'', ''standard deviation'' and ''entropy'' with, respectively, in vivo mid-term root mean square average coefficient of variation (RMSCV)%=1.79, 4.24 and 2.05. The ''mean'' and ''entropy'' parameters had a better reproducibility but ''standard deviation'' showed a better discriminant power. Thus, for univariate analysis, the difference between subjects with fractures and controls was significant (P -3 ) and significant for each fracture group independently (P -4 for HF, P=0.025 for VF and P -3 for OF). After multivariate analysis with adjustment for age and total hip BMD, the difference concerning the ''standard deviation'' parameter remained statistically significant between the control group and the HF and VF groups (P -5 , and P=0.04, respectively). No significant correlation between these Laws' masks parameters and

  19. A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Chunren Lai

    2017-12-01

    Full Text Available It is crucial to differentiate patients with temporal lobe epilepsy (TLE from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV, and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM, and the support vector machine-recursive feature elimination (SVM-RFE were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy, followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

  20. The use of principal component, discriminate and rough sets analysis methods of radiological data

    International Nuclear Information System (INIS)

    Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

    2006-01-01

    In this work, computational methods of finding clusters of multivariate data points were explored using principal component analysis (PCA), discriminate analysis (DA) and rough set analysis (RSA) methods. The variables were the concentrations of four natural isotopes and the texture characteristics of 100 sand samples from the coast of North Sinai, Egypt. Beach and dune sands are the two types of samples included. These methods were used to reduce the dimensionality of multivariate data and as classification and clustering methods. The results showed that the classification of sands in the environment of North Sinai is dependent upon the radioactivity contents of the naturally occurring radioactive materials and not upon the characteristics of the sand. The application of DA enables the creation of a classification rule for sand type and it revealed that samples with high negatively values of the first score have the highest contamination of black sand. PCA revealed that radioactivity concentrations alone can be considered to predict the classification of other samples. The results of RSA showed that only one of the concentrations of 238 U, 226 Ra and 232 Th with 40 K content, can characterize the clusters together with characteristics of the sand. Both PCA and RSA result in the following conclusion: 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one/two of them may not be considered without affecting the body of knowledge

  1. Classification of root canal microorganisms using electronic-nose and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Özbilge Hatice

    2010-11-01

    Full Text Available Abstract Background Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument. Method Our microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance. Results We have found that quadratic type dicriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and

  2. Improving the analysis of near-spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

    Science.gov (United States)

    Gemignani, Jessica; Middell, Eike; Barbour, Randall L; Graber, Harry L; Blankertz, Benjamin

    2018-04-04

    The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions

  3. Structural Discrimination

    DEFF Research Database (Denmark)

    Thorsen, Mira Skadegård

    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...

  4. Race, Sex, and Discrimination in School Settings: A Multilevel Analysis of Associations with Delinquency

    Science.gov (United States)

    Chambers, Brittany D.; Erausquin, Jennifer Toller

    2018-01-01

    Background: Adolescence is a critical phase of development and experimentation with delinquent behaviors. There is a growing body of literature exploring individual and structural impacts of discrimination on health outcomes and delinquent behaviors. However, there is limited research assessing how school diversity and discrimination impact…

  5. 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…

  6. 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

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

    DEFF Research Database (Denmark)

    Askjær, Sune; Langgård, Morten

    2008-01-01

    The criterion of success for the initial stages of a ligand-based drug-discovery project is dual. First, a set of suitable lead compounds has to be identified. Second, a level of a preliminary structure-activity relationship (SAR) of the identified ligands has to be established in order to guide ...... by the protein-binding site known from X-ray complexes. The result of this analysis assists in explaining the efficiency of 2D pharmacophore fingerprints as descriptors in virtual screening....... 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...

  8. Molecular discrimination of lactobacilli used as starter and probiotic cultures by amplified ribosomal DNA restriction analysis.

    Science.gov (United States)

    Roy, D; Sirois, S; Vincent, D

    2001-04-01

    Lactic acid bacteria such as Lactobacillus helveticus, L. delbrueckii subsp. delbrueckii, L. delbrueckii subsp. lactis, L. delbrueckii subsp. bulgaricus, L. acidophilus, and L. casei related taxa which are widely used as starter or probiotic cultures can be identified by amplified ribosomal DNA restriction analysis (ARDRA). The genetic discrimination of the related species belonging to these groups was first obtained by PCR amplifications by using group-specific or species-specific 16S rDNA primers. The numerical analysis of the ARDRA patterns obtained by using CfoI, HinfI, Tru9I, and ScrFI was an efficient typing tool for identification of species of the L. acidophilus and L. casei complex. ARDRA by using CfoI was a reliable method for differentiation of L. delbrueckii subsp. bulgaricus and L. delbrueckii subsp. lactis. Finally, strains ATCC 393 and ATCC 15820 exhibited unique ARDRA patterns with CfoI and Tru9I restriction enzymes as compared with the other strains of L. casei, L. paracasei, and L. rhamnosus.

  9. Using stable isotope analysis to discriminate gasoline on the basis of its origin.

    Science.gov (United States)

    Heo, Su-Young; Shin, Woo-Jin; Lee, Sin-Woo; Bong, Yeon-Sik; Lee, Kwang-Sik

    2012-03-15

    Leakage of gasoline and diesel from underground tanks has led to a severe environmental problem in many countries. Tracing the production origin of gasoline and diesel is required to enable the development of dispute resolution and appropriate remediation strategies for the oil-contaminated sites. We investigated the bulk and compound-specific isotopic compositions of gasoline produced by four oil companies in South Korea: S-Oil, SK, GS and Hyundai. The relative abundance of several compounds in gasoline was determined by the peak height of the major ion (m/z 44). The δ(13)C(Bulk) and δD(Bulk) values of gasoline produced by S-Oil were significantly different from those of SK, GS and Hyundai. In particular, the compound-specific isotopic value (δ(13)C(CSIA)) of methyl tert-butyl ether (MTBE) in S-Oil gasoline was significantly lower than that of gasoline produced by other oil companies. The abundance of several compounds in gasoline, such as n-pentane, MTBE, n-hexane, toluene, ethylbenzene and o-xylene, differed widely among gasoline from different oil companies. This study shows that gasoline can be forensically discriminated according to the oil company responsible for its manufacture using stable isotope analysis combined with multivariate statistical analysis. Copyright © 2012 John Wiley & Sons, Ltd.

  10. 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.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

    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.

  12. Further studies of crania from ancient northern Africa: an analysis of crania from first dynasty Egyptian tombs, using discriminant functions.

    Science.gov (United States)

    Keita, S O

    1992-03-01

    An analysis of First Dynasty crania from Abydos was undertaken using multiple discriminant functions. The results demonstrate greater affinity with Upper Nile Valley patterns, but also suggest change from earlier craniometric trends. Gene flow and movement of northern officials to the important southern city may explain the findings.

  13. Signal Detection Methods and Discriminant Analysis Applied to Categorization of Newspaper and Government Documents: A Preliminary Study.

    Science.gov (United States)

    Ng, Kwong Bor; Rieh, Soo Young; Kantor, Paul

    2000-01-01

    Discussion of natural language processing focuses on experiments using linear discriminant analysis to distinguish "Wall Street Journal" texts from "Federal Register" tests using information about the frequency of occurrence of word boundaries, sentence boundaries, and punctuation marks. Displays and interprets results in terms…

  14. Search for the standard model Higgs boson in $e^{+}e^{-}$ four- jet topology using neural networks and discriminant analysis

    CERN Document Server

    Mjahed, M

    2003-01-01

    We present an attempt to separate between Higgs boson events (e/sup + /e/sup -/ to ZH to qqbb) and other physics processes in the 4-jet channel (e/sup +/e/sup -/ to Z/ gamma , W/sup +/W, ZZ to 4jets), using the discriminant analysis and neural networks methods. Events were produced at LEP2 energies, using the Lund Monte Carlo generator and the Aleph package. The most discriminant variables as the reconstructed jet mass, the jet properties (b-tag, rapidity weighted moments) and other variables are used. (8 refs).

  15. Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

    Science.gov (United States)

    Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

    2018-01-16

    Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

  16. Diagnosing basal cell carcinoma in vivo by near-infrared Raman spectroscopy: a Principal Components Analysis discrimination algorithm

    Science.gov (United States)

    Silveira, Landulfo, Jr.; Silveira, Fabrício L.; Bodanese, Benito; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.

    2012-02-01

    This work demonstrated the discrimination among basal cell carcinoma (BCC) and normal human skin in vivo using near-infrared Raman spectroscopy. Spectra were obtained in the suspected lesion prior resectional surgery. After tissue withdrawn, biopsy fragments were submitted to histopathology. Spectra were also obtained in the adjacent, clinically normal skin. Raman spectra were measured using a Raman spectrometer (830 nm) with a fiber Raman probe. By comparing the mean spectra of BCC with the normal skin, it has been found important differences in the 800-1000 cm-1 and 1250-1350 cm-1 (vibrations of C-C and amide III, respectively, from lipids and proteins). A discrimination algorithm based on Principal Components Analysis and Mahalanobis distance (PCA/MD) could discriminate the spectra of both tissues with high sensitivity and specificity.

  17. A LDA-based approach to promoting ranking diversity for genomics information retrieval.

    Science.gov (United States)

    Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

    2012-06-11

    In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search

  18. Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

    Science.gov (United States)

    Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

    2014-01-01

    Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

  19. NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution.

    Science.gov (United States)

    Casale, Monica; Bagnasco, Lucia; Giordani, Paolo; Mariotti, Mauro Giorgio; Malaspina, Paola

    2015-09-01

    Lichens are used as biomonitors of air pollution because they are extremely sensitive to the presence of substances that alter atmospheric composition. Fifty-one thalli of two different varieties of Pseudevernia furfuracea (var. furfuracea and var. ceratea) were collected far from local sources of air pollution. Twenty-six of these thalli were then exposed to the air for one month in the industrial port of Genoa, which has high levels of environmental pollution. The possibility of using Near-infrared spectroscopy (NIRS) for generating a 'fingerprint' of lichens was investigated. Chemometric methods were successfully applied to discriminate between samples from polluted and non-polluted areas. In particular, Principal Component Analysis (PCA) was applied as a multivariate display method on the NIR spectra to visualise the data structure. This showed that the difference between samples of different varieties was not significant in comparison to the difference between samples exposed to different levels of environmental pollution. Then Linear Discriminant Analysis (LDA) was carried out to discriminate between lichens based on their exposure to pollutants. The distinction between control samples (not exposed) and samples exposed to the air in the industrial port of Genoa was evaluated. On average, 95.2% of samples were correctly classified, 93.0% of total internal prediction (5 cross-validation groups) and 100.0% of external prediction (on the test set) was achieved. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

    2017-09-01

    In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  1. 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.

  2. Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

    Science.gov (United States)

    Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

    2017-12-15

    Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

  3. 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

  4. Digital discrimination of neutrons and γ-rays in liquid scintillators using pulse gradient analysis

    International Nuclear Information System (INIS)

    D'Mellow, B.; Aspinall, M.D.; Mackin, R.O.; Joyce, M.J.; Peyton, A.J.

    2007-01-01

    A method for the digital discrimination of neutrons and γ-rays in mixed radiation fields is described. Pulses in the time domain, arising from the interaction of photons and neutrons in a liquid scintillator, have been produced using an accepted empirical model and from experimental measurements with an americium-beryllium source. Neutrons and γ-rays have been successfully discriminated in both of these data sets in the digital domain. The digital discrimination method described in this paper is simple and exploits samples early in the life of the pulse. It is thus compatible with current embedded system technologies, offers a degree of immunity to pulse pile-up and heralds a real-time means for neutron/γ discrimination that is fundamental to many potential industrial applications

  5. Pseudogap in normal underdoped phase of Bi2212: LDA + DMFT + Σk

    International Nuclear Information System (INIS)

    Nekrasov, I.A.; Kuchinskii, E.Z.; Pchelkina, Z.V.; Sadovskii, M.V.

    2007-01-01

    Pseudogap phenomena are observed for normal underdoped phase of different high-T c cuprates. Among others Bi 2 Sr 2 CaCu 2 O 8-δ (Bi2212) compound is one of the most studied experimentally [A. Damascelli, Z. Hussain, Z.-X. Shen, Rev. Mod. Phys. 75 (2003) 473; J.C. Campuzano, M.R. Norman, M. Randeria, in: K.H. Bennemann, J.B. Ketterson (Eds.), Physics of Superconductors, vol. 2, Springer, Berlin, 2004, p. 167; J. Fink et al., (cond-mat/0512307); X.J. Zhou et al., (cond-mat/0604284)]. To describe pseudogap regime in Bi2212, we employ novel generalized DMFT + Σ k approach [E.Z. Kuchinskii, I.A. Nekrasov, M.V. Sadovskii, JETP Lett. 82 (2005) 198; M.V. Sadovskii et al., Phys. Rev. B 72 (2005) 155105, and these proceedings, (doi:10.1016/j.physc.2007.03.367)]. This approach gives possibility to preserve conventional dynamical mean-field theory (DMFT) equations [A. Georges et al., Rev. Mod. Phys. 68 (1996) 13] and include an additional (momentum dependent) self-energy Σ k . In the present case, Σ k describes non-local dynamical correlations induced by short-ranged collective Heisenberg-like antiferromagnetic spin fluctuations [M.V. Sadovskii, Physics-Uspekhi 44 (2001) 515, (cond-mat/0408489)]. The effective single impurity problem in the DMFT + Σ k is solved by numerical renormalization group (NRG) [R. Bulla, A.C. Hewson, Th. Pruschke, J. Phys. Cond. Mat. 10 (1998) 8365; R. Bulla, Phys. Rev. Lett. 83 (1999) 136]. To take into account material specific properties of two neighboring CuO 2 layers of Bi2212 we employ local density approximation (LDA) to calculate necessary model parameters, e.g. the values of intra- and interlayer hopping integrals between Cu-sites. Onsite Coulomb interaction U for x 2 -y 2 orbital was calculated in constrained LDA method [O. Gunnarsson et al., Phys. Rev. B 39 (1989) 1708]. The value of pseudogap potential Δ was obtained within DMFT(NRG) [E.Z. Kuchinskii, I.A. Nekrasov, M.V. Sadovskii, JETP Lett. 82 (2005) 198; M.V. Sadovskii et al

  6. Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

    Science.gov (United States)

    Safi’ie, M. A.; Utami, E.; Fatta, H. A.

    2018-03-01

    Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

  7. A liquid-liquid transition in supercooled aqueous solution related to the HDA-LDA transition

    Science.gov (United States)

    Woutersen, Sander; Ensing, Bernd; Hilbers, Michiel; Zhao, Zuofeng; Angell, C. Austen

    2018-03-01

    Simulations and theory suggest that the thermodynamic anomalies of water may be related to a phase transition between two supercooled liquid states, but so far this phase transition has not been observed experimentally because of preemptive ice crystallization. We used calorimetry, infrared spectroscopy, and molecular dynamics simulations to investigate a water-rich hydrazinium trifluoroacetate solution in which the local hydrogen bond structure surrounding a water molecule resembles that in neat water at elevated pressure, but which does not crystallize upon cooling. Instead, this solution underwent a sharp, reversible phase transition between two homogeneous liquid states. The hydrogen-bond structures of these two states are similar to those established for high- and low-density amorphous (HDA and LDA) water. Such structural similarity supports theories that predict a similar sharp transition in pure water under pressure if ice crystallization could be suppressed.

  8. Understanding flood-induced water chemistry variability extracting temporal patterns with the LDA method

    Science.gov (United States)

    Aubert, A. H.; Tavenard, R.; Emonet, R.; De Lavenne, A.; Malinowski, S.; Guyet, T.; Quiniou, R.; Odobez, J.; Merot, P.; Gascuel-odoux, C.

    2013-12-01

    Studying floods has been a major issue in hydrological research for years, both in quantitative and qualitative hydrology. Stream chemistry is a mix of solutes, often used as tracers, as they originate from various sources in the catchment and reach the stream by various flow pathways. Previous studies (for instance (1)) hypothesized that stream chemistry reaction to a rainfall event is not unique but varies seasonally, and according to the yearly meteorological conditions. Identifying a typology of flood temporal chemical patterns is a way to better understand catchment processes at the flood and seasonal time scale. We applied a probabilistic model (Latent Dirichlet Allocation or LDA (2)) mining recurrent sequential patterns from a dataset of floods. A set of 472 floods was automatically extracted from a daily 12-year long record of nitrate, dissolved organic carbon, sulfate and chloride concentrations. Rainfall, discharge, water table depth and temperature are also considered. Data comes from a long-term hydrological observatory (AgrHys, western France) located at Kervidy-Naizin. From each flood, a document has been generated that is made of a set of "hydrological words". Each hydrological word corresponds to a measurement: it is a triplet made of the considered variable, the time at which the measurement is made (relative to the beginning of the flood), and its magnitude (that can be low, medium or high). The documents and the number of pattern to be mined are used as input data to the LDA algorithm. LDA relies on spotting co-occurrences (as an alternative to the more traditional study of correlation) between words that appear within the flood documents. It has two nice properties that are its ability to easily deal with missing data and its additive property that allows a document to be seen as a mixture of several flood patterns. The output of LDA is a set of patterns easily represented in graphics. These patterns correspond to typical reactions to rainfall

  9. Discrimination of side-window glass of Korean autos by laser ablation inductively coupled plasma mass spectrometry.

    Science.gov (United States)

    Lee, Sin-Woo; Ryu, Jong-Sik; Min, Ji-Sook; Choi, Man-Yong; Lee, Kwang-Sik; Shin, Woo-Jin

    2016-07-15

    Fragments of glass from cars are often found at crime scenes and can be crucial evidence for solving the crime. The glass fragments are important as trace evidence at crime scenes related to car accidents and burgled homes. By identifying the origin of glass fragments, it is possible to infer the identity of a suspect. Our results represent a promising approach to a thorough forensic investigation of car glass. Thirty-five samples from the side windows of cars produced and used in South Korea were collected from the official agencies of five car manufacturers and from two glassmakers. In addition, 120 samples from side mirrors were collected from the same suppliers as well as from small businesses. Their chemical compositions (including Pb isotopes) were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and linear discriminant analysis (LDA) was performed. The percentages of major elements (Si, Ca, and Fe) in side-window glass varied within narrow ranges (30.0 ± 2.36%, 5.93 ± 0.52%, and 0.33 ± 0.05%, respectively), while the differences among Pb isotope ratios were not significant. In contrast, light rare earth elements (LREEs) were different from each glassmaker. From the LDA, the types of side-window glass were successfully discriminated according to car manufacturer, glassmaker, and even glass thickness. However, glass from side mirrors cannot be used for good forensic identifiers. Discrimination techniques for side-window glass, although not for side mirrors, using chemical compositions combined with multivariate statistical analyses provide evidence for forensic investigations. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. Discrimination of Clover and Citrus Honeys from Egypt According to Floral Type Using Easily Assessable Physicochemical Parameters and Discriminant Analysis: An External Validation of the Chemometric Approach

    Directory of Open Access Journals (Sweden)

    Ioannis K. Karabagias

    2018-05-01

    Full Text Available Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014–2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx, total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin (p < 0.05. Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone.

  11. Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

    Science.gov (United States)

    Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

    2018-03-01

    This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

  12. Sex determination using discriminant function analysis in Indigenous (Kurubas children and adolescents of Coorg, Karnataka, India: A lateral cephalometric study

    Directory of Open Access Journals (Sweden)

    Darshan Devang Divakar

    2016-11-01

    Full Text Available 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 < 0.05 (Table 5. Also, significant differences (p < 0.05 were observed in all the variables according to age (Table 6. Out of 24 variables, only ULTc predicts the gender. The reliability of the derived discriminant function was assessed among study subjects; 100% of males and females were recognized correctly. Conclusion: The final outcome of this study validates the existence of sexual dimorphism in the skeleton as early as 6.5 years of age. There is a need for further research to determine other landmarks that can help in sex determination and norms for Indigenous (Kuruba population and also other Indigenous population of Coorg, Karnataka, India. Keywords: Discriminant function analysis, Forensic investigation, Indigenous, Lateral cephalograms, Sex determination

  13. 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.

  14. Online LDA BASED brain-computer interface system to aid disabled people

    Directory of Open Access Journals (Sweden)

    Apdullah Yayık

    2017-06-01

    Full Text Available This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance.

  15. [External therapy of plasma cell mastitis by jiuyi powder using partial least-squares discriminant analysis: a safety analysis].

    Science.gov (United States)

    Ye, Mei-na; Yang, Ming; Cheng, Yi-qin; Wang, Bing; Zhu, Ying; Xia, Ya-ru; Meng, Tian; Chen, Hao; Chen, Li-ying; Cheng, Hong-feng

    2015-04-01

    To evaluate the safety and the clinical value of external use of jiuyi Powder (JP) in treating plasma cell mastitis using partial least-squares discriminant analysis (PLSDA). Totally 50 patients with plasma cell mastitis treated by external use of JP were observed and biochemical examinations of blood and urine detected before application, at day 4 after application, at day 1 and 14 after discontinuation. Blood mercury and urinary mercury were detected before application, at day 1, 4, and 7 after application, at day 1 and 14 after discontinuation. Urinary mercury was also detected at 28 after discontinuation and 3 months after discontinuation. The information of wound, days of external application and the total dosage of external application were recorded before application, at day 1, 4, and 7 after application, as well as at day 1 after discontinuation. Then a discriminant model covering potential safety factors was set up by PLSDA after screening safety indices with important effects. The applicability of the model was assessed using area under ROC curve. Potential safety factors were assessed using variable importance in the projection (VIP). Urinary β2-microglobulin (β2-MG), urinary N-acetyl-β-D-glucosaminidase (NAG), 24 h urinary protein, and urinary α1-microglobulin (α1-MG) were greatly affected by external use of JP in treating plasma cell mastitis. The accuracy rate of PLSDA discriminate model was 74. 00%. The sensitivity, specificity, and the area under ROC curve was 0. 7826, 0. 7037, and 0. 8084, respectively. Three factors with greater effect on the potential safety were screened as follows: pre-application volume of the sore cavity, days of external application, and the total dosage of external application. PLSDA method could be used in analyzing bioinformation of clinical Chinese medicine. Urinary β2-MG and urinary NAG were two main safety monitoring indices. Days of external application and the total dosage of external application were main

  16. 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 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.

  17. Subclassification and Detection of New Markers for the Discrimination of Primary Liver Tumors by Gene Expression Analysis Using Oligonucleotide Arrays.

    Science.gov (United States)

    Hass, Holger G; Vogel, Ulrich; Scheurlen, Michael; Jobst, Jürgen

    2017-12-26

    The failure to correctly differentiate between intrahepatic cholangiocarcinoma [CC] and hepatocellular carcinoma [HCC] is a significant clinical problem, particularly in terms of the different treatment goals for both cancers. In this study a specific gene expression profile to discriminate these two subgroups of liver cancer was established and potential diagnostic markers for clinical use were analyzed. To evaluate the gene expression profiles of HCC and intrahepatic CC, Oligonucleotide arrays ( Affymetrix U133A) were used. Overexpressed genes were checked for their potential use as new markers for discrimination and their expression values were validated by reverse transcription polymerase chain reaction and immunohistochemistry analyses. 695 genes/expressed sequence tags (ESTs) in HCC (245 up-/450 down-regulated) and 552 genes/ESTs in CC (221 up-/331 down-regulated) were significantly dysregulated (p〈0.05, fold change >2, ≥70%). Using a supervised learning method, and one-way analysis of variance a specific 270-gene expression profile that enabled rapid, reproducible differentiation between both tumors and non-malignant liver tissues was established. A panel of 12 genes (e.g. HSP90β, ERG1, GPC3, TKT, ACLY, and NME1 for HCC; SPT2, T4S3, CNX43, TTD1, HBD01 for CC) were detected and partly described for the first time as potential discrimination markers. A specific gene expression profile for discrimination of primary liver cancer was identified and potential marker genes with feasible clinical impact were described.

  18. 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.

  19. DNA content analysis allows discrimination between Trypanosoma cruzi and Trypanosoma rangeli.

    Science.gov (United States)

    Naves, Lucila Langoni; da Silva, Marcos Vinícius; Fajardo, Emanuella Francisco; da Silva, Raíssa Bernardes; De Vito, Fernanda Bernadelli; Rodrigues, Virmondes; Lages-Silva, Eliane; Ramírez, Luis Eduardo; Pedrosa, André Luiz

    2017-01-01

    Trypanosoma cruzi, a human protozoan parasite, is the causative agent of Chagas disease. Currently the species is divided into six taxonomic groups. The genome of the CL Brener clone has been estimated to be 106.4-110.7 Mb, and DNA content analyses revealed that it is a diploid hybrid clone. Trypanosoma rangeli is a hemoflagellate that has the same reservoirs and vectors as T. cruzi; however, it is non-pathogenic to vertebrate hosts. The haploid genome of T. rangeli was previously estimated to be 24 Mb. The parasitic strains of T. rangeli are divided into KP1(+) and KP1(-). Thus, the objective of this study was to investigate the DNA content in different strains of T. cruzi and T. rangeli by flow cytometry. All T. cruzi and T. rangeli strains yielded cell cycle profiles with clearly identifiable G1-0 (2n) and G2-M (4n) peaks. T. cruzi and T. rangeli genome sizes were estimated using the clone CL Brener and the Leishmania major CC1 as reference cell lines because their genome sequences have been previously determined. The DNA content of T. cruzi strains ranged from 87,41 to 108,16 Mb, and the DNA content of T. rangeli strains ranged from 63,25 Mb to 68,66 Mb. No differences in DNA content were observed between KP1(+) and KP1(-) T. rangeli strains. Cultures containing mixtures of the epimastigote forms of T. cruzi and T. rangeli strains resulted in cell cycle profiles with distinct G1 peaks for strains of each species. These results demonstrate that DNA content analysis by flow cytometry is a reliable technique for discrimination between T. cruzi and T. rangeli isolated from different hosts.

  20. 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

  1. 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

  2. An investigation of particle behavior in gas-solid horizontal pipe flow by an extended LDA technique

    Energy Technology Data Exchange (ETDEWEB)

    Yong Lu; Donald H. Glass; William J. Easson [University of Edinburgh, Edinburgh (United Kingdom). Institute for Materials and Processes

    2009-12-15

    An extended Laser Doppler Anemometry (LDA) technique has been developed to measure the distributions of particle velocities and particle number rates over a whole pipe cross-section in a dilute pneumatic conveying system. The first extension concentrates on the transform matrix for predicting the laser beams' cross point in a pipe according to the shift coordinate of the 3D computer-controlled traverse system on which the probes of the LDA system were mounted. The second focuses on the proper LDA sample rate for the measurement of gas-solid pipe flow with polydisperse particles. A suitable LDA sample rate should ensure that enough data is recorded in the measurement interval to precisely calculate the particle mean velocity or other statistical values at every sample point. The present study explores the methodology as well as the fundamentals of measurements, using a laser facility, of the cross-sectional distributions of solid phase. In the horizontal gas-solid pipe flow (glass beads less than 110 {mu}m), the experimental data show that the cross-sectional flow patterns of the solid phase can be classified by annulus-like flow describing the axial particle velocity contours and stratified flow characterising particle number rate distribution over a cross-section. Thus, the cross-sectional flow pattern of the solid phase in a horizontal pipe may be annular or stratified dependent on whether the axial particle velocity or particle number rate is the phenomenon studied. 13 refs., 16 figs., 1 tab.

  3. Calibration of a micromachined particle velocity microphone in a standing wave tube using a LDA photon-correlation technique

    NARCIS (Netherlands)

    Raangs, R.; Schlicke, Ted; Barham, Richard

    2005-01-01

    In this paper, a new method of calibrating an acoustic particle velocity sensor using laser Doppler anemometry (LDA) is discussed. The results were compared and were in good agreement with the results obtained by conventional methods, where the sensitivity of the microflown is obtained with the use

  4. Experimental study of the polyamorphism of water. I. The isobaric transitions from amorphous ices to LDA at 4 MPa

    Science.gov (United States)

    Handle, Philip H.; Loerting, Thomas

    2018-03-01

    The existence of more than one solid amorphous state of water is an extraordinary feature. Since polyamorphism might be connected to the liquid-liquid critical point hypothesis, it is particularly important to study the relations amongst the different amorphous ices. Here we study the polyamorphic transformations of several high pressure amorphous ices to low-density amorphous ice (LDA) at 4 MPa by isobaric heating utilising in situ volumetry and ex situ X-ray diffraction. We find that very-high density amorphous ice (VHDA) and unannealed high density amorphous ice (HDA) show significant relaxation before transforming to LDA, whereby VHDA is seen to relax toward HDA. By contrast, expanded HDA shows almost no relaxation prior to the transformation. The transition to LDA itself obeys criteria for a first-order-like transition in all cases. In the case of VHDA, even macroscopic phase separation is observed. These findings suggest that HDA and LDA are two clearly distinct polyamorphs. We further present evidence that HDA reaches the metastable equilibrium at 140 K and 0.1 GPa but only comes close to that at 140 K and 0.2 GPa. The most important is the path independence of the amorphous phase reached at 140 K and 0.1 GPa.

  5. LDA-PIV Diagnostics and 3D Simulation of Oscillating Swirl Flow in a Closed Cylindrical Container

    DEFF Research Database (Denmark)

    Naumov, Igor; Okulov, V. L.; Meyer, Knud Erik

    2003-01-01

    Results on unsteady vortex breakdown are obained simultaneously using two diagnostics methods: a) determination of velocity fields by particle tracks (Particle Image Velocimeter - PIV), b) determination of velocity fields by Laser Doppler Anemometry (LDA), are presented.The experiments data are i...

  6. Differential discriminator

    International Nuclear Information System (INIS)

    Dukhanov, V.I.; Mazurov, I.B.

    1981-01-01

    A principal flowsheet of a differential discriminator intended for operation in a spectrometric circuit with statistical time distribution of pulses is described. The differential discriminator includes four integrated discriminators and a channel of piled-up signal rejection. The presence of the rejection channel enables the discriminator to operate effectively at loads of 14x10 3 pulse/s. The temperature instability of the discrimination thresholds equals 250 μV/ 0 C. The discrimination level changes within 0.1-5 V, the level shift constitutes 0.5% for the filling ratio of 1:10. The rejection coefficient is not less than 90%. Alpha spectrum of the 228 Th source is presented to evaluate the discriminator operation with the rejector. The rejector provides 50 ns time resolution

  7. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

    Directory of Open Access Journals (Sweden)

    Hojjat Seyed Mousavi

    2015-01-01

    Full Text Available Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images. To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG and high-grade glioma (HGG which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1 A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2 a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP. In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1 Successful detection rates of pseudopalisading necrosis

  8. Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

    International Nuclear Information System (INIS)

    Hooman, A.; Mohammadzadeh, M.

    2008-01-01

    Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curve (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions

  9. 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.

  10. 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.

  11. An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

    Science.gov (United States)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

  12. Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

    Czech Academy of Sciences Publication Activity Database

    Farokhi, S.; Shamsuddin, S. M.; Flusser, Jan; Sheikh, U. U.; Khansari, M.; Jafari-Khouzani, K.

    2013-01-01

    Roč. 22, č. 1 (2013), s. 1-11 ISSN 1017-9909 R&D Projects: GA ČR GAP103/11/1552 Keywords : face recognition * infrared imaging * image moments Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.850, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/flusser-rotation and noise invariant near-infrared face recognition by means of zernike moments and spectral regression discriminant analysis.pdf

  13. A qualitative analysis of hate speech reported to the Romanian National Council for Combating Discrimination (2003‑2015)

    OpenAIRE

    Adriana Iordache

    2015-01-01

    The article analyzes the specificities of Romanian hate speech over a period of twelve years through a qualitative analysis of 384 Decisions of the National Council for Combating Discrimination. The study employs a coding methodology which allows one to separate decisions according to the group that was the victim of hate speech. The article finds that stereotypes employed are similar to those encountered in the international literature. The main target of hate speech is the Roma, who are ...

  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. A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra

    International Nuclear Information System (INIS)

    Chen LiMei; Carpita, N.C.; Reiter, W.D.; Wilson, R.H.; Jeffries, C.; McCann, M.C.

    1998-01-01

    We have developed a rapid method to screen large numbers of mutant plants for a broad range of cell wall phenotypes using Fourier transform infrared (FTIR) microspectroscopy of leaves. We established and validated a model that can discriminate between the leaves of wild-type and a previously defined set of cell-wall mutants of Arabidopsis. Exploratory principal component analysis indicated that mutants deficient in different cell-wall sugars can be distinguished from each other. Discrimination of cell-wall mutants from wild-type was independent of variability in starch content or additional unrelated mutations that might be present in a heavily mutagenised population. We then developed an analysis of FTIR spectra of leaves obtained from over 1000 mutagenised flax plants, and selected 59 plants whose spectral variation from wild-type was significantly out of the range of a wild-type population, determined by Mahalanobis distance. Cell wall sugars from the leaves of selected putative mutants were assayed by gas chromatography-mass spectrometry and 42 showed significant differences in neutral sugar composition. The FTIR spectra indicated that six of the remaining 17 plants have altered ester or protein content. We conclude that linear discriminant analysis of FTIR spectra is a robust method to identify a broad range of structural and architectural alterations in cell walls, appearing as a consequence of developmental regulation, environmental adaptation or genetic modification. (author)

  16. Multiple endmember spectral-angle-mapper (SAM) analysis improves discrimination of Savanna tree species

    CSIR Research Space (South Africa)

    Cho, Moses A

    2009-08-01

    Full Text Available of this paper was to evaluate the classification performance of a multiple-endmember spectral angle mapper (SAM) classification approach in discriminating seven common African savanna tree species and to compare the results with the traditional SAM classifier...

  17. An Information Analysis of 2-, 3-, and 4-Word Verbal Discrimination Learning.

    Science.gov (United States)

    Arima, James K.; Gray, Francis D.

    Information theory was used to qualify the difficulty of verbal discrimination (VD) learning tasks and to measure VD performance. Words for VD items were selected with high background frequency and equal a priori probabilities of being selected as a first response. Three VD lists containing only 2-, 3-, or 4-word items were created and equated for…

  18. 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…

  19. LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation.

    Science.gov (United States)

    Pyo, Shinjee; Kim, Eunhui; Kim, Munchurl

    2015-08-01

    Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average

  20. Gender-based discrimination in South Africa: A quantitative analysis of fairness of remuneration

    Directory of Open Access Journals (Sweden)

    Renier Steyn

    2015-05-01

    Full Text Available Equity is important to most individuals and its perceived absence  may impact negatively on individual and organisational performance. The concept of equity presupposes fair treatment, while discrimination implies unfair treatment. The perceptions of discrimination, or being treated unfairly, may result from psycho-social processes, or from data that justifies discrimination and is quantifiable. Objectives: To assess whether differences in post grading and remuneration for males and females are based on gender, rather than on quantifiable variables that could justify these differences. Method: Biographical information was gathered from 1740 employees representing 29 organisations. The data collected included self-reported post grading (dependent variable and 14 independent variables, which may predict the employees’ post gradings. The independent variables related primarily to education, tenure and family responsibility. Results: Males reported higher post gradings and higher salaries than those of females, but the difference was not statistically significant and the practical significance of this difference was slight. Qualification types, job specific training, and membership of professional bodies did not affect post grading along gender lines. The ways in which work experience was measured had no influence on post grading or salary for either males or females. Furthermore, family responsibility, union membership and the type of work the employees performed did not influence the employees’ post grading. The only difference found concerned the unfair treatment of males, particularly those who were well-qualified.   Conclusions: Objective evidence of unfair gender-based discrimination affecting post grading and salary is scarce, and the few differences that do occur have little statistical and practical significance. Perceptions of being discriminated against may therefore more often be seen as the result of psycho-social processes and

  1. 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.

  2. Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue.

    Science.gov (United States)

    Yu, HaiYan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xia

    2015-08-01

    To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. 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.

  4. 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.

  5. Comparative PIV and LDA studies of Newtonian and non-Newtonian flows in an agitated tank.

    Science.gov (United States)

    Story, Anna; Jaworski, Zdzisław; Simmons, Mark J; Nowak, Emilia

    2018-01-01

    The paper presents results of an experimental study of the fluid velocity field in a stirred tank equipped with a Prochem Maxflo T (PMT) type impeller which was rotating at a constant frequency of N  = 4.1 or 8.2 s -1 inducing transitional ( Re  = 499 or 1307) or turbulent ( Re  = 2.43 × 10 4 ) flow of the fluid. The experiments were performed for a Newtonian fluid (water) and a non-Newtonian fluid (0.2 wt% aqueous solution of carboxymethyl cellulose, CMC) exhibiting mild viscoelastic properties. Measurements were carried out using laser light scattering on tracer particles which follow the flow (2-D PIV). For both the water and the CMC solution one primary and two secondary circulation loops were observed within the fluid volume; however, the secondary loops were characterized by much lower intensity. The applied PMT-type impeller produced in the Newtonian fluid an axial primary flow, whilst in the non-Newtonian fluid the flow was more radial. The results obtained in the form of the local mean velocity components were in satisfactory agreement with the literature data from LDA. Distribution of the shear rate in the studied system was also analyzed. For the non-Newtonian fluid an area was computed where the elastic force dominates over the viscous one. The area was nearly matching the region occupied by the primary circulation loop.

  6. Spectral discrimination of macrophyte species during different seasons in a tropical wetland using in-situ hyperspectral remote sensing

    Science.gov (United States)

    Saluja, Ridhi; Garg, J. K.

    2017-10-01

    Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

  7. An Initial Analysis of LANDSAT-4 Thematic Mapper Data for the Discrimination of Agricultural, Forested Wetland, and Urban Land Covers

    Science.gov (United States)

    Quattrochi, D. A.

    1984-01-01

    An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.

  8. 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.

  9. Micro-PIXE analysis of fish otoliths. Methodology and evaluation of first results for stock discrimination

    International Nuclear Information System (INIS)

    Sie, S.H.; Thresher, R.E.

    1992-01-01

    Micro-PIXE has been used to measure the trace element distribution in otoliths from several species of ocean fish, in order to investigate its possible use in stock discrimination. Trace elements detected include Sr, Fe, Mn, Ni, Zn, Cu, Se, Cd, Br, Hg and Pb. Trace elements Na, K, Cl, S and Cl were detected with the electron microprobe. The high sensitivity of PIXE demands a meticulous sample preparation procedure to avoid contamination problems. Practical problems associated with the application of the technique were investigated in detail. Preliminary results indicate that most trace elements except Sr, are present at close to the limits of detection at few ppm, but biologically significant data can be obtained for stock discrimination applications. (author)

  10. Analysis of Child Gender Discrimination Based on Adults' Consumption Patterns: Microdata Evidence from China

    OpenAIRE

    Feridoon Koohi-Kamali; R. Liu; Y. Liu

    2015-01-01

    The applications of the Rothbarth model of inferring child gender discrimination from the variations in parental living standard have consistently failed to uncover evidence for bias from surveys in countries with some of the world's worst welfare outcomes for girls. This paper demonstrates the importance of the remedies required for an effective implementation of that model with an application to a survey from urban China. The paper obtains econometric evidence for the presence of child gend...

  11. Post-Apartheid Trends in Gender Discrimination in South Africa: Analysis through Decomposition Techniques

    OpenAIRE

    Debra Shepherd

    2008-01-01

    Using appropriate econometric methods and 11 representative household surveys, this paper empirically assesses the extent and evolution of gender discrimination in the South African labour market over the post-apartheid period. Attention is also paid to the role that anti-discriminatory legislation has had to play in effecting change in the South African labour market. Much of the paper’s focus is placed on African women who would have benefited most from the new legislative environment. Afri...

  12. Prion strain discrimination based on rapid in vivo amplification and analysis by the cell panel assay.

    Directory of Open Access Journals (Sweden)

    Yervand Eduard Karapetyan

    Full Text Available Prion strain identification has been hitherto achieved using time-consuming incubation time determinations in one or more mouse lines and elaborate neuropathological assessment. In the present work, we make a detailed study of the properties of PrP-overproducing Tga20 mice. We show that in these mice the four prion strains examined are rapidly and faithfully amplified and can subsequently be discriminated by a cell-based procedure, the Cell Panel Assay.

  13. Classification of cultivated mussels from Galicia (Northwest Spain) with European Protected Designation of Origin using trace element fingerprint and chemometric analysis

    International Nuclear Information System (INIS)

    Costas-Rodriguez, M.; Lavilla, I.; Bendicho, C.

    2010-01-01

    Inductively coupled plasma-mass spectrometry (ICP-MS) in combination with different supervised chemometric approaches has been used to classify cultivated mussels in Galicia (Northwest of Spain) under the European Protected Designation of Origin (PDO). 158 mussel samples, collected in the five rias on the basis of the production, along with minor and trace elements, including high field strength elements (HFSEs) and rare earth elements (REEs), were used with this aim. The classification of samples was achieved according to their origin: Galician vs. other regions (from Tarragona, Spain, and Ethang de Thau, France) and between the Galician Rias. The ability of linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and artificial neural network (ANN) to classify the samples was investigated. Correct assignations for Galician and non-Galician samples were obtained when LDA and SIMCA were used. ANNs were more effective when a classification according to the ria of origin was to be applied.

  14. Pinpointing the classifiers of English language writing ability: A discriminant function analysis approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Shams

    2013-02-01

    Full Text Available     The major aim of this paper was to investigate the validity of language and intelligence factors for classifying Iranian English learners` writing performance. Iranian participants of the study took three tests for grammar, breadth, and depth of vocabulary, and two tests for verbal and narrative intelligence. They also produced a corpus of argumentative writings in answer to IELTS specimen. Several runs of discriminant function analyses were used to examine the classifying power of the five variables for discriminating between low and high ability L2 writers. The results revealed that among language factors, depth of vocabulary (collocational knowledge produces the best discriminant function. In general, narrative intelligence was found to be the most reliable predictor for membership in low or high groups. It was also found that, among the five sub-abilities of narrative intelligence, emplotment carries the highest classifying value. Finally, the applications and implications of the results for second language researchers, cognitive scientists, and applied linguists were discussed.Â

  15. Classification of adulterated honeys by multivariate analysis.

    Science.gov (United States)

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

    In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. 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  gender. The reliability of the derived discriminant function was assessed among study subjects; 100% of males and females were recognized correctly. Conclusion: The final outcome of this study validates the existence of sexual dimorphism in the skeleton as early as 6.5 years of age. There is a need for further research to determine other landmarks that can help in sex determination and norms for Indigenous (Kuruba) population and also other Indigenous population of Coorg, Karnataka, India.

  17. Genotypic and Phenotypic Analysis of Dairy Lactococcus lactis Biodiversity in Milk: Volatile Organic Compounds as Discriminating Markers

    Science.gov (United States)

    Dhaisne, Amandine; Guellerin, Maeva; Laroute, Valérie; Laguerre, Sandrine; Le Bourgeois, Pascal; Loubiere, Pascal

    2013-01-01

    The diversity of nine dairy strains of Lactococcus lactis subsp. lactis in fermented milk was investigated by both genotypic and phenotypic analyses. Pulsed-field gel electrophoresis and multilocus sequence typing were used to establish an integrated genotypic classification. This classification was coherent with discrimination of the L. lactis subsp. lactis bv. diacetylactis lineage and reflected clonal complex phylogeny and the uniqueness of the genomes of these strains. To assess phenotypic diversity, 82 variables were selected as important dairy features; they included physiological descriptors and the production of metabolites and volatile organic compounds (VOCs). Principal-component analysis (PCA) demonstrated the phenotypic uniqueness of each of these genetically closely related strains, allowing strain discrimination. A method of variable selection was developed to reduce the time-consuming experimentation. We therefore identified 20 variables, all associated with VOCs, as phenotypic markers allowing discrimination between strain groups. These markers are representative of the three metabolic pathways involved in flavor: lipolysis, proteolysis, and glycolysis. Despite great phenotypic diversity, the strains could be divided into four robust phenotypic clusters based on their metabolic orientations. Inclusion of genotypic diversity in addition to phenotypic characters in the classification led to five clusters rather than four being defined. However, genotypic characters make a smaller contribution than phenotypic variables (no genetic distances selected among the most contributory variables). This work proposes an original method for the phenotypic differentiation of closely related strains in milk and may be the first step toward a predictive classification for the manufacture of starters. PMID:23709512

  18. DFT calculations of electronic and optical properties of SrS with LDA, GGA and mGGA functionals

    Energy Technology Data Exchange (ETDEWEB)

    Sharma, Shatendra, E-mail: shatendra@gmai.com [University Science Instrumentation Centre, Jawaharlal Nehru University, New Delhi-110067 (India); Sharma, Jyotsna [School of Basic & Applied Sciences, K. R. Mangalam University, Sohna Road, Gurgaon-122103 (India); Sharma, Yogita [Department of Applied Sciences, KIIT, Sohna Road, Gurgaon-122103 (India)

    2016-05-06

    The theoretical investigations of electronic and optical properties of SrS are made using the first principle DFT calculations. The calculations are performed for the local-density approximation (LDA), generalized gradient approximation (GGA) and for an alternative form of GGA i.e. metaGGA for both rock salt type (B1, Fm3m) and cesium chloride (B2, Pm3m) structures. The band structure, density of states and optical spectra are calculated under various available functional. The calculations with LDA and GGA functional underestimate the values of band gaps with all functional, however the values with mGGA show reasonably good agreement with experimental and those calculated by using other methods.

  19. Application of the exploratory analysis of data in the geographical discrimination of okra of Rio Grande do Norte and Pernambuco

    Directory of Open Access Journals (Sweden)

    Francisco Santos Panero

    2009-11-01

    Full Text Available The contents of Cu, Zn, Na, Fe, K, Ca, Mn, Mg, PO43-, Cl- and SO42- were determined in samples of okra of the municipal districts of Caruaru and Vitória de Santo Antão, in Pernambuco, as well as in the municipal districts of Ceará-Mirim, Macaíba and Extremoz in the state of Rio Grande do Norte. The objective of this work is the application of two methods of  exploratory analysis of data: Principal Component Analysis - PCA and Hierarquical Cluster Analysis - HCA in the geographical discrimination of okra originating in the states of Rio Grande do Norte and Pernambuco. The results showed that Cl- and Na were the main elements for the differentiation of the samples of Rio Grande do Norte and, the samples of Pernambuco presented the largest amount of Fe, Cu, Mn, Mg, Ca, Zn, K, PO43-, and SO42-. Boths the methods of exploratory analysis of data investigated are efficient for geographical discrimination of okra originating in Rio Grande do Norte and Pernambuco.

  20. 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.

  1. A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

    2016-11-23

    A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

  2. 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

  3. 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...

  4. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

    Science.gov (United States)

    Ying, Xiaoguo; Lin, Han; Hui, Guohua

    2015-01-01

    Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

  5. PIV and LDA measurements of the wake behind a wind turbine model

    Science.gov (United States)

    Naumov, I. V.; Mikkelsen, R. F.; Okulov, V. L.; Sørensen, J. N.

    2014-06-01

    In the present work we review the results of a series of measurements of the flow behind a model scale of a horizontal axis wind turbine rotor carried out at the water flume at Technical University of Denmark (DTU). The rotor is three-bladed and designed using Glauert theory for tip speed ratio λ =5 with a constant design lift coefficient along the span, CLdesign= 0.8. The measurements include dye visualization, Particle Image Velocimetry and Laser Doppler Anemometry. The wake instability has been studied in the range λ =3 - 9 at different cross-sections from the very near wake up to 10 rotor diameters downstream from the rotor. The initial flume flow was subject to a very low turbulence level with a uniform velocity profile, limiting the influence of external disturbances on the development of the inherent vortex instability. Using PIV measurements and visualizations, special attention was paid to detect and categorize different types of wake instabilities and the development of the flow in the near and the far wake. In parallel to PIV, LDA measurements provided data for various rotor regimes, revealing the existence of three main regular frequencies governing the development of different processes and instabilities in the rotor wake. In the far wake a constant frequency corresponding to the Strouhal number was found for the long-scale instabilities. This Strouhal number is in good agreement with the well-known constant that usually characterizes the oscillation in wakes behind bluff bodies. From associated visualizations and reconstructions of the flow field, it was found that the dynamics of the far wake is associated with the precession (rotation) of a helical vortex core. The data indicate that Strouhal number of this precession is independent of the rotor angular speed.

  6. PIV and LDA measurements of the wake behind a wind turbine model

    International Nuclear Information System (INIS)

    Naumov, I V; Okulov, V L; Mikkelsen, R F; Sørensen, J N

    2014-01-01

    In the present work we review the results of a series of measurements of the flow behind a model scale of a horizontal axis wind turbine rotor carried out at the water flume at Technical University of Denmark (DTU). The rotor is three-bladed and designed using Glauert theory for tip speed ratio λ =5 with a constant design lift coefficient along the span, C Ldesign = 0.8. The measurements include dye visualization, Particle Image Velocimetry and Laser Doppler Anemometry. The wake instability has been studied in the range λ =3 – 9 at different cross-sections from the very near wake up to 10 rotor diameters downstream from the rotor. The initial flume flow was subject to a very low turbulence level with a uniform velocity profile, limiting the influence of external disturbances on the development of the inherent vortex instability. Using PIV measurements and visualizations, special attention was paid to detect and categorize different types of wake instabilities and the development of the flow in the near and the far wake. In parallel to PIV, LDA measurements provided data for various rotor regimes, revealing the existence of three main regular frequencies governing the development of different processes and instabilities in the rotor wake. In the far wake a constant frequency corresponding to the Strouhal number was found for the long-scale instabilities. This Strouhal number is in good agreement with the well-known constant that usually characterizes the oscillation in wakes behind bluff bodies. From associated visualizations and reconstructions of the flow field, it was found that the dynamics of the far wake is associated with the precession (rotation) of a helical vortex core. The data indicate that Strouhal number of this precession is independent of the rotor angular speed

  7. Numerical experiment on different validation cases of water coolant flow in supercritical pressure test sections assisted by discriminated dimensional analysis part I: the dimensional analysis

    International Nuclear Information System (INIS)

    Kiss, A.; Aszodi, A.

    2011-01-01

    As recent studies prove in contrast to 'classical' dimensional analysis, whose application is widely described in heat transfer textbooks despite its poor results, the less well known and used discriminated dimensional analysis approach can provide a deeper insight into the physical problems involved and much better results in all cases where it is applied. As a first step of this ongoing research discriminated dimensional analysis has been performed on supercritical pressure water pipe flow heated through the pipe solid wall to identify the independent dimensionless groups (which play an independent role in the above mentioned thermal hydraulic phenomena) in order to serve a theoretical base to comparison between well known supercritical pressure water pipe heat transfer experiments and results of their validated CFD simulations. (author)

  8. 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.

  9. 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)

  10. 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)

  11. Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

    Directory of Open Access Journals (Sweden)

    Yongbin Chen

    Full Text Available Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD, which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

  12. Stock discrimination in Great Lakes Walleye using mitochondrial DNA restriction analysis

    International Nuclear Information System (INIS)

    Billington, N.; Hebert, P.D.N.

    1986-01-01

    Over the past two years it has become evident that because of its strict maternal inheritance and rapid rate of evolutionary differentiation, mitochondrial (mt) DNA diversity offers exceptional promise in the discrimination of fish stocks. The current project aims to determine the extent of mt DNA variation among stocks of walleye (Stizostedion vitreum) from the Great Lakes. At this point, mt DNA has been isolated from 68 walleye representing the Thames River stock and a reef breeding stock from western Lake Erie, as well as from individuals of S. canadense, a species which hybridizes with S. vitreum. Mitochondrial DNA was extracted from livers of these fish, purified by CsCl density gradient centrifugation and digested using 20 endonucleases. Polymorphisms were detected with 8 of the enzymes. There was a great deal of variation among fish from both spawning populations, so much so that individual fish could be identified by this technique. No single enzyme allowed discrimination of the two stocks, but restriction pattern variation following Dde I digestion permitted separation of 50% of Lake Erie fish from Thames River stock. Comparison of mt DNA restriction patterns of walleye and sauger showed that two species are easily separable, setting the stage for a more detailed study of hybridization between the taxa

  13. 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.

  14. Mass discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Broeckman, A. [Rijksuniversiteit Utrecht (Netherlands)

    1978-12-15

    In thermal ionization mass spectrometry the phenomenon of mass discrimination has led to the use of a correction factor for isotope ratio-measurements. The correction factor is defined as the measured ratio divided by the true or accepted value of this ratio. In fact this factor corrects for systematic errors of the whole procedure; however mass discrimination is often associated just with the mass spectrometer.

  15. How discriminating are discriminative instruments?

    Science.gov (United States)

    Hankins, Matthew

    2008-05-27

    The McMaster framework introduced by Kirshner & Guyatt is the dominant paradigm for the development of measures of health status and health-related quality of life (HRQL). The framework defines the functions of such instruments as evaluative, predictive or discriminative. Evaluative instruments are required to be sensitive to change (responsiveness), but there is no corresponding index of the degree to which discriminative instruments are sensitive to cross-sectional differences. This paper argues that indices of validity and reliability are not sufficient to demonstrate that a discriminative instrument performs its function of discriminating between individuals, and that the McMaster framework would be augmented by the addition of a separate index of discrimination. The coefficient proposed by Ferguson (Delta) is easily adapted to HRQL instruments and is a direct, non-parametric index of the degree to which an instrument distinguishes between individuals. While Delta should prove useful in the development and evaluation of discriminative instruments, further research is required to elucidate the relationship between the measurement properties of discrimination, reliability and responsiveness.

  16. How discriminating are discriminative instruments?

    Directory of Open Access Journals (Sweden)

    Hankins Matthew

    2008-05-01

    Full Text Available Abstract The McMaster framework introduced by Kirshner & Guyatt is the dominant paradigm for the development of measures of health status and health-related quality of life (HRQL. The framework defines the functions of such instruments as evaluative, predictive or discriminative. Evaluative instruments are required to be sensitive to change (responsiveness, but there is no corresponding index of the degree to which discriminative instruments are sensitive to cross-sectional differences. This paper argues that indices of validity and reliability are not sufficient to demonstrate that a discriminative instrument performs its function of discriminating between individuals, and that the McMaster framework would be augmented by the addition of a separate index of discrimination. The coefficient proposed by Ferguson (Delta is easily adapted to HRQL instruments and is a direct, non-parametric index of the degree to which an instrument distinguishes between individuals. While Delta should prove useful in the development and evaluation of discriminative instruments, further research is required to elucidate the relationship between the measurement properties of discrimination, reliability and responsiveness.

  17. Quantitative analysis of the clinical data on leukemia, 5. Specificity of clinical features in acute myelocytic leukemia with 8; 21 translocation by multiple logistic discriminant analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ueoka, Hiroshi; Kamada, Nanao; Yamamoto, Hisashi; Ohtaki, Megu; Takimoto, Yasuo; Kuramoto, Atsushi; Munaka, Masaki

    1984-11-01

    In order to determine the necessity of chromosome analysis required for the evaluation of 8;21 translocation, multiple logistic discriminant analysis was made on 124 patients with acute non-lymphocytic leukemia experienced in the authors' institution. Variables which showed positive correlation with the presence of 8;21 translocation were the presence of Auer body and granular abnormality of the cells, numbers of peripheral promyelocytes, myelocytes and metamyelocytes, and bone marrow promyelocytes, myelocytes, and the sum of rods and segments. Those which showed negative correlation with 8;21 translocation were peripheral platelet count, neutrocytealkaline phosphatase (N-AP) score, numbers of eosinocytes, monocytes and erythroblasts, and erythroblasts on myelogram. Auer body, four peripheral hematological features (platelet count, N-AP score, metamyelocytes and monocytes), and three myelogram features (myelocytes, reticular cells and granulocytes/eosionocytes) were used for the multiple logistic discriminant analysis. By the analysis, 2 of the 22 patients (9.1%) with translocation were judged not to have 8;21 translocation and 3 of the 102 patients (2.9%) without translocation were judged to have it. Therefore, this multiple logistic discriminant method has proved to be simple and useful in clinically evaluating acute non-lymphocytic leukemia. (Namekawa, K.).

  18. 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

  19. Acromegaly determination using discriminant analysis of the three-dimensional facial classification in Taiwanese.

    Science.gov (United States)

    Wang, Ming-Hsu; Lin, Jen-Der; Chang, Chen-Nen; Chiou, Wen-Ko

    2017-08-01

    The aim of this study was to assess the size, angles and positional characteristics of facial anthropometry between "acromegalic" patients and control subjects. We also identify possible facial soft tissue measurements for generating discriminant functions toward acromegaly determination in males and females for acromegaly early self-awareness. This is a cross-sectional study. Subjects participating in this study included 70 patients diagnosed with acromegaly (35 females and 35 males) and 140 gender-matched control individuals. Three-dimensional facial images were collected via a camera system. Thirteen landmarks were selected. Eleven measurements from the three categories were selected and applied, including five frontal widths, three lateral depths and three lateral angular measurements. Descriptive analyses were conducted using means and standard deviations for each measurement. Univariate and multivariate discriminant function analyses were applied in order to calculate the accuracy of acromegaly detection. Patients with acromegaly exhibit soft-tissue facial enlargement and hypertrophy. Frontal widths as well as lateral depth and angle of facial changes were evident. The average accuracies of all functions for female patient detection ranged from 80.0-91.40%. The average accuracies of all functions for male patient detection were from 81.0-94.30%. The greatest anomaly observed was evidenced in the lateral angles, with greater enlargement of "nasofrontal" angles for females and greater "mentolabial" angles for males. Additionally, shapes of the lateral angles showed changes. The majority of the facial measurements proved dynamic for acromegaly patients; however, it is problematic to detect the disease with progressive body anthropometric changes. The discriminant functions of detection developed in this study could help patients, their families, medical practitioners and others to identify and track progressive facial change patterns before the possible patients

  20. Sensitivity of cognitive tests in four cognitive domains in discriminating MDD patients from healthy controls: a meta-analysis.

    Science.gov (United States)

    Lim, JaeHyoung; Oh, In Kyung; Han, Changsu; Huh, Yu Jeong; Jung, In-Kwa; Patkar, Ashwin A; Steffens, David C; Jang, Bo-Hyoung

    2013-09-01

    We performed a meta-analysis in order to determine which neuropsychological domains and tasks would be most sensitive for discriminating between patients with major depressive disorder (MDD) and healthy controls. Relevant articles were identified through a literature search of the PubMed and Cochrane Library databases for the period between January 1997 and May 2011. A meta-analysis was conducted using the standardized means of individual cognitive tests in each domain. The heterogeneity was assessed, and subgroup analyses according to age and medication status were performed to explore the sources of heterogeneity. A total of 22 trials involving 955 MDD patients and 7,664 healthy participants were selected for our meta-analysis. MDD patients showed significantly impaired results compared with healthy participants on the Digit Span and Continuous Performance Test in the attention domain; the Trail Making Test A (TMT-A) and the Digit Symbol Test in the processing speed domain; the Stroop Test, the Wisconsin Card Sorting Test, and Verbal Fluency in the executive function domain; and immediate verbal memory in the memory domain. The Finger Tapping Task, TMT-B, delayed verbal memory, and immediate and delayed visual memory failed to separate MDD patients from healthy controls. The results of subgroup analysis showed that performance of Verbal Fluency was significantly impaired in younger depressed patients (memory was significantly reduced in depressed patients using antidepressants. Our findings have inevitable limitations arising from methodological issues inherent in the meta-analysis and we could not explain high heterogeneity between studies. Despite such limitations, current study has the strength of being the first meta-analysis which tried to specify cognitive function of depressed patients compared with healthy participants. And our findings may provide clinicians with further evidences that some cognitive tests in specific cognitive domains have sensitivity

  1. DNA-Catalytically Active Gold Nanoparticle Conjugates-Based Colorimetric Multidimensional Sensor Array for Protein Discrimination.

    Science.gov (United States)

    Wei, Xiangcong; Chen, Zhengbo; Tan, Lulu; Lou, Tianhong; Zhao, Yan

    2017-01-03

    A series of single-strand oligonucleotides functionalized catalytically active gold nanoparticle (AuNPs) as nonspecific receptors have been designed to build a protein sensing array. We take advantage of the correlation between the catalytic activity and the exposed surface area of AuNPs, i.e., DNA-proteins interactions mask the surface area of AuNPs, leading to poor catalytic performance of AuNPs. As the number of DNA-bound proteins increases, the surfaces of AuNPs become more masked; thus, the time of 4- nitrophenol/NaBH 4 reaction for color change (yellow → colorless) of the solution increases. Taking advantage of three nonspecific SH-labeled DNA sequences (A15, C15, and T15) as array sensing elements and the color-change time (CCT) of the solution as signal readout, colorimetric response patterns can be obtained on the array and identified via linear discriminant analysis (LDA). Eleven proteins have been completely distinguished with 100% accuracy with the naked eye at the 30 nM level. Remarkably, two similar proteins (bovine serum albumin and human serum albumin), two different proteins (bovine serum albumin and concanavalin) at the same concentration, and the mixtures of the two proteins with different molar ratios have been discriminated with 100%. The practicability of this sensor array is further validated by high accuracy (100%) identification of 11 proteins in human serum samples.

  2. Proteome comparison for discrimination between honeydew and floral honeys from botanical species Mimosa scabrella Bentham by principal component analysis.

    Science.gov (United States)

    Azevedo, Mônia Stremel; Valentim-Neto, Pedro Alexandre; Seraglio, Siluana Katia Tischer; da Luz, Cynthia Fernandes Pinto; Arisi, Ana Carolina Maisonnave; Costa, Ana Carolina Oliveira

    2017-10-01

    Due to the increasing valuation and appreciation of honeydew honey in many European countries and also to existing contamination among different types of honeys, authentication is an important aspect of quality control with regard to guaranteeing the origin in terms of source (honeydew or floral) and needs to be determined. Furthermore, proteins are minor components of the honey, despite the importance of their physiological effects, and can differ according to the source of the honey. In this context, the aims of this study were to carry out protein extraction from honeydew and floral honeys and to discriminate these honeys from the same botanical species, Mimosa scabrella Bentham, through proteome comparison using two-dimensional gel electrophoresis and principal component analysis. The results showed that the proteome profile and principal component analysis can be a useful tool for discrimination between these types of honey using matched proteins (45 matched spots). Also, the proteome profile showed 160 protein spots in honeydew honey and 84 spots in the floral honey. The protein profile can be a differential characteristic of this type of honey, in view of the importance of proteins as bioactive compounds in honey. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  3. HDclassif : An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

    Directory of Open Access Journals (Sweden)

    Laurent Berge

    2012-01-01

    Full Text Available This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA. In a similar manner, the associated clustering method iscalled high dimensional data clustering (HDDC and uses the expectation-maximization algorithm for inference. In order to correctly t the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

  4. Discriminant analysis of characteristics determining acceptance or rejection of nuclear power

    International Nuclear Information System (INIS)

    Holsapple, C.W.; Whinston, A.B.

    1977-01-01

    This study utilizes the linear discriminant model to analyze demographic and attitudinal data concerning the construction of a nuclear power facility at the Bailly site in northern Indiana. The objective is to ascertain the extent to which various respondent characteristics are useful in distinguishing among respondent attitudes (opposed, in favor, unsure) toward the Bailly project. Examination of reduced space characteristics leads the authors to postulate an interpretation of its two dimensions as respondent uncertainty and respondent resistance. The largest contributor (positive) to uncertainty was found to be a divorced or separated marital status; the greatest contributer (negative) to resistance was found to be home ownership. Both of these respondent characteristics were significant in the univariate sense. A particularly striking trend was the reliance of the opposed group upon electronic media as the source of most local news, whereas the other two groups tended to rely most heavily on newspapers

  5. 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

    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...

  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. 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

  8. Generalized Hyperalgesia in Children and Adults Diagnosed With Hypermobility Syndrome and Ehlers-Danlos Syndrome Hypermobility Type: A Discriminative Analysis.

    Science.gov (United States)

    Scheper, M C; Pacey, V; Rombaut, L; Adams, R D; Tofts, L; Calders, P; Nicholson, L L; Engelbert, R H H

    2017-03-01

    Lowered pressure-pain thresholds have been demonstrated in adults with Ehlers-Danlos syndrome hypermobility type (EDS-HT), but whether these findings are also present in children is unclear. Therefore, the objectives of the study were to determine whether generalized hyperalgesia is present in children with hypermobility syndrome (HMS)/EDS-HT, explore potential differences in pressure-pain thresholds between children and adults with HMS/EDS-HT, and determine the discriminative value of generalized hyperalgesia. Patients were classified in 1 of 3 groups: HMS/EDS-HT, hypermobile (Beighton score ≥4 of 9), and healthy controls. Descriptive data of age, sex, body mass index, Beighton score, skin laxity, and medication usage were collected. Generalized hyperalgesia was quantified by the average pressure-pain thresholds collected from 12 locations. Confounders collected were pain locations/intensity, fatigue, and psychological distress. Comparisons between children with HMS/EDS-HT and normative values, between children and adults with HMS/EDS-HT, and corrected confounders were analyzed with multivariate analysis of covariance. The discriminative value of generalized hyperalgesia employed to differentiate between HMS/EDS-HT, hypermobility, and controls was quantified with logistic regression. Significantly lower pressure-pain thresholds were found in children with HMS/EDS-HT compared to normative values (range -22.0% to -59.0%; P ≤ 0.05). When applying a threshold of 30.8 N/cm 2 for males and 29.0 N/cm 2 for females, the presence of generalized hyperalgesia discriminated between individuals with HMS/EDS-HT, hypermobility, and healthy controls (odds ratio 6.0). Children and adults with HMS/EDS-HT are characterized by hypermobility, chronic pain, and generalized hyperalgesia. The presence of generalized hyperalgesia may indicate involvement of the central nervous system in the development of chronic pain. © 2016, American College of Rheumatology.

  9. 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. © 2015 Institute of Food Technologists®

  10. 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

  11. 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

  12. Does cognitive performance map to categorical diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder? A discriminant functions analysis.

    Science.gov (United States)

    Van Rheenen, Tamsyn E; Bryce, Shayden; Tan, Eric J; Neill, Erica; Gurvich, Caroline; Louise, Stephanie; Rossell, Susan L

    2016-03-01

    Despite known overlaps in the pattern of cognitive impairments in individuals with bipolar disorder (BD), schizophrenia (SZ) and schizoaffective disorder (SZA), few studies have examined the extent to which cognitive performance validates traditional diagnostic boundaries in these groups. Individuals with SZ (n=49), schizoaffective disorder (n=33) and BD (n=35) completed a battery of cognitive tests measuring the domains of processing speed, immediate memory, semantic memory, learning, working memory, executive function and sustained attention. A discriminant functions analysis revealed a significant function comprising semantic memory, immediate memory and processing speed that maximally separated patients with SZ from those with BD. Initial classification scores on the basis of this function showed modest diagnostic accuracy, owing in part to the misclassification of SZA patients as having SZ. When SZA patients were removed from the model, a second cross-validated classifier yielded slightly improved diagnostic accuracy and a single function solution, of which semantic memory loaded most heavily. A cluster of non-executive cognitive processes appears to have some validity in mapping onto traditional nosological boundaries. However, since semantic memory performance was the primary driver of the discrimination between BD and SZ, it is possible that performance differences between the disorders in this cognitive domain in particular, index separate underlying aetiologies. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM

    Directory of Open Access Journals (Sweden)

    Yi Jin

    2014-01-01

    Full Text Available In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR, which is called Multiview Smooth Discriminant Analysis (MSDA based on Extreme Learning Machines (ELM. Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. The MSDA aims at finding a multiview learning based common discriminative feature space and it can then fully utilize the underlying relationship of features from different views. To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM is adopted to train the single hidden layer feedforward neural networks (SLFNs. To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. Simulations show that our new proposed method generally outperforms several recent approaches with a fast training speed.

  14. 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.

  15. Total body calcium by neutron activation analysis in normals and osteoporotic populations: a discriminator of significant bone mass loss

    International Nuclear Information System (INIS)

    Ott, S.M.; Murano, R.; Lewellen, T.K.; Nelp, W.B.; Chesnut, C.M.

    1983-01-01

    Measurements of total body calcium by neutron activation (TBC) in 94 normal individuals and 86 osteoporotic patients are reported. The ability of TBC to discriminate normal from osteoporotic females was evaluated with decision analysis. Bone mineral content (BMC) by single-photon absorptiometry was also measured. TBC was higher in males (range 826 to 1363 gm vs 537 to 1054 in females) and correlated with height in all normals. In females over age 55 there was a negative correlation with age. Thus, for normals an algorithm was derived to allow comparison between measured TBC and that predicted by sex, age, and height (TBCp). In the 28 normal females over age 55, the TBC was 764 +/- 115 gm vs. 616 +/- 90 in the osteoporotics. In 63 of the osteoporotic females an estimated height, from tibial length, was used to predict TBC. In normals the TBC/TBCp ratio was 1.00 +/- 0.12, whereas in osteoporotic females it was 0.80 +/- 0.12. A receiver operating characteristic curve showed better discrimination of osteoporosis with TBC/TBCp than with wrist BMC. By using Bayes' theorem, with a 25% prevalence of osteoporosis (estimate for postmenopausal women), the posttest probability of disease was 90% when the TBC/TBCp ratio was less than 0.84. The authors conclude that a low TBC/TBCp ratio is very helpful in determining osteoporosis

  16. High Resolution Melting Analysis Targeting hsp70 as a Fast and Efficient Method for the Discrimination of Leishmania Species.

    Science.gov (United States)

    Zampieri, Ricardo Andrade; Laranjeira-Silva, Maria Fernanda; Muxel, Sandra Marcia; Stocco de Lima, Ana Carolina; Shaw, Jeffrey Jon; Floeter-Winter, Lucile Maria

    2016-02-01

    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.

  17. 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.

  18. 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

  19. Can anthropometry measure gender discrimination? An analysis using WHO standards to assess the growth of Bangladeshi children.

    Science.gov (United States)

    Moestue, Helen

    2009-08-01

    To examine the potential of anthropometry as a tool to measure gender discrimination, with particular attention to the WHO growth standards. Surveillance data collected from 1990 to 1999 were analysed. Height-for-age Z-scores were calculated using three norms: the WHO standards, the 1978 National Center for Health Statistics (NCHS) reference and the 1990 British growth reference (UK90). Bangladesh. Boys and girls aged 6-59 months (n 504 358). The three sets of growth curves provided conflicting pictures of the relative growth of girls and boys by age and over time. Conclusions on sex differences in growth depended also on the method used to analyse the curves, be it according to the shape or the relative position of the sex-specific curves. The shapes of the WHO-generated curves uniquely implied that Bangladeshi girls faltered faster or caught up slower than boys throughout their pre-school years, a finding consistent with the literature. In contrast, analysis of the relative position of the curves suggested that girls had higher WHO Z-scores than boys below 24 months of age. Further research is needed to help establish whether and how the WHO international standards can measure gender discrimination in practice, which continues to be a serious problem in many parts of the world.

  20. Analysis of trace element compositions in adhesive cloth tapes using high-energy x-ray fluorescence spectrometer with three-dimensional polarization optics for forensic discrimination

    International Nuclear Information System (INIS)

    Goto, Akiko; Hokura, Akiko; Nakai, Izumi

    2008-01-01

    The forensic discrimination of adhesive cloth tapes often used in crimes was developed using a high-energy energy-dispersive X-ray fluorescence spectrometer with 3-dimensional polarization optics. The best measurement condition for discrimination of the tape was as follows: secondary targets, Rh and Al 2 O 3 ; measurement time, 300 s for Rh and 600 s for Al 2 O 3 ; 14 elements (Ca, Ti, Cr, Mn, Fe, Ni, Zn, Sr, Zr, Nb, Mo, Sb, Ba and Pb) were used for discrimination. It is found that the combined information of yarn density and the XRF peak intensity of the 14 elements successfully discriminated 29 out of 31 samples, of which 2 probably had the same origin. This technique is useful for forensic analysis, because it is nondestructive, rapid and easy. Therefore, it can be applied to actual forensic identification. (author)

  1. 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.

  2. Discrimination between ginseng from Korea and China by light stable isotope analysis

    Energy Technology Data Exchange (ETDEWEB)

    Horacek, Micha, E-mail: micha.horacek@ait.ac.at [Department of Environmental Resources and Technology, Austrian Institute of Technology, 2444 Seibersdorf (Austria); Min, Ji-Sook; Heo, Sang-Cheol [National Institute of Scientific Investigation, 331-1 Shinwol-7dong, Yangcheon-ku, Seoul 158-707 (Korea, Republic of); Soja, Gerhard [Department of Environmental Resources and Technology, Austrian Institute of Technology, 2444 Seibersdorf (Austria)

    2010-12-03

    Ginseng is a health food and traditional medicine highly valued in Asia. Ginseng from certain origins is higher valued than from other origins, so that a reliable method for differentiation of geographical origin is important for the economics of ginseng production. To discriminate between ginseng samples from South Korea and PR China, 29 samples have been analyzed for the isotopic composition of the elements H, C and N. The results showed {delta}{sup 2}H values between -94 and -79 per mille , for {delta}{sup 13}C -27.9 to -23.7 per mille and for {delta}{sup 15}N 1.3-5.4 per mille for Chinese ginseng. Korean ginseng gave {delta}{sup 2}H ratios between -91 and -69 per mille , {delta}{sup 13}C ratios between -31.2 and -22.4 per mille and {delta}{sup 15}N ratios between -2.4 and +7 per mille . Despite the overlap between the values for individual isotopes, a combination of the isotope systems gave a reasonable differentiation between the two geographic origins. Especially the statistically significant difference in {delta}{sup 2}H ratios facilitated the differentiation between Korean and Chinese ginseng samples.

  3. Discrimination between ginseng from Korea and China by light stable isotope analysis

    International Nuclear Information System (INIS)

    Horacek, Micha; Min, Ji-Sook; Heo, Sang-Cheol; Soja, Gerhard

    2010-01-01

    Ginseng is a health food and traditional medicine highly valued in Asia. Ginseng from certain origins is higher valued than from other origins, so that a reliable method for differentiation of geographical origin is important for the economics of ginseng production. To discriminate between ginseng samples from South Korea and PR China, 29 samples have been analyzed for the isotopic composition of the elements H, C and N. The results showed δ 2 H values between -94 and -79 per mille , for δ 13 C -27.9 to -23.7 per mille and for δ 15 N 1.3-5.4 per mille for Chinese ginseng. Korean ginseng gave δ 2 H ratios between -91 and -69 per mille , δ 13 C ratios between -31.2 and -22.4 per mille and δ 15 N ratios between -2.4 and +7 per mille . Despite the overlap between the values for individual isotopes, a combination of the isotope systems gave a reasonable differentiation between the two geographic origins. Especially the statistically significant difference in δ 2 H ratios facilitated the differentiation between Korean and Chinese ginseng samples.

  4. 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.

  5. Femtomolar detection of single mismatches by discriminant analysis of DNA hybridization events using gold nanoparticles.

    Science.gov (United States)

    Ma, Xingyi; Sim, Sang Jun

    2013-03-21

    Even though DNA-based nanosensors have been demonstrated for quantitative detection of analytes and diseases, hybridization events have never been numerically investigated for further understanding of DNA mediated interactions. Here, we developed a nanoscale platform with well-designed capture and detection gold nanoprobes to precisely evaluate the hybridization events. The capture gold nanoprobes were mono-laid on glass and the detection probes were fabricated via a novel competitive conjugation method. The two kinds of probes combined in a suitable orientation following the hybridization with the target. We found that hybridization efficiency was markedly dependent on electrostatic interactions between DNA strands, which can be tailored by adjusting the salt concentration of the incubation solution. Due to the much lower stability of the double helix formed by mismatches, the hybridization efficiencies of single mismatched (MMT) and perfectly matched DNA (PMT) were different. Therefore, we obtained an optimized salt concentration that allowed for discrimination of MMT from PMT without stringent control of temperature or pH. The results indicated this to be an ultrasensitive and precise nanosensor for the diagnosis of genetic diseases.

  6. Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI Data for Burned Area Discrimination

    Directory of Open Access Journals (Sweden)

    Haiyan Huang

    2016-10-01

    Full Text Available Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area mapping. This study examines the performance of the Sentinel-2A Multi Spectral Instrument (MSI bands and derived spectral indices to differentiate between unburned and burned areas. For this purpose, five pairs of pre-fire and post-fire top of atmosphere (TOA reflectance and atmospherically corrected (surface reflectance images were studied. The pixel values of locations that were unburned in the first image and burned in the second image, as well as the values of locations that were unburned in both images which served as a control, were compared and the discrimination of individual bands and spectral indices were evaluated using parametric (transformed divergence and non-parametric (decision tree approaches. Based on the results, the most suitable MSI bands to detect burned areas are the 20 m near-infrared, short wave infrared and red-edge bands, while the performance of the spectral indices varied with location. The atmospheric correction only significantly influenced the separability of the visible wavelength bands. The results provide insights that are useful for developing Sentinel-2 burned area mapping algorithms.

  7. Reliable discrimination of 10 ungulate species using high resolution melting analysis of faecal DNA.

    Directory of Open Access Journals (Sweden)

    Ana Ramón-Laca

    Full Text Available Identifying species occupying an area is essential for many ecological and conservation studies. Faecal DNA is a potentially powerful method for identifying cryptic mammalian species. In New Zealand, 10 species of ungulate (Order: Artiodactyla have established wild populations and are managed as pests because of their impacts on native ecosystems. However, identifying the ungulate species present within a management area based on pellet morphology is unreliable. We present a method that enables reliable identification of 10 ungulate species (red deer, sika deer, rusa deer, fallow deer, sambar deer, white-tailed deer, Himalayan tahr, Alpine chamois, feral sheep, and feral goat from swabs of faecal pellets. A high resolution melting (HRM assay, targeting a fragment of the 12S rRNA gene, was developed. Species-specific primers were designed and combined in a multiplex PCR resulting in fragments of different length and therefore different melting behaviour for each species. The method was developed using tissue from each of the 10 species, and was validated in blind trials. Our protocol enabled species to be determined for 94% of faecal pellet swabs collected during routine monitoring by the New Zealand Department of Conservation. Our HRM method enables high-throughput and cost-effective species identification from low DNA template samples, and could readily be adapted to discriminate other mammalian species from faecal DNA.

  8. Energetic, electronic and optical properties of lanthanide doped TiO2: An ab initio LDA+U study

    CSIR Research Space (South Africa)

    Mulwa, WM

    2016-05-01

    Full Text Available potential, J. Chem. Phys. 118 (2003) 8207. doi:10.1063/1.1564060. [23] X. Ren, Beyond LDA and GGA - Tackling exact exchange , hybrid functional , MP2 , and RPA with numeric atom-centered orbitals The Fritz-Haber-Institute ab initio molecular simulations.... Calzolari, A. Ruini, A. Catellani, Anchor Group versus Conjugation: Toward the Gap-State Engineering of Functionalized ZnO (101̅0) Surface for Optoelectronic Applications, J. Am. Chem. Soc. 133 (2011) 5893–5899. [36] R. Gillen, S.J. Clark, J. Robertson...

  9. Plano de marketing para a venda de camiões Volvo em Portugal – Auto Sueco, Lda.

    OpenAIRE

    Martins, José Nuno da Rocha

    2014-01-01

    O principal objetivo deste estudo foi o desenvolvimento de um plano de marketing para a venda de camiões Volvo em Portugal. O estudo pretende dotar a Auto Sueco Lda. de ferramentas que lhe permitam melhorar a performance no negócio da venda de camiões Volvo em Portugal. Nesse sentido, o plano começa por fazer uma caracterização detalhada da envolvente externa, bem como elaborar um diagnóstico da situação interna da empresa. Mediante esta análise, são enumerados os pontos que a empresa deve...

  10. Comparing GW+DMFT and LDA+DMFT for the testbed material SrVO{sub 3}

    Energy Technology Data Exchange (ETDEWEB)

    Taranto, Ciro; Toschi, Alessandro; Held, Karsten [Institute for Solid State Physics, Vienna University of Technology (Austria); Kaltak, Merzuk; Kresse, Georg [University of Vienna, Faculty of Physics and Center for Computational Materials Science (Austria); Parragh, Nicolaus; Sangiovanni, Giorgio [Institut fuer Theoretische Physik und Astrophysik, Universitaet Wuerzburg (Germany)

    2013-07-01

    We have implemented the GW+dynamical mean field theory (DMFT) approach in the Vienna ab initio simulation package. Employing the interaction values obtained from the locally unscreened random phase approximation (RPA), we compare GW+DMFT and LDA+DMFT against each other and against experiment for SrVO{sub 3}. We observed a partial compensation of stronger electronic correlations due to the reduced GW bandwidth and weaker correlations due to a larger screening of the RPA interaction, so that the obtained spectra are quite similar and well agree with experiment. Noteworthily, the GW+DMFT better reproduces the position of the lower Hubbard side band.

  11. 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.

  12. Pulse Shape Analysis and Discrimination for Silicon-Photomultipliers in Helium-4 Gas Scintillation Neutron Detector

    Science.gov (United States)

    Barker, Cathleen; Zhu, Ting; Rolison, Lucas; Kiff, Scott; Jordan, Kelly; Enqvist, Andreas

    2018-01-01

    Using natural helium (helium-4), the Arktis 180-bar pressurized gas scintillator is capable of detecting and distinguishing fast neutrons and gammas. The detector has a unique design of three optically separated segments in which 12 silicon-photomultiplier (SiPM) pairs are positioned equilaterally across the detector to allow for them to be fully immersed in the helium-4 gas volume; consequently, no additional optical interfaces are necessary. The SiPM signals were amplified, shaped, and readout by an analog board; a 250 MHz, 14-bit digitizer was used to examine the output pulses from each SiPMpair channel. The SiPM over-voltage had to be adjusted in order to reduce pulse clipping and negative overshoot, which was observed for events with high scintillation production. Pulse shaped discrimination (PSD) was conducted by evaluating three different parameters: time over threshold (TOT), pulse amplitude, and pulse integral. In order to differentiate high and low energy events, a 30ns gate window was implemented to group pulses from two SiPM channels or more for the calculation of TOT. It was demonstrated that pulses from a single SiPM channel within the 30ns window corresponded to low-energy gamma events while groups of pulses from two-channels or more were most likely neutron events. Due to gamma pulses having lower pulse amplitude, the percentage of measured gamma also depends on the threshold value in TOT calculations. Similarly, the threshold values were varied for the optimal PSD methods of using pulse amplitude and pulse area parameters. Helium-4 detectors equipped with SiPMs are excellent for in-the-field radiation measurement of nuclear spent fuel casks. With optimized PSD methods, the goal of developing a fuel cask content monitoring and inspection system based on these helium-4 detectors will be achieved.

  13. DISCRIMINATIVE ANALYSIS OF TESTS FOR EVALUATING SITUATIONMOTORIC ABILITIES BETWEEN TWO GROUPS OF BASKETBALL PLAYERS SELECTED BY THE TEST OF SOCIOMETRY

    Directory of Open Access Journals (Sweden)

    Abdulla Elezi

    2011-09-01

    Full Text Available Determining differences between the two groups of basketball players selected with the modified sociometric test (Paranosić and Lazarević in some tests for assessing situation-motor skills, was the aim of this work. The test sample was consisted of 20 basketball players who had most positive points and 20 basketball players who had most negative points, in total- 40 players. T-test was applied to determine whether there are differences between the two groups of basketball players who had been elected with the help of the sociometric test. Analyses were made with the program SPSS 8.0. The discriminative analysis has determined that the differences in the arithmetic means between the groups of basketball players who had most positive points and the group of basketball players who had most negative points in some tests for assessing situation-motor abilities do not exist

  14. Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

    Science.gov (United States)

    Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

    2000-01-01

    Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

  15. Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Farics Éva

    2017-10-01

    Full Text Available The main aim of this paper is to determine the depositional environments of an Upper-Eocene coarse-grained clastic succession in the Buda Hills, Hungary. First of all, we measured some commonly used parameters of samples (size, amount, roundness and sphericity in a much more objective overall and faster way than with traditional measurement approaches, using the newly developed Rock Analyst application. For the multivariate data obtained, we applied Combined Cluster and Discriminant Analysis (CCDA in order to determine homogeneous groups of the sampling locations based on the quantitative composition of the conglomerate as well as the shape parameters (roundness and sphericity. The result is the spatial pattern of these groups, which assists with the interpretation of the depositional processes. According to our concept, those sampling sites which belong to the same homogeneous groups were likely formed under similar geological circumstances and by similar geological processes.

  16. Forensic Discrimination of Concrete Pieces by Elemental Analysis of Acid-soluble Component with Inductively Coupled Plasma-Mass Spectrometry.

    Science.gov (United States)

    Kasamatsu, Masaaki; Igawa, Takao; Suzuki, Shinichi; Suzuki, Yasuhiro

    2018-01-01

    Since fragments of concrete can be evidence of crime, a determination of whether or not they come from the same origin is required. The authors focused on nitric acid-soluble components in the fragments of concrete. As a result of qualitative analysis with ICP-MS, it was confirmed that elements such as Cu, Zn, Rb, Sr, Zr, Ba, La, Ce, Nd, and Pb were contained in the fragments. After the nitric acid-soluble components in the fragments of concrete were separated by dissolving them in nitric acid, the concentrations of these elements in the dissolved solution were quantitatively determined by ICP-MS. The concentration ratios of nine elements compared to La were used as indicators. By comparing these indicators, it was possible to discriminate between the fragments of concrete.

  17. Signal discrimination of ULF electromagnetic data with using singular spectrum analysis – an attempt to detect train noise

    Directory of Open Access Journals (Sweden)

    S. Saito

    2011-07-01

    Full Text Available Electromagnetic phenomena associated with crustal activities have been reported in a wide frequency range (DC-HF. In particular, ULF electromagnetic phenomena are the most promising among them because of the deeper skin depth. However, ULF geoelctromagnetic data are a superposition of signals of different origins. They originated from interactions between the geomagnetic field and the solar wind, leak current by a DC-driven train (train noise, precipitation, and so on. In general, the intensity of electromagnetic signals associated with crustal activity is smaller than the above variations. Therefore, in order to detect a smaller signal, signal discrimination such as noise reduction or identification of noises is very important. In this paper, the singular spectrum analysis (SSA has been performed to detect the DC-driven train noise in geoelectric potential difference data. The aim of this paper is to develop an effective algorithm for the DC-driven train noise detection.

  18. Incremental Support Vector Machine Combined with Ultraviolet-Visible Spectroscopy for Rapid Discriminant Analysis of Red Wine

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2018-01-01

    Full Text Available The aim of this work is to develop a new method to overcome the increased training time when a recognition model is updated based on the condition of new features extracted from new samples. As a common complex system, red wine has a rich chemical composition and is used as an object of this research. The novel method based on incremental learning support vector machine (I-SVM combined with ultraviolet–visible (UV-Vis spectroscopy was applied to discriminant analysis of the brands of red wine for the first time. In this method, new features included in the new training samples were introduced into the recognition model through iterative learning in each iteration, and the recognition model was rapidly updated without significantly increasing the training time. Experimental results show that the recognition model established by this method obtains a good balance between training efficiency and recognition accuracy.

  19. A qualitative analysis of hate speech reported to the Romanian National Council for Combating Discrimination (2003‑2015

    Directory of Open Access Journals (Sweden)

    Adriana Iordache

    2015-12-01

    Full Text Available The article analyzes the specificities of Romanian hate speech over a period of twelve years through a qualitative analysis of 384 Decisions of the National Council for Combating Discrimination. The study employs a coding methodology which allows one to separate decisions according to the group that was the victim of hate speech. The article finds that stereotypes employed are similar to those encountered in the international literature. The main target of hate speech is the Roma, who are seen as „dirty“, „uncivilized“ and a threat to Romania’s image abroad. Other stereotypes encountered were that of the „disloyal“ Hungarian and of the sexually promiscuous woman. Moreover, women are seen as unfit for management positions. The article also discusses stereotypes about homosexuals, who are seen as „sick“ and about non-orthodox religions, portrayed as „sectarian“.

  20. Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

    Science.gov (United States)

    Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

    2015-01-01

    The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

  1. Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil.

    Science.gov (United States)

    Alves, Junia O; Botelho, Bruno G; Sena, Marcelo M; Augusti, Rodinei

    2013-10-01

    Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils. Copyright © 2013 John Wiley & Sons, Ltd.

  2. Blind Time-Frequency Analysis for Source Discrimination in Multisensor Array Processing

    National Research Council Canada - National Science Library

    Amin, Moeness

    1999-01-01

    .... We have clearly demonstrated, through analysis and simulations, the offerings of time-frequency distributions in solving key problems in sensor array processing, including direction finding, source...

  3. A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part II: discriminant analysis

    Science.gov (United States)

    Michailidou, C.; Maheras, P.; Arseni-Papadimititriou, A.; Kolyva-Machera, F.; Anagnostopoulou, C.

    2009-06-01

    A discriminant analysis is applied in order to determine the relationships between circulation types in the middle troposphere and prevailing weather types over two major Greek cities, Athens and Thessaloniki. In order to describe the synoptic conditions, an automatic classification scheme for the Greek region is used. For each circulation type identified (14 in total), several meteorological parameters at the 500 hPa level are calculated such as geopotential heights and their anomalies, temperature and relative vorticity. Weather group-types that reflect the conditions at the surface, were previously defined using a two-step cluster analysis. These types result from a combination of five meteorological parameters—maximum temperature, precipitation amount, relative humidity, wind velocity and sunshine duration. The study period is 43 years long (1958-2000) and is restricted to the cold and wet period of the year, from December until March. For Athens, six weather types are developed, whereas for Thessaloniki five are produced. By means of a stepwise discriminant analysis (DA) model, the most important variables from the 500 hPa level are found and are used to generate the necessary functions that can discriminate weather types over the two stations. The aim of the present study is first to discriminate weather types effectively and to identify the most important discriminating variables, and second, to connect these weather types to elements of the prevailing synoptic pattern, through mathematical functions provided by DA. The results of the evaluation of the aforementioned procedure are considered to be very satisfactory.

  4. Neutron/gamma discrimination employing the power spectrum analysis of the signal from the liquid scintillator BC501A

    International Nuclear Information System (INIS)

    Luo, X.L.; Wang, Y.K; Yang, J.; Liu, G.; Lin, C.B.; Hu, Q.Q.; Peng, J.X.

    2013-01-01

    A digital method for the discrimination of neutron and γ-ray events based on analyzing the power spectra of the signals from a BC501A liquid scintillator detector was presented and investigated in this paper. In order to evaluate the feasibility of this novel pulse shape discrimination method, a 5GSample/s 8-bit oscilloscope was used to acquire waveforms for n/γ discrimination. Furthermore, the performance of this novel n/γ discrimination method was compared with that of a widely used method called the reference-pulses method which averaged a large number of neutron and γ-ray pulses to obtain the reference-pulse as the criterion for n/γ discrimination. The results showed that the proposed method performed well over the reference-pulses method, which was verified by the considerable decrease in the error rate of n/γ discrimination and the improvement of the Figure of Merit

  5. Multimodal optical analysis discriminates freshly extracted human sample of gliomas, metastases and meningiomas from their appropriate controls

    Science.gov (United States)

    Zanello, Marc; Poulon, Fanny; Pallud, Johan; Varlet, Pascale; Hamzeh, H.; Abi Lahoud, Georges; Andreiuolo, Felipe; Ibrahim, Ali; Pages, Mélanie; Chretien, Fabrice; di Rocco, Federico; Dezamis, Edouard; Nataf, François; Turak, Baris; Devaux, Bertrand; Abi Haidar, Darine

    2017-02-01

    Delineating tumor margins as accurately as possible is of primordial importance in surgical oncology: extent of resection is associated with survival but respect of healthy surrounding tissue is necessary for preserved quality of life. The real-time analysis of the endogeneous fluorescence signal of brain tissues is a promising tool for defining margins of brain tumors. The present study aims to demonstrate the feasibility of multimodal optical analysis to discriminate fresh samples of gliomas, metastases and meningiomas from their appropriate controls. Tumor samples were studied on an optical fibered endoscope using spectral and fluorescence lifetime analysis and then on a multimodal set-up for acquiring spectral, one and two-photon fluorescence images, second harmonic generation signals and two-photon fluorescence lifetime datasets. The obtained data allowed us to differentiate healthy samples from tumor samples. These results confirmed the possible clinical relevance of this real-time multimodal optical analysis. This technique can be easily applied to neurosurgical procedures for a better delineation of surgical margins.

  6. Revealing discriminating power of the elements in edible sea salts: Line-intensity correlation analysis from laser-induced plasma emission spectra

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yonghoon, E-mail: yhlee@mokpo.ac.kr [Department of Chemistry, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Ham, Kyung-Sik [Department of Food Engineering, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Han, Song-Hee [Division of Maritime Transportation System, Mokpo National Maritime University, Jeonnam 530-729 (Korea, Republic of); Yoo, Jonghyun, E-mail: jyoo@appliedspectra.com [Applied Spectra, Inc., 46665 Fremont Boulevard, Fremont, CA 94538 (United States); Jeong, Sungho [School of Mechatronics, Gwangju Institute of Science and Technology, Gwangju 500-712 (Korea, Republic of)

    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. - Highlights: • Broadband LIBS spectra of various edible sea salts were obtained. • Intensity correlation of emission lines of the elements in edible sea salts was analyzed. • The elements were categorized into three groups with independent discriminating power. • The effective combination of a few lines can provide dependable classification models.

  7. Evidence of Employment Discrimination Based on Sexual Orientation and Gender Identity: An Analysis of Complaints Filed with State Enforcement Agencies

    OpenAIRE

    Mallory, Christy; Sears, Brad

    2015-01-01

    LGBT people use sexual orientation and gender identity employment non-discrimination laws as frequently as people of color and women use race and sex non-discrimination laws. This study examines complaints filed based on sexual orientation or gender identity, race, and sex and adjusts them by the number of people in the workforce most likely to experience each type of discrimination – LGBT people, people of color and women. Data on discrimination complaints were requested from the 22 states t...

  8. Analyses of absorption distribution of a rubidium cell side-pumped by a Laser-Diode-Array (LDA)

    Science.gov (United States)

    Yu, Hang; Han, Juhong; Rong, Kepeng; Wang, Shunyan; Cai, He; An, Guofei; Zhang, Wei; Yu, Qiang; Wu, Peng; Wang, Hongyuan; Wang, You

    2018-01-01

    A diode-pumped alkali laser (DPAL) has been regarded as one of the most potential candidates to achieve high power performances of next generation. In this paper, we investigate the physical properties of a rubidium cell side-pumped by a Laser-Diode-Array (LDA) in this study. As the saturated concentration of a gain medium inside a vapor cell is extremely sensitive to the temperature, the populations of every energy-level of the atomic alkali are strongly relying on the vapor temperature. Thus, the absorption characteristics of a DPAL are mainly dominated by the temperature distribution. In this paper, the temperature, absorption, and lasing distributions in the cross-section of a rubidium cell side-pumped by a LDA are obtained by means of a complicated mathematic procedure. Based on the original end-pumped mode we constructed before, a novel one-direction side-pumped theoretical mode has been established to explore the distribution properties in the transverse section of a rubidium vapor cell by combining the procedures of heat transfer and laser kinetics together. It has been thought the results might be helpful for design of a side-pumped configuration in a high-powered DPAL.

  9. Texture-based analysis of 100 MR examinations of head and neck tumors. Is it possible to discriminate between benign and malignant masses in a multicenter trial?

    International Nuclear Information System (INIS)

    Fruehwald-Pallamar, J.; Czerny, C.; Hesselink, J.R.; Mafee, M.F.; Holzer-Fruehwald, L.; Mayerhoefer, M.E.

    2016-01-01

    To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2D and 3D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2D, and on contrast-enhanced T1-TSE with fat saturation for 3D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data.

  10. Texture-based analysis of 100 MR examinations of head and neck tumors. Is it possible to discriminate between benign and malignant masses in a multicenter trial?

    Energy Technology Data Exchange (ETDEWEB)

    Fruehwald-Pallamar, J.; Czerny, C. [Medical University of Vienna (Austria). Subdiv. of Neuroradiology and Musculoskeletal Radiology; Hesselink, J.R.; Mafee, M.F. [UCSD Medical Center, San Diego, CA (United States). Dept. of Radiology; Holzer-Fruehwald, L.; Mayerhoefer, M.E. [Medical University of Vienna (Austria). Dept. of Biomedical Imaging and Image-Guided Therapy

    2016-02-15

    To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2D and 3D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2D, and on contrast-enhanced T1-TSE with fat saturation for 3D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data.

  11. Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis.

    Science.gov (United States)

    Van Weehaeghe, Donatienne; Ceccarini, Jenny; Delva, Aline; Robberecht, Wim; Van Damme, Philip; Van Laere, Koen

    2016-08-01

    An objective biomarker for early identification and accurate differential diagnosis of amyotrophic lateral sclerosis (ALS) is lacking. (18)F-FDG PET brain imaging with advanced statistical analysis may provide a tool to facilitate this. The objective of this work was to validate volume-of-interest (VOI) and voxel-based (using a support vector machine [SVM] approach) (18)F-FDG PET analysis methods to differentiate ALS from controls in an independent prospective large cohort, using a priori-derived classifiers. Furthermore, the prognostic value of (18)F-FDG PET was evaluated. A prospective cohort of patients with a suspected diagnosis of a motor neuron disorder (n = 119; mean age ± SD, 61 ± 12 y; 81 men and 38 women) was recruited. One hundred five patients were diagnosed with ALS (mean age ± SD, 61.0 ± 12 y; 74 men and 31 women) (group 2), 10 patients with primary lateral sclerosis (mean age ± SD, 55.5 ± 12 y; 3 men and 7 women), and 4 patients with progressive muscular atrophy (mean age ± SD, 59.2 ± 5 y; 4 men). The mean disease duration of all patients was 15.0 ± 13.4 mo at diagnosis, with PET conducted 15.2 ± 13.3 mo after the first symptoms. Data were compared with a previously gathered dataset of 20 screened healthy subjects (mean age ± SD, 62.4 ± 6.4 y; 12 men and 8 women) and 70 ALS patients (mean age ± SD, 62.2 ± 12.5 y; 44 men and 26 women) (group 1). Data were spatially normalized and analyzed on a VOI basis (statistical software (using the Hammers atlas) and voxel basis using statistical parametric mapping. Discriminant analysis and SVM were used to classify new cases based on the classifiers derived from group 1. Compared with controls, ALS patients showed a nearly identical pattern of hypo- and hypermetabolism in groups 1 and 2. VOI-based discriminant analysis resulted in an 88.8% accuracy in predicting the new ALS cases. For the SVM approach, this accuracy was 100%. Brain metabolism between ALS and primary lateral sclerosis patients was

  12. 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-...

  13. Analysis of ancient fine white porcelains by SRXRF and preliminary study of the discriminating criteria

    International Nuclear Information System (INIS)

    Feng Xiangqian; Feng Songlin; Xu Qing; Zhang Ying; Lei Yong; Fan Dongyu; Cheng Lin; Huang Yuying; He Wei

    2002-01-01

    30 Samples were chosen from the fine white porcelain shards excavated in Xing, Xing and Jingxing kilns of Hebei province. Their bodies and glaze were analyzed for 19 elements by SRXRF, followed by the cluster analysis and principal factor analysis. The statistical results show that porcelains of each kiln are classified as one class according to their elemental composition. It indicates that provenance of the fine white porcelains may be identified based on their chemical components

  14. Characteristic Chromatogram: A Method of Discriminate and Quantitative Analysis for Quality Evaluation of Uncaria Stem with Hooks.

    Science.gov (United States)

    Hou, Jinjun; Feng, Ruihong; Zhang, Yibei; Pan, Huiqin; Yao, Shuai; Han, Sumei; Feng, Zijin; Cai, Luying; Wu, Wanying; Guo, De-An

    2018-04-01

    It remains a challenge to establish new monographs for herbal drugs derived from multiple botanical sources. Specifically, the difficulty involves discriminating and quantifying these herbs with components whose levels vary markedly among different samples. Using Uncaria stem with hooks as an example, a characteristic chromatogram was proposed to discriminate its five botanical origins and to quantify its characteristic components in the chromatogram. The characteristic chromatogram with respect to the components of Uncaria stem with hooks with the five botanical origins was established using 0.02% diethylamine and acetonitrile as the mobile phase. The total analysis time was 50 min and the detection wavelength was 245 nm. Using the same chromatogram parameters, the single standard to determine multicomponents method was validated to simultaneously quantify nine indole alkaloids, including vincosamide, 3 α -dihydrocadambine, isocorynoxeine, corynoxeine, isorhynchophylline, rhynchophylline, hirsuteine, hirsutine, and geissoschizine methyl ether. The results showed that only the Uncaria stem with hooks from Uncaria rhynchophylla , the most widely used in the herbal market, showed the presence of these nine alkaloids. The conversion factors were 1.27, 2.32, 0.98, 1.04, 1.00, 1.02, 1.26, 1.33, and 1.25, respectively. The limits of quantitation were lower than 700 ng/mL. The total contents of 31 batches of Uncaria stem with hooks were in the range of 0.1 - 0.6%, except for Uncaria hirsuta Havil and Uncaria sinensis (Oliv.) Havil. The results also showed that the total content of indole alkaloids tended to decrease with an increase in the hook diameter. This showed that the characteristic chromatogram is practical for controlling the quality of traditional Chinese medicines with multiple botanical origins. Georg Thieme Verlag KG Stuttgart · New York.

  15. The geographical origin and chemical composition in phellinus mushrooms measured by instrumental neutron activation analysis

    International Nuclear Information System (INIS)

    Lim, J.M.; Sun, G.M.; Moon, J.H.; Chung, Y.S.; Lee, J.H.; Kim, K.H.

    2012-01-01

    In order to expand the utilization of phellinus mushrooms as a dietary supplement, we attempted to evaluate the chemical composition by measuring its inorganic elemental content with the aid of instrumental neutron activation analysis (INAA). Twenty seven phellinus mushrooms samples were collected from Korea, Cambodia, and Vietnam. A total of 28 elements were analyzed in the phellinus mushroom samples using the INAA. The concentrations of Ca, K, and Mg are much higher than those of other elements in phellinus mushroom samples. The sum of determined elemental concentration in Cambodia samples was about 2-6 times higher than those in Korea and Vietnam samples, respectively. Based on our measurement data, we attempted to discriminate the geographical origin using principal components analysis (PCA) and linear discriminant analysis (LDA). The geographical origins of all samples were clearly classified with correct classification rate of 100%. (author)

  16. Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria, E-mail: zezell@usp.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2017-11-01

    Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

  17. Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

    International Nuclear Information System (INIS)

    Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria

    2017-01-01

    Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

  18. Discrimination of Vegetation Height Categories With Passive Satellite Sensor Imagery Using Texture Analysis

    NARCIS (Netherlands)

    Petrou, Z.; Manakos, I.; Stathaki, T.; Mücher, C.A.; Adamo, M.

    2015-01-01

    Vegetation height is a crucial factor in environmental studies, landscape analysis, and mapping applications. Its estimation may prove cost and resource demanding, e.g., employing light detection and ranging (LiDAR) data. This study presents a cost-effective framework for height estimation, built

  19. Neutron-gamma discrimination by pulse analysis with superheated drop detector

    International Nuclear Information System (INIS)

    Das, Mala; Seth, S.; Saha, S.; Bhattacharya, S.; Bhattacharjee, P.

    2010-01-01

    Superheated drop detector (SDD) consisting of drops of superheated liquid of halocarbon is irradiated to neutrons and gamma-rays from 252 Cf fission neutron source and 137 Cs gamma source, respectively, separately. Analysis of pulse height of signals at the neutron and gamma-ray sensitive temperature provides significant information on the identification of neutron and gamma-ray induced events.

  20. Recognition of beer brand based on multivariate analysis of volatile fingerprint.

    Science.gov (United States)

    Cajka, Tomas; Riddellova, Katerina; Tomaniova, Monika; Hajslova, Jana

    2010-06-18

    Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  1. MRI characteristics in focal hepatic disease before and after administration of MnDPDP: discriminant analysis as a diagnostic tool

    International Nuclear Information System (INIS)

    Helmberger, Thomas K.; Reiser, Maximilian F.; Jung, Gregor; Sievers, Klaus; Doehring, Wilfried; Meurer, Karoline

    2002-01-01

    The aim of this study was to determine if different types of focal hepatic lesions can be differentiated by specific quantitative and qualitative imaging characteristics pre- and post-Mangafodipir trisodium (MnDPDP) administration using a computerized multivariable, discriminant analysis (DA). In a multicenter trial, 151 patients with focal liver disease were studied at 1.5 and 1.0 T using gradient-recalled echo T1 and fast spin-echo T2-weighted images pre and post MnDPDP (0.005 mmol/kg b.w.) i.v. administration. Analysis could be performed in 141 of 151 of the patients. The variables used in both single variable analysis and DA included contrast-to-noise ratios pre and post MnDPDP, presence of rim enhancement, margin, and heterogeneity of a lesion pre and post MnDPDP. The classification of diagnoses using DA was compared with a standard of reference (HCC in 23%, metastases in 25%, cyst in 13%, FNH in 10%, hemangioma in 11%, and other or no lesion in 18% of the patients; histology in 49%, long-term follow-up in 51% of the cases). In the differentiation of the various hepatic lesions, CNR together with the presence of heterogeneity or rim enhancement as variables for DA gave the highest sensitivity, specificity, and accuracy which ranged between 65 and 93, 44 and 83, and 65 and 86%, respectively. The DA models based on post-MnDPDP variables showed better classification results than the models based on pre-MnDPDP variables. An improvement of accuracy was observed when differentiating HCC from FNH lesion groups (48.9-67.4%; p≤0.05), and when differentiating HCC from metastasis lesion groups (68.3-84.1%; p≤0.01). In all regards there was no difference for T2-weighted images pre and post MnDPDP. By combining quantitative and qualitative variables, DA proved to be a useful tool in lesion discrimination. Due to considerable heterogeneity within some of the lesion type groups, the definite diagnostic impact of MnDPDP cannot be completely established yet, and further

  2. Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

    OpenAIRE

    Sikirzhytski, Vitali; Virkler, Kelly; Lednev, Igor K.

    2010-01-01

    Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wave...

  3. Serum Immunoglobulin G4 in Discriminating Autoimmune Pancreatitis From Pancreatic Cancer: A Diagnostic Meta-analysis.

    Science.gov (United States)

    Dai, Cong; Cao, Qin; Jiang, Min; Sun, Ming-Jun

    2018-03-01

    Differentiation between autoimmune pancreatitis (AIP) and pancreatic cancer (PC) is a clinical challenge. Emerging published data on the accuracy of serum immunoglobulin G4 (IgG4) for the differential diagnosis between AIP and PC are inconsistent. The objective of our study was to perform a meta-analysis evaluating the clinical utility of serum IgG4 in the differential diagnosis between AIP and PC. We performed a systematic literature search of multiple electronic databases. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies checklist. Random-effects model was used to summarize the diagnostic odds ratio and other measures of accuracy. Eleven studies comprising 523 AIP patients and 771 PC patients were included in the meta-analysis. The summary estimates for serum IgG4 in distinguishing AIP from PC were as follows: diagnostic odds ratio, 57.30 (95% confidence interval [CI], 23.17-141.67); sensitivity, 0.72 (95% CI, 0.68-0.76); specificity, 0.93 (95% CI, 0.91-0.94). The area under the curve of serum IgG4 in distinguishing AIP from PC was 0.9200. Our meta-analysis found that serum IgG4 has high specificity and relatively low sensitivity in the differential diagnosis between AIP and PC. Therefore, serum IgG4 is useful in distinguishing AIP from PC.

  4. Characteristic fingerprinting based on macamides for discrimination of maca (Lepidium meyenii) by LC/MS/MS and multivariate statistical analysis.

    Science.gov (United States)

    Pan, Yu; Zhang, Ji; Li, Hong; Wang, Yuan-Zhong; Li, Wan-Yi

    2016-10-01

    Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca (Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty-seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC-UV/MS/MS). Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS-DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. When combined with a PLS-DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  5. Barcoding melting curve analysis for rapid, sensitive, and discriminating authentication of saffron (Crocus sativus L.) from its adulterants.

    Science.gov (United States)

    Jiang, Chao; Cao, Liang; Yuan, Yuan; Chen, Min; Jin, Yan; Huang, Luqi

    2014-01-01

    Saffron (Crocus sativus L.) is one of the most important and expensive medicinal spice products in the world. Because of its high market value and premium price, saffron is often adulterated through the incorporation of other materials, such as Carthamus tinctorius L. and Calendula officinalis L. flowers, Hemerocallis L. petals, Daucus carota L. fleshy root, Curcuma longa L. rhizomes, Zea may L., and Nelumbo nucifera Gaertn. stigmas. To develop a straightforward, nonsequencing method for rapid, sensitive, and discriminating detection of these adulterants in traded saffron, we report here the application of a barcoding melting curve analysis method (Bar-MCA) that uses the universal chloroplast plant DNA barcoding region trnH-psbA to identify adulterants. When amplified at DNA concentrations and annealing temperatures optimized for the curve analysis, peaks were formed at specific locations for saffron (81.92°C) and the adulterants: D. carota (81.60°C), C. tinctorius (80.10°C), C. officinalis (79.92°C), Dendranthema morifolium (Ramat.) Tzvel. (79.62°C), N. nucifera (80.58°C), Hemerocallis fulva (L.) L. (84.78°C), and Z. mays (84.33°C). The constructed melting curves for saffron and its adulterants have significantly different peak locations or shapes. In conclusion, Bar-MCA could be a faster and more cost-effective method to authenticate saffron and detect its adulterants.

  6. Barcoding Melting Curve Analysis for Rapid, Sensitive, and Discriminating Authentication of Saffron (Crocus sativus L. from Its Adulterants

    Directory of Open Access Journals (Sweden)

    Chao Jiang

    2014-01-01

    Full Text Available Saffron (Crocus sativus L. is one of the most important and expensive medicinal spice products in the world. Because of its high market value and premium price, saffron is often adulterated through the incorporation of other materials, such as Carthamus tinctorius L. and Calendula officinalis L. flowers, Hemerocallis L. petals, Daucus carota L. fleshy root, Curcuma longa L. rhizomes, Zea may L., and Nelumbo nucifera Gaertn. stigmas. To develop a straightforward, nonsequencing method for rapid, sensitive, and discriminating detection of these adulterants in traded saffron, we report here the application of a barcoding melting curve analysis method (Bar-MCA that uses the universal chloroplast plant DNA barcoding region trnH-psbA to identify adulterants. When amplified at DNA concentrations and annealing temperatures optimized for the curve analysis, peaks were formed at specific locations for saffron (81.92°C and the adulterants: D. carota (81.60°C, C. tinctorius (80.10°C, C. officinalis (79.92°C, Dendranthema morifolium (Ramat. Tzvel. (79.62°C, N. nucifera (80.58°C, Hemerocallis fulva (L. L. (84.78°C, and Z. mays (84.33°C. The constructed melting curves for saffron and its adulterants have significantly different peak locations or shapes. In conclusion, Bar-MCA could be a faster and more cost-effective method to authenticate saffron and detect its adulterants.

  7. Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

    Full Text Available High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

  8. Provenance Establishment of Stingless Bee Honey Using Multi-element Analysis in Combination with Chemometrics Techniques.

    Science.gov (United States)

    Shadan, Aidil Fahmi; Mahat, Naji A; Wan Ibrahim, Wan Aini; Ariffin, Zaiton; Ismail, Dzulkiflee

    2018-01-01

    As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location-specific, multi-element distribution patterns may prove useful for provenancing such product. Using the inductively coupled-plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi-elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross-validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi-element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported. © 2017 American Academy of Forensic Sciences.

  9. The Effect of Unequal Samples, Heterogeneity of Covariance Matrices, and Number of Variables on Discriminant Analysis Classification Tables and Related Statistics.

    Science.gov (United States)

    Spearing, Debra; Woehlke, Paula

    To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…

  10. The Relation of Racial Identity, Ethnic Identity, and Racial Socialization to Discrimination-Distress: A Meta-Analysis of Black Americans

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2013-01-01

    This meta-analysis synthesized the results of 27 studies examining the relations of racial identity, ethnic identity, and racial socialization to discrimination-distress for Black Americans. The purpose was to uncover which constructs connected to racial identity, ethnic identity, and racial socialization most strongly correlate with racial…

  11. Application of artificial intelligence to electrofacies identification: neural networks versus discriminant analysis; Aplicacao de inteligencia artificial na identificacao de eletrofacies redes neuroniais versus analise discriminante

    Energy Technology Data Exchange (ETDEWEB)

    Silva Rodrigues, F da [PETROBRAS, Rio de Janeiro, RJ (Brazil); Queiroz Neto, I.A. de [PETROBRAS, Rio de Janeiro, RJ (Brazil). Centro de Pesquisas

    1992-07-01

    Electro-facies are identified by neural network trained with well log and core data. Differences between neural network and expert system are discussed. According the author, the combination of neural network computing and traditional computing methods, like discriminant analysis, can help in the solution of many problems in electro-facies identification. 5 figs., 1 tab., 11 refs.

  12. The Use of Raman Tweezers and Chemometric Analysis to Discriminate the Urological Cell Lines, PC-3, LNCaP, BPH and MGH-U1

    Science.gov (United States)

    Harvey, T. J.; Hughes, C.; Ward, A. D.; Gazi, E.; Faria, E. Correia; Clarke, N. W.; Brown, M.; Snook, R.; Gardner, P.

    2008-11-01

    Here we report on investigations into using Raman optical tweezers to analyse both live and chemically fixed prostate and bladder cells. Spectra were subjected to chemometric analysis to discriminate and classify the cell types based on their spectra. Subsequent results revealed the potential of Raman tweezers as a potential clinical diagnostic tool.

  13. Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma. A pilot study

    Energy Technology Data Exchange (ETDEWEB)

    Lisson, Catharina S.; Lisson, Christoph G.; Flosdorf, Kerstin; Meier, Reinhard; Beer, Meinrad; Schmidt, Stefan A. [University Hospital of Ulm, Department of Diagnostic and Interventional Radiology, Ulm (Germany); Mayer-Steinacker, Regine [University Hospital of Ulm, Department of Internal Medicine III, Ulm (Germany); Schultheiss, Markus; Baer, Alexandra von [University Hospital of Ulm, Department of Trauma Surgery, Ulm (Germany); Barth, Thomas F.E. [University of Ulm, Institute of Pathology, Ulm (Germany); Beer, Ambros J. [University Hospital of Ulm, Department of Nuclear Medicine, Ulm (Germany); Baumhauer, Matthias [Mint Medical, Dossenheim (Germany)

    2018-02-15

    To explore the diagnostic value of MRI-based 3D texture analysis to identify texture features that can be used for discrimination of low-grade chondrosarcoma from enchondroma. Eleven patients with low-grade chondrosarcoma and 11 patients with enchondroma were retrospectively evaluated. Texture analysis was performed using mint Lesion: Kurtosis, entropy, skewness, mean of positive pixels (MPP) and uniformity of positive pixel distribution (UPP) were obtained in four MRI sequences and correlated with histopathology. The Mann-Whitney U-test and receiver operating characteristic (ROC) analysis were performed to identify most discriminative texture features. Sensitivity, specificity, accuracy and optimal cut-off values were calculated. Significant differences were found in four of 20 texture parameters with regard to the different MRI sequences (p<0.01). The area under the ROC curve values to discriminate chondrosarcoma from enchondroma were 0.876 and 0.826 for kurtosis and skewness in contrast-enhanced T1 (ceT1w), respectively; in non-contrast T1, values were 0.851 and 0.822 for entropy and UPP, respectively. The highest discriminatory power had kurtosis in ceT1w with a cut-off ≥3.15 to identify low-grade chondrosarcoma (82 % sensitivity, 91 % specificity, accuracy 86 %). MRI-based 3D texture analysis might be able to discriminate low-grade chondrosarcoma from enchondroma by a variety of texture parameters. (orig.)

  14. Chemotaxonomic Diversity of Three Ficus Species: Their Discrimination Using Chemometric Analysis and Their Role in Combating Oxidative Stress.

    Science.gov (United States)

    Al-Musayeib, Nawal; Ebada, Sherif S; Gad, Haidy A; Youssef, Fadia S; Ashour, Mohamed Lotfy

    2017-10-01

    Genus Ficus (Moraceae) constitutes more than 850 species and about 2000 varieties and it acts as a golden mine that could afford effective and safe remedies combating many health disorders. Discrimination of Ficus cordata , Ficus ingens , and Ficus palmata using chemometric analysis and assessment of their role in combating oxidative stress. Phytochemical profiling of the methanol extracts of the three Ficus species and their successive fractions was performed using high-performance liquid chromatography/electrospray ionization mass spectrometry. Their discrimination was carried out using the obtained spectral data applying chemometric unsupervised pattern-recognition techniques, namely, principal component analysis and hierarchical cluster analysis. In vitro hepatoprotective and antioxidant evaluation of the samples was performed using human hepatocellular carcinoma cells challenged by carbon tetrachloride (CCl 4 ). Altogether, 22 compounds belonging to polyphenolics, flavonoids, and furanocoumarins were identified in the three Ficus species. Aviprin is the most abundant compound in F. cordata while chlorogenic acid and psoralen were present in high percentages in F. ingens and F. palmata , respectively. Chemometric analyses showed that F. palmata and F. cordata are more closely related chemically to each other rather than F. ingens . The ethyl acetate fractions of all the examined species showed a marked hepatoprotective efficacy accounting for 54.78%, 55.46%, and 56.42% reduction in serum level of alanine transaminase and 56.82%, 54.16%, and 57.06% suppression in serum level of aspartate transaminase, respectively, at 100 μg/mL comparable to CCl 4 -treated cells. Ficus species exhibited a no table antioxidant and hepatoprotective activity owing to their richness in polyphenolics and furanocoumarins. Ficus cordata , Ficus ingens , and Ficus palmata were analyzed using high-performance liquid chromatography/electrospray ionization mass spectrometry that revealed

  15. Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds

    DEFF Research Database (Denmark)

    Shetty, Nisha; Olesen, Merete Halkjær; Gislum, René

    2012-01-01

    Because of the difficulties in obtaining homogenous germination of spinach seeds for baby leaf production, the possibility of using partial least squares discriminant analysis (PLS-DA) on features extracted from multispectral images of spinach seeds was investigated. The objective has been...... to discriminate between different seed sizes, as well as to predict germination ability and germ length. Images of 300 seeds including small, medium, and large seeds were taken, and the seeds were examined for germination ability and germ length. PLS-DA loadings plots were used to reduce the multidimensional...

  16. Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs

    Energy Technology Data Exchange (ETDEWEB)

    Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.; Schubert, R.; Haenni, M.; Hengg, C.; Majumdar, S.; Link, T. M. [Department of Radiology and Biomedical Imaging, University of California, 400 Parnassus Avenue, San Francisco, California 94143 (United States); University of Health Sciences, Medical Informatics and Technology, 6060 Hall (Austria); AO Development Institute, 7270 Davos Platz (Switzerland); Medical University Innsbruck, 6020 Innsbruck (Austria); Department of Radiology and Biomedical Imaging, University of California, 400 Parnassus Avenue, San Francisco, California 94143 (United States)

    2009-11-15

    Purpose: Texture analysis of femur radiographs may serve as a potential low cost technique to predict osteoporotic fracture risk and has received considerable attention in the past years. A further application of this technique may be the measurement of the quality of specific bone compartments to provide useful information for treatment of bone fractures. Two challenges of texture analysis are the selection of the best suitable texture measure and reproducible placement of regions of interest (ROIs). The goal of this in vitro study was to automatically place ROIs in radiographs of proximal femur specimens and to calculate correlations between various different texture analysis methods and the femurs' anchorage strength. Methods: Radiographs were obtained from 14 femoral specimens and bone mineral density (BMD) was measured in the femoral neck. Biomechanical testing was performed to assess the anchorage strength in terms of failure load, breakaway torque, and number of cycles. Images were segmented using a framework that is based on the usage of level sets and statistical in-shape models. Five ROIs were automatically placed in the head, upper and lower neck, trochanteric, and shaft compartment in an atlas subject. All other subjects were registered rigidly, affinely, and nonlinearly, and the resulting transformation was used to map the five ROIs onto the individual femora. Results: In each ROI, texture features were extracted using gray level co-occurence matrices (GLCM), third-order GLCM, morphological gradients (MGs), Minkowski dimensions (MDs), Minkowski functionals (MFs), Gaussian Markov random fields, and scaling index method (SIM). Coefficients of determination for each texture feature with parameters of anchorage strength were computed. In a stepwise multiregression analysis, the most predictive parameters were identified in different models. Texture features were highly correlated with anchorage strength estimated by the failure load of up to R{sup 2

  17. Discriminant analysis of Raman spectra for body fluid identification for forensic purposes.

    Science.gov (United States)

    Sikirzhytski, Vitali; Virkler, Kelly; Lednev, Igor K

    2010-01-01

    Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence.

  18. Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

    Directory of Open Access Journals (Sweden)

    Vitali Sikirzhytski

    2010-03-01

    Full Text Available Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence.

  19. Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology

    Science.gov (United States)

    Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.

    1998-02-01

    We applied multiresolution wavelet analysis to the sequence of times between human heartbeats ( R-R intervals) and have found a scale window, between 16 and 32 heartbeat intervals, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as belonging either to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of the presence of heart failure from the R-R intervals alone. Comparison is made with previous approaches, which have provided only statistically significant measures.

  20. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology

    OpenAIRE

    Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.

    1997-01-01

    We applied multiresolution wavelet analysis to the sequence of times between human heartbeats (R-R intervals) and have found a scale window, between 16 and 32 heartbeats, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as either belonging to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of...

  1. Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs

    International Nuclear Information System (INIS)

    Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.; Schubert, R.; Haenni, M.; Hengg, C.; Majumdar, S.; Link, T. M.

    2009-01-01

    Purpose: Texture analysis of femur radiographs may serve as a potential low cost technique to predict osteoporotic fracture risk and has received considerable attention in the past years. A further application of this technique may be the measurement of the quality of specific bone compartments to provide useful information for treatment of bone fractures. Two challenges of texture analysis are the selection of the best suitable texture measure and reproducible placement of regions of interest (ROIs). The goal of this in vitro study was to automatically place ROIs in radiographs of proximal femur specimens and to calculate correlations between various different texture analysis methods and the femurs' anchorage strength. Methods: Radiographs were obtained from 14 femoral specimens and bone mineral density (BMD) was measured in the femoral neck. Biomechanical testing was performed to assess the anchorage strength in terms of failure load, breakaway torque, and number of cycles. Images were segmented using a framework that is based on the usage of level sets and statistical in-shape models. Five ROIs were automatically placed in the head, upper and lower neck, trochanteric, and shaft compartment in an atlas subject. All other subjects were registered rigidly, affinely, and nonlinearly, and the resulting transformation was used to map the five ROIs onto the individual femora. Results: In each ROI, texture features were extracted using gray level co-occurence matrices (GLCM), third-order GLCM, morphological gradients (MGs), Minkowski dimensions (MDs), Minkowski functionals (MFs), Gaussian Markov random fields, and scaling index method (SIM). Coefficients of determination for each texture feature with parameters of anchorage strength were computed. In a stepwise multiregression analysis, the most predictive parameters were identified in different models. Texture features were highly correlated with anchorage strength estimated by the failure load of up to R 2 =0.61 (MF

  2. Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis

    Science.gov (United States)

    Mabood, Fazal; Boqué, Ricard; Folcarelli, Rita; Busto, Olga; Al-Harrasi, Ahmed; Hussain, Javid

    2015-05-01

    We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720 nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20 nm, 40 nm, 60 nm and 80 nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20 nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

  3. Antigen-antibody biorecognition events as discriminated by noise analysis of force spectroscopy curves.

    Science.gov (United States)

    Bizzarri, Anna Rita; Cannistraro, Salvatore

    2014-08-22

    Atomic force spectroscopy is able to extract kinetic and thermodynamic parameters of biomolecular complexes provided that the registered unbinding force curves could be reliably attributed to the rupture of the specific complex interactions. To this aim, a commonly used strategy is based on the analysis of the stretching features of polymeric linkers which are suitably introduced in the biomolecule-substrate immobilization procedure. Alternatively, we present a method to select force curves corresponding to specific biorecognition events, which relies on a careful analysis of the force fluctuations of the biomolecule-functionalized cantilever tip during its approach to the partner molecules immobilized on a substrate. In the low frequency region, a characteristic 1/f (α) noise with α equal to one (flickering noise) is found to replace white noise in the cantilever fluctuation power spectrum when, and only when, a specific biorecognition process between the partners occurs. The method, which has been validated on a well-characterized antigen-antibody complex, represents a fast, yet reliable alternative to the use of linkers which may involve additional surface chemistry and reproducibility concerns.

  4. Classification of Malaysia aromatic rice using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-01-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

  5. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  6. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Science.gov (United States)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  7. A rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by direct analysis in real-time mass spectrometry

    Directory of Open Access Journals (Sweden)

    Jang Young

    2011-06-01

    Full Text Available Abstract Background Efficient high throughput screening systems of useful mutants are prerequisite for study of plant functional genomics and lots of application fields. Advance in such screening tools, thanks to the development of analytic instruments. Direct analysis in real-time (DART-mass spectrometry (MS by ionization of complex materials at atmospheric pressure is a rapid, simple, high-resolution analytical technique. Here we describe a rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by DART-MS. Results To determine whether this DART-MS combined by multivariate analysis can perform genetic discrimination based on global metabolic profiling, intact Arabidopsis thaliana mutant seeds were subjected to DART-MS without any sample preparation. Partial least squares-discriminant analysis (PLS-DA of DART-MS spectral data from intact seeds classified 14 different lines of seeds into two distinct groups: Columbia (Col-0 and Landsberg erecta (Ler ecotype backgrounds. A hierarchical dendrogram based on partial least squares-discriminant analysis (PLS-DA subdivided the Col-0 ecotype into two groups: mutant lines harboring defects in the phenylpropanoid biosynthetic pathway and mutants without these defects. These results indicated that metabolic profiling with DART-MS could discriminate intact Arabidopsis seeds at least ecotype level and metabolic pathway level within same ecotype. Conclusion The described DART-MS combined by multivariate analysis allows for rapid screening and metabolic characterization of lots of Arabidopsis mutant seeds without complex metabolic preparation steps. Moreover, potential novel metabolic markers can be detected and used to clarify the genetic relationship between Arabidopsis cultivars. Furthermore this technique can be applied to predict the novel gene function of metabolic mutants regardless of morphological phenotypes.

  8. Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

    Directory of Open Access Journals (Sweden)

    Ranjana Jaiswara

    Full Text Available Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species

  9. Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

    Science.gov (United States)

    Ganga, G M D; Esposto, K F; Braatz, D

    2012-01-01

    The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

  10. Rapid classification of turmeric based on DNA fingerprint by near-infrared spectroscopy combined with moving window partial least squares-discrimination analysis

    International Nuclear Information System (INIS)

    Kasemsumran, Sumaporn; Suttiwijitpukdee, Nattaporn; Keeratinijakal, Vichein

    2017-01-01

    In this research, near-infrared (NIR) spectroscopy in combination with moving window partial least squares-discrimination analysis (MWPLS-DA) was utilized to discriminate the variety of turmeric based on DNA markers, which correlated to the quantity of curcuminoid. Curcuminoid was used as a marker compound in variety identification due to the most pharmacological properties of turmeric possessed from it. MWPLS-DA optimized informative NIR spectral regions for the fitting and prediction to {-1/1}-coded turmeric varieties, indicating variables in the development of latent variables in discrimination analysis. Consequently, MWPLS-DA benefited in the selection of combined informative NIR spectral regions of 1100 – 1260, 1300 – 1500 and 1880 – 2500 nm for classification modeling of turmeric variety with 148 calibration samples, and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. An effective and rapid strategy of using NIR in combination with MWPLS-DA provided the best variety identification results of 100% in both specificity and total accuracy for 48 test samples. (author)

  11. Phytoliths analysis for the discrimination of Foxtail millet (Setaria italica) and Common millet (Panicum miliaceum).

    Science.gov (United States)

    Lu, Houyuan; Zhang, Jianping; Wu, Naiqin; Liu, Kam-Biu; Xu, Deke; Li, Quan

    2009-01-01

    Foxtail millet (Setaria italica) and Common millet (Panicum miliaceum) are the oldest domesticated dry farming crops in Eurasia. Identifying these two millets in the archaeobotanical remains are still problematic, especially because the millet grains preserve only when charred. Phytoliths analysis provides a viable method for identifying this important crop. However, to date, the identification of millet phytoliths has been questionable, because very little study has been done on their morphometry and taxonomy. Particularly, no clear diagnostic feature has been used to distinguish between Foxtail millet and Common millet. Here we examined the anatomy and silicon structure patterns in the glumes, lemmas, and paleas from the inflorescence bracts in 27 modern plants of Foxtail millet, Common millet, and closely related grasses, using light microscopy with phase-contrast and microscopic interferometer. Our research shows that five key diagnostic characteristics in phytolith morphology can be used to distinguish Foxtail millet from Common millet based on the presence of cross-shaped type, regularly arranged papillae, Omega-undulated type, endings structures of epidermal long cell, and surface ridgy line sculpture in the former species. We have established identification criteria that, when used together, give the only reliable way of distinguishing between Foxtail millet and Common millet species based on their phytoliths characteristics, thus making a methodological contribution to phytolith research. Our findings also have important implications in the fields of plant taxonomy, agricultural archaeology, and the culture history of ancient civilizations.

  12. Phytoliths analysis for the discrimination of Foxtail millet (Setaria italica and Common millet (Panicum miliaceum.

    Directory of Open Access Journals (Sweden)

    Houyuan Lu

    Full Text Available Foxtail millet (Setaria italica and Common millet (Panicum miliaceum are the oldest domesticated dry farming crops in Eurasia. Identifying these two millets in the archaeobotanical remains are still problematic, especially because the millet grains preserve only when charred. Phytoliths analysis provides a viable method for identifying this important crop. However, to date, the identification of millet phytoliths has been questionable, because very little study has been done on their morphometry and taxonomy. Particularly, no clear diagnostic feature has been used to distinguish between Foxtail millet and Common millet. Here we examined the anatomy and silicon structure patterns in the glumes, lemmas, and paleas from the inflorescence bracts in 27 modern plants of Foxtail millet, Common millet, and closely related grasses, using light microscopy with phase-contrast and microscopic interferometer. Our research shows that five key diagnostic characteristics in phytolith morphology can be used to distinguish Foxtail millet from Common millet based on the presence of cross-shaped type, regularly arranged papillae, Omega-undulated type, endings structures of epidermal long cell, and surface ridgy line sculpture in the former species. We have established identification criteria that, when used together, give the only reliable way of distinguishing between Foxtail millet and Common millet species based on their phytoliths characteristics, thus making a methodological contribution to phytolith research. Our findings also have important implications in the fields of plant taxonomy, agricultural archaeology, and the culture history of ancient civilizations.

  13. Application of L.D.A. to measure instantaneous flow velocity field in the exhaust of a combustion engine

    International Nuclear Information System (INIS)

    Boutrif, M.S.; Thelliez, M.

    1993-01-01

    We present experimental results of instantaneous velocity measurement, which were obtained by application of the laser Doppler anemometry (L.D.A.) at the exhaust pipe of a reciprocating engine under real working conditions. First of all, we show that the instantaneous velocity is monodimensional along a straight exhaust pipe, and that the boundary layer develops within a 2 mm thickness. We also show that the cylinder discharges in two phases: the blow down period and the fi