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.)

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.

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...

Generic top-down discrimination for sorting and partitioning in linear time

DEFF Research Database (Denmark)

Henglein, Fritz

2012-01-01

orders for rst-order recursive types, the discriminators execute in worst-case linear time. The generic discriminators can be coded compactly using list comprehensions, with order expressions specied using Generalized Algebraic Data Types (GADTs). We give some examples of the uses of discriminators....... The basic multiset discriminator for references, originally due to Paige et al., is shown to be both ecient and fully abstract: it nds all duplicates of all references occurring in a list in linear time without leaking information about their representation. In particular, it behaves deterministically...

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

Directory of Open Access Journals (Sweden)

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

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)

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.

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.

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.

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.

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

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

Science.gov (United States)

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

2015-11-03

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

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

Science.gov (United States)

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.

Thurstonian models for sensory discrimination tests as generalized linear models

DEFF Research Database (Denmark)

Brockhoff, Per B.; Christensen, Rune Haubo Bojesen

2010-01-01

as a so-called generalized linear model. The underlying sensory difference 6 becomes directly a parameter of the statistical model and the estimate d' and it's standard error becomes the "usual" output of the statistical analysis. The d' for the monadic A-NOT A method is shown to appear as a standard......Sensory discrimination tests such as the triangle, duo-trio, 2-AFC and 3-AFC tests produce binary data and the Thurstonian decision rule links the underlying sensory difference 6 to the observed number of correct responses. In this paper it is shown how each of these four situations can be viewed...

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.

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.

Dead time effects from linear amplifiers and discriminators in single detector systems

International Nuclear Information System (INIS)

Funck, E.

1986-01-01

The dead-time losses originating from a linear amplifier combined with a discriminator for pulse-height selection are investigated. Measurements are carried out to determine the type of dead time represented by the amplifier-discriminator combination. The corrections involved by feeding the discriminator output pulses into an electronic module producing a blocking time are discussed and practical hints are given to reduce them. (orig.)

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

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

Directory of Open Access Journals (Sweden)

Cheng-Yuan Shih

2010-01-01

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

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.

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.

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.

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.

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

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

Implementing nonprojective measurements via linear optics: An approach based on optimal quantum-state discrimination

International Nuclear Information System (INIS)

Loock, Peter van; Nemoto, Kae; Munro, William J.; Raynal, Philippe; Luetkenhaus, Norbert

2006-01-01

We discuss the problem of implementing generalized measurements [positive operator-valued measures (POVMs)] with linear optics, either based upon a static linear array or including conditional dynamics. In our approach, a given POVM shall be identified as a solution to an optimization problem for a chosen cost function. We formulate a general principle: the implementation is only possible if a linear-optics circuit exists for which the quantum mechanical optimum (minimum) is still attainable after dephasing the corresponding quantum states. The general principle enables us, for instance, to derive a set of necessary conditions for the linear-optics implementation of the POVM that realizes the quantum mechanically optimal unambiguous discrimination of two pure nonorthogonal states. This extends our previous results on projection measurements and the exact discrimination of orthogonal states

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).

Research of Face Recognition with Fisher Linear Discriminant

Science.gov (United States)

Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

2018-01-01

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

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.

A Fisher’s Criterion-Based Linear Discriminant Analysis for Predicting the Critical Values of Coal and Gas Outbursts Using the Initial Gas Flow in a Borehole

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Xiaowei Li

2017-01-01

Full Text Available The risk of coal and gas outbursts can be predicted using a method that is linear and continuous and based on the initial gas flow in the borehole (IGFB; this method is significantly superior to the traditional point prediction method. Acquiring accurate critical values is the key to ensuring accurate predictions. Based on ideal rock cross-cut coal uncovering model, the IGFB measurement device was developed. The present study measured the data of the initial gas flow over 3 min in a 1 m long borehole with a diameter of 42 mm in the laboratory. A total of 48 sets of data were obtained. These data were fuzzy and chaotic. Fisher’s discrimination method was able to transform these spatial data, which were multidimensional due to the factors influencing the IGFB, into a one-dimensional function and determine its critical value. Then, by processing the data into a normal distribution, the critical values of the outbursts were analyzed using linear discriminant analysis with Fisher’s criterion. The weak and strong outbursts had critical values of 36.63 L and 80.85 L, respectively, and the accuracy of the back-discriminant analysis for the weak and strong outbursts was 94.74% and 92.86%, respectively. Eight outburst tests were simulated in the laboratory, the reverse verification accuracy was 100%, and the accuracy of the critical value was verified.

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

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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.

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.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

Science.gov (United States)

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

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)

Use of linear discriminant analysis to characterise three dairy cattle breeds on the basis of several milk characteristics

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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.

Discriminant analysis in Polish manufacturing sector performance assessment

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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.

[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.

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)

The time-walk of analog constant fraction discriminators using very fast scintillator detectors with linear and non-linear energy response

Energy Technology Data Exchange (ETDEWEB)

Regis, J.-M., E-mail: regis@ikp.uni-koeln.de [Institut fuer Kernphysik der Universitaet zu Koeln, Zuelpicher Str. 77, 50937 Koeln (Germany); Rudigier, M.; Jolie, J.; Blazhev, A.; Fransen, C.; Pascovici, G.; Warr, N. [Institut fuer Kernphysik der Universitaet zu Koeln, Zuelpicher Str. 77, 50937 Koeln (Germany)

2012-08-21

The electronic {gamma}-{gamma} fast timing technique allows for direct nuclear lifetime determination down to the few picoseconds region by measuring the time difference between two coincident {gamma}-ray transitions. Using high resolution ultra-fast LaBr{sub 3}(Ce) scintillator detectors in combination with the recently developed mirror symmetric centroid difference method, nuclear lifetimes are measured with a time resolving power of around 5 ps. The essence of the method is to calibrate the energy dependent position (centroid) of the prompt response function of the setup which is obtained for simultaneously occurring events. This time-walk of the prompt response function induced by the analog constant fraction discriminator has been determined by systematic measurements using different photomultiplier tubes and timing adjustments of the constant fraction discriminator. We propose a universal calibration function which describes the time-walk or the combined {gamma}-{gamma} time-walk characteristics, respectively, for either a linear or a non-linear amplitude versus energy dependency of the scintillator detector output pulses.

Improvement of CPU time of Linear Discriminant Function based on MNM criterion by IP

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Shuichi Shinmura

2014-05-01

Full Text Available Revised IP-OLDF (optimal linear discriminant function by integer programming is a linear discriminant function to minimize the number of misclassifications (NM of training samples by integer programming (IP. However, IP requires large computation (CPU time. In this paper, it is proposed how to reduce CPU time by using linear programming (LP. In the first phase, Revised LP-OLDF is applied to all cases, and all cases are categorized into two groups: those that are classified correctly or those that are not classified by support vectors (SVs. In the second phase, Revised IP-OLDF is applied to the misclassified cases by SVs. This method is called Revised IPLP-OLDF.In this research, it is evaluated whether NM of Revised IPLP-OLDF is good estimate of the minimum number of misclassifications (MNM by Revised IP-OLDF. Four kinds of the real data—Iris data, Swiss bank note data, student data, and CPD data—are used as training samples. Four kinds of 20,000 re-sampling cases generated from these data are used as the evaluation samples. There are a total of 149 models of all combinations of independent variables by these data. NMs and CPU times of the 149 models are compared with Revised IPLP-OLDF and Revised IP-OLDF. The following results are obtained: 1 Revised IPLP-OLDF significantly improves CPU time. 2 In the case of training samples, all 149 NMs of Revised IPLP-OLDF are equal to the MNM of Revised IP-OLDF. 3 In the case of evaluation samples, most NMs of Revised IPLP-OLDF are equal to NM of Revised IP-OLDF. 4 Generalization abilities of both discriminant functions are concluded to be high, because the difference between the error rates of training and evaluation samples are almost within 2%.   Therefore, Revised IPLP-OLDF is recommended for the analysis of big data instead of Revised IP-OLDF. Next, Revised IPLP-OLDF is compared with LDF and logistic regression by 100-fold cross validation using 100 re-sampling samples. Means of error rates of

Remote unambiguous discrimination of linearly independent symmetric d-level quantum states

International Nuclear Information System (INIS)

Chen Libing; Liu Yuhua; Tan Peng; Lu Hong

2009-01-01

A set of linearly independent nonorthogonal symmetric d-level quantum states can be discriminated remotely and unambiguously with the aid of two-level Einstein-Podolsky-Rosen (EPR) states. We present a scheme for such a kind of remote unambiguous quantum state discrimination (UD). The probability of discrimination is in agreement with the optimal probability for local unambiguous discrimination among d symmetric states (Chefles and Barnettt 1998 Phys. Lett. A 250 223). This scheme consists of a remote generalized measurement described by a positive operator valued measurement (POVM). This remote POVM can be realized by performing a nonlocal 2d x 2d unitary operation on two spatially separated systems, one is the qudit which is encoded by one of the d symmetric nonorthogonal states to be distinguished and the other is an ancillary qubit, and a conventional local von Neumann orthogonal measurement on the ancilla. By decomposing the evolution process from the initial state to the final state, we construct a quantum network for realizing the remote POVM with a set of two-level nonlocal controlled-rotation gates, and thus provide a feasible physical means to realize the remote UD. A two-level nonlocal controlled-rotation gate can be implemented by using a two-level EPR pair in addition to local operations and classical communications (LOCCs)

Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

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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.

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.

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.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Directory of Open Access Journals (Sweden)

Santana Isabel

2011-08-01

Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

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.

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...

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.

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.

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.

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…

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...

Quantum-state discrimination

International Nuclear Information System (INIS)

Roa, Luis; Retamal, Juan Carlos; Saavedra, Carlos

2002-01-01

A proposal for a physical implementation of a quantum-state discrimination protocol using an ion in a linear trap is studied, where two nonorthogonal quantum states are codified using two electronic states of the ion. In addition, a protocol is given for discriminating superpositions of nonorthogonal entangled states between ions inside widely separated optical cavities. The discrimination protocol is extended to the case of N linearly independent nonorthogonal quantum states lying in a space of 2N-1 dimensions

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

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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.

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.

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...

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.

The pigeon's discrimination of visual entropy: a logarithmic function.

Science.gov (United States)

Young, Michael E; Wasserman, Edward A

2002-11-01

We taught 8 pigeons to discriminate 16-icon arrays that differed in their visual variability or "entropy" to see whether the relationship between entropy and discriminative behavior is linear (in which equivalent differences in entropy should produce equivalent changes in behavior) or logarithmic (in which higher entropy values should be less discriminable from one another than lower entropy values). Pigeons received a go/no-go task in which the lower entropy arrays were reinforced for one group and the higher entropy arrays were reinforced for a second group. The superior discrimination of the second group was predicted by a theoretical analysis in which excitatory and inhibitory stimulus generalization gradients fall along a logarithmic, but not a linear scale. Reanalysis of previously published data also yielded results consistent with a logarithmic relationship between entropy and discriminative behavior.

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

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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.

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

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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.

Credit scoring analysis using kernel discriminant

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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.

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

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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.

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.

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

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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.

Application of Discriminant Analysis on Romanian Insurance Market

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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.

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

determination of sex in south african blacks by discriminant function analysis of mandibular linear dimensions : A preliminary investigation using the zulu local population.

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Franklin, Daniel; O'Higgins, Paul; Oxnard, Charles E; Dadour, Ian

2006-12-01

The determination of sex is a critical component in forensic anthropological investigation. The literature attests to numerous metrical standards, each utilizing diffetent skeletal elements, for sex determination in South A frican Blacks. Metrical standards are popular because they provide a high degree of expected accuracy and are less error-prone than subjective nonmetric visual techniques. We note, however, that there appears to be no established metric mandible discriminant function standards for sex determination in this population.We report here on a preliminary investigation designed to evaluate whether the mandible is a practical element for sex determination in South African Blacks. The sample analyzed comprises 40 nonpathological Zulu individuals drawn from the R.A. Dart Collection. Ten linear measurements, obtained from mathematically trans-formed three-dimensional landmark data, are analyzed using basic univariate statistics and discriminant function analyses. Seven of the 10 measurements examined are found to be sexually dimorphic; the dimensions of the ramus are most dimorphic. The sex classification accuracy of the discriminant functions ranged from 72.5 to 87.5% for the univariate method, 92.5% for the stepwise method, and 57.5 to 95% for the direct method. We conclude that the mandible is an extremely useful element for sex determination in this population.

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.

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

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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%).

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

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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%.

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)

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

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Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

The 95% confidence intervals of error rates and discriminant coefficients

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Shuichi Shinmura

2015-02-01

Full Text Available Fisher proposed a linear discriminant function (Fisher’s LDF. From 1971, we analysed electrocardiogram (ECG data in order to develop the diagnostic logic between normal and abnormal symptoms by Fisher’s LDF and a quadratic discriminant function (QDF. Our four years research was inferior to the decision tree logic developed by the medical doctor. After this experience, we discriminated many data and found four problems of the discriminant analysis. A revised Optimal LDF by Integer Programming (Revised IP-OLDF based on the minimum number of misclassification (minimum NM criterion resolves three problems entirely [13, 18]. In this research, we discuss fourth problem of the discriminant analysis. There are no standard errors (SEs of the error rate and discriminant coefficient. We propose a k-fold crossvalidation method. This method offers a model selection technique and a 95% confidence intervals (C.I. of error rates and discriminant coefficients.

Minimum error discrimination for an ensemble of linearly independent pure states

International Nuclear Information System (INIS)

Singal, Tanmay; Ghosh, Sibasish

2016-01-01

Inspired by the work done by Belavkin (1975 Stochastics 1 315) and independently by Mochon, (2006 Phys. Rev. A 73 032328), we formulate the problem of minimum error discrimination (MED) of any ensemble of n linearly independent pure states by stripping the problem of its rotational covariance and retaining only the rotationally invariant aspect of the problem. This is done by embedding the optimal conditions in a matrix equality as well as matrix inequality. Employing the implicit function theorem in these conditions we get a set of first-order coupled ordinary nonlinear differential equations which can be used to drag the solution from an initial point (where solution is known) to another point (whose solution is sought). This way of obtaining the solution can be done through a simple Taylor series expansion and analytic continuation when required. Thus, we complete the work done by Belavkin and Mochon by ultimately leading their theory to a solution for the MED problem of linearly independent pure state ensembles. We also compare the computational complexity of our technique with the barrier-type interior point method of SDP and show that our technique is computationally as efficient as (actually, a bit more than) the SDP algorithm, with the added advantage of being much simpler to implement. (paper)

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

General tensor discriminant analysis and gabor features for gait recognition.

Science.gov (United States)

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

Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

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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.

A face recognition algorithm based on multiple individual discriminative models

DEFF Research Database (Denmark)

Fagertun, Jens; Gomez, David Delgado; Ersbøll, Bjarne Kjær

2005-01-01

Abstract—In this paper, a novel algorithm for facial recognition is proposed. The technique combines the color texture and geometrical configuration provided by face images. Landmarks and pixel intensities are used by Principal Component Analysis and Fisher Linear Discriminant Analysis to associate...

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)

PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

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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.

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

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Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple

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.

Discrimination and Acculturative Stress among First-Generation Dominicans

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Dawson, Beverly Araujo; Panchanadeswaran, Subadra

2010-01-01

The present study examined the relationship between discriminatory experiences and acculturative stress levels among a sample of 283 Dominican immigrants. Findings from a linear regression analysis revealed that experiences of daily racial discrimination and major racist events were significant predictors of acculturative stress after controlling…

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.))

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.

Linear Algebraic Method for Non-Linear Map Analysis

International Nuclear Information System (INIS)

Yu, L.; Nash, B.

2009-01-01

We present a newly developed method to analyze some non-linear dynamics problems such as the Henon map using a matrix analysis method from linear algebra. Choosing the Henon map as an example, we analyze the spectral structure, the tune-amplitude dependence, the variation of tune and amplitude during the particle motion, etc., using the method of Jordan decomposition which is widely used in conventional linear algebra.

Discriminating four tectonic settings: Five new geochemical ...

Indian Academy of Sciences (India)

of basic and ultrabasic rocks from four tectonic settings of island arc, continental rift, ocean-island, and mid-ocean ridge. These diagrams were ...... 9. Macdonald et al (1995). 8. 1. 35.9. 3.7. Kenya, Turkana Rift (Bird Nest,. Central). 3 ...... reproduction in diverse applications. 4.2 Comparison of linear discriminant analysis.

Spherically symmetric analysis on open FLRW solution in non-linear massive gravity

Energy Technology Data Exchange (ETDEWEB)

Chiang, Chien-I; Izumi, Keisuke; Chen, Pisin, E-mail: chienichiang@berkeley.edu, E-mail: izumi@phys.ntu.edu.tw, E-mail: chen@slac.stanford.edu [Leung Center for Cosmology and Particle Astrophysics, National Taiwan University, Taipei 10617, Taiwan (China)

2012-12-01

We study non-linear massive gravity in the spherically symmetric context. Our main motivation is to investigate the effect of helicity-0 mode which remains elusive after analysis of cosmological perturbation around an open Friedmann-Lemaitre-Robertson-Walker (FLRW) universe. The non-linear form of the effective energy-momentum tensor stemming from the mass term is derived for the spherically symmetric case. Only in the special case where the area of the two sphere is not deviated away from the FLRW universe, the effective energy momentum tensor becomes completely the same as that of cosmological constant. This opens a window for discriminating the non-linear massive gravity from general relativity (GR). Indeed, by further solving these spherically symmetric gravitational equations of motion in vacuum to the linear order, we obtain a solution which has an arbitrary time-dependent parameter. In GR, this parameter is a constant and corresponds to the mass of a star. Our result means that Birkhoff's theorem no longer holds in the non-linear massive gravity and suggests that energy can probably be emitted superluminously (with infinite speed) on the self-accelerating background by the helicity-0 mode, which could be a potential plague of this theory.

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.

Rainfall induced landslide susceptibility mapping using weight-of-evidence, linear and quadratic discriminant and logistic model tree method

Science.gov (United States)

Hong, H.; Zhu, A. X.

2017-12-01

Climate change is a common phenomenon and it is very serious all over the world. The intensification of rainfall extremes with climate change is of key importance to society and then it may induce a large impact through landslides. This paper presents GIS-based new ensemble data mining techniques that weight-of-evidence, logistic model tree, linear and quadratic discriminant for landslide spatial modelling. This research was applied in Anfu County, which is a landslide-prone area in Jiangxi Province, China. According to a literature review and research the study area, we select the landslide influencing factor and their maps were digitized in a GIS environment. These landslide influencing factors are the altitude, plan curvature, profile curvature, slope degree, slope aspect, topographic wetness index (TWI), Stream Power Index (SPI), Topographic Wetness Index (SPI), distance to faults, distance to rivers, distance to roads, soil, lithology, normalized difference vegetation index and land use. According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the government of Jiangxi Meteorological Bureau of China, 367 landslides were identified in the study area. The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme. In this research, Pearson's correlation was used for the evaluation of the relationship between the landslides and influencing factors. In the next step, three data mining techniques combined with the weight-of-evidence, logistic model tree, linear and quadratic discriminant, were used for the landslide spatial modelling and its zonation. Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve. The results showed that the area under the curve (AUC) of all of the models was > 0.80. At the same time, the highest AUC value was for the linear and quadratic

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

Science.gov (United States)

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

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.

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.

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.

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.

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).

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)

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

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.

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...

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.

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

[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

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.

On the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model

International Nuclear Information System (INIS)

Unkel, Steffen; Belka, Claus; Lauber, Kirsten

2016-01-01

The most frequently used method to quantitatively describe the response to ionizing irradiation in terms of clonogenic survival is the linear-quadratic (LQ) model. In the LQ model, the logarithm of the surviving fraction is regressed linearly on the radiation dose by means of a second-degree polynomial. The ratio of the estimated parameters for the linear and quadratic term, respectively, represents the dose at which both terms have the same weight in the abrogation of clonogenic survival. This ratio is known as the α/β ratio. However, there are plausible scenarios in which the α/β ratio fails to sufficiently reflect differences between dose-response curves, for example when curves with similar α/β ratio but different overall steepness are being compared. In such situations, the interpretation of the LQ model is severely limited. Colony formation assays were performed in order to measure the clonogenic survival of nine human pancreatic cancer cell lines and immortalized human pancreatic ductal epithelial cells upon irradiation at 0-10 Gy. The resulting dataset was subjected to LQ regression and non-linear log-logistic regression. Dimensionality reduction of the data was performed by cluster analysis and principal component analysis. Both the LQ model and the non-linear log-logistic regression model resulted in accurate approximations of the observed dose-response relationships in the dataset of clonogenic survival. However, in contrast to the LQ model the non-linear regression model allowed the discrimination of curves with different overall steepness but similar α/β ratio and revealed an improved goodness-of-fit. Additionally, the estimated parameters in the non-linear model exhibit a more direct interpretation than the α/β ratio. Dimensionality reduction of clonogenic survival data by means of cluster analysis was shown to be a useful tool for classifying radioresistant and sensitive cell lines. More quantitatively, principal component analysis allowed

A comparison between linear and non-linear analysis of flexible pavements

Energy Technology Data Exchange (ETDEWEB)

Soleymani, H.R.; Berthelot, C.F.; Bergan, A.T. [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Mechanical Engineering

1995-12-31

Computer pavement analysis programs, which are based on mathematical simulation models, were compared. The programs included in the study were: ELSYM5, an Elastic Linear (EL) pavement analysis program, MICH-PAVE, a Finite Element Non-Linear (FENL) and Finite Element Linear (FEL) pavement analysis program. To perform the analysis different tire pressures, pavement material properties and asphalt layer thicknesses were selected. Evaluation criteria used in the analysis were tensile strain in bottom of the asphalt layer, vertical compressive strain at the top of the subgrade and surface displacement. Results showed that FENL methods predicted more strain and surface deflection than the FEL and EL analysis methods. Analyzing pavements with FEL does not offer many advantages over the EL method. Differences in predicted strains between the three methods of analysis in some cases was found to be close to 100% It was suggested that these programs require more calibration and validation both theoretically and empirically to accurately correlate with field observations. 19 refs., 4 tabs., 9 figs.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

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

NARCIS (Netherlands)

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

2006-01-01

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

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.

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.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

Science.gov (United States)

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data

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

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.

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

Advanced analysis technique for the evaluation of linear alternators and linear motors

Science.gov (United States)

Holliday, Jeffrey C.

1995-01-01

A method for the mathematical analysis of linear alternator and linear motor devices and designs is described, and an example of its use is included. The technique seeks to surpass other methods of analysis by including more rigorous treatment of phenomena normally omitted or coarsely approximated such as eddy braking, non-linear material properties, and power losses generated within structures surrounding the device. The technique is broadly applicable to linear alternators and linear motors involving iron yoke structures and moving permanent magnets. The technique involves the application of Amperian current equivalents to the modeling of the moving permanent magnet components within a finite element formulation. The resulting steady state and transient mode field solutions can simultaneously account for the moving and static field sources within and around the device.

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.

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.

Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach.

Science.gov (United States)

Roldan-Valadez, Ernesto; Rios, Camilo; Cortez-Conradis, David; Favila, Rafael; Moreno-Jimenez, Sergio

2014-06-01

Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics. Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed. The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks' λ = 0.324, χ(2) (3) = 38.907, p tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases.

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.

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…

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

Science.gov (United States)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Introduction to integral discriminants

International Nuclear Information System (INIS)

Morozov, A.; Shakirov, Sh.

2009-01-01

The simplest partition function, associated with homogeneous symmetric forms S of degree r in n variables, is integral discriminant J n|r (S) = ∫e -S(x 1 ,...,x n ) dx 1 ...dx n . Actually, S-dependence remains the same if e -S in the integrand is substituted by arbitrary function f(S), i.e. integral discriminant is a characteristic of the form S itself, and not of the averaging procedure. The aim of the present paper is to calculate J n|r in a number of non-Gaussian cases. Using Ward identities - linear differential equations, satisfied by integral discriminants - we calculate J 2|3 ,J 2|4 ,J 2|5 and J 3|3 . In all these examples, integral discriminant appears to be a generalized hypergeometric function. It depends on several SL(n) invariants of S, with essential singularities controlled by the ordinary algebraic discriminant of S.

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 ...

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…

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

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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

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.

Optimum unambiguous discrimination between subsets of nonorthogonal quantum states

International Nuclear Information System (INIS)

Sun Yuqing; Hillery, Mark; Bergou, Janos A.

2002-01-01

It is known that unambiguous discrimination among nonorthogonal but linearly independent quantum states is possible with a certain probability of success. Here, we consider a variant of that problem. Instead of discriminating among all of the different states, we shall only discriminate between two subsets of them. In particular, for the case of three nonorthogonal states, { vertical bar ψ 1 >, vertical bar ψ 2 >, vertical bar ψ 3 >}, we show that the optimal strategy to distinguish vertical bar ψ 1 > from the set { vertical bar ψ 2 >, vertical bar ψ 3 >} has a higher success rate than if we wish to discriminate among all three states. Somewhat surprisingly, for unambiguous discrimination the subsets need not be linearly independent. A fully analytical solution is presented, and we also show how to construct generalized interferometers (multiport) which provide an optical implementation of the optimal strategy

Assay based on electrical impedance spectroscopy to discriminate between normal and cancerous mammalian cells

Science.gov (United States)

Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés

2018-03-01

In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.

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.

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.

The estimation of genetic distance and discriminant variables on breed of duck (Alabio, Bali, Khaki Campbell, Mojosari and Pegagan by morphological analysis

Directory of Open Access Journals (Sweden)

B Brahmantiyo

2003-03-01

Full Text Available A study on morphological body conformation of Alabio, Bali, Khaki Campbell, Mojosari and Pegagan ducks was carried out to determine the genetic distance and discriminant variables. This research was held in Research Institute for Animal Production, Ciawi, Bogor using 65 Alabio ducks, 40 Bali ducks, 36 Khaki Campbell ducks, 60 Mojosari ducks and 30 Pegagan ducks. Seven different body parts were measured, they were the length of femur, tibia, tarsometatarsus, the circumference of tarsometatarsus, the length of third digits, wing and maxilla. General Linear Models and simple discriminant analysis were used in this observation (SAS package program. Male and female Pegagan ducks had morphological size bigger than Alabio, Bali, Khaki Campbell and Mojosari ducks. Khaki Campbell ducks were mixed with Bali ducks (47.22% and Pegagan ducks from isolated location in South Sumatera were lightly mixed with Alabio and Bali. Mahalanobis genetic distance showed that Bali and Khaki Campbell ducks, also, Alabio and Mojosari ducks had similarity, with genetic distance of 1.420 and 1.548, respectively. Results from canonical analysis showed that the most discriminant variables were obtained from the length of femur, tibia and third digits.

Linear Parametric Sensitivity Analysis of the Constraint Coefficient Matrix in Linear Programs

OpenAIRE

Zuidwijk, Rob

2005-01-01

textabstractSensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an optimal solution are investigated, and the optimal solution is studied on a so-called critical range of the initial data, in which certain properties such as the optimal basis in linear programming are ...

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.

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.,.

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, ...

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

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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

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

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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.

Exponential local discriminant embedding and its application to face recognition.

Science.gov (United States)

Dornaika, Fadi; Bosaghzadeh, Alireza

2013-06-01

Local discriminant embedding (LDE) has been recently proposed to overcome some limitations of the global linear discriminant analysis method. In the case of a small training data set, however, LDE cannot directly be applied to high-dimensional data. This case is the so-called small-sample-size (SSS) problem. The classical solution to this problem was applying dimensionality reduction on the raw data (e.g., using principal component analysis). In this paper, we introduce a novel discriminant technique called "exponential LDE" (ELDE). The proposed ELDE can be seen as an extension of LDE framework in two directions. First, the proposed framework overcomes the SSS problem without discarding the discriminant information that was contained in the null space of the locality preserving scatter matrices associated with LDE. Second, the proposed ELDE is equivalent to transforming original data into a new space by distance diffusion mapping (similar to kernel-based nonlinear mapping), and then, LDE is applied in such a new space. As a result of diffusion mapping, the margin between samples belonging to different classes is enlarged, which is helpful in improving classification accuracy. The experiments are conducted on five public face databases: Yale, Extended Yale, PF01, Pose, Illumination, and Expression (PIE), and Facial Recognition Technology (FERET). The results show that the performances of the proposed ELDE are better than those of LDE and many state-of-the-art discriminant analysis techniques.

Boosting Discriminant Learners for Gait Recognition Using MPCA Features

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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.

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.

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.

Perceived Discrimination in LGBTIQ Discourse: A Typology of Verbal Discrimination

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Sol Rojas Lizana

2014-08-01

Full Text Available New within the field of Discourse Analysis, Perceived Discrimination (PD is the study of discourse that focuses on the perspective of the victims of discrimination. This article explores the experiences of verbal discrimination as reported by eighteen LGBTIQ participants during semi-structured, co-constructed interviews. Data were classified in order to develop a taxonomy of discrimination based on Mellor’s (2003, 2004. This taxonomy foregrounds two types of discrimination: verbal and behavioural. In this paper, I exemplify the forms of verbal discrimination encountered and offer an analysis of the discourse used in the construction of the experiences and of the effects reported. The results show that verbal discrimination is an overt phenomenon and that participants are stressed by the ever present possibility of facing it. Verbal discrimination is mainly triggered by a perceived transgression to the normalised standards of people’s behaviour, movements and look in a heterosexist society. It presents three subtypes: name calling, abuse and remarks. These subtypes are described through the analysis of keywords, effects and expressions (such as faggot, gay, dyke, queer, the pronoun ‘it’, religious comments and other remarks. The type of discrimination used was associated with the level of acquaintance perpetrators have with the experiencers; that is, name calling was used by people unknown to the victims while abuse and remarks by acquaintances and family members. Participants resorted to several discursive strategies to convey their intentions. They used mitigation strategies when wanting to minimize the experience, hedging and repetition were used for emphasis, and to convey urgency and pervasiveness. Metaphorical expressions related to internal or external injuries were also used to express the powerful effect of verbal discrimination on people.

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.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

Science.gov (United States)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

International Nuclear Information System (INIS)

Hosseini-Golgoo, S M; Bozorgi, H; Saberkari, A

2015-01-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively. (paper)

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

Life satisfaction and trauma in clinical and non-clinical children living in a war-torn environment: A discriminant analysis.

Science.gov (United States)

Veronese, Guido; Pepe, Alessandro

2017-07-01

The aim of this work was to discriminate between healthy children and children at risk of developing mental impairments by evaluating the impact on contextual and individual factors of a context characterized by war. We tested the hypothesis that a linear discriminant function composed of trauma, life satisfaction, and affect balance has the power to classify the children as community or clinical referred. Membership of the clinical-referred group was associated with poorer life satisfaction and higher levels of trauma. Community-referred profiles were associated with lesser trauma. Perceived life satisfaction regarding family and school was the main contributor to the discriminant function.

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.

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?

Science.gov (United States)

Fruehwald-Pallamar, J; Hesselink, J R; Mafee, M F; Holzer-Fruehwald, L; Czerny, C; Mayerhoefer, M E

2016-02-01

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 2 D and 3 D 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 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D 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. 2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol. © Georg Thieme Verlag KG Stuttgart · New York.

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.

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.

Perturbation analysis of linear control problems

International Nuclear Information System (INIS)

Petkov, Petko; Konstantinov, Mihail

2017-01-01

The paper presents a brief overview of the technique of splitting operators, proposed by the authors and intended for perturbation analysis of control problems involving unitary and orthogonal matrices. Combined with the technique of Lyapunov majorants and the implementation of the Banach and Schauder fixed point principles, it allows to obtain rigorous non-local perturbation bounds for a set of sensitivity analysis problems. Among them are the reduction of linear systems into orthogonal canonical forms, the feedback synthesis problem and pole assignment problem in particular, as well as other important problems in control theory and linear algebra. Key words: perturbation analysis, canonical forms, feedback synthesis

Seismic analysis of equipment system with non-linearities such as gap and friction using equivalent linearization method

International Nuclear Information System (INIS)

Murakami, H.; Hirai, T.; Nakata, M.; Kobori, T.; Mizukoshi, K.; Takenaka, Y.; Miyagawa, N.

1989-01-01

Many of the equipment systems of nuclear power plants contain a number of non-linearities, such as gap and friction, due to their mechanical functions. It is desirable to take such non-linearities into account appropriately for the evaluation of the aseismic soundness. However, in usual design works, linear analysis method with rough assumptions is applied from engineering point of view. An equivalent linearization method is considered to be one of the effective analytical techniques to evaluate non-linear responses, provided that errors to a certain extent are tolerated, because it has greater simplicity in analysis and economization in computing time than non-linear analysis. The objective of this paper is to investigate the applicability of the equivalent linearization method to evaluate the maximum earthquake response of equipment systems such as the CANDU Fuelling Machine which has multiple non- linearities

Common pitfalls in statistical analysis: Linear regression analysis

Directory of Open Access Journals (Sweden)

Rakesh Aggarwal

2017-01-01

Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Social Status Correlates of Reporting Racial Discrimination and Gender Discrimination among Racially Diverse Women

Science.gov (United States)

Ro, Annie E.; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design. PMID:19485231

Social status correlates of reporting gender discrimination and racial discrimination among racially diverse women.

Science.gov (United States)

Ro, Annie E; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in North California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.

Discrimination between biological interfaces and crystal-packing contacts

Directory of Open Access Journals (Sweden)

Yuko Tsuchiya

2008-11-01

Full Text Available Yuko Tsuchiya1, Haruki Nakamura2, Kengo Kinoshita1,31Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minatoku, Tokyo, 108-8639, Japan; 2Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan; 3Bioinformatics Research and Development, JST, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, JapanAbstract: A discrimination method between biologically relevant interfaces and artificial crystal-packing contacts in crystal structures was constructed. The method evaluates protein-protein interfaces in terms of complementarities for hydrophobicity, electrostatic potential and shape on the protein surfaces, and chooses the most probable biological interfaces among all possible contacts in the crystal. The method uses a discriminator named as “COMP”, which is a linear combination of the complementarities for the above three surface features and does not correlate with the contact area. The discrimination of homo-dimer interfaces from symmetry-related crystal-packing contacts based on the COMP value achieved the modest success rate. Subsequent detailed review of the discrimination results raised the success rate to about 88.8%. In addition, our discrimination method yielded some clues for understanding the interaction patterns in several examples in the PDB. Thus, the COMP discriminator can also be used as an indicator of the “biological-ness” of protein-protein interfaces.Keywords: protein-protein interaction, complementarity analysis, homo-dimer interface, crystal-packing contact, biological interfaces

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

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.

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...

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)

Analysis of Disperse Dyes Using Liquid Chromatography/Linear Ion Trap Mass Spectrometry (LC/LIT-MSn) and Database Construction.

Science.gov (United States)

Kato, Takao; Ikeue, Takahisa; Suzuki, Yasuhiro; Handa, Makoto

2016-01-01

Liquid chromatography/linear ion trap mass spectrometry (LC/LIT-MS(n)) was used to construct a database of disperse dyes. Fifty-three standard dyes were subjected to LC/LIT-MS(n) and characterized based on their mass spectra (MS, MS(2), and MS(3)), values of λmax (maximum absorption wavelength in the UV-visible spectrum), and retention times. The results demonstrate that it is possible to reliably identify coexisting dyes that cannot be separated by LC or detected by diode array detection due to their low molecular absorption coefficients. In addition, the by-products included in the standard dyes were found to provide important information for the identification and discrimination of dyestuffs synthesized using different processes. The confirmation of the effectiveness of LC/LIT-MS(n) analysis in detecting small amounts of disperse dyes in this study shows its potential for use in the discrimination of dyed fibers obtained at crime scenes.

Fast neutron-gamma discrimination on neutron emission profile measurement on JT-60U

International Nuclear Information System (INIS)

Ishii, K.; Okamoto, A.; Kitajima, S.; Sasao, M.; Shinohara, K.; Ishikawa, M.; Baba, M.; Isobe, M.

2010-01-01

A digital signal processing (DSP) system is applied to stilbene scintillation detectors of the multichannel neutron emission profile monitor in JT-60U. Automatic analysis of the neutron-γ pulse shape discrimination is a key issue to diminish the processing time in the DSP system, and it has been applied using the two-dimensional (2D) map. Linear discriminant function is used to determine the dividing line between neutron events and γ-ray events on a 2D map. In order to verify the validity of the dividing line determination, the pulse shape discrimination quality is evaluated. As a result, the γ-ray contamination in most of the beam heating phase was negligible compared with the statistical error with 10 ms time resolution.

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.

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

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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.

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.

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.

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)

Analysis of Nonlinear Dynamics in Linear Compressors Driven by Linear Motors

Science.gov (United States)

Chen, Liangyuan

2018-03-01

The analysis of dynamic characteristics of the mechatronics system is of great significance for the linear motor design and control. Steady-state nonlinear response characteristics of a linear compressor are investigated theoretically based on the linearized and nonlinear models. First, the influence factors considering the nonlinear gas force load were analyzed. Then, a simple linearized model was set up to analyze the influence on the stroke and resonance frequency. Finally, the nonlinear model was set up to analyze the effects of piston mass, spring stiffness, driving force as an example of design parameter variation. The simulating results show that the stroke can be obtained by adjusting the excitation amplitude, frequency and other adjustments, the equilibrium position can be adjusted by adjusting the DC input, and to make the more efficient operation, the operating frequency must always equal to the resonance frequency.

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)

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.

Programmable quantum-state discriminator by nuclear magnetic resonance

International Nuclear Information System (INIS)

Gopinath, T.; Das, Ranabir; Kumar, Anil

2005-01-01

A programmable quantum-state discriminator is implemented by using nuclear magnetic resonance. We use a two-qubit spin-1/2 system, one for the data qubit and one for the ancilla (program) qubit. This device does the unambiguous (error-free) discrimination of a pair of states of the data qubit that are symmetrically located about a fixed state. The device is used to discriminate both linearly polarized states and elliptically polarized states. The maximum probability of successful discrimination is achieved by suitably preparing the ancilla qubit. It is also shown that the probability of discrimination depends on the angle of the unitary operator of the protocol and ellipticity of the data qubit state

Second-order capillary electrophoresis diode array detector data modeled with the Tucker3 algorithm: A novel strategy for Argentinean white wine discrimination respect to grape variety.

Science.gov (United States)

Azcarate, Silvana M; de Araújo Gomes, Adriano; Vera-Candioti, Luciana; Cesar Ugulino de Araújo, Mário; Camiña, José M; Goicoechea, Héctor C

2016-07-01

Data obtained by capillary electrophoresis with diode array detection (CE-DAD) were modeled with the purpose to discriminate Argentinean white wines samples produced from three grape varieties (Torrontés, Chardonnay, and Sauvignon blanc). Thirty-eight samples of commercial white wine from four wine-producing provinces of Argentina (Mendoza, San Juan, Salta, and Rio Negro) were analyzed. CE-DAD matrices with dimensions of 421 elution times (from 1.17 to 7.39 minutes) × 71 wavelengths (from 227 to 367 nm) were joined in a three way data array and decomposed by Tucker3 method under non-negativity constraint, employing 18, 18 and six factors in the modes 1, 2 and 3, respectively. Using the scores of Tucker model, it was possible to discriminate samples of Argentinean white wine by linear discriminant analysis and Kernel linear discriminant analysis. Core element analysis of the Tucker3 model allows identifying the loading profiles in spectral mode related to Argentinean white wine samples. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear Parametric Sensitivity Analysis of the Constraint Coefficient Matrix in Linear Programs

NARCIS (Netherlands)

R.A. Zuidwijk (Rob)

2005-01-01

textabstractSensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an

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.

Asymmetry and irregularity border as discrimination factor between melanocytic lesions

Science.gov (United States)

Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo

2015-06-01

Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.

Meta-analytic review of the development of face discrimination in infancy: Face race, face gender, infant age, and methodology moderate face discrimination.

Science.gov (United States)

Sugden, Nicole A; Marquis, Alexandra R

2017-11-01

Infants show facility for discriminating between individual faces within hours of birth. Over the first year of life, infants' face discrimination shows continued improvement with familiar face types, such as own-race faces, but not with unfamiliar face types, like other-race faces. The goal of this meta-analytic review is to provide an effect size for infants' face discrimination ability overall, with own-race faces, and with other-race faces within the first year of life, how this differs with age, and how it is influenced by task methodology. Inclusion criteria were (a) infant participants aged 0 to 12 months, (b) completing a human own- or other-race face discrimination task, (c) with discrimination being determined by infant looking. Our analysis included 30 works (165 samples, 1,926 participants participated in 2,623 tasks). The effect size for infants' face discrimination was small, 6.53% greater than chance (i.e., equal looking to the novel and familiar). There was a significant difference in discrimination by race, overall (own-race, 8.18%; other-race, 3.18%) and between ages (own-race: 0- to 4.5-month-olds, 7.32%; 5- to 7.5-month-olds, 9.17%; and 8- to 12-month-olds, 7.68%; other-race: 0- to 4.5-month-olds, 6.12%; 5- to 7.5-month-olds, 3.70%; and 8- to 12-month-olds, 2.79%). Multilevel linear (mixed-effects) models were used to predict face discrimination; infants' capacity to discriminate faces is sensitive to face characteristics including race, gender, and emotion as well as the methods used, including task timing, coding method, and visual angle. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Applied linear algebra and matrix analysis

CERN Document Server

Shores, Thomas S

2018-01-01

In its second edition, this textbook offers a fresh approach to matrix and linear algebra. Its blend of theory, computational exercises, and analytical writing projects is designed to highlight the interplay between these aspects of an application. This approach places special emphasis on linear algebra as an experimental science that provides tools for solving concrete problems. The second edition’s revised text discusses applications of linear algebra like graph theory and network modeling methods used in Google’s PageRank algorithm. Other new materials include modeling examples of diffusive processes, linear programming, image processing, digital signal processing, and Fourier analysis. These topics are woven into the core material of Gaussian elimination and other matrix operations; eigenvalues, eigenvectors, and discrete dynamical systems; and the geometrical aspects of vector spaces. Intended for a one-semester undergraduate course without a strict calculus prerequisite, Applied Linear Algebra and M...

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.

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.

Perceived Interpersonal Discrimination and Older Women’s Mental Health: Accumulation Across Domains, Attributions, and Time

Science.gov (United States)

Bécares, Laia; Zhang, Nan

2018-01-01

Abstract Experiencing discrimination is associated with poor mental health, but how cumulative experiences of perceived interpersonal discrimination across attributes, domains, and time are associated with mental disorders is still unknown. Using data from the Study of Women’s Health Across the Nation (1996–2008), we applied latent class analysis and generalized linear models to estimate the association between cumulative exposure to perceived interpersonal discrimination and older women’s mental health. We found 4 classes of perceived interpersonal discrimination, ranging from cumulative exposure to discrimination over attributes, domains, and time to none or minimal reports of discrimination. Women who experienced cumulative perceived interpersonal discrimination over time and across attributes and domains had the highest risk of depression (Center for Epidemiologic Studies Depression Scale score ≥16) compared with women in all other classes. This was true for all women regardless of race/ethnicity, although the type and severity of perceived discrimination differed across racial/ethnic groups. Cumulative exposure to perceived interpersonal discrimination across attributes, domains, and time has an incremental negative long-term association with mental health. Studies that examine exposure to perceived discrimination due to a single attribute in 1 domain or at 1 point in time underestimate the magnitude and complexity of discrimination and its association with health. PMID:29036550

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 ...

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.

Discriminant analysis of Social Work’s performance in licensure examination

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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.

Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

Science.gov (United States)

Wang, C. L.; Funk, L. L.; Riedel, R. A.; Berry, K. D.

2017-05-01

3He gas based neutron Linear-Position-Sensitive Detectors (LPSDs) have been used for many neutron scattering instruments. Traditional Pulse-height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (NGD ratio) on the order of 105-106. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher Linear Discriminant Analysis (FLDA) and three Multivariate Analyses (MVAs) of the features were performed. The NGD ratios are improved by about 102-103 times compared with the traditional PHA method. Our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.

Latent log-linear models for handwritten digit classification.

Science.gov (United States)

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

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.

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

Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics.

Science.gov (United States)

Peckner, Ryan; Myers, Samuel A; Jacome, Alvaro Sebastian Vaca; Egertson, Jarrett D; Abelin, Jennifer G; MacCoss, Michael J; Carr, Steven A; Jaffe, Jacob D

2018-05-01

Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

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

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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.

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.

A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

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Baofeng Shi

2015-01-01

Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

Non-linear seismic analysis of structures coupled with fluid

International Nuclear Information System (INIS)

Descleve, P.; Derom, P.; Dubois, J.

1983-01-01

This paper presents a method to calculate non-linear structure behaviour under horizontal and vertical seismic excitation, making possible the full non-linear seismic analysis of a reactor vessel. A pseudo forces method is used to introduce non linear effects and the problem is solved by superposition. Two steps are used in the method: - Linear calculation of the complete model. - Non linear analysis of thin shell elements and calculation of seismic induced pressure originating from linear and non linear effects, including permanent loads and thermal stresses. Basic aspects of the mathematical formulation are developed. It has been applied to axi-symmetric shell element using a Fourier series solution. For the fluid interaction effect, a comparison is made with a dynamic test. In an example of application, the displacement and pressure time history are given. (orig./GL)

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

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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

The Best Model of the Swiss Banknote Data -Validation by the 95% CI of coefficients and t-test of discriminant scores

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Shuichi Shinmura

2016-06-01

Full Text Available The discriminant analysis is not the inferential statistics since there are no equations for standard error (SE of error rate and discriminant coefficient based on the normal distribution. In this paper, we proposed the “k-fold cross validation for small sample” and can obtain the 95% confidence interval (CI of error rates and discriminant coefficients. This method is the computer-intensive approach by statistical and mathematical programming (MP software such as JMP and LINGO. By the proposed approach, we can choose the best model with the minimum mean of error rate in the validation samples (Minimum M2 Standard. In this research, we examine the sixteen linear separable models of Swiss banknote data by eight linear discriminant functions (LDFs. M2 of the best model of Revised IP-OLDF is the smallest value of all models. We find all coefficients of six Revised IP-OLDF among sixteen models rejected by the 95% CI of discriminant coefficients (Discriminant coefficient standard. We compare t-values of the discriminant scores. The t-value of the best model has the maximum values among sixteen models (Maximum t-value Standard. Moreover, we can conclude that all standards support the best model of Revised IP-OLDF.

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

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...

A new comprehensive index for discriminating adulteration in bovine raw milk.

Science.gov (United States)

Liu, Jing; Ren, Jing; Liu, Zhen-Min; Guo, Ben-Heng

2015-04-01

This paper proposes a new comprehensive index, called Q, which can effectively discriminate artificial adulterated milk from unadulterated milk. Both normal and adulterated samples of bovine raw milk were analysed by Fourier transform infrared spectroscopic instrument to measure the traditional indices of quality, including fat (FAT), protein (PRO), lactose (LAC), total solids (TS), non-fat solid (NFS), freezing point (FP) and somatic cell counts (SCC). From these traditional indices, this paper elaborates a method to build the index Q. First, correlated analysis and principle component analysis were used to select parameter pairs TS-FAT and FP-LAC as predominant variables. Second, linear-regression analysis and residual analysis are applied to determine the index Q and its discriminating ranges. The verification and two-blind trial results suggested that index Q could accurately detect milk adulteration with maltodextrin and water (as low as 1.0% of adulteration proportions), and with other nine kinds of synthetic adulterants (as low as 0.5% of adulteration proportions). Copyright © 2014 Elsevier Ltd. All rights reserved.

Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling.

Science.gov (United States)

Amorello, Diana; Orecchio, Santino; Pace, Andrea; Barreca, Salvatore

2016-09-01

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.

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

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

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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.

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.

DISCRIMINATION OF WOMEN IN THE LABOUR MARKET OF SR AND MODELS OF DISCRIMINATION

OpenAIRE

Ján Vravec; Radovan Baèík

2012-01-01

IThe paper deals with the problem of women’s discrimination in the labour market. Significant differences, among women and men in the labour market, are especially in unemployment rate in reward system, and high horizontal and vertical segregation of women. The aspects of discrimination arise despite of existing legislation, which gender discrimination strictly prohibits. An analysis of arguments, consequences and models of women’s discrimination in the labour market can significantly help to...

Data analysis in high-dimensional sparse spaces

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

Relative sensitivity of depth discrimination for ankle inversion and plantar flexion movements.

Science.gov (United States)

Black, Georgia; Waddington, Gordon; Adams, Roger

2014-02-01

25 participants (20 women, 5 men) were tested for sensitivity in discrimination between sets of six movements centered on 8 degrees, 11 degrees, and 14 degrees, and separated by 0.3 degrees. Both inversion and plantar flexion movements were tested. Discrimination of the extent of inversion movement was observed to decline linearly with increasing depth; however, for plantar flexion, the discrimination function for movement extent was found to be non-linear. The relatively better discrimination of plantar flexion movements than inversion movements at around 11 degrees from horizontal is interpreted as an effect arising from differential amounts of practice through use, because this position is associated with the plantar flexion movement made in normal walking. The fact that plantar flexion movements are discriminated better than inversion at one region but not others argues against accounts of superior proprioceptive sensitivity for plantar flexion compared to inversion that are based on general properties of plantar flexion such as the number of muscle fibres on stretch.

Lattice Boltzmann methods for global linear instability analysis

Science.gov (United States)

Pérez, José Miguel; Aguilar, Alfonso; Theofilis, Vassilis

2017-12-01

Modal global linear instability analysis is performed using, for the first time ever, the lattice Boltzmann method (LBM) to analyze incompressible flows with two and three inhomogeneous spatial directions. Four linearization models have been implemented in order to recover the linearized Navier-Stokes equations in the incompressible limit. Two of those models employ the single relaxation time and have been proposed previously in the literature as linearization of the collision operator of the lattice Boltzmann equation. Two additional models are derived herein for the first time by linearizing the local equilibrium probability distribution function. Instability analysis results are obtained in three benchmark problems, two in closed geometries and one in open flow, namely the square and cubic lid-driven cavity flow and flow in the wake of the circular cylinder. Comparisons with results delivered by classic spectral element methods verify the accuracy of the proposed new methodologies and point potential limitations particular to the LBM approach. The known issue of appearance of numerical instabilities when the SRT model is used in direct numerical simulations employing the LBM is shown to be reflected in a spurious global eigenmode when the SRT model is used in the instability analysis. Although this mode is absent in the multiple relaxation times model, other spurious instabilities can also arise and are documented herein. Areas of potential improvements in order to make the proposed methodology competitive with established approaches for global instability analysis are discussed.

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 %.

A discrimination problem from seismology

International Nuclear Information System (INIS)

Elvers, E.

1975-12-01

Seismic discrimination between earthquakes and underground nuclear explosions is studied, utilizing magnitudes of two kinds from several seismic stations. A model is given first, where the mean values of the magnitudes are linear fuctions of a parameter describing event size. It is shown how the model parameters can be estimated after a minor restriction on their space. When the discrimination rule is derived from the model, a few different approaches are considered, and they are shown to coincide. It is found reasonabel to use a discriminant, which is linear in the magnitudes, and explicit formulas are obtained. The power of the method is expressed by a measure of separation between the alternatives, which also shows the importance of the individual magnitudes. Missing data is a frequent problem in practice, and the case is treated where there is a detection threshold for one of the magnitudes. The classicfication probabilities are computed when applying the rule to the available magnitudes, and they depend on the event size. The method is not optimal, and it is shown that it can be improved by using the technique of identification by negative evidence, i.e. by utilizing the threshold as upper bound for a missing magnitude. The model is one of general use, and the results thus have a wider applicability. (author)

Multispectral Image Analysis for Robust Prediction of Astaxanthin Coating

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Frosch, Stina; Nielsen, Michael Engelbrecht

2013-01-01

The aim of this study was to investigate the possibility of predicting the type and concentration level of astaxanthin coating of aquaculture feed pellets using multispectral image analysis. We used both natural and synthetic astaxanthin, and we used several different concentration levels...... of synthetic astaxanthin in combination with four different recipes of feed pellets. We used a VideometerLab with 20 spectral bands in the range of 385-1050 nm. We used linear discriminant analysis and sparse linear discriminant analysis for classification and variable selection. We used partial least squares...

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.

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...

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...

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.

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.

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.

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.

Signals and transforms in linear systems analysis

CERN Document Server

Wasylkiwskyj, Wasyl

2013-01-01

Signals and Transforms in Linear Systems Analysis covers the subject of signals and transforms, particularly in the context of linear systems theory. Chapter 2 provides the theoretical background for the remainder of the text. Chapter 3 treats Fourier series and integrals. Particular attention is paid to convergence properties at step discontinuities. This includes the Gibbs phenomenon and its amelioration via the Fejer summation techniques. Special topics include modulation and analytic signal representation, Fourier transforms and analytic function theory, time-frequency analysis and frequency dispersion. Fundamentals of linear system theory for LTI analogue systems, with a brief account of time-varying systems, are covered in Chapter 4 . Discrete systems are covered in Chapters 6 and 7.  The Laplace transform treatment in Chapter 5 relies heavily on analytic function theory as does Chapter 8 on Z -transforms. The necessary background on complex variables is provided in Appendix A. This book is intended to...

Design and Analysis of MEMS Linear Phased Array

Directory of Open Access Journals (Sweden)

Guoxiang Fan

2016-01-01

Full Text Available A structure of micro-electro-mechanical system (MEMS linear phased array based on “multi-cell” element is designed to increase radiation sound pressure of transducer working in bending vibration mode at high frequency. In order to more accurately predict the resonant frequency of an element, the theoretical analysis of the dynamic equation of a fixed rectangular composite plate and finite element method simulation are adopted. The effects of the parameters both in the lateral and elevation direction on the three-dimensional beam directivity characteristics are comprehensively analyzed. The key parameters in the analysis include the “cell” number of element, “cell” size, “inter-cell” spacing and the number of elements, element width. The simulation results show that optimizing the linear array parameters both in the lateral and elevation direction can greatly improve the three-dimensional beam focusing for MEMS linear phased array, which is obviously different from the traditional linear array.

Non linear stability analysis of parallel channels with natural circulation

Energy Technology Data Exchange (ETDEWEB)

Mishra, Ashish Mani; Singh, Suneet, E-mail: suneet.singh@iitb.ac.in

2016-12-01

Highlights: • Nonlinear instabilities in natural circulation loop are studied. • Generalized Hopf points, Sub and Supercritical Hopf bifurcations are identified. • Bogdanov–Taken Point (BT Point) is observed by nonlinear stability analysis. • Effect of parameters on stability of system is studied. - Abstract: Linear stability analysis of two-phase flow in natural circulation loop is quite extensively studied by many researchers in past few years. It can be noted that linear stability analysis is limited to the small perturbations only. It is pointed out that such systems typically undergo Hopf bifurcation. If the Hopf bifurcation is subcritical, then for relatively large perturbation, the system has unstable limit cycles in the (linearly) stable region in the parameter space. Hence, linear stability analysis capturing only infinitesimally small perturbations is not sufficient. In this paper, bifurcation analysis is carried out to capture the non-linear instability of the dynamical system and both subcritical and supercritical bifurcations are observed. The regions in the parameter space for which subcritical and supercritical bifurcations exist are identified. These regions are verified by numerical simulation of the time-dependent, nonlinear ODEs for the selected points in the operating parameter space using MATLAB ODE solver.

Non-linear finite element analysis in structural mechanics

CERN Document Server

Rust, Wilhelm

2015-01-01

This monograph describes the numerical analysis of non-linearities in structural mechanics, i.e. large rotations, large strain (geometric non-linearities), non-linear material behaviour, in particular elasto-plasticity as well as time-dependent behaviour, and contact. Based on that, the book treats stability problems and limit-load analyses, as well as non-linear equations of a large number of variables. Moreover, the author presents a wide range of problem sets and their solutions. The target audience primarily comprises advanced undergraduate and graduate students of mechanical and civil engineering, but the book may also be beneficial for practising engineers in industry.

Analysis of Linear Hybrid Systems in CLP

DEFF Research Database (Denmark)

Banda, Gourinath; Gallagher, John Patrick

2009-01-01

In this paper we present a procedure for representing the semantics of linear hybrid automata (LHAs) as constraint logic programs (CLP); flexible and accurate analysis and verification of LHAs can then be performed using generic CLP analysis and transformation tools. LHAs provide an expressive...

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.

Lower bound on inconclusive probability of unambiguous discrimination

International Nuclear Information System (INIS)

Feng Yuan; Zhang Shengyu; Duan Runyao; Ying Mingsheng

2002-01-01

We derive a lower bound on the inconclusive probability of unambiguous discrimination among n linearly independent quantum states by using the constraint of no signaling. It improves the bound presented in the paper of Zhang, Feng, Sun, and Ying [Phys. Rev. A 64, 062103 (2001)], and when the optimal discrimination can be reached, these two bounds coincide with each other. An alternative method of constructing an appropriate measurement to prove the lower bound is also presented

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.

Functional discriminant method and neuronal net

International Nuclear Information System (INIS)

Minh-Quan Tran.

1993-02-01

The ZEUS detector at the ep storage ring HERA at DESY is equipped with a 3 level trigger system. This enormous effort is necessary to fight against the high proton beamgas background that was estimated to be at the level of 100 kHz. In this thesis two methods were investigated to calculate a trigger decision from a set of various trigger parameters. The Functional Discriminant Analysis evalutes a decision parameter that is optimized by means of a linear algebra technic. A method is shown how to determine the most important trigger parameters. A 'feed forward' neuralnetwork was analyzed in order to allow none lineare cuts in the n dimensinal configuration space spanned by the trigger parameters. The error back propagation method was used to teach the neural network. It is shown that both decision methods are able to abstract the important characteristics of event samples. As soon as they are tought they will seperate events from these classes even though they were not part of the training sample. (orig.) [de

NEIGHBOURHOOD POVERTY, PERCEIVED DISCRIMINATION AND CENTRAL ADIPOSITY IN THE USA: INDEPENDENT ASSOCIATIONS IN A REPEATED MEASURES ANALYSIS.

Science.gov (United States)

Kwarteng, Jamila L; Schulz, Amy J; Mentz, Graciela B; Israel, Barbara A; Shanks, Trina R; Perkins, Denise White

2016-11-01

This study examines the independent effects of neighbourhood context (i.e. neighbourhood poverty) and exposure to perceived discrimination in shaping risk of obesity over time. Weighted three-level hierarchical linear regression models for a continuous outcome were used to assess the independent effects of neighbourhood poverty and perceived discrimination on obesity over time in a sample of 157 non-Hispanic Black, non-Hispanic White and Hispanic adults in Detroit, USA, in 2002/2003 and 2007/2008. Independent associations were found between neighbourhood poverty and perceived discrimination with central adiposity over time. Residents of neighbourhoods with high concentrations of poverty were more likely to show increases in central adiposity compared with those in neighbourhoods with lower concentrations of poverty. In models adjusted for BMI, neighbourhood poverty at baseline was associated with a greater change in central adiposity among participants who lived in neighbourhoods in the second (B=3.79, p=0.025) and third (B=3.73, p=0.024) poverty quartiles, compared with those in the lowest poverty neighbourhoods. The results from models that included both neighbourhood poverty and perceived discrimination showed that both were associated with increased risk of increased central adiposity over time. Residents of neighbourhoods in the second (B=9.58, ppoverty were more likely to show greater increases in central adiposity over time, compared with those in the lowest poverty quartile, with mean discrimination at baseline independently and positively associated with increases in central adiposity over time (B=2.36, p=0.020). The results suggest that neighbourhood poverty and perceived discrimination are independently associated with a heightened risk of increase in central adiposity over time. Efforts to address persistent disparities in central adiposity in the USA should include strategies to reduce high concentrations of neighbourhood poverty as well as

Discrimination and mental health problems among homeless minority young people.

Science.gov (United States)

Milburn, Norweeta G; Batterham, Philip; Ayala, George; Rice, Eric; Solorio, Rosa; Desmond, Kate; Lord, Lynwood; Iribarren, Javier; Rotheram-Borus, Mary Jane

2010-01-01

We examined the associations among perceived discrimination, racial/ethnic identification, and emotional distress in newly homeless adolescents. We assessed a sample of newly homeless adolescents (n=254) in Los Angeles, California, with measures of perceived discrimination and racial/ethnic identification. We assessed emotional distress using the Brief Symptom Inventory and used multivariate linear regression modeling to gauge the impact of discrimination and racial identity on emotional distress. Controlling for race and immigration status, gender, and age, young people with a greater sense of ethnic identification experienced less emotional distress. Young people with a history of racial/ethnic discrimination experienced more emotional distress. Intervention programs that contextualize discrimination and enhance racial/ethnic identification and pride among homeless young people are needed.

Ethnic identity, racial discrimination and attenuated psychotic symptoms in an urban population of emerging adults.

Science.gov (United States)

Anglin, Deidre M; Lui, Florence; Espinosa, Adriana; Tikhonov, Aleksandr; Ellman, Lauren

2018-06-01

Studies suggest strong ethnic identity generally protects against negative mental health outcomes associated with racial discrimination. In light of evidence suggesting racial discrimination may enhance psychosis risk in racial and ethnic minority (REM) populations, the present study explored the relationship between ethnic identity and attenuated positive psychotic symptoms (APPS) and whether ethnic identity moderates the association between racial discrimination and these symptoms. A sample of 644 non-help-seeking REM emerging adults was administered self-report inventories for psychosis risk, experiences of discrimination and ethnic identity. Latent class analysis was applied to determine the nature and number of ethnic identity types in this population. The direct association between ethnic identity and APPS and the interaction between ethnic identity and racial discrimination on APPS were determined in linear regression analyses. Results indicated three ethnic identity classes (very low, moderate to high and very high). Ethnic identity was not directly related to APPS; however, it was related to APPS under racially discriminating conditions. Specifically, participants who experienced discrimination in the moderate to high or very high ethnic identity classes reported fewer symptoms than participants who experienced discrimination in the very low ethnic identity class. Strong ethnic group affiliation and connection may serve a protective function for psychosis risk in racially discriminating environments and contexts among REM young adults. The possible social benefits of strong ethnic identification among REM youth who face racial discrimination should be explored further in clinical high-risk studies. © 2016 John Wiley & Sons Australia, Ltd.

Combustion monitoring of a water tube boiler using a discriminant radial basis network.

Science.gov (United States)

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

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...

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

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...

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...

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)

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.

Hard and soft age discrimination: the dual nature of workplace discrimination.

Science.gov (United States)

Stypinska, Justyna; Turek, Konrad

2017-03-01

The paper concentrates on the problem of age discrimination in the labour market and the way it can be conceptualised and measured in a multi-disciplinary way. The approach proposed here combines two understandings of age discrimination-a sociological and legal one, what allows for a fuller and expanded understanding of ageism in the workplace. At the heart of the study is a survey carried out in Poland with a sample of 1000 men and women aged 45-65 years. The study takes a deeper and innovative look into the issue of age discrimination in employment. Confirmatory factor analysis with WLSMV estimation and logistic regressions were used to test the hypotheses. The study shows that age discrimination in labour market can take on different forms: hard and soft, where the hard type of age discrimination mirrors the legally prohibited types of behaviours and those which relate to the actual decisions of employers which can impact on the employee's career development. The soft discrimination corresponds with those occurrences, which are not inscribed in the legal system per se, are occurring predominantly in the interpersonal sphere, but can nevertheless have negative consequences. Soft discrimination was experienced more often (28.6% of respondents) than hard discrimination (15.7%) with higher occurrences among women, persons in precarious job situation or residents of urban areas. The role of education was not confirmed to influence the levels of perceived age discrimination.

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.

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.

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)

The analysis and design of linear circuits

CERN Document Server

Thomas, Roland E; Toussaint, Gregory J

2009-01-01

The Analysis and Design of Linear Circuits, 6e gives the reader the opportunity to not only analyze, but also design and evaluate linear circuits as early as possible. The text's abundance of problems, applications, pedagogical tools, and realistic examples helps engineers develop the skills needed to solve problems, design practical alternatives, and choose the best design from several competing solutions. Engineers searching for an accessible introduction to resistance circuits will benefit from this book that emphasizes the early development of engineering judgment.

CFD analysis of linear compressors considering load conditions

Science.gov (United States)

Bae, Sanghyun; Oh, Wonsik

2017-08-01

This paper is a study on computational fluid dynamics (CFD) analysis of linear compressor considering load conditions. In the conventional CFD analysis of the linear compressor, the load condition was not considered in the behaviour of the piston. In some papers, behaviour of piston is assumed as sinusoidal motion provided by user defined function (UDF). In the reciprocating type compressor, the stroke of the piston is restrained by the rod, while the stroke of the linear compressor is not restrained, and the stroke changes depending on the load condition. The greater the pressure difference between the discharge refrigerant and the suction refrigerant, the more the centre point of the stroke is pushed backward. And the behaviour of the piston is not a complete sine wave. For this reason, when the load condition changes in the CFD analysis of the linear compressor, it may happen that the ANSYS code is changed or unfortunately the modelling is changed. In addition, a separate analysis or calculation is required to find a stroke that meets the load condition, which may contain errors. In this study, the coupled mechanical equations and electrical equations are solved using the UDF, and the behaviour of the piston is solved considering the pressure difference across the piston. Using the above method, the stroke of the piston with respect to the motor specification of the analytical model can be calculated according to the input voltage, and the piston behaviour can be realized considering the thrust amount due to the pressure difference.

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.

Measuring HIV- and AIDS-related stigma and discrimination in Nicaragua: results from a community-based study.

Science.gov (United States)

Ugarte, William J; Högberg, Ulf; Valladares, Eliette C; Essén, Birgitta

2013-04-01

Psychometric properties of external HIV-related stigma and discrimination scales and their predictors were investigated. A cross-sectional community-based study was carried out among 520 participants using an ongoing health and demographic surveillance system in León, Nicaragua. Participants completed an 18-item HIV stigma scale and 19 HIV and AIDS discrimination-related statements. A factor analysis found that 15 of the 18 items in the stigma scale and 18 of the 19 items in the discrimination scale loaded clearly into five- and four-factor structures, respectively. Overall Cronbach's alpha of .81 for the HIV stigma scale and .91 for the HIV discrimination scale provided evidence of internal consistency. Hierarchical multiple linear regression analysis identified that females, rural residents, people with insufficient HIV-related transmission knowledge, those not tested for HIV, those reporting an elevated self-perception of HIV risk, and those unwilling to disclose their HIV status were associated with higher stigmatizing attitudes and higher discriminatory actions towards HIV-positive people. This is the first community-based study in Nicaragua that demonstrates that overall HIV stigma and discrimination scales were reliable and valid in a community-based sample comprised of men and women of reproductive age. Stigma and discrimination were reported high in the general population, especially among sub-groups. The findings in the current study suggest community-based strategies, including the monitoring of stigma and discrimination, and designing and implementing stigma reduction interventions, are greatly needed to reduce inequities and increase acceptance of persons with HIV.

Computer-aided diagnostic system of diffuse liver diseases using scintiscanning, 1. Application of linear discriminant function

Energy Technology Data Exchange (ETDEWEB)

Maeda, T; Ogawa, F; Okabe, H; Murakami, K [Kyoto Prefectural Univ. of Medicine (Japan); Yoshida, S

1976-07-01

An approach to an automated diagnostic system for diffuse parenchymal diseases of the liver is made based on hepatic scintigraphy and an electronic computer. The findings of hepatic scintigram with /sup 198/Au-colloid were analysed for 7 items, various patterns of hepatic image on anterior view, various patterns of hepatic image on right lateral view, criteria for visualization of bone marrow on anterior view, criteria for visualization of bone marrow on right lateral view, criteria for visualization of spleen on anterior view, degree of splenomegaly, and value of effective hepatic blood flow (KL-value). Each item was subdivided into several categories. Multivariate discriminant analysis was used for differential diagnosis of liver diseases with a dummy variable, based on the 25 categories of the 7 item on 100 abnormal hepato-scintigrams confirmed histologically, and on 20 normal subjects. This study was 90.0% accurate in normal liver, 81.1% accurate in acute hepatitis, 71.1% in inactive chronic hepatitis, 78.2% in active chronic hepatitis, 93.3% in Ko-type of liver cirrhosis, and 77.8% in the Otu-type of liver cirrhosis. The final diagnostic accuracy was 81.7% in all cases for training group. The accuracy of hepatic scintigraphy was 13% and 19% higher than that of laboratory findings in differentiating two groups of liver cirrhosis and two groups of chronic hepatitis respectively. To obtain maximum diagnostic information for discriminating among etiologies, ranges of discriminant function coefficients in these items were compared with in each group of liver diseases. The most useful diagnostic finding were the configurations of the hepatic images on the anterior and the right lateral views. Visualization of the spleen and the degree of splenomegaly were also useful for differentiation in each group of chronic hepatitis and liver cirrhosis.

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.

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.

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.

Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin.

Science.gov (United States)

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®

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.

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

Fast differential pulse discriminator-counter

International Nuclear Information System (INIS)

Shelevoj, K.D.

1985-01-01

The flowsheet of a differential pulse discriminator counter is described; the result of discrimination here is independent from the shape of the input pulse. Rate of the analysis of input pulses with minimum amplitude up to 0.3 mV coming out from the photomultiplier makes up 220 MHz. The flowsheet of the discriminator used in the system of photon counting for atmosphere probing is presented

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…

Discrimination of radiation quality through second harmonic out-of-phase cw-ESR detection.

Science.gov (United States)

Marrale, Maurizio; Longo, Anna; Brai, Maria; Barbon, Antonio; Brustolon, Marina

2014-02-01

The ability to discriminate the quality of ionizing radiation is important because the biological effects produced in tissue strongly depends on both absorbed dose and linear energy transfer (LET) of ionizing particles. Here we present an experimental electron spin resonance (ESR) analysis aimed at discriminating the effective LETs of various radiation beams (e.g., 19.3 MeV protons, (60)Co photons and thermal neutrons). The measurement of the intensities of the continuous wave spectrometer signal channel first harmonic in-phase and the second harmonic out-of-phase components are used to distinguish the radiation quality. A computational analysis, was carried out to evaluate the dependence of the first harmonic in-phase and second harmonic out-of-phase components on microwave power, modulation amplitude and relaxation times, and highlights that these components could be used to point out differences in the relaxation times. On the basis of this numerical analysis the experimental results are discussed. The methodology described in this study has the potential to provide information on radiation quality.

Darker Skin Tone Increases Perceived Discrimination among Male but Not Female Caribbean Black Youth

Directory of Open Access Journals (Sweden)

Shervin Assari

2017-12-01

Full Text Available Background: Among most minority groups, males seem to report higher levels of exposure and vulnerability to racial discrimination. Although darker skin tone may increase exposure to racial discrimination, it is yet unknown whether skin tone similarly influences perceived discrimination among male and female Caribbean Black youth. Objective: The current cross-sectional study tests the role of gender on the effects of skin tone on perceived discrimination among Caribbean Black youth. Methods: Data came from the National Survey of American Life-Adolescent Supplement (NSAL-A, 2003–2004, which included 360 Caribbean Black youth (ages 13 to 17. Demographic factors (age and gender, socioeconomic status (SES; family income, income to needs ratio, and subjective SES, skin tone, and perceived everyday discrimination were measured. Linear regressions were used for data analysis. Results: In the pooled sample, darker skin tone was associated with higher levels of perceived discrimination among Caribbean Black youth (b = 0.48; 95% Confidence Interval (CI = 0.07–0.89. A significant interaction was found between gender and skin tone (b = 1.17; 95% CI = 0.49–1.86, suggesting a larger effect of skin tone on perceived discrimination for males than females. In stratified models, darker skin tone was associated with more perceived discrimination for males (b = 1.20; 95% CI = 0.69–0.72 but not females (b = 0.06; 95% CI = −0.42–0.55. Conclusion: Similar to the literature documenting male gender as a vulnerability factor to the effects of racial discrimination, we found that male but not female Caribbean Black youth with darker skin tones perceive more discrimination.

Linear regression and sensitivity analysis in nuclear reactor design

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

2015-01-01

Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

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.

Comparison of equivalent linear and non linear methods on ground response analysis: case study at West Bangka site

International Nuclear Information System (INIS)

Eko Rudi Iswanto; Eric Yee

2016-01-01

Within the framework of identifying NPP sites, site surveys are performed in West Bangka (WB), Bangka-Belitung Island Province. Ground response analysis of a potential site has been carried out using peak strain profiles and peak ground acceleration. The objective of this research is to compare Equivalent Linear (EQL) and Non Linear (NL) methods of ground response analysis on the selected NPP site (West Bangka) using Deep Soil software. Equivalent linear method is widely used because requires soil data in simple way and short time of computational process. On the other hand, non linear method is capable of representing the actual soil behaviour by considering non linear soil parameter. The results showed that EQL method has similar trends to NL method. At surface layer, the acceleration values for EQL and NL methods are resulted as 0.425 g and 0.375 g respectively. NL method is more reliable in capturing higher frequencies of spectral acceleration compared to EQL method. (author)

Using digital images to measure and discriminate small particles in cotton

Science.gov (United States)

Taylor, Robert A.; Godbey, Luther C.

1991-02-01

Inages from conventional video systems are being digitized in coraputers for the analysis of small trash particles in cotton. The method has been developed to automate particle counting and area measurements for bales of cotton prepared for market. Because the video output is linearly proportional to the amount of light reflected the best spectral band for optimum particle discrimination should be centered at the wavelength of maximum difference between particles and their surroundings. However due to the spectral distribution of the illumination energy and the detector sensitivity peak image performance bands were altered. Reflectance from seven mechanically cleaned cotton lint samples and trash removed were examined for spectral contrast in the wavelength range of camera sensitivity. Pixel intensity histograms from the video systent are reported for simulated trashmeter area reference samples (painted dots on panels) and for cotton containing trash to demonstrate the particle discrimination mechanism. 2.

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.

Error Analysis on Plane-to-Plane Linear Approximate Coordinate ...

Indian Academy of Sciences (India)

Abstract. In this paper, the error analysis has been done for the linear approximate transformation between two tangent planes in celestial sphere in a simple case. The results demonstrate that the error from the linear transformation does not meet the requirement of high-precision astrometry under some conditions, so the ...

Controllability analysis of decentralised linear controllers for polymeric fuel cells

Energy Technology Data Exchange (ETDEWEB)

Serra, Maria; Aguado, Joaquin; Ansede, Xavier; Riera, Jordi [Institut de Robotica i Informatica Industrial, Universitat Politecnica de Catalunya - Consejo Superior de Investigaciones Cientificas, C. Llorens i Artigas 4, 08028 Barcelona (Spain)

2005-10-10

This work deals with the control of polymeric fuel cells. It includes a linear analysis of the system at different operating points, the comparison and selection of different control structures, and the validation of the controlled system by simulation. The work is based on a complex non linear model which has been linearised at several operating points. The linear analysis tools used are the Morari resiliency index, the condition number, and the relative gain array. These techniques are employed to compare the controllability of the system with different control structures and at different operating conditions. According to the results, the most promising control structures are selected and their performance with PI based diagonal controllers is evaluated through simulations with the complete non linear model. The range of operability of the examined control structures is compared. Conclusions indicate good performance of several diagonal linear controllers. However, very few have a wide operability range. (author)

Optimizing relational algebra operations using generic equivalence discriminators and lazy products

DEFF Research Database (Denmark)

Henglein, Fritz

2010-01-01

We show how to efficiently evaluate generic map-filter-product queries, generalizations of select-project-join (SPJ) queries in re- lational algebra, based on a combination of two novel techniques: generic discrimination-based joins and lazy (formal) products. Discrimination-based joins are based...... that discriminators can be constructed generically (by structural recursion on equivalence expressions), purely func- tionally, and efficiently (worst-case linear time). The array-based basic multiset discrimination algorithm of Cai and Paige (1995) provides a base discriminator that is both asymptotically and prac...... on relational algebra equalities, without need for a query preprocessing phase. They require no indexes and behave purely functionally. They can be considered a form of symbolic execution of set expressions that automate and encapsulate dynamic program transformation of such expressions and lead to asymptotic...

Supervised linear dimensionality reduction with robust margins for object recognition

Science.gov (United States)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

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.

Linearized spectrum correlation analysis for line emission measurements.

Science.gov (United States)

Nishizawa, T; Nornberg, M D; Den Hartog, D J; Sarff, J S

2017-08-01

A new spectral analysis method, Linearized Spectrum Correlation Analysis (LSCA), for charge exchange and passive ion Doppler spectroscopy is introduced to provide a means of measuring fast spectral line shape changes associated with ion-scale micro-instabilities. This analysis method is designed to resolve the fluctuations in the emission line shape from a stationary ion-scale wave. The method linearizes the fluctuations around a time-averaged line shape (e.g., Gaussian) and subdivides the spectral output channels into two sets to reduce contributions from uncorrelated fluctuations without averaging over the fast time dynamics. In principle, small fluctuations in the parameters used for a line shape model can be measured by evaluating the cross spectrum between different channel groupings to isolate a particular fluctuating quantity. High-frequency ion velocity measurements (100-200 kHz) were made by using this method. We also conducted simulations to compare LSCA with a moment analysis technique under a low photon count condition. Both experimental and synthetic measurements demonstrate the effectiveness of LSCA.

Botanical discrimination of Greek unifloral honeys with physico-chemical and chemometric analyses.

Science.gov (United States)

Karabagias, Ioannis K; Badeka, Anastasia V; Kontakos, Stavros; Karabournioti, Sofia; Kontominas, Michael G

2014-12-15

The aim of the present study was to investigate the possibility of characterisation and classification of Greek unifloral honeys (pine, thyme, fir and orange blossom) according to botanical origin using volatile compounds, conventional physico-chemical parameters and chemometric analyses (MANOVA and Linear Discriminant Analysis). For this purpose, 119 honey samples were collected during the harvesting period 2011 from 14 different regions in Greece known to produce unifloral honey of good quality. Physico-chemical analysis included the identification and semi quantification of fifty five volatile compounds performed by Headspace Solid Phase Microextraction coupled to gas chromatography/mass spectroscopy and the determination of conventional quality parameters such as pH, free, lactonic, total acidity, electrical conductivity, moisture, ash, lactonic/free acidity ratio and colour parameters L, a, b. Results showed that using 40 diverse variables (30 volatile compounds of different classes and 10 physico-chemical parameters) the honey samples were satisfactorily classified according to botanical origin using volatile compounds (84.0% correct prediction), physicochemical parameters (97.5% correct prediction), and the combination of both (95.8% correct prediction) indicating that multi element analysis comprises a powerful tool for honey discrimination purposes. Copyright © 2014 Elsevier Ltd. All rights reserved.

A parametric FE modeling of brake for non-linear analysis

Energy Technology Data Exchange (ETDEWEB)

Ahmed,Ibrahim; Fatouh, Yasser [Automotive and Tractors Technology Department, Faculty of Industrial Education, Helwan University, Cairo (Egypt); Aly, Wael [Refrigeration and Air-Conditioning Technology Department, Faculty of Industrial Education, Helwan University, Cairo (Egypt)

2013-07-01

A parametric modeling of a drum brake based on 3-D Finite Element Methods (FEM) for non-contact analysis is presented. Many parameters are examined during this study such as the effect of drum-lining interface stiffness, coefficient of friction, and line pressure on the interface contact. Firstly, the modal analysis of the drum brake is also studied to get the natural frequency and instability of the drum to facilitate transforming the modal elements to non-contact elements. It is shown that the Unsymmetric solver of the modal analysis is efficient enough to solve this linear problem after transforming the non-linear behavior of the contact between the drum and the lining to a linear behavior. A SOLID45 which is a linear element is used in the modal analysis and then transferred to non-linear elements which are Targe170 and Conta173 that represent the drum and lining for contact analysis study. The contact analysis problems are highly non-linear and require significant computer resources to solve it, however, the contact problem give two significant difficulties. Firstly, the region of contact is not known based on the boundary conditions such as line pressure, and drum and friction material specs. Secondly, these contact problems need to take the friction into consideration. Finally, it showed a good distribution of the nodal reaction forces on the slotted lining contact surface and existing of the slot in the middle of the lining can help in wear removal due to the friction between the lining and the drum. Accurate contact stiffness can give a good representation for the pressure distribution between the lining and the drum. However, a full contact of the front part of the slotted lining could occur in case of 20, 40, 60 and 80 bar of piston pressure and a partially contact between the drum and lining can occur in the rear part of the slotted lining.

Joint entropy for space and spatial frequency domains estimated from psychometric functions of achromatic discrimination.

Science.gov (United States)

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint

The Linear Time Frequency Analysis Toolbox

DEFF Research Database (Denmark)

Søndergaard, Peter Lempel; Torrésani, Bruno; Balazs, Peter

2011-01-01

The Linear Time Frequency Analysis Toolbox is a Matlab/Octave toolbox for computational time-frequency analysis. It is intended both as an educational and computational tool. The toolbox provides the basic Gabor, Wilson and MDCT transform along with routines for constructing windows (lter...... prototypes) and routines for manipulating coe cients. It also provides a bunch of demo scripts devoted either to demonstrating the main functions of the toolbox, or to exemplify their use in specic signal processing applications. In this paper we describe the used algorithms, their mathematical background...

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.

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.

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.

Application of linearized model to the stability analysis of the pressurized water reactor

International Nuclear Information System (INIS)

Li Haipeng; Huang Xiaojin; Zhang Liangju

2008-01-01

A Linear Time-Invariant model of the Pressurized Water Reactor is formulated through the linearization of the nonlinear model. The model simulation results show that the linearized model agrees well with the nonlinear model under small perturbation. Based upon the Lyapunov's First Method, the linearized model is applied to the stability analysis of the Pressurized Water Reactor. The calculation results show that the methodology of linearization to stability analysis is conveniently feasible. (authors)

Comparison of modal spectral and non-linear time history analysis of a piping system

International Nuclear Information System (INIS)

Gerard, R.; Aelbrecht, D.; Lafaille, J.P.

1987-01-01

A typical piping system of the discharge line of the chemical and volumetric control system, outside the containment, between the penetration and the heat exchanger, an operating power plant was analyzed using four different methods: Modal spectral analysis with 2% constant damping, modal spectral analysis using ASME Code Case N411 (PVRC damping), linear time history analysis, non-linear time history analysis. This paper presents an estimation of the conservatism of the linear methods compared to the non-linear analysis. (orig./HP)

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.

Salient Region Detection via Feature Combination and Discriminative Classifier

Directory of Open Access Journals (Sweden)

Deming Kong

2015-01-01

Full Text Available We introduce a novel approach to detect salient regions of an image via feature combination and discriminative classifier. Our method, which is based on hierarchical image abstraction, uses the logistic regression approach to map the regional feature vector to a saliency score. Four saliency cues are used in our approach, including color contrast in a global context, center-boundary priors, spatially compact color distribution, and objectness, which is as an atomic feature of segmented region in the image. By mapping a four-dimensional regional feature to fifteen-dimensional feature vector, we can linearly separate the salient regions from the clustered background by finding an optimal linear combination of feature coefficients in the fifteen-dimensional feature space and finally fuse the saliency maps across multiple levels. Furthermore, we introduce the weighted salient image center into our saliency analysis task. Extensive experiments on two large benchmark datasets show that the proposed approach achieves the best performance over several state-of-the-art approaches.

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.

Spatial Analysis of Linear Structures in the Exploration of Groundwater

Directory of Open Access Journals (Sweden)

Abdramane Dembele

2017-11-01

Full Text Available The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding, Principal Component Analysis (PCA, and band ratio methods. The automatic extraction method of linear structures has permitted the obtaining of a structural map with 82,659 linear structures, distributed on different stratigraphic stages. The intensity study shows an accentuation in density over 12.52% of the total area, containing 22.02% of the linear structures. The density and nodes (intersections of fractures formed by the linear structures on the different lithologies allowed to observe the behavior of the region’s aquifers in the exploration of subsoil resources. The central density, in relation to the hydrographic network of the lowlands, shows the conditioning of the flow and retention of groundwater in the region, and in-depth fluids. The node areas and high-density linear structures, have shown an ability to have rejections in deep (pores that favor the formation of structural traps for oil resources.

Generalized Linear Covariance Analysis

Science.gov (United States)

Carpenter, James R.; Markley, F. Landis

2014-01-01

This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

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.

A Colorimetric Sensor for Qualitative Discrimination and Quantitative Detection of Volatile Amines

Directory of Open Access Journals (Sweden)

Zhonglin Tang

2010-06-01

Full Text Available We have developed a novel colorimetric sensor based on a digital camera and white LED illumination. Colorimetric sensor arrays (CSAs were made from a set of six chemically responsive dyes impregnated on an inert substrate plate by solution casting. Six common amine aqueous solutions, including dimethylamine, triethylamine, diisopropyl-amine, aniline, cyclohexylamine, and pyridine vaporized at 25 °C and six health-related trimethylamine (TMA concentrations including 170 ppm, 51 ppm, 8 ppm, 2 ppm, 125 ppb and 50 ppb were analyzed by the sensor to test its ability for the qualitative discrimination and quantitative detection of volatile amines. We extracted the feature vectors of the CSA's response to the analytes from a fusional color space, which was obtained by conducting a joint search algorithm of sequential forward selection and sequential backward selection (SFS&SBS based on the linear discriminant criteria (LDC in a mixed color space composed of six common color spaces. The principle component analysis (PCA followed by the hierarchical cluser analysis (HCA were utilized to discriminate 12 analytes. Results showed that the colorimetric sensor grouped the six amine vapors and five TMA concentrations correctly, while TMA concentrations of 125 ppb and 50 ppb were indiscriminable from each other. The limitation of detection (LOD of the sensor for TMA was found to be lower than 50 ppb. The CSAs were reusable for TMA concentrations below 8 ppm.

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.

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

 Optimizing relational algebra operations using discrimination-based joins and lazy products

DEFF Research Database (Denmark)

Henglein, Fritz

We show how to implement in-memory execution of the core re- lational algebra operations of projection, selection and cross-product eciently, using discrimination-based joins and lazy products. We introduce the notion of (partitioning) discriminator, which par- titions a list of values according...... to a specied equivalence relation on keys the values are associated with. We show how discriminators can be dened generically, purely functionally, and eciently (worst-case linear time) on top of the array-based basic multiset discrimination algorithm of Cai and Paige (1995). Discriminators provide the basis...... the selection operation to recognize on the y whenever it is applied to a cross-product, in which case it can choose an ecient discrimination-based equijoin implementation. The techniques subsume most of the optimization techniques based on relational algebra equalities, without need for a query preprocessing...

Object recognition with hierarchical discriminant saliency networks.

Science.gov (United States)

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

Science.gov (United States)

Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

2014-01-01

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406

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.

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

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)

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.

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.

Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification

International Nuclear Information System (INIS)

Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

2009-01-01

Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate cepstral coefficients are selected by a proposed separability criterion to construct DFC features. For the post-processing, FLPP which combines features from several analysis windows is used to improve the feature performance further. In this work, two classifiers (a linear discriminant classifier and quadratic discriminant classifier) without hyper-parameter optimization are employed to simplify the training procedure and avoid the possible bias of feature evaluation. Experimental results of the 11-motion problem show that the proposed DFC feature outperforms traditional features such as time-domain statistics and autoregressive-derived cepstrum in terms of the classification accuracy, and it is a promising method for the multi-functionality and high-accuracy control of myoelectric prostheses

Tribal vs. Public Schools: Perceived Discrimination and School Adjustment among Indigenous Children from Early to Mid-Adolescence.

Science.gov (United States)

Crawford, Devan M; Cheadle, Jacob E; Whitbeck, Les B

2010-04-01

The purpose of this study is to assess the differential effects of perceived discrimination by type of school on positive school adjustment among Indigenous children during late elementary and early middle school years. The analysis utilizes a sample of 654 Indigenous children from four reservations in the Northern Midwest and four Canadian First Nation reserves. Multiple group linear growth modeling within a structural equation framework is employed to investigate the moderating effects of school type on the relationship between discrimination and positive school adjustment. Results show that students in all school types score relatively high on positive school adjustment at time one (ages 10-12). However, in contrast to students in tribal schools for whom positive school adjustment remains stable, those attending public schools and those moving between school types show a decline in school adjustment over time. Furthermore, the negative effects of discrimination on positive school adjustment are greater for those attending public schools and those moving between schools. Possible reasons for this finding and potential explanations for why tribal schools may provide protection from the negative effects of discrimination are discussed.

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.

Stigma and discrimination against people living with HIV by healthcare providers, Southwest Ethiopia

Directory of Open Access Journals (Sweden)

Feyissa Garumma T

2012-07-01

Full Text Available Abstract Background Stigma and discrimination against people living with human immunodeficiency virus (HIV are obstacles in the way of effective responses to HIV. Understanding the extent of stigma / discrimination and the underlying causes is necessary for developing strategies to reduce them. This study was conducted to explore stigma and discrimination against PLHIV amongst healthcare providers in Jimma zone, Southwest Ethiopia. Methods A cross-sectional study, employing quantitative and qualitative methods, was conducted in 18 healthcare institutions of Jimma zone, during March 14 to April 14, 2011. A total of 255 healthcare providers responded to questionnaires asking about sociodemographic characteristics, HIV knowledge, perceived institutional support and HIV-related stigma and discrimination. Factor analysis was employed to create measurement scales for stigma and factor scores were used in one way analysis of variance (ANOVA, T-tests, Pearson’s correlation and multiple linear regression analyses. Qualitative data collected using key-informant interviews and Focus Group Discussions (FGDs were employed to triangulate with the findings from the quantitative survey. Results Mean stigma scores (as the percentages of maximum scale scores were: 66.4 for the extra precaution scale, 52.3 for the fear of work-related HIV transmission, 49.4 for the lack of feelings of safety, 39.0 for the value-driven stigma, 37.4 for unethical treatment of PLHIV, 34.4 for discomfort around PLHIV and 31.1 for unofficial disclosure. Testing and disclosing test results without consent, designating HIV clients and unnecessary referral to other healthcare institutions and refusal to treat clients were identified. Having in-depth HIV knowledge, the perception of institutional support, attending training on stigma and discrimination, educational level of degree or higher, high HIV case loads, the presence of ART service in the healthcare facility and claiming to be

Non linear seismic analysis of charge/discharge machine

International Nuclear Information System (INIS)

Dostal, M.; Trbojevic, V.M.; Nobile, M.

1987-01-01

The main conclusions of the seismic analysis of the Latina CDM are: i. The charge machine has been demonstrated to be capable of withstanding the effects of a 0.1 g earthquake. Stresses and displacements were all within allowable limits and the stability criteria were fully satisfied for all positions of the cross-travel bogie on the gantry. ii. Movements due to loss of friction between the cross-travel bogie wheels and the rail was found to be small, i.e. less than 2 mm for all cases considered. The modes of rocking of the fixed and hinged legs preclude any possibility of excessive movement between the long travel bogie wheels and the rail. iii. The non-linear analysis incorporating contact and friction has given more realistic results than any of the linear verification analyses. The method of analysis indicates that even the larger structures can be efficiently solved on a mini computer for a long forcing input (16 s). (orig.)

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.

Standardized Index of Shape (SIS): a quantitative DCE-MRI parameter to discriminate responders by non-responders after neoadjuvant therapy in LARC

Energy Technology Data Exchange (ETDEWEB)

Petrillo, Antonella; Fusco, Roberta; Petrillo, Mario; Granata, Vincenza [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy). Div. of Radiology; Sansone, Mario [Naples Univ. ' ' Federico II' ' (Italy). Dept. of Biomedical, Electronics and Telecommunications Engineering; Avallone, Antonio [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy). Div. of Gastrointestinal Medical Oncology; Delrio, Paolo [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy). Div. of Gastrointestinal surgical Oncology; Pecori, Biagio [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy). Div. of Radiotherapy; Tatangelo, Fabiana [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy). Div. of Diagnostic Pathology; Ciliberto, Gennaro [Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS, Naples (Italy)

2015-07-15

To investigate the potential of DCE-MRI to discriminate responders from non-responders after neoadjuvant chemo-radiotherapy (CRT) for locally advanced rectal cancer (LARC). We investigated several shape parameters for the time-intensity curve (TIC) in order to identify the best combination of parameters between two linear parameter classifiers. Seventy-four consecutive patients with LARC were enrolled in a prospective study approved by our ethics committee. Each patient gave written informed consent. After surgery, pathological TNM and tumour regression grade (TRG) were estimated. DCE-MRI semi-quantitative analysis (sqMRI) was performed to identify the best parameter or parameter combination to discriminate responders from non-responders in response monitoring to CRT. Percentage changes of TIC shape descriptors from the baseline to the presurgical scan were assessed and correlated with TRG. Receiver operating characteristic analysis and linear classifier were applied. Forty-six patients (62.2 %) were classified as responders, while 28 subjects (37.8 %) were considered as non-responders. sqMRI reached a sensitivity of 93.5 % and a specificity of 82.1 % combining the percentage change in Maximum Signal Difference (ΔMSD) and Wash-out Slope (ΔWOS), the Standardized Index of Shape (SIS). SIS obtains the best result in discriminating responders from non-responders after CRT in LARC, with a cut-off value of -3.0 %. (orig.)

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.

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.

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.

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...

[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

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.

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.

Theoretical analysis of balanced truncation for linear switched systems

DEFF Research Database (Denmark)

Petreczky, Mihaly; Wisniewski, Rafal; Leth, John-Josef

2012-01-01

In this paper we present theoretical analysis of model reduction of linear switched systems based on balanced truncation, presented in [1,2]. More precisely, (1) we provide a bound on the estimation error using L2 gain, (2) we provide a system theoretic interpretation of grammians and their singu......In this paper we present theoretical analysis of model reduction of linear switched systems based on balanced truncation, presented in [1,2]. More precisely, (1) we provide a bound on the estimation error using L2 gain, (2) we provide a system theoretic interpretation of grammians...... for showing this independence is realization theory of linear switched systems. [1] H. R. Shaker and R. Wisniewski, "Generalized gramian framework for model/controller order reduction of switched systems", International Journal of Systems Science, Vol. 42, Issue 8, 2011, 1277-1291. [2] H. R. Shaker and R....... Wisniewski, "Switched Systems Reduction Framework Based on Convex Combination of Generalized Gramians", Journal of Control Science and Engineering, 2009....

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

Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

Directory of Open Access Journals (Sweden)

Tunjo Perić

2017-01-01

Full Text Available This paper presents and analyzes the applicability of three linearization techniques used for solving multi-objective linear fractional programming problems using the goal programming method. The three linearization techniques are: (1 Taylor’s polynomial linearization approximation, (2 the method of variable change, and (3 a modification of the method of variable change proposed in [20]. All three linearization techniques are presented and analyzed in two variants: (a using the optimal value of the objective functions as the decision makers’ aspirations, and (b the decision makers’ aspirations are given by the decision makers. As the criteria for the analysis we use the efficiency of the obtained solutions and the difficulties the analyst comes upon in preparing the linearization models. To analyze the applicability of the linearization techniques incorporated in the linear goal programming method we use an example of a financial structure optimization problem.

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.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

Energy Technology Data Exchange (ETDEWEB)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2014-05-15

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

International Nuclear Information System (INIS)

Jing, Yaqi; Meng, Qinghao; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen

2014-01-01

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively

Workplace discrimination: experiences of practicing physicians.

Science.gov (United States)

Coombs, Alice A Tolbert; King, Roderick K

2005-04-01

In response to a growing concern regarding physician discrimination in the workplace, this study was developed to: (1) describe the types of discrimination that exist for the practicing physician and (2) determine which groups of physicians are more likely to experience the various forms of discrimination. Surveys were mailed to 1930 practicing physicians in Massachusetts. Participants were asked if they had encountered discrimination, how significant the discrimination was against a specific group, the frequency of personal discrimination, and the type of discrimination. Factor analysis identified four types of discrimination: career advancement, punitive behaviors, practice barriers and hiring barriers. A total of 445 responses were received (a 24% response rate). Sixty-three percent of responding physicians had experienced some form of discrimination. Respondents were women (46%), racial/ethnic minorities (42%) and international medical graduates (IMGs) (40%). In addition, 26% of those classified as white were also IMGs. Over 60% of respondents believed discrimination against IMGs was very or somewhat significant. Almost 27% of males acknowledged that gender bias against females was very or somewhat significant. IMGs were more likely to indicate that discrimination against IMGs was significant in their current organization. Of U.S. medical graduates (USMGs) 44% reported that discrimination against IMGs in their current organization was significant. Nonwhites were more likely to report that discrimination based on race/ethnicity was significant. Nearly 29% of white respondents also believed that such discrimination was very or somewhat significant. Physicians practicing in academic, research, and private practice sectors experience discrimination based on gender, ethnic/racial, and IMG status.

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

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)

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

Science.gov (United States)

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

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)

Age and Workplace Discrimination in Lithuania

OpenAIRE

Braziene, Ruta

2017-01-01

This paper aims to disclose an expression of age and workplace discrimination in the Lithuanian labor market. The paper is discussing theoretical aspects of age discrimination and presents the results of the sociological survey research results carried out in 2014. The purpose of this paper is to disclose age and workplace discrimination at the Lithuanian labor market. Analysis of scientific literature and quantitative research results allows to state that older adults are experiencing discri...

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.

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...

CFORM- LINEAR CONTROL SYSTEM DESIGN AND ANALYSIS: CLOSED FORM SOLUTION AND TRANSIENT RESPONSE OF THE LINEAR DIFFERENTIAL EQUATION

Science.gov (United States)

Jamison, J. W.

1994-01-01

CFORM was developed by the Kennedy Space Center Robotics Lab to assist in linear control system design and analysis using closed form and transient response mechanisms. The program computes the closed form solution and transient response of a linear (constant coefficient) differential equation. CFORM allows a choice of three input functions: the Unit Step (a unit change in displacement); the Ramp function (step velocity); and the Parabolic function (step acceleration). It is only accurate in cases where the differential equation has distinct roots, and does not handle the case for roots at the origin (s=0). Initial conditions must be zero. Differential equations may be input to CFORM in two forms - polynomial and product of factors. In some linear control analyses, it may be more appropriate to use a related program, Linear Control System Design and Analysis (KSC-11376), which uses root locus and frequency response methods. CFORM was written in VAX FORTRAN for a VAX 11/780 under VAX VMS 4.7. It has a central memory requirement of 30K. CFORM was developed in 1987.

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.

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Directory of Open Access Journals (Sweden)

Pinar Deniz Tosun

2017-12-01

Full Text Available Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG. Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS, i.e., wakefulness, rapid-eye movement (REM and non-rapid-eye movement (NREM sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel–Ziv complexity (PLZC, a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD. The results obtained with PLZC were contrasted with a related non-linear method, Lempel–Ziv complexity (LZC. Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

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.

Experienced discrimination in home mortgage lending

DEFF Research Database (Denmark)

Secchi, Davide; Seri, Raffaello

2017-01-01

This article proposes a framework for the analysis of experienced discrimination in home mortgages. It addresses the problem of home mortgage lending discrimination in one of the richest areas of northern Italy. Employees of a local hospital were interviewed to study their perception (or experien...

Perceived age discrimination in older adults.

Science.gov (United States)

Rippon, Isla; Kneale, Dylan; de Oliveira, Cesar; Demakakos, Panayotes; Steptoe, Andrew

2014-05-01

to examine perceived age discrimination in a large representative sample of older adults in England. this cross-sectional study of over 7,500 individuals used data from the fifth wave of the English Longitudinal Study of Ageing (ELSA), a longitudinal cohort study of men and women aged 52 years and older in England. Wave 5 asked respondents about the frequency of five everyday discriminatory situations. Participants who attributed any experiences of discrimination to their age were treated as cases of perceived age discrimination. Multivariable logistic regression analysis was used to estimate the odds ratios of experiencing perceived age discrimination in relation to selected sociodemographic factors. approximately a third (33.3%) of all respondents experienced age discrimination, rising to 36.8% in those aged 65 and over. Perceived age discrimination was associated with older age, higher education, lower levels of household wealth and being retired or not in employment. The correlates of age discrimination across the five discriminatory situations were similar. understanding age discrimination is vital if we are to develop appropriate policies and to target future interventions effectively. These findings highlight the scale of the challenge of age discrimination for older adults in England and illustrate that those groups are particularly vulnerable to this form of discrimination.

Linear and nonlinear stability analysis, associated to experimental fast reactors

International Nuclear Information System (INIS)

Amorim, E.S. do; Moura Neto, C. de; Rosa, M.A.P.

1980-07-01

Phenomena associated to the physics of fast neutrons were analysed by linear and nonlinear Kinetics with arbitrary feedback. The theoretical foundations of linear kinetics and transfer functions aiming at the analysis of fast reactors stability, are established. These stability conditions were analitically proposed and investigated by digital and analogic programs. (E.G.) [pt

Investigation on n/γ discrimination methods for liquid scintillator detector

International Nuclear Information System (INIS)

Li Kuinian; Li Yang; Zhang Mei; Zhang Zhongbing; Li Binkang; Zhang Xiaodong; Liu Jun; Zhang Xianpeng

2014-01-01

To obtain the n/γ discrimination ability of different digital pulse shape discrimination methods, four methods (rising time method, charge comparison method, pulse gradient analysis and frequency gradient analysis) in americium-beryllium mixed radiation fields were demonstrated. The signals from EJ-301 and BC501A scintillator detectors were digitized using oscilloscope. A comparison was taken among the four discrimination methods. The discrimination results of the four methods in liquid scintillator detectors show that the rising time method is the best and it provides a good choice in real-time n/γ discrimination system. (authors)

Discrimination and Anti-discrimination in Denmark

DEFF Research Database (Denmark)

Olsen, Tore Vincents

The purpose of this report is to describe and analyse Danish anti-discrimination legislation and the debate about discrimination in Denmark in order to identify present and future legal challenges. The main focus is the implementation of the EU anti-discrimination directives in Danish law...

Tribal vs. Public Schools: Perceived Discrimination and School Adjustment among Indigenous Children from Early to Mid-Adolescence*

OpenAIRE

Crawford, Devan M.; Cheadle, Jacob E.; Whitbeck, Les B.

2010-01-01

The purpose of this study is to assess the differential effects of perceived discrimination by type of school on positive school adjustment among Indigenous children during late elementary and early middle school years. The analysis utilizes a sample of 654 Indigenous children from four reservations in the Northern Midwest and four Canadian First Nation reserves. Multiple group linear growth modeling within a structural equation framework is employed to investigate the moderating effects of s...

Social Determinants of Perceived Discrimination among Black Youth: Intersection of Ethnicity and Gender.

Science.gov (United States)

Assari, Shervin; Caldwell, Cleopatra Howard

2018-02-15

Most of the existing sociological and epidemiological literature has focused on the protective effects of high socioeconomic status (SES) on population health through reducing exposure to risk factors and increasing human and material resources that can mitigate adversities. Recent studies, however, have documented poor mental health of high SES Blacks, particularly African American males and Caribbean Black females. The literature also shows a link between perceived discrimination and poor mental health. To better understand the extra costs of upward social mobility for minority populations, this study explored ethnic by gender variations in the associations between SES indicators and perceived discrimination in an ethnically diverse national sample of Black youth. This study included 810 African American and 360 Caribbean Black youth who were sampled in the National Survey of American Life-Adolescent supplement (NSAL-A). Three SES indicators (financial hardship, family income, and income to needs ratio) were the independent variables. The dependent variable was perceived (daily) discrimination. Age was the covariate. Ethnicity and gender were the focal moderators. Linear regressions were used for data analysis in the pooled sample and also based on the intersection of ethnicity and gender. Considerable gender by ethnicity variations were found in the patterns of the associations between SES indicators and perceived discrimination. Financial hardship was a risk factor for perceived discrimination in African American males only. High family income and income to needs ratio were associated with high (but not low) perceived discrimination in African American males and Caribbean Black females. SES indicators were not associated with perceived discrimination for African American females or Caribbean Black males. When it comes to Black youth, high SES is not always protective. Whether SES reduces or increases perceived discrimination among Black youth depends on the

Social Determinants of Perceived Discrimination among Black Youth: Intersection of Ethnicity and Gender

Directory of Open Access Journals (Sweden)

Shervin Assari

2018-02-01

Full Text Available Most of the existing sociological and epidemiological literature has focused on the protective effects of high socioeconomic status (SES on population health through reducing exposure to risk factors and increasing human and material resources that can mitigate adversities. Recent studies, however, have documented poor mental health of high SES Blacks, particularly African American males and Caribbean Black females. The literature also shows a link between perceived discrimination and poor mental health. To better understand the extra costs of upward social mobility for minority populations, this study explored ethnic by gender variations in the associations between SES indicators and perceived discrimination in an ethnically diverse national sample of Black youth. This study included 810 African American and 360 Caribbean Black youth who were sampled in the National Survey of American Life—Adolescent supplement (NSAL-A. Three SES indicators (financial hardship, family income, and income to needs ratio were the independent variables. The dependent variable was perceived (daily discrimination. Age was the covariate. Ethnicity and gender were the focal moderators. Linear regressions were used for data analysis in the pooled sample and also based on the intersection of ethnicity and gender. Considerable gender by ethnicity variations were found in the patterns of the associations between SES indicators and perceived discrimination. Financial hardship was a risk factor for perceived discrimination in African American males only. High family income and income to needs ratio were associated with high (but not low perceived discrimination in African American males and Caribbean Black females. SES indicators were not associated with perceived discrimination for African American females or Caribbean Black males. When it comes to Black youth, high SES is not always protective. Whether SES reduces or increases perceived discrimination among Black youth

Small-scale quantum information processing with linear optics

International Nuclear Information System (INIS)

Bergou, J.A.; Steinberg, A.M.; Mohseni, M.

2005-01-01

Full text: Photons are the ideal systems for carrying quantum information. Although performing large-scale quantum computation on optical systems is extremely demanding, non scalable linear-optics quantum information processing may prove essential as part of quantum communication networks. In addition efficient (scalable) linear-optical quantum computation proposal relies on the same optical elements. Here, by constructing multirail optical networks, we experimentally study two central problems in quantum information science, namely optimal discrimination between nonorthogonal quantum states, and controlling decoherence in quantum systems. Quantum mechanics forbids deterministic discrimination between nonorthogonal states. This is one of the central features of quantum cryptography, which leads to secure communications. Quantum state discrimination is an important primitive in quantum information processing, since it determines the limitations of a potential eavesdropper, and it has applications in quantum cloning and entanglement concentration. In this work, we experimentally implement generalized measurements in an optical system and demonstrate the first optimal unambiguous discrimination between three non-orthogonal states with a success rate of 55 %, to be compared with the 25 % maximum achievable using projective measurements. Furthermore, we present the first realization of unambiguous discrimination between a pure state and a nonorthogonal mixed state. In a separate experiment, we demonstrate how decoherence-free subspaces (DFSs) may be incorporated into a prototype optical quantum algorithm. Specifically, we present an optical realization of two-qubit Deutsch-Jozsa algorithm in presence of random noise. By introduction of localized turbulent airflow we produce a collective optical dephasing, leading to large error rates and demonstrate that using DFS encoding, the error rate in the presence of decoherence can be reduced from 35 % to essentially its pre

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)

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 ...

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

Science.gov (United States)

Andrés, J M; Bona, M T

2006-11-15

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.

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.

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

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.

[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

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

Science.gov (United States)

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Using Linear Algebra to Introduce Computer Algebra, Numerical Analysis, Data Structures and Algorithms (and To Teach Linear Algebra, Too).

Science.gov (United States)

Gonzalez-Vega, Laureano

1999-01-01

Using a Computer Algebra System (CAS) to help with the teaching of an elementary course in linear algebra can be one way to introduce computer algebra, numerical analysis, data structures, and algorithms. Highlights the advantages and disadvantages of this approach to the teaching of linear algebra. (Author/MM)

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.

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.

The Role of Discrimination in Care Postponement Among Trans-Feminine Individuals in the U.S. National Transgender Discrimination Survey.

Science.gov (United States)

Glick, Jennifer L; Theall, Katherine P; Andrinopoulos, Katherine M; Kendall, Carl

2018-04-01

This study examines the associations between discrimination experiences (types and locations) and care postponement among trans-feminine individuals in the United States. This secondary, cross-sectional study utilized a subset of the data from the National Transgender Discrimination Survey (n = 2248), specifically for trans-feminine individuals. In this analysis, we examined the relationship between discrimination and primary care postponement. Twenty-six percent (26.25%) of the study sample reported delaying preventive care due to fear of discrimination; 23.98%-46.66% of respondents reported past experiences of discrimination (setting dependent). Discrimination in health and non-health settings and different types of discrimination-being denied services, verbally harassed, or physically assaulted-were all significantly associated with delaying care; respondents reporting discrimination were up to 20 times more likely to postpone care. While discrimination at a health location had the strongest association with care postponement (adjusted odds ratio = 9.65, confidence interval = 7.60-12.24), discrimination in all non-health-related locations was also important. Individuals reporting discrimination in greater numbers of locations and multiple types of discrimination were more likely to postpone care. To promote preventive care-seeking, these results affirm the importance of interventions that promote discrimination-free environments for gender minorities.

Item validity vs. item discrimination index: a redundancy?

Science.gov (United States)

Panjaitan, R. L.; Irawati, R.; Sujana, A.; Hanifah, N.; Djuanda, D.

2018-03-01

In several literatures about evaluation and test analysis, it is common to find that there are calculations of item validity as well as item discrimination index (D) with different formula for each. Meanwhile, other resources said that item discrimination index could be obtained by calculating the correlation between the testee’s score in a particular item and the testee’s score on the overall test, which is actually the same concept as item validity. Some research reports, especially undergraduate theses tend to include both item validity and item discrimination index in the instrument analysis. It seems that these concepts might overlap for both reflect the test quality on measuring the examinees’ ability. In this paper, examples of some results of data processing on item validity and item discrimination index were compared. It would be discussed whether item validity and item discrimination index can be represented by one of them only or it should be better to present both calculations for simple test analysis, especially in undergraduate theses where test analyses were included.

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)

Perceived Discrimination and Health: A Meta-Analytic Review

Science.gov (United States)

Pascoe, Elizabeth A.; Richman, Laura Smart

2009-01-01

Perceived discrimination has been studied with regard to its impact on several types of health effects. This meta-analysis provides a comprehensive account of the relationships between multiple forms of perceived discrimination and both mental and physical health outcomes. In addition, this meta-analysis examines potential mechanisms by which…

Development of non-linear vibration analysis code for CANDU fuelling machine

International Nuclear Information System (INIS)

Murakami, Hajime; Hirai, Takeshi; Horikoshi, Kiyomi; Mizukoshi, Kaoru; Takenaka, Yasuo; Suzuki, Norio.

1988-01-01

This paper describes the development of a non-linear, dynamic analysis code for the CANDU 600 fuelling machine (F-M), which includes a number of non-linearities such as gap with or without Coulomb friction, special multi-linear spring connections, etc. The capabilities and features of the code and the mathematical treatment for the non-linearities are explained. The modeling and numerical methodology for the non-linearities employed in the code are verified experimentally. Finally, the simulation analyses for the full-scale F-M vibration testing are carried out, and the applicability of the code to such multi-degree of freedom systems as F-M is demonstrated. (author)

Robust Linear Models for Cis-eQTL Analysis.

Science.gov (United States)

Rantalainen, Mattias; Lindgren, Cecilia M; Holmes, Christopher C

2015-01-01

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

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

Discriminative Bayesian Dictionary Learning for Classification.

Science.gov (United States)

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Linear and Nonlinear Analysis of Brain Dynamics in Children with Cerebral Palsy

Science.gov (United States)

Sajedi, Firoozeh; Ahmadlou, Mehran; Vameghi, Roshanak; Gharib, Masoud; Hemmati, Sahel

2013-01-01

This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26…

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

Discrimination Issues in the Process of Personnel Selection

OpenAIRE

Krinitsyna, Zoya Vasilievna; Menshikova, Ekaterina Valentinovna

2015-01-01

The paper discusses the concept of employment discrimination and its types, depending on the causes. The analysis of problems of social (gender and age) and psychological (racial and religious affiliation, disability) discrimination is given. The consequences of discrimination from the perspective of the employee and the employer are considered. The unfavorable situation in Russia in terms of high levels of discrimination is shown. The main objective trend, which will lead inevitably to the r...

Bayes-Based Fault Discrimination in Wide Area Backup Protection

Directory of Open Access Journals (Sweden)

WANG, Z.

2012-02-01

Full Text Available Multivariate statistical analysis is an effective tool to finish the fault location for electric power system. In Bayesian discriminant analysis as a subbranch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. In this paper, based on Bayesian discriminant analysis principle, a great number of simulation examples have confirmed that the results of Bayesian fault discriminant in wide area backup protection are accurate and reliable.

Functional linear models for association analysis of quantitative traits.

Science.gov (United States)

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY

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)

An analysis of the electromagnetic field in multi-polar linear induction system

International Nuclear Information System (INIS)

Chervenkova, Todorka; Chervenkov, Atanas

2002-01-01

In this paper a new method for determination of the electromagnetic field vectors in a multi-polar linear induction system (LIS) is described. The analysis of the electromagnetic field has been done by four dimensional electromagnetic potentials in conjunction with theory of the magnetic loops . The electromagnetic field vectors are determined in the Minkovski's space as elements of the Maxwell's tensor. The results obtained are compared with those got from the analysis made by the finite elements method (FEM).With the method represented in this paper one can determine the electromagnetic field vectors in the multi-polar linear induction system using four-dimensional potential. A priority of this method is the obtaining of analytical results for the electromagnetic field vectors. These results are also valid for linear media. The dependencies are valid also at high speeds of movement. The results of the investigated linear induction system are comparable to those got by the finite elements method. The investigations may be continued in the determination of other characteristics such as drag force, levitation force, etc. The method proposed in this paper for an analysis of linear induction system can be used for optimization calculations. (Author)

Associations of racial discrimination and parental discrimination coping messages with African American adolescent racial identity.

Science.gov (United States)

Richardson, Bridget L; Macon, Tamarie A; Mustafaa, Faheemah N; Bogan, Erin D; Cole-Lewis, Yasmin; Chavous, Tabbye M

2015-06-01

Research links racial identity to important developmental outcomes among African American adolescents, but less is known about the contextual experiences that shape youths' racial identity. In a sample of 491 African American adolescents (48% female), associations of youth-reported experiences of racial discrimination and parental messages about preparation for racial bias with adolescents' later racial identity were examined. Cluster analysis resulted in four profiles of adolescents varying in reported frequency of racial discrimination from teachers and peers at school and frequency of parental racial discrimination coping messages during adolescents' 8th grade year. Boys were disproportionately over-represented in the cluster of youth experiencing more frequent discrimination but receiving fewer parental discrimination coping messages, relative to the overall sample. Also examined were clusters of adolescents' 11th grade racial identity attitudes about the importance of race (centrality), personal group affect (private regard), and perceptions of societal beliefs about African Americans (public regard). Girls and boys did not differ in their representation in racial identity clusters, but 8th grade discrimination/parent messages clusters were associated with 11th grade racial identity cluster membership, and these associations varied across gender groups. Boys experiencing more frequent discrimination but fewer parental coping messages were over-represented in the racial identity cluster characterized by low centrality, low private regard, and average public regard. The findings suggest that adolescents who experience racial discrimination but receive fewer parental supports for negotiating and coping with discrimination may be at heightened risk for internalizing stigmatizing experiences. Also, the findings suggest the need to consider the context of gender in adolescents' racial discrimination and parental racial socialization.

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)

Stability Analysis for Multi-Parameter Linear Periodic Systems

DEFF Research Database (Denmark)

Seyranian, A.P.; Solem, Frederik; Pedersen, Pauli

1999-01-01

This paper is devoted to stability analysis of general linear periodic systems depending on real parameters. The Floquet method and perturbation technique are the basis of the development. We start out with the first and higher-order derivatives of the Floquet matrix with respect to problem...

Evaluation of errors for mass-spectrometric analysis with surface-ionization type mass-spectrometer (statistical evaluation of mass-discrimination effect)

International Nuclear Information System (INIS)

Wada, Y.

1981-01-01

The surface-ionization type mass-spectrometer is widely used as an apparatus for quality assurance, accountability and safeguarding of nuclear materials, and for this analysis it has become an important factor to statistically evaluate an analytical error which consists of a random error and a systematic error. The major factor of this systematic error was the mass-discrimination effect. In this paper, various assays for evaluating the factor of variation on the mass-discrimination effect were studied and the data obtained were statistically evaluated. As a result of these analyses, it was proved that the factor of variation on the mass-discrimination effect was not attributed to the acid concentration of sample, sample size on the filament and supplied voltage for a multiplier, but mainly to the filament temperature during the mass-spectrometric analysis. The mass-discrimination effect values β which were usually calculated from the measured data of uranium, plutonium or boron isotopic standard sample were not so significant dependently of the difference of U-235, Pu-239 or B-10 isotopic abundance. Furthermore, in the case of U and Pu, measurement conditions and the mass range of these isotopes were almost similar, and these values β were not statistically significant between U and Pu. On the other hand, the value β for boron was about a third of the value β for U or Pu, but compared with the coefficient of the correction on the mass-discrimination effect for the difference of mass-number, ΔM, these coefficient values were almost the same among U, Pu, and B.As for the isotopic analysis error of U, Pu, Nd and B, it was proved that the isotopic abundance of these elements and the isotopic analysis error were in a relationship of quadratic curves on a logarithmic-logarithmic scale

Linearization effect in multifractal analysis: Insights from the Random Energy Model

Science.gov (United States)

Angeletti, Florian; Mézard, Marc; Bertin, Eric; Abry, Patrice

2011-08-01

The analysis of the linearization effect in multifractal analysis, and hence of the estimation of moments for multifractal processes, is revisited borrowing concepts from the statistical physics of disordered systems, notably from the analysis of the so-called Random Energy Model. Considering a standard multifractal process (compound Poisson motion), chosen as a simple representative example, we show the following: (i) the existence of a critical order q∗ beyond which moments, though finite, cannot be estimated through empirical averages, irrespective of the sample size of the observation; (ii) multifractal exponents necessarily behave linearly in q, for q>q∗. Tailoring the analysis conducted for the Random Energy Model to that of Compound Poisson motion, we provide explicative and quantitative predictions for the values of q∗ and for the slope controlling the linear behavior of the multifractal exponents. These quantities are shown to be related only to the definition of the multifractal process and not to depend on the sample size of the observation. Monte Carlo simulations, conducted over a large number of large sample size realizations of compound Poisson motion, comfort and extend these analyses.

Non-linear Dynamics of Speech in Schizophrenia

DEFF Research Database (Denmark)

Fusaroli, Riccardo; Simonsen, Arndis; Weed, Ethan

(regularity and complexity) of speech. Our aims are (1) to achieve a more fine-grained understanding of the speech patterns in schizophrenia than has previously been achieved using traditional, linear measures of prosody and fluency, and (2) to employ the results in a supervised machine-learning process......-effects inference. SANS and SAPS scores were predicted using a 10-fold cross-validated multiple linear regression. Both analyses were iterated 1000 to test for stability of results. Results: Voice dynamics allowed discrimination of patients with schizophrenia from healthy controls with a balanced accuracy of 85...

The linear attenuation coefficients as features of multiple energy CT image classification

International Nuclear Information System (INIS)

Homem, M.R.P.; Mascarenhas, N.D.A.; Cruvinel, P.E.

2000-01-01

We present in this paper an analysis of the linear attenuation coefficients as useful features of single and multiple energy CT images with the use of statistical pattern classification tools. We analyzed four CT images through two pointwise classifiers (the first classifier is based on the maximum-likelihood criterion and the second classifier is based on the k-means clustering algorithm) and one contextual Bayesian classifier (ICM algorithm - Iterated Conditional Modes) using an a priori Potts-Strauss model. A feature extraction procedure using the Jeffries-Matusita (J-M) distance and the Karhunen-Loeve transformation was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials

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

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

Science.gov (United States)

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, Pdiscrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

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.

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.

Dielectrophoresis-based discrimination of bacteria at the strain level based on their surface properties.

Directory of Open Access Journals (Sweden)

William A Braff

Full Text Available Insulator-based dielectrophoresis can be used to manipulate biological particles, but has thus far found limited practical applications due to low sensitivity. We present linear sweep three-dimensional insulator-based dielectrophoresis as a considerably more sensitive approach for strain-level discrimination bacteria. In this work, linear sweep three-dimensional insulator-based dielectrophoresis was performed on Pseudomonas aeruginosa PA14 along with six isogenic mutants as well as Streptococcus mitis SF100 and PS344. Strain-level discrimination was achieved between these clinically important pathogens with applied electric fields below 10 V/mm. This low voltage, high sensitivity technique has potential applications in clinical diagnostics as well as microbial physiology research.

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.

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

Discrimination of curvature from motion during smooth pursuit eye movements and fixation.

Science.gov (United States)

Ross, Nicholas M; Goettker, Alexander; Schütz, Alexander C; Braun, Doris I; Gegenfurtner, Karl R

2017-09-01

Smooth pursuit and motion perception have mainly been investigated with stimuli moving along linear trajectories. Here we studied the quality of pursuit movements to curved motion trajectories in human observers and examined whether the pursuit responses would be sensitive enough to discriminate various degrees of curvature. In a two-interval forced-choice task subjects pursued a Gaussian blob moving along a curved trajectory and then indicated in which interval the curve was flatter. We also measured discrimination thresholds for the same curvatures during fixation. Motion curvature had some specific effects on smooth pursuit properties: trajectories with larger amounts of curvature elicited lower open-loop acceleration, lower pursuit gain, and larger catch-up saccades compared with less curved trajectories. Initially, target motion curvatures were underestimated; however, ∼300 ms after pursuit onset pursuit responses closely matched the actual curved trajectory. We calculated perceptual thresholds for curvature discrimination, which were on the order of 1.5 degrees of visual angle (°) for a 7.9° curvature standard. Oculometric sensitivity to curvature discrimination based on the whole pursuit trajectory was quite similar to perceptual performance. Oculometric thresholds based on smaller time windows were higher. Thus smooth pursuit can quite accurately follow moving targets with curved trajectories, but temporal integration over longer periods is necessary to reach perceptual thresholds for curvature discrimination. NEW & NOTEWORTHY Even though motion trajectories in the real world are frequently curved, most studies of smooth pursuit and motion perception have investigated linear motion. We show that pursuit initially underestimates the curvature of target motion and is able to reproduce the target curvature ∼300 ms after pursuit onset. Temporal integration of target motion over longer periods is necessary for pursuit to reach the level of precision found

Transgender women of color: discrimination and depression symptoms

Science.gov (United States)

Jefferson, Kevin; Neilands, Torsten B.; Sevelius, Jae

2014-01-01

Purpose Trans women of color contend with multiple marginalizations; the purpose of this study is to examine associations between experiencing discriminatory (racist/transphobic) events and depression symptoms. It uses a categorical measure of combined discrimination, and examines a protective association of transgender identity on depression symptoms. Design/methodology/approach Data from a subset of trans women of color participants in the Sheroes study were analyzed with linear and logistic regression. Associations of depression symptoms with racist and transphobic events, combined discrimination, coping self-efficacy, and transgender identity were assessed with odds ratios. Findings Exposure to discriminatory events and combined discrimination positively associated with depression symptom odds. Increased transgender identity associated with increased coping self-efficacy, which negatively associated with depression symptom odds. Research limitations/implications Cross-sectional study data prohibits inferring causality; results support conducting longitudinal research on discrimination’s health effects, and research on transgender identity. Results also support operationalizing intersectionality in health research. The study’s categorical approach to combined discrimination may be replicable in studies with hard to reach populations and small sample sizes. Practical implications Health programs could pursue psychosocial interventions and anti-discrimination campaigns. Interventions might advocate increasing participants’ coping self-efficacy while providing space to explore and develop social identity. Social implications There is a need for policy and health programs to center trans women of color concerns. Originality/value This study examines combined discrimination and identity in relation to depression symptoms among trans women of color, an underserved population. Paper type Research paper PMID:25346778

Discrimination in the workplace, reported by people with major depressive disorder: a cross-sectional study in 35 countries

Science.gov (United States)

Brouwers, E P M; Mathijssen, J; Van Bortel, T; Knifton, L; Wahlbeck, K; Van Audenhove, C; Kadri, N; Chang, Ch; Goud, B R; Ballester, D; Tófoli, LF; Bello, R; Jorge-Monteiro, M F; Zäske, H; Milaćić, I; Uçok, A; Bonetto, C; Lasalvia, A; Thornicroft, G; Van Weeghel, J

2016-01-01

Objective Whereas employment has been shown to be beneficial for people with Major Depressive Disorder (MDD) across different cultures, employers’ attitudes have been shown to be negative towards workers with MDD. This may form an important barrier to work participation. Today, little is known about how stigma and discrimination affect work participation of workers with MDD, especially from their own perspective. We aimed to assess, in a working age population including respondents with MDD from 35 countries: (1) if people with MDD anticipate and experience discrimination when trying to find or keep paid employment; (2) if participants in high, middle and lower developed countries differ in these respects; and (3) if discrimination experiences are related to actual employment status (ie, having a paid job or not). Method Participants in this cross-sectional study (N=834) had a diagnosis of MDD in the previous 12 months. They were interviewed using the Discrimination and Stigma Scale (DISC-12). Analysis of variance and generalised linear mixed models were used to analyse the data. Results Overall, 62.5% had anticipated and/or experienced discrimination in the work setting. In very high developed countries, almost 60% of respondents had stopped themselves from applying for work, education or training because of anticipated discrimination. Having experienced workplace discrimination was independently related to unemployment. Conclusions Across different countries and cultures, people with MDD very frequently reported discrimination in the work setting. Effective interventions are needed to enhance work participation in people with MDD, focusing simultaneously on decreasing stigma in the work environment and on decreasing self-discrimination by empowering workers with MDD. PMID:26908523

Socioeconomic status discrimination and C-reactive protein in African-American and White adults.

Science.gov (United States)

Van Dyke, Miriam E; Vaccarino, Viola; Dunbar, Sandra B; Pemu, Priscilla; Gibbons, Gary H; Quyyumi, Arshed A; Lewis, Tené T

2017-08-01

We examined the association between socioeconomic status (SES) discrimination and C-reactive protein (CRP) in a biracial cohort of middle-aged adults using an intersectionality framework. Participants were 401 African-American and White adults from a population-based cohort in the Southeastern United States. SES discrimination was self-reported with a modified Experiences of Discrimination Scale, and CRP levels were assayed from blood samples. Linear regression analyses were used to examine the associations among SES discrimination, race, education, and CRP after controlling for age, gender, racial and gender discrimination, financial and general stress, body mass index, smoking, sleep quality, and depressive symptoms. Intersectional effects were tested using race×SES discrimination, education×SES discrimination and race×education×SES discrimination interactions. Adjusting for sociodemographics, racial discrimination, gender discrimination, and all relevant two-way interaction terms, we observed a significant race×education×SES discrimination interaction (p=0.019). In adjusted models stratified by race and education, SES discrimination was associated with elevated CRP among higher educated African-Americans (β=0.29, p=0.018), but not lower educated African-Americans (β=-0.13, p=0.32); or lower educated (β=-0.02, p=0.92) or higher educated (β=-0.01, p=0.90) Whites. Findings support the relevance of SES discrimination as an important discriminatory stressor for CRP specifically among higher educated African-Americans. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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...

Discrimination of Xihulongjing tea grade using an electronic tongue ...

African Journals Online (AJOL)

Five grades of Xihulongjing tea (grade: AAA, AA, A, B and C, from the same region and processed with the same processing method) were discriminated using -Astree II electronic tongue (e-tongue) coupled with pattern recognition methods including principal component analysis (PCA), canonical discriminant analysis ...

Linear stability analysis in a solid-propellant rocket motor

Energy Technology Data Exchange (ETDEWEB)

Kim, K.M.; Kang, K.T.; Yoon, J.K. [Agency for Defense Development, Taejon (Korea, Republic of)

1995-10-01

Combustion instability in solid-propellant rocket motors depends on the balance between acoustic energy gains and losses of the system. The objective of this paper is to demonstrate the capability of the program which predicts the standard longitudinal stability using acoustic modes based on linear stability analysis and T-burner test results of propellants. Commercial ANSYS 5.0A program can be used to calculate the acoustic characteristic of a rocket motor. The linear stability prediction was compared with the static firing test results of rocket motors. (author). 11 refs., 17 figs.

Linear regression models and k-means clustering for statistical analysis of fNIRS data.

Science.gov (United States)

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-02-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

Linear stability analysis of collective neutrino oscillations without spurious modes

Science.gov (United States)

Morinaga, Taiki; Yamada, Shoichi

2018-01-01

Collective neutrino oscillations are induced by the presence of neutrinos themselves. As such, they are intrinsically nonlinear phenomena and are much more complex than linear counterparts such as the vacuum or Mikheyev-Smirnov-Wolfenstein oscillations. They obey integro-differential equations, for which it is also very challenging to obtain numerical solutions. If one focuses on the onset of collective oscillations, on the other hand, the equations can be linearized and the technique of linear analysis can be employed. Unfortunately, however, it is well known that such an analysis, when applied with discretizations of continuous angular distributions, suffers from the appearance of so-called spurious modes: unphysical eigenmodes of the discretized linear equations. In this paper, we analyze in detail the origin of these unphysical modes and present a simple solution to this annoying problem. We find that the spurious modes originate from the artificial production of pole singularities instead of a branch cut on the Riemann surface by the discretizations. The branching point singularities on the Riemann surface for the original nondiscretized equations can be recovered by approximating the angular distributions with polynomials and then performing the integrals analytically. We demonstrate for some examples that this simple prescription does remove the spurious modes. We also propose an even simpler method: a piecewise linear approximation to the angular distribution. It is shown that the same methodology is applicable to the multienergy case as well as to the dispersion relation approach that was proposed very recently.

Everyday discrimination is associated with nicotine dependence among African American, Latino, and White smokers.

Science.gov (United States)

Kendzor, Darla E; Businelle, Michael S; Reitzel, Lorraine R; Rios, Debra M; Scheuermann, Taneisha S; Pulvers, Kim; Ahluwalia, Jasjit S

2014-06-01

Discrimination is a commonly perceived stressor among African Americans and Latinos, and previous research has linked stress with substance dependence. Although studies have shown a link between discrimination and smoking, little is known about the relationship between discrimination and nicotine dependence. A total of 2,376 African American (33.4%; n = 794), Latino (33.1%; n = 786), and White (33.5%; n = 796) smokers completed an online survey. Everyday discrimination experiences were described in total and by race/ethnicity. Covariate-adjusted linear regression analyses were conducted to evaluate the associations between everyday discrimination and indicators of nicotine dependence. Most participants (79.1%), regardless of race/ethnicity, reported experiencing everyday discrimination. However, total scores on the discrimination measure were higher among Latinos and African Americans than among Whites (p Whites. Regression analyses indicated that everyday discrimination was positively associated with indicators of nicotine dependence, including the Heaviness of Smoking Index (HSI; p < .001) and the Brief Wisconsin Inventory of Smoking Dependence Motives (WISDM) scales (all ps < .001). There was a significant interaction between race/ethnicity and discrimination, such that discrimination was associated with the HSI only among Latinos. Similarly, discrimination was most strongly associated with the WISDM scales among Latinos. Analyses indicated that discrimination is a common stressor associated with nicotine dependence. Findings suggest that greater nicotine dependence is a potential pathway through which discrimination may influence health.

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)

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.

[Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].

Science.gov (United States)

Zhang, Yan-nan; Chen, Lan-zhen; Xue, Xiao-feng; Wu, Li-ming; Li, Yi; Yang, Juan

2015-09-01

At present, the rice syrup as a low price of the sweeteners was often adulterated into acacia honey and the adulterated honeys were sold in honey markets, while there is no suitable and fast method to identify honey adulterated with rice syrup. In this study, Near infrared spectroscopy (NIR) combined with chemometric methods were used to discriminate authenticity of honey. 20 unprocessed acacia honey samples from the different honey producing areas, mixed? with different proportion of rice syrup, were prepared of seven different concentration gradient? including 121 samples. The near infrared spectrum (NIR) instrument and spectrum processing software have been applied in the? spectrum? scanning and data conversion on adulterant samples, respectively. Then it was analyzed by Principal component analysis (PCA) and canonical discriminant analysis methods in order to discriminating adulterated honey. The results showed that after principal components analysis, the first two principal components accounted for 97.23% of total variation, but the regionalism of the score plot of the first two PCs was not obvious, so the canonical discriminant analysis was used to make the further discrimination, all samples had been discriminated correctly, the first two discriminant functions accounted for 91.6% among the six canonical discriminant functions, Then the different concentration of adulterant samples can be discriminated correctly, it illustrate that canonical discriminant analysis method combined with NIR spectroscopy is not only feasible but also practical for rapid and effective discriminate of the rice syrup adulterant of acacia honey.

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.

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.).

Discrimination and depressive symptoms among sexual minority youth: is gay-affirming religious affiliation a protective factor?

Science.gov (United States)

Gattis, Maurice N; Woodford, Michael R; Han, Yoonsun

2014-11-01

Researchers have examined perceived discrimination as a risk factor for depression among sexual minorities; however, the role of religion as a protective factor is under-investigated, especially among sexual minority youth. Drawing on a cross-sectional study investigating campus climate at a large public university in the U.S. midwest, we examined the role of affiliation with a gay-affirming denomination (i.e., endorsing same-sex marriage) as a moderating factor in the discrimination-depression relationship among self-identified sexual minority (n = 393) and heterosexual youth (n = 1,727). Using multivariate linear regression analysis, religious affiliation was found to moderate the discrimination-depression relationship among sexual minorities. Specifically, the results indicated that the harmful effects of discrimination among sexual minority youth affiliated with denominations that endorsed same-sex marriage were significantly less than those among peers who affiliated with denominations opposing same-sex marriage or who identified as secular. In contrast, religious affiliation with gay-affirming denominations did not moderate the discrimination-depression relationship among heterosexual participants. The findings suggest that, although religion and same-sex sexuality are often seen as incompatible topics, it is important when working with sexual minority clients for clinicians to assess religious affiliation, as it could be either a risk or a protective factor, depending on the religious group's stance toward same-sex sexuality. To promote the well-being of sexual minority youth affiliated with denominations opposed to same-sex marriage, the results suggest these faith communities may be encouraged to reconsider their position and/or identify ways to foster youth's resilience to interpersonal discrimination.

Is Perceived Discrimination in Pregnancy Prospectively Linked to Postpartum Depression? Exploring the Role of Education.

Science.gov (United States)

Stepanikova, Irena; Kukla, Lubomir

2017-08-01

Objectives The role of perceived discrimination in postpartum depression is largely unknown. We investigate whether perceived discrimination reported in pregnancy contributes to postpartum depression, and whether its impact varies by education level. Methods Prospective data are a part of European Longitudinal Study of Pregnancy and Childhood, the Czech Republic. Surveys were collected in mid-pregnancy and at 6 months after delivery. Depression was measured using Edinburgh Postnatal Depression Scale. Generalized linear models were estimated to test the effects of perceived discrimination on postpartum depression. Results Multivariate models revealed that among women with low education, discrimination in pregnancy was prospectively associated with 2.43 times higher odds of postpartum depression (p discrimination was not linked to postpartum depression among women with high education. Conclusions Perceived discrimination is a risk factor for postpartum depression among women with low education. Screening for discrimination and socio-economic disadvantage during pregnancy could benefit women who are at risk for mental health problems.

Linear functional analysis for scientists and engineers

CERN Document Server

Limaye, Balmohan V

2016-01-01

This book provides a concise and meticulous introduction to functional analysis. Since the topic draws heavily on the interplay between the algebraic structure of a linear space and the distance structure of a metric space, functional analysis is increasingly gaining the attention of not only mathematicians but also scientists and engineers. The purpose of the text is to present the basic aspects of functional analysis to this varied audience, keeping in mind the considerations of applicability. A novelty of this book is the inclusion of a result by Zabreiko, which states that every countably subadditive seminorm on a Banach space is continuous. Several major theorems in functional analysis are easy consequences of this result. The entire book can be used as a textbook for an introductory course in functional analysis without having to make any specific selection from the topics presented here. Basic notions in the setting of a metric space are defined in terms of sequences. These include total boundedness, c...

Non-linear analysis of skew thin plate by finite difference method

International Nuclear Information System (INIS)

Kim, Chi Kyung; Hwang, Myung Hwan

2012-01-01

This paper deals with a discrete analysis capability for predicting the geometrically nonlinear behavior of skew thin plate subjected to uniform pressure. The differential equations are discretized by means of the finite difference method which are used to determine the deflections and the in-plane stress functions of plates and reduced to several sets of linear algebraic simultaneous equations. For the geometrically non-linear, large deflection behavior of the plate, the non-linear plate theory is used for the analysis. An iterative scheme is employed to solve these quasi-linear algebraic equations. Several problems are solved which illustrate the potential of the method for predicting the finite deflection and stress. For increasing lateral pressures, the maximum principal tensile stress occurs at the center of the plate and migrates toward the corners as the load increases. It was deemed important to describe the locations of the maximum principal tensile stress as it occurs. The load-deflection relations and the maximum bending and membrane stresses for each case are presented and discussed

Genetic Discrimination: A Legal Or Biological Issue?

Directory of Open Access Journals (Sweden)

Bárbara Augusta de Paula Araujo Myssior

2016-12-01

Full Text Available This essay debates the technological evolution that, from the decoding of the human genome has opened up many scientific benefits, and yet brings up a new kind of segregation: genetic discrimination. Based on the right to privacy, as well as the concept of genetic identity, as well as data protection and information, worked up the genetic discrimination. Therefore, documentary research and critical analysis of scientific papers were taken, using up of the inductive reasoning method. As a result, elucidate how such discrimination affects individuals, it is possible to conclude that regardless of the type of discrimination, all should be restrained by law.

On the efficacy of linear system analysis of renal autoregulation in rats

DEFF Research Database (Denmark)

Chon, K H; Chen, Y M; Holstein-Rathlou, N H

1993-01-01

In order to assess the linearity of the mechanisms subserving renal blood flow autoregulation, broad-band arterial pressure fluctuations at three different power levels were induced experimentally and the resulting renal blood flow responses were recorded. Linear system analysis methods were...

Linear and nonlinear analysis of high-power rf amplifiers

International Nuclear Information System (INIS)

Puglisi, M.

1983-01-01

After a survey of the state variable analysis method the final amplifier for the CBA is analyzed taking into account the real beam waveshape. An empirical method for checking the stability of a non-linear system is also considered

Tunnel Diode Discriminator with Fixed Dead Time

DEFF Research Database (Denmark)

Diamond, J. M.

1965-01-01

A solid state discriminator for the range 0.4 to 10 V is described. Tunnel diodes are used for the discriminator element and in a special fixed dead time circuit. An analysis of temperature stability is presented. The regulated power supplies are described, including a special negative resistance...

Plastic limit analysis with non linear kinematic strain hardening for metalworking processes applications

International Nuclear Information System (INIS)

Chaaba, Ali; Aboussaleh, Mohamed; Bousshine, Lahbib; Boudaia, El Hassan

2011-01-01

Limit analysis approaches are widely used to deal with metalworking processes analysis; however, they are applied only for perfectly plastic materials and recently for isotropic hardening ones excluding any kind of kinematic hardening. In the present work, using Implicit Standard Materials concept, sequential limit analysis approach and the finite element method, our objective consists in extending the limit analysis application for including linear and non linear kinematic strain hardenings. Because this plastic flow rule is non associative, the Implicit Standard Materials concept is adopted as a framework of non standard plasticity modeling. The sequential limit analysis procedure which considers the plastic behavior with non linear kinematic strain hardening as a succession of perfectly plastic behavior with yielding surfaces updated after each sequence of limit analysis and geometry updating is applied. Standard kinematic finite element method together with a regularization approach is used for performing two large compression cases (cold forging) in plane strain and axisymmetric conditions