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Sample records for multivariate statistical approach

  1. Synthetic environmental indicators: A conceptual approach from the multivariate statistics

    International Nuclear Information System (INIS)

    Escobar J, Luis A

    2008-01-01

    This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

  2. Multivariate statistical analysis a high-dimensional approach

    CERN Document Server

    Serdobolskii, V

    2000-01-01

    In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

  3. Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

    Science.gov (United States)

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

    Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

  4. Applied multivariate statistical analysis

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  5. A primer of multivariate statistics

    CERN Document Server

    Harris, Richard J

    2014-01-01

    Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

  6. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

    Directory of Open Access Journals (Sweden)

    Charmaine eDemanuele

    2015-10-01

    Full Text Available Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from fMRI blood oxygenation level dependent (BOLD time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC, but not in the primary visual cortex (V1. Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel

  7. Multivariate Statistical Process Control

    DEFF Research Database (Denmark)

    Kulahci, Murat

    2013-01-01

    As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim...... is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive...... amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. Applications of these control charts...

  8. Multivariate Location Estimation Using Extension of $R$-Estimates Through $U$-Statistics Type Approach

    OpenAIRE

    Chaudhuri, Probal

    1992-01-01

    We consider a class of $U$-statistics type estimates for multivariate location. The estimates extend some $R$-estimates to multivariate data. In particular, the class of estimates includes the multivariate median considered by Gini and Galvani (1929) and Haldane (1948) and a multivariate extension of the well-known Hodges-Lehmann (1963) estimate. We explore large sample behavior of these estimates by deriving a Bahadur type representation for them. In the process of developing these asymptoti...

  9. The intervals method: a new approach to analyse finite element outputs using multivariate statistics

    Directory of Open Access Journals (Sweden)

    Jordi Marcé-Nogué

    2017-10-01

    Full Text Available Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches.

  10. The intervals method: a new approach to analyse finite element outputs using multivariate statistics

    Science.gov (United States)

    De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

    2017-01-01

    Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

  11. Applied multivariate statistics with R

    CERN Document Server

    Zelterman, Daniel

    2015-01-01

    This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...

  12. A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

    Science.gov (United States)

    2017-09-01

    application of statistical inference. Even when human forecasters leverage their professional experience, which is often gained through long periods of... application throughout statistics and Bayesian data analysis. The multivariate form of 2( , )  (e.g., Figure 12) is similarly analytically...data (i.e., no systematic manipulations with analytical functions), it is common in the statistical literature to apply mathematical transformations

  13. Aspects of multivariate statistical theory

    CERN Document Server

    Muirhead, Robb J

    2009-01-01

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to pen

  14. Multivariate Statistical Process Control Charts: An Overview

    OpenAIRE

    Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

    2006-01-01

    In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...

  15. Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles.

    Science.gov (United States)

    Dhat, Shalaka; Pund, Swati; Kokare, Chandrakant; Sharma, Pankaj; Shrivastava, Birendra

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed 95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation. Copyright © 2016 Elsevier B.V. All

  16. Assessment of Near-Bottom Water Quality of Southwestern Coast of Sarawak, Borneo, Malaysia: A Multivariate Statistical Approach

    Directory of Open Access Journals (Sweden)

    Chen-Lin Soo

    2017-01-01

    Full Text Available The study on Sarawak coastal water quality is scarce, not to mention the application of the multivariate statistical approach to investigate the spatial variation of water quality and to identify the pollution source in Sarawak coastal water. Hence, the present study aimed to evaluate the spatial variation of water quality along the coastline of the southwestern region of Sarawak using multivariate statistical techniques. Seventeen physicochemical parameters were measured at 11 stations along the coastline with approximately 225 km length. The coastal water quality showed spatial heterogeneity where the cluster analysis grouped the 11 stations into four different clusters. Deterioration in coastal water quality has been observed in different regions of Sarawak corresponding to land use patterns in the region. Nevertheless, nitrate-nitrogen exceeded the guideline value at all sampling stations along the coastline. The principal component analysis (PCA has determined a reduced number of five principal components that explained 89.0% of the data set variance. The first PC indicated that the nutrients were the dominant polluting factors, which is attributed to the domestic, agricultural, and aquaculture activities, followed by the suspended solids in the second PC which are related to the logging activities.

  17. Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

    Science.gov (United States)

    Varekar, Vikas; Karmakar, Subhankar; Jha, Ramakar

    2016-02-01

    The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders

  18. Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach.

    Science.gov (United States)

    Moya, Claudio E; Raiber, Matthias; Taulis, Mauricio; Cox, Malcolm E

    2015-03-01

    The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na-Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na-HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous-Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the

  19. Multivariate statistical methods a primer

    CERN Document Server

    Manly, Bryan FJ

    2004-01-01

    THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o

  20. Multivariate statistics exercises and solutions

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

  1. Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India).

    Science.gov (United States)

    Mavukkandy, Musthafa Odayooth; Karmakar, Subhankar; Harikumar, P S

    2014-09-01

    The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. The effectiveness of existing river water quality monitoring

  2. Multivariate statistical methods a first course

    CERN Document Server

    Marcoulides, George A

    2014-01-01

    Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

  3. MIDAS: Regionally linear multivariate discriminative statistical mapping.

    Science.gov (United States)

    Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

    2018-07-01

    Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the

  4. Detecting relationships between the interannual variability in climate records and ecological time series using a multivariate statistical approach - four case studies for the North Sea region

    Energy Technology Data Exchange (ETDEWEB)

    Heyen, H. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik

    1998-12-31

    A multivariate statistical approach is presented that allows a systematic search for relationships between the interannual variability in climate records and ecological time series. Statistical models are built between climatological predictor fields and the variables of interest. Relationships are sought on different temporal scales and for different seasons and time lags. The possibilities and limitations of this approach are discussed in four case studies dealing with salinity in the German Bight, abundance of zooplankton at Helgoland Roads, macrofauna communities off Norderney and the arrival of migratory birds on Helgoland. (orig.) [Deutsch] Ein statistisches, multivariates Modell wird vorgestellt, das eine systematische Suche nach potentiellen Zusammenhaengen zwischen Variabilitaet in Klima- und oekologischen Zeitserien erlaubt. Anhand von vier Anwendungsbeispielen wird der Klimaeinfluss auf den Salzgehalt in der Deutschen Bucht, Zooplankton vor Helgoland, Makrofauna vor Norderney, und die Ankunft von Zugvoegeln auf Helgoland untersucht. (orig.)

  5. Sediment contaminants and biological effects in southern California: Use of a multivariate statistical approach to assess biological impact

    International Nuclear Information System (INIS)

    Maxon, C.L.; Barnett, A.M.; Diener, D.R.

    1997-01-01

    This study attempts to predict biological toxicity and benthic community impact in sediments collected from two southern California sites. Contaminant concentrations and grain size were evaluated as predictors using a two-step multivariate approach. The first step used principal component analysis (PCA) to describe contamination type and magnitude present at each site. Four dominant PC vectors, explaining 88% of the total variance, each corresponded to a unique physical and/or chemical signature. The four PC vectors, in decreasing order of importance, were: (1) high molecular weight polynuclear aromatic hydrocarbons (PAH), most likely from combusted or weathered petroleum; (2) low molecular weight alkylated PAH, primarily from weathered fuel product; (3) low molecular weight nonalkylated PAH, indicating a fresh petroleum-related origin; and (4) fine-grained sediments and metals. The second step used stepwise regression analysis to predict individual biological effects (dependent) variables using the four PC vectors as independent variables. Results showed that sediment grain size alone was the best predictor of amphipod mortality. Contaminant vectors showed discrete depositional areas independent of grain size. Neither contaminant concentrations nor PCA vectors were good predictors of biological effects, most likely due to the low concentrations in sediments

  6. Water quality assessment in the Bétaré-Oya gold mining area (East-Cameroon): Multivariate Statistical Analysis approach.

    Science.gov (United States)

    Rakotondrabe, Felaniaina; Ndam Ngoupayou, Jules Remy; Mfonka, Zakari; Rasolomanana, Eddy Harilala; Nyangono Abolo, Alexis Jacob; Ako Ako, Andrew

    2018-01-01

    The influence of gold mining activities on the water quality in the Mari catchment in Bétaré-Oya (East Cameroon) was assessed in this study. Sampling was performed within the period of one hydrological year (2015 to 2016), with 22 sampling sites consisting of groundwater (06) and surface water (16). In addition to measuring the physicochemical parameters, such as pH, electrical conductivity, alkalinity, turbidity, suspended solids and CN - , eleven major elements (Na + , K + , Ca 2+ , Mg 2+ , NH 4 + , Cl - , NO 3 - , HCO 3 - , SO 4 2- , PO 4 3- and F - ) and eight heavy metals (Pb, Zn, Cd, Fe, Cu, As, Mn and Cr) were also analyzed using conventional hydrochemical methods, Multivariate Statistical Analysis and the Heavy metal Pollution Index (HPI). The results showed that the water from Mari catchment and Lom River was acidic to basic (5.40water quality, except for nitrates in some wells, which was found at a concentration >50mg NO 3 - /L. This water was found as two main types: calcium magnesium bicarbonate (CaMg-HCO 3 ), which was the most represented, and sodium bicarbonate potassium (NaK-HCO 3 ). As for trace elements in surface water, the contents of Pb, Cd, Mn, Cr and Fe were higher than recommended by the WHO guidelines, and therefore, the surface water was unsuitable for human consumption. Three phenomena were responsible for controlling the quality of the water in the study area: hydrolysis of silicate minerals of plutono-metamorphic rocks, which constitute the geological basement of this area; vegetation and soil leaching; and mining activities. The high concentrations of TSS and trace elements found in this basin were mainly due to gold mining activities (exploration and exploitation) as well as digging of rivers beds, excavation and gold amalgamation. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Multivariate statistical assessment of coal properties

    Czech Academy of Sciences Publication Activity Database

    Klika, Z.; Serenčíšová, J.; Kožušníková, Alena; Kolomazník, I.; Študentová, S.; Vontorová, J.

    2014-01-01

    Roč. 128, č. 128 (2014), s. 119-127 ISSN 0378-3820 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : coal properties * structural,chemical and petrographical properties * multivariate statistics Subject RIV: DH - Mining, incl. Coal Mining Impact factor: 3.352, year: 2014 http://dx.doi.org/10.1016/j.fuproc.2014.06.029

  8. Geochemistry of natural and anthropogenic fall-out (aerosol and precipitation) collected from the NW Mediterranean: two different multivariate statistical approaches

    International Nuclear Information System (INIS)

    Molinaroli, E.; Pistolato, M.; Rampazzo, G.; Guerzoni, S.

    1999-01-01

    The chemical characteristics of the mineral fractions of aerosol and precipitation collected in Sardinia (NW Mediterranean) are highlighted by means of two multivariate statistical approaches. Two different combinations of classification and statistical methods for geochemical data are presented. It is shown that the application of cluster analysis subsequent to Q-Factor analysis better distinguishes among Saharan dust, background pollution (Europe-Mediterranean) and local aerosol from various source regions (Sardinia). Conversely, the application of simple cluster analysis was able to distinguish only between aerosols and precipitation particles, without assigning the sources (local or distant) to the aerosol. This method also highlighted the fact that crust-enriched precipitation is similar to desert-derived aerosol. Major elements (Al, Na) and trace metal (Pb) turn out to be the most discriminating elements of the analysed data set. Independent use of mineralogical, granulometric and meteorological data confirmed the results derived from the statistical methods employed. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

  9. The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

    Science.gov (United States)

    Marković, Snežana; Kerč, Janez; Horvat, Matej

    2017-03-01

    We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

  10. Multivariate methods and forecasting with IBM SPSS statistics

    CERN Document Server

    Aljandali, Abdulkader

    2017-01-01

    This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...

  11. Multivariate statistical analysis of wildfires in Portugal

    Science.gov (United States)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  12. Method for statistical data analysis of multivariate observations

    CERN Document Server

    Gnanadesikan, R

    1997-01-01

    A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

  13. Multivariate ordination statistics workshop with R slides

    OpenAIRE

    Strack, Michael

    2015-01-01

    2-hour workshop given at Macquarie University Department of Biological Sciences, 4 November 2015. Workshop was an introduction to the family of techniques falling under multivariate ordination, using the R language and drawing heavily from the book "Numerical Ecology with R" by Borcard et. al (2012).

  14. Hydrogeochemistry and quality of surface water and groundwater in the vicinity of Lake Monoun, West Cameroon: approach from multivariate statistical analysis and stable isotopic characterization.

    Science.gov (United States)

    Kamtchueng, Brice T; Fantong, Wilson Y; Wirmvem, Mengnjo J; Tiodjio, Rosine E; Takounjou, Alain F; Ndam Ngoupayou, Jules R; Kusakabe, Minoru; Zhang, Jing; Ohba, Takeshi; Tanyileke, Gregory; Hell, Joseph V; Ueda, Akira

    2016-09-01

    With the use of conventional hydrogeochemical techniques, multivariate statistical analysis, and stable isotope approaches, this paper investigates for the first time surface water and groundwater from the surrounding areas of Lake Monoun (LM), West Cameroon. The results reveal that waters are generally slightly acidic to neutral. The relative abundance of major dissolved species are Ca(2+) > Mg(2+) > Na(+) > K(+) for cations and HCO3 (-) ≫ NO3 (-) > Cl(-) > SO4 (2-) for anions. The main water type is Ca-Mg-HCO3. Observed salinity is related to water-rock interaction, ion exchange process, and anthropogenic activities. Nitrate and chloride have been identified as the most common pollutants. These pollutants are attributed to the chlorination of wells and leaching from pit latrines and refuse dumps. The stable isotopic compositions in the investigated water sources suggest evidence of evaporation before recharge. Four major groups of waters were identified by salinity and NO3 concentrations using the Q-mode hierarchical cluster analysis (HCA). Consistent with the isotopic results, group 1 represents fresh unpolluted water occurring near the recharge zone in the general flow regime; groups 2 and 3 are mixed water whose composition is controlled by both weathering of rock-forming minerals and anthropogenic activities; group 4 represents water under high vulnerability of anthropogenic pollution. Moreover, the isotopic results and the HCA showed that the CO2-rich bottom water of LM belongs to an isolated hydrological system within the Foumbot plain. Except for some springs, groundwater water in the area is inappropriate for drinking and domestic purposes but good to excellent for irrigation.

  15. Multivariate Approaches to Classification in Extragalactic Astronomy

    Directory of Open Access Journals (Sweden)

    Didier eFraix-Burnet

    2015-08-01

    Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

  16. Application of multivariate statistical techniques in microbial ecology.

    Science.gov (United States)

    Paliy, O; Shankar, V

    2016-03-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

  17. Multivariate statistics high-dimensional and large-sample approximations

    CERN Document Server

    Fujikoshi, Yasunori; Shimizu, Ryoichi

    2010-01-01

    A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

  18. Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison.

    Science.gov (United States)

    Cardot, J-M; Roudier, B; Schütz, H

    2017-07-01

    The f 2 test is generally used for comparing dissolution profiles. In cases of high variability, the f 2 test is not applicable, and the Multivariate Statistical Distance (MSD) test is frequently proposed as an alternative by the FDA and EMA. The guidelines provide only general recommendations. MSD tests can be performed either on raw data with or without time as a variable or on parameters of models. In addition, data can be limited-as in the case of the f 2 test-to dissolutions of up to 85% or to all available data. In the context of the present paper, the recommended calculation included all raw dissolution data up to the first point greater than 85% as a variable-without the various times as parameters. The proposed MSD overcomes several drawbacks found in other methods.

  19. Identifying sources of soil inorganic pollutants on a regional scale using a multivariate statistical approach: Role of pollutant migration and soil physicochemical properties

    International Nuclear Information System (INIS)

    Zhang Changbo; Wu Longhua; Luo Yongming; Zhang Haibo; Christie, Peter

    2008-01-01

    Principal components analysis (PCA) and correlation analysis were used to estimate the contribution of four components related to pollutant sources on the total variation in concentrations of Cu, Zn, Pb, Cd, As, Se, Hg, Fe and Mn in surface soil samples from a valley in east China with numerous copper and zinc smelters. Results indicate that when carrying out source identification of inorganic pollutants their tendency to migrate in soils may result in differences between the pollutant composition of the source and the receptor soil, potentially leading to errors in the characterization of pollutants using multivariate statistics. The stability and potential migration or movement of pollutants in soils must therefore be taken into account. Soil physicochemical properties may offer additional useful information. Two different mechanisms have been hypothesized for correlations between soil heavy metal concentrations and soil organic matter content and these may be helpful in interpreting the statistical analysis. - Principal components analysis with Varimax rotation can help identify sources of soil inorganic pollutants but pollutant migration and soil properties can exert important effects

  20. Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS

    OpenAIRE

    Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita

    2016-01-01

    This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

  1. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Science.gov (United States)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-06-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  2. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    International Nuclear Information System (INIS)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-01-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  3. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Energy Technology Data Exchange (ETDEWEB)

    Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

    2004-06-15

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  4. Multivariate statistical characterization of groundwater quality in Ain ...

    African Journals Online (AJOL)

    Administrator

    depends much on the sustainability of the available water resources. Water of .... 18 wells currently in use were selected based on the preliminary field survey carried out to ... In recent times, multivariate statistical methods have been applied ...

  5. Hydro-geochemical paths of multi-layer groundwater system in coal mining regions - Using multivariate statistics and geochemical modeling approaches.

    Science.gov (United States)

    Liu, Pu; Hoth, Nils; Drebenstedt, Carsten; Sun, Yajun; Xu, Zhimin

    2017-12-01

    Groundwater is an important drinking water resource that requires protection in North China. Coal mining industry in the area may influence the water quality evolution. To provide primary characterization of the hydrogeochemical processes and paths that control the water quality evolution, a complex multi-layer groundwater system in a coal mining area is investigated. Multivariate statistical methods involving hierarchical cluster analysis (HCA) and principal component analysis (PCA) are applied, 6 zones and 3 new principal components are classified as major reaction zones and reaction factors. By integrating HCA and PCA with hydrogeochemical correlations analysis, potential phases, reactions and connections between various zones are presented. Carbonates minerals, gypsum, clay minerals as well as atmosphere gases - CO 2 , H 2 O and NH 3 are recognized as major reactants. Mixtures, evaporation, dissolution/precipitation of minerals and cation exchange are potential reactions. Inverse modeling is finally used, and it verifies the detailed processes and diverse paths. Consequently, 4 major paths are found controlling the variations of groundwater chemical properties. Shallow and deep groundwater is connected primarily by the flow of deep groundwater up through fractures and faults into the shallow aquifers. Mining does not impact the underlying aquifers that represent the most critical groundwater resource. But controls should be taken to block the mixing processes from highly polluted mine water. The paper highlights the complex hydrogeochemical evolution of a multi-layer groundwater system under mining impact, which could be applied to further groundwater quality management in the study area, as well as most of the other coalfields in North China. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

    Science.gov (United States)

    Butaciu, Sinziana; Senila, Marin; Sarbu, Costel; Ponta, Michaela; Tanaselia, Claudiu; Cadar, Oana; Roman, Marius; Radu, Emil; Sima, Mihaela; Frentiu, Tiberiu

    2017-04-01

    The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - , Cl - , F - , SO 4 2- , PO 4 3- , NO 3 - ), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F - species and the anthropogenic one for NO 3 - , SO 4 2- , PO 4 3- and K + and (ii) classification of groundwater based on content of arsenic species. The HCO 3 - type of local groundwater and alkaline pH (8.31-8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F - but by different paths. The PO 4 3- -AsO 4 3- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na + -F - -pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. A multivariate statistical approaches on physicochemical characteristics of ground water in and around Nagapattinam district, Cauvery deltaic region of Tamil Nadu, India

    Directory of Open Access Journals (Sweden)

    Venkatramanan Senapathi

    2013-07-01

    Full Text Available Ground water samples collected at different locations in and around the Nagapattinam district were analyzed for their physicochemical characteristics. The ground water samples were collected from fifty two dug and deep wells during the monsoon and summer seasons in June and December, 2011. The present investigation is focused on the determination of physico-chemical parameters such as pH, EC, TDS, Ca, Mg, Na, K, HCO3, SO4 and Cl. Ground water suitability for drinking, domestic and agri- cultural purposes was examined by using WHO standards. Correlation, factor and cluster analyses were applied to classify the ground water qualities and to categorize the geochemical processes controlling ground water geochemistry. Factor analysis indicates that seawater intrusion and agriculture runoff are dominant factors controlling the hydrogeochemistry of ground water in the study area. Cluster analysis was helpful for the classification on the basis of contamination characteristics of ground water quality. This study also elucidates that multivariate statistical analyses can be used to improve the understanding of ground water status and assessment of ground water quality.  Resumen En este estudio se analizan las características fisicoquímicas de muestras de aguas subterráneas tomadas en diferentes locaciones en y alrededor del distrito de Nagapattinam. Las muestras se recolectaron en 52 pozos cavados y perforaciones profundas durante las subestaciones del monzón y el verano, en los meses de junio y diciembre (2011. La presente investigación está enfocada en la determinación de parametros fisicoquímicos como pH, EC, TDS, Ca, Mg, Na, K, HCO3, SO4 y Cl. Se examinó la pertinencia de estas aguas para consumo y para irrigación a la luz de los estándares de la Organización Mundial de la Salud. Se aplicaron análisis de correlación, factores y cúmulos para clasificar las muestras y categorizar los procesos geoquímicos que controlan las aguas subterr

  8. Statistical Inference for a Class of Multivariate Negative Binomial Distributions

    DEFF Research Database (Denmark)

    Rubak, Ege H.; Møller, Jesper; McCullagh, Peter

    This paper considers statistical inference procedures for a class of models for positively correlated count variables called -permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...... studied in the literature, while this is the first statistical paper on -permanental random fields. The focus is on maximum likelihood estimation, maximum quasi-likelihood estimation and on maximum composite likelihood estimation based on uni- and bivariate distributions. Furthermore, new results...

  9. Multivariate statistical analysis of major and trace element data for ...

    African Journals Online (AJOL)

    Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

  10. Statistical inference for a class of multivariate negative binomial distributions

    DEFF Research Database (Denmark)

    Rubak, Ege Holger; Møller, Jesper; McCullagh, Peter

    This paper considers statistical inference procedures for a class of models for positively correlated count variables called α-permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...

  11. A unifying framework for k-statistics, polykays and their multivariate generalizations.

    OpenAIRE

    DI NARDO, Elvira; GUARINO G, G.; Senato, D.

    2008-01-01

    Through the classical umbral calculus, we provide a unifying syntax for single and multivariate $k$-statistics, polykays and multivariate polykays. From a combinatorial point of view, we revisit the theory as exposed by Stuart and Ord, taking into account the Doubilet approach to symmetric functions. Moreover, by using exponential polynomials rather than set partitions, we provide a new formula for $k$-statistics that results in a very fast algorithm to generate such estimators.

  12. The Inappropriate Symmetries of Multivariate Statistical Analysis in Geometric Morphometrics.

    Science.gov (United States)

    Bookstein, Fred L

    In today's geometric morphometrics the commonest multivariate statistical procedures, such as principal component analysis or regressions of Procrustes shape coordinates on Centroid Size, embody a tacit roster of symmetries -axioms concerning the homogeneity of the multiple spatial domains or descriptor vectors involved-that do not correspond to actual biological fact. These techniques are hence inappropriate for any application regarding which we have a-priori biological knowledge to the contrary (e.g., genetic/morphogenetic processes common to multiple landmarks, the range of normal in anatomy atlases, the consequences of growth or function for form). But nearly every morphometric investigation is motivated by prior insights of this sort. We therefore need new tools that explicitly incorporate these elements of knowledge, should they be quantitative, to break the symmetries of the classic morphometric approaches. Some of these are already available in our literature but deserve to be known more widely: deflated (spatially adaptive) reference distributions of Procrustes coordinates, Sewall Wright's century-old variant of factor analysis, the geometric algebra of importing explicit biomechanical formulas into Procrustes space. Other methods, not yet fully formulated, might involve parameterized models for strain in idealized forms under load, principled approaches to the separation of functional from Brownian aspects of shape variation over time, and, in general, a better understanding of how the formalism of landmarks interacts with the many other approaches to quantification of anatomy. To more powerfully organize inferences from the high-dimensional measurements that characterize so much of today's organismal biology, tomorrow's toolkit must rely neither on principal component analysis nor on the Procrustes distance formula, but instead on sound prior biological knowledge as expressed in formulas whose coefficients are not all the same. I describe the problems

  13. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    International Nuclear Information System (INIS)

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T.

    1993-01-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs

  14. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    Energy Technology Data Exchange (ETDEWEB)

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

    1993-07-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

  15. Classification of Malaysia aromatic rice using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-01-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

  16. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  17. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Science.gov (United States)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  18. Identification of mine waters by statistical multivariate methods

    Energy Technology Data Exchange (ETDEWEB)

    Mali, N [IGGG, Ljubljana (Slovenia)

    1992-01-01

    Three water-bearing aquifers are present in the Velenje lignite mine. The aquifer waters have differing chemical composition; a geochemical water analysis can therefore determine the source of mine water influx. Mine water samples from different locations in the mine were analyzed, the results of chemical content and of electric conductivity of mine water were statistically processed by means of MICROGAS, SPSS-X and IN STATPAC computer programs, which apply three multivariate statistical methods (discriminate, cluster and factor analysis). Reliability of calculated values was determined with the Kolmogorov and Smirnov tests. It is concluded that laboratory analysis of single water samples can produce measurement errors, but statistical processing of water sample data can identify origin and movement of mine water. 15 refs.

  19. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

  20. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  1. Classification of Specialized Farms Applying Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Zuzana Hloušková

    2017-01-01

    Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

  2. Multivariate approach to matrimonial mobility in Catalonia.

    Science.gov (United States)

    Calafell, F; Hernández, M

    1993-10-01

    Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.

  3. Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

    Science.gov (United States)

    Warner, Rebecca M.

    2007-01-01

    This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

  4. Multivariate statistical analysis of atom probe tomography data

    International Nuclear Information System (INIS)

    Parish, Chad M.; Miller, Michael K.

    2010-01-01

    The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.

  5. Multivariate statistical analysis of a multi-step industrial processes

    DEFF Research Database (Denmark)

    Reinikainen, S.P.; Høskuldsson, Agnar

    2007-01-01

    Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized...... efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse...... multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors...

  6. Data classification and MTBF prediction with a multivariate analysis approach

    International Nuclear Information System (INIS)

    Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

    2012-01-01

    The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

  7. Adjustment of geochemical background by robust multivariate statistics

    Science.gov (United States)

    Zhou, D.

    1985-01-01

    Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.

  8. Multivariate statistical tools for the radiometric features of volcanic islands

    International Nuclear Information System (INIS)

    Basile, S.; Brai, M.; Marrale, M.; Micciche, S.; Lanzo, G.; Rizzo, S.

    2009-01-01

    The Aeolian Islands represents a Quaternary volcanic arc related to the subduction of the Ionian plate beneath the Calabrian Arc. The geochemical variability of the islands has led to a broad spectrum of magma rocks. Volcanic products from calc-alkaline (CA) to calc-alkaline high in potassium (HKCA) are present throughout the Archipelago, but products belonging to shoshonitic (SHO) and potassium (KS) series characterize the southern portion of Lipari, Vulcano and Stromboli. Tectonics also plays an important role in the process of the islands differentiation. In this work, we want to review and cross-analyze the data on Lipari, Stromboli and Vulcano, collected in measurement and sampling campaigns over the last years. Chemical data were obtained by X-ray fluorescence. High resolution gamma-ray spectrometry with germanium detectors was used to measure primordial radionuclide activities. The activity of primordial radionuclides in the volcanic products of these three islands is strongly dependent on their chemism. The highest contents are found in more differentiated products (rhyolites). The CA products have lower concentrations, while the HKCA and Shoshonitic product concentrations are in between. Calculated dose rates have been correlated with the petrochemical features in order to gain further insight in evolution and differentiation of volcanic products. Ratio matching technique and multivariate statistical analyses, such as Principal Component Analysis and Minimum Spanning Tree, have been applied as an additional tool helpful to better describe the lithological affinities of the samples. (Author)

  9. Multivariate statistical modelling based on generalized linear models

    CERN Document Server

    Fahrmeir, Ludwig

    1994-01-01

    This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

  10. Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

    Science.gov (United States)

    Boccippio, Dennis

    2004-01-01

    Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

  11. Multivariate statistical analysis - an application to lunar materials

    International Nuclear Information System (INIS)

    Deb, M.

    1978-01-01

    The compositional characteristics of clinopyroxenes and spinels - two minerals considered to be very useful in deciphering lunar history, have been studied using the multivariate statistical method of principal component analysis. The mineral-chemical data used are from certain lunar rocks and fines collected by Apollo 11, 12, 14 and 15 and Luna 16 and 20 missions, representing mainly the mare basalts and also non-mare basalts, breccia and rock fragments from the highland regions, in which a large number of these minerals have been analyzed. The correlations noted in the mineral compositions, indicating substitutional relationships, have been interpreted on the basis of available crystal-chemical and petrological informations. Compositional trends for individual specimens have been delineated and compared by producing ''principal latent vector diagrams''. The percent variance of the principal components denoted by the eigenvalues, have been evaluated in terms of the crystallization history of the samples. Some of the major petrogenetic implications of this study concern the role of early formed cumulate phases in the near-surface fractionation of mare basalts, mixing of mineral compositions in the highland regolith and the subsolidus reduction trends in lunar spinels. (auth.)

  12. Mulch materials in processing tomato: a multivariate approach

    Directory of Open Access Journals (Sweden)

    Marta María Moreno

    2013-08-01

    Full Text Available Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L. crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2, one oxo-biodegradable material (OB, two types of paper (PP1, PP2, and one barley straw cover (BS were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]. A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

  13. Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

    Science.gov (United States)

    Gulgundi, Mohammad Shahid; Shetty, Amba

    2018-03-01

    Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

  14. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

    International Nuclear Information System (INIS)

    Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

    2004-01-01

    The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

  15. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    International Nuclear Information System (INIS)

    Weathers, J.B.; Luck, R.; Weathers, J.W.

    2009-01-01

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  16. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    Energy Technology Data Exchange (ETDEWEB)

    Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

    2009-11-15

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  17. A multivariate statistical study on a diversified data gathering system for nuclear power plants

    International Nuclear Information System (INIS)

    Samanta, P.K.; Teichmann, T.; Levine, M.M.; Kato, W.Y.

    1989-02-01

    In this report, multivariate statistical methods are presented and applied to demonstrate their use in analyzing nuclear power plant operational data. For analyses of nuclear power plant events, approaches are presented for detecting malfunctions and degradations within the course of the event. At the system level, approaches are investigated as a means of diagnosis of system level performance. This involves the detection of deviations from normal performance of the system. The input data analyzed are the measurable physical parameters, such as steam generator level, pressurizer water level, auxiliary feedwater flow, etc. The study provides the methodology and illustrative examples based on data gathered from simulation of nuclear power plant transients and computer simulation of a plant system performance (due to lack of easily accessible operational data). Such an approach, once fully developed, can be used to explore statistically the detection of failure trends and patterns and prevention of conditions with serious safety implications. 33 refs., 18 figs., 9 tabs

  18. Attitudes toward Advanced and Multivariate Statistics When Using Computers.

    Science.gov (United States)

    Kennedy, Robert L.; McCallister, Corliss Jean

    This study investigated the attitudes toward statistics of graduate students who studied advanced statistics in a course in which the focus of instruction was the use of a computer program in class. The use of the program made it possible to provide an individualized, self-paced, student-centered, and activity-based course. The three sections…

  19. Application of a Multivariate Statistical Technique to Interpreting Data from Multichannel Equipment for the Example of the KLEM Spectrometer

    International Nuclear Information System (INIS)

    Podorozhnyi, D.M.; Postnikov, E.B.; Sveshnikova, L.G.; Turundaevsky, A.N.

    2005-01-01

    A multivariate statistical procedure for solving problems of estimating physical parameters on the basis of data from measurements with multichannel equipment is described. Within the multivariate procedure, an algorithm is constructed for estimating the energy of primary cosmic rays and the exponent in their power-law spectrum. They are investigated by using the KLEM spectrometer (NUCLEON project) as a specific example of measuring equipment. The results of computer experiments simulating the operation of the multivariate procedure for this equipment are given, the proposed approach being compared in these experiments with the one-parameter approach presently used in data processing

  20. Evaluation of droplet size distributions using univariate and multivariate approaches

    DEFF Research Database (Denmark)

    Gauno, M.H.; Larsen, C.C.; Vilhelmsen, T.

    2013-01-01

    of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose...... in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution....... Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions...

  1. Evaluation of droplet size distributions using univariate and multivariate approaches.

    Science.gov (United States)

    Gaunø, Mette Høg; Larsen, Crilles Casper; Vilhelmsen, Thomas; Møller-Sonnergaard, Jørn; Wittendorff, Jørgen; Rantanen, Jukka

    2013-01-01

    Pharmaceutically relevant material characteristics are often analyzed based on univariate descriptors instead of utilizing the whole information available in the full distribution. One example is droplet size distribution, which is often described by the median droplet size and the width of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution. Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions. The multivariate data analysis was proven to be an efficient tool for evaluating the full information contained in a distribution.

  2. HORIZONTAL BRANCH MORPHOLOGY OF GLOBULAR CLUSTERS: A MULTIVARIATE STATISTICAL ANALYSIS

    International Nuclear Information System (INIS)

    Jogesh Babu, G.; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar; Mondal, Saptarshi

    2009-01-01

    The proper interpretation of horizontal branch (HB) morphology is crucial to the understanding of the formation history of stellar populations. In the present study a multivariate analysis is used (principal component analysis) for the selection of appropriate HB morphology parameter, which, in our case, is the logarithm of effective temperature extent of the HB (log T effHB ). Then this parameter is expressed in terms of the most significant observed independent parameters of Galactic globular clusters (GGCs) separately for coherent groups, obtained in a previous work, through a stepwise multiple regression technique. It is found that, metallicity ([Fe/H]), central surface brightness (μ v ), and core radius (r c ) are the significant parameters to explain most of the variations in HB morphology (multiple R 2 ∼ 0.86) for GGC elonging to the bulge/disk while metallicity ([Fe/H]) and absolute magnitude (M v ) are responsible for GGC belonging to the inner halo (multiple R 2 ∼ 0.52). The robustness is tested by taking 1000 bootstrap samples. A cluster analysis is performed for the red giant branch (RGB) stars of the GGC belonging to Galactic inner halo (Cluster 2). A multi-episodic star formation is preferred for RGB stars of GGC belonging to this group. It supports the asymptotic giant branch (AGB) model in three episodes instead of two as suggested by Carretta et al. for halo GGC while AGB model is suggested to be revisited for bulge/disk GGC.

  3. Multivariate Statistical Process Optimization in the Industrial Production of Enzymes

    DEFF Research Database (Denmark)

    Klimkiewicz, Anna

    of productyield. The potential of NIR technology to monitor the activity of the enzyme has beenthe subject of a feasibility study presented in PAPER I. It included (a) evaluation onwhich of the two real-time NIR flow cell configurations is the preferred arrangementfor monitoring of the retentate stream downstream...... strategies for theorganization of these datasets, with varying number of timestamps, into datastructures fit for latent variable (LV) modeling, have been compared. The ultimateaim of the data mining steps is the construction of statistical ‘soft models’ whichcapture the principle or latent behavior...

  4. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    International Nuclear Information System (INIS)

    Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.; Sales, Brian C.; Sefat, Athena S.

    2014-01-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe 0.55 Se 0.45 (T c = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe 1−x Se x structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces

  5. Multivariate statistical models for disruption prediction at ASDEX Upgrade

    International Nuclear Information System (INIS)

    Aledda, R.; Cannas, B.; Fanni, A.; Sias, G.; Pautasso, G.

    2013-01-01

    In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed

  6. Multivariate analysis of 2-DE protein patterns - Practical approaches

    DEFF Research Database (Denmark)

    Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

    2007-01-01

    Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

  7. Artificial intelligence approaches in statistics

    International Nuclear Information System (INIS)

    Phelps, R.I.; Musgrove, P.B.

    1986-01-01

    The role of pattern recognition and knowledge representation methods from Artificial Intelligence within statistics is considered. Two areas of potential use are identified and one, data exploration, is used to illustrate the possibilities. A method is presented to identify and separate overlapping groups within cluster analysis, using an AI approach. The potential of such ''intelligent'' approaches is stressed

  8. DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

    OpenAIRE

    Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

    2017-01-01

    International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

  9. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  10. Advances in statistical monitoring of complex multivariate processes with applications in industrial process control

    CERN Document Server

    Kruger, Uwe

    2012-01-01

    The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applica

  11. Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques.

    Directory of Open Access Journals (Sweden)

    Nsikak U Benson

    Full Text Available Trace metals (Cd, Cr, Cu, Ni and Pb concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria. The degree of contamination was assessed using the individual contamination factors (ICF and global contamination factor (GCF. Multivariate statistical approaches including principal component analysis (PCA, cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources.

  12. Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

    Czech Academy of Sciences Publication Activity Database

    Novák, J.; Sůvová, H.; Vondráček, Jiří

    2002-01-01

    Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

  13. Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

    Directory of Open Access Journals (Sweden)

    Md. Bodrud-Doza

    2016-04-01

    Full Text Available This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI, degree of contamination (Cd, heavy metal evaluation index (HEI reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA suggests that groundwater quality is mainly related to geogenic (rock–water interaction and anthropogenic source (agrogenic and domestic sewage in the study area. Subsequently, the findings of cluster analysis (CA and correlation matrix (CM are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh.

  14. Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

    International Nuclear Information System (INIS)

    Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

    2009-01-01

    In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)

  15. Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

    Science.gov (United States)

    Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

    2017-01-01

    High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Multivariate statistical methods and data mining in particle physics (4/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  17. Multivariate statistical methods and data mining in particle physics (2/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  18. Multivariate statistical methods and data mining in particle physics (1/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  19. Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

    Science.gov (United States)

    Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

    2013-06-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

  20. Generalized Tensor-Based Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors

    OpenAIRE

    Lepore, Natasha; Brun, Caroline; Chou, Yi-Yu; Chiang, Ming-Chang; Dutton, Rebecca A.; Hayashi, Kiralee M.; Luders, Eileen; Lopez, Oscar L.; Aizenstein, Howard J.; Toga, Arthur W.; Becker, James T.; Thompson, Paul M.

    2008-01-01

    This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor...

  1. Multivariate statistical treatment of PIXE analysis of some traditional Chinese medicines

    International Nuclear Information System (INIS)

    Xiaofeng Zhang; Jianguo Ma; Junfa Qin; Lun Xiao

    1991-01-01

    Elements in two kinds of 30 traditional Chinese medicines were analyzed by PIXE method, and the data were treated by multivariate statistical methods. The results show that these two kinds of traditional Chinese medicines are almost separable according to their elemental contents. The results are congruous with the traditional Chinese medicine practice. (author) 7 refs.; 2 figs.; 2 tabs

  2. Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China

    International Nuclear Information System (INIS)

    Chen Kouping; Jiao, Jiu J.; Huang Jianmin; Huang Runqiu

    2007-01-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies. - Multivariate statistical analysis was used to investigate relationships among trace elements and factors controlling trace element distribution in groundwater

  3. Monitoring a PVC batch process with multivariate statistical process control charts

    NARCIS (Netherlands)

    Tates, A. A.; Louwerse, D. J.; Smilde, A. K.; Koot, G. L. M.; Berndt, H.

    1999-01-01

    Multivariate statistical process control charts (MSPC charts) are developed for the industrial batch production process of poly(vinyl chloride) (PVC). With these MSPC charts different types of abnormal batch behavior were detected on-line. With batch contribution plots, the probable causes of these

  4. Multivariate statistical analysis of electron energy-loss spectroscopy in anisotropic materials

    International Nuclear Information System (INIS)

    Hu Xuerang; Sun Yuekui; Yuan Jun

    2008-01-01

    Recently, an expression has been developed to take into account the complex dependence of the fine structure in core-level electron energy-loss spectroscopy (EELS) in anisotropic materials on specimen orientation and spectral collection conditions [Y. Sun, J. Yuan, Phys. Rev. B 71 (2005) 125109]. One application of this expression is the development of a phenomenological theory of magic-angle electron energy-loss spectroscopy (MAEELS), which can be used to extract the isotropically averaged spectral information for materials with arbitrary anisotropy. Here we use this expression to extract not only the isotropically averaged spectral information, but also the anisotropic spectral components, without the restriction of MAEELS. The application is based on a multivariate statistical analysis of core-level EELS for anisotropic materials. To demonstrate the applicability of this approach, we have conducted a study on a set of carbon K-edge spectra of multi-wall carbon nanotube (MWCNT) acquired with energy-loss spectroscopic profiling (ELSP) technique and successfully extracted both the averaged and dichroic spectral components of the wrapped graphite-like sheets. Our result shows that this can be a practical alternative to MAEELS for the study of electronic structure of anisotropic materials, in particular for those nanostructures made of layered materials

  5. Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

    DEFF Research Database (Denmark)

    Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

    2007-01-01

    The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

  6. Provenance Study of Archaeological Ceramics from Syria Using XRF Multivariate Statistical Analysis and Thermoluminescence Dating

    OpenAIRE

    Bakraji, Elias Hanna; Abboud, Rana; Issa, Haissm

    2014-01-01

    Thermoluminescence (TL) dating and multivariate statistical methods based on radioisotope X-ray fluorescence analysis have been utilized to date and classify Syrian archaeological ceramics fragment from Tel Jamous site. 54 samples were analyzed by radioisotope X-ray fluorescence; 51 of them come from Tel Jamous archaeological site in Sahel Akkar region, Syria, which fairly represent ceramics belonging to the Middle Bronze Age (2150 to 1600 B.C.) and the remaining three samples come from Mar-T...

  7. Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

    Science.gov (United States)

    Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

    2017-04-01

    This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

  8. Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

    Science.gov (United States)

    Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

    2017-04-01

    Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

  9. Application of multivariate statistical technique for hydrogeochemical assessment of groundwater within the Lower Pra Basin, Ghana

    Science.gov (United States)

    Tay, C. K.; Hayford, E. K.; Hodgson, I. O. A.

    2017-06-01

    Multivariate statistical technique and hydrogeochemical approach were employed for groundwater assessment within the Lower Pra Basin. The main objective was to delineate the main processes that are responsible for the water chemistry and pollution of groundwater within the basin. Fifty-four (54) (No) boreholes were sampled in January 2012 for quality assessment. PCA using Varimax with Kaiser Normalization method of extraction for both rotated space and component matrix have been applied to the data. Results show that Spearman's correlation matrix of major ions revealed expected process-based relationships derived mainly from the geochemical processes, such as ion-exchange and silicate/aluminosilicate weathering within the aquifer. Three main principal components influence the water chemistry and pollution of groundwater within the basin. The three principal components have accounted for approximately 79% of the total variance in the hydrochemical data. Component 1 delineates the main natural processes (water-soil-rock interactions) through which groundwater within the basin acquires its chemical characteristics, Component 2 delineates the incongruent dissolution of silicate/aluminosilicates, while Component 3 delineates the prevalence of pollution principally from agricultural input as well as trace metal mobilization in groundwater within the basin. The loadings and score plots of the first two PCs show grouping pattern which indicates the strength of the mutual relation among the hydrochemical variables. In terms of proper management and development of groundwater within the basin, communities, where intense agriculture is taking place, should be monitored and protected from agricultural activities. especially where inorganic fertilizers are used by creating buffer zones. Monitoring of the water quality especially the water pH is recommended to ensure the acid neutralizing potential of groundwater within the basin thereby, curtailing further trace metal

  10. Pattern recognition by the use of multivariate statistical evaluation of macro- and micro-PIXE results

    International Nuclear Information System (INIS)

    Tapper, U.A.S.; Malmqvist, K.G.; Loevestam, N.E.G.; Swietlicki, E.; Salford, L.G.

    1991-01-01

    The importance of statistical evaluation of multielemental data is illustrated using the data collected in a macro- and micro-PIXE analysis of human brain tumours. By employing a multivariate statistical classification methodology (SIMCA) it was shown that the total information collected from each specimen separates three types of tissue: High malignant, less malignant and normal brain tissue. This makes a classification of a given specimen possible based on the elemental concentrations. Partial least squares regression (PLS), a multivariate regression method, made it possible to study the relative importance of the examined nine trace elements, the dry/wet weight ratio and the age of the patient in predicting the survival time after operation for patients with the high malignant form, astrocytomas grade III-IV. The elemental maps from a microprobe analysis were also subjected to multivariate analysis. This showed that the six elements sorted into maps could be presented in three maps containing all the relevant information. The intensity in these maps is proportional to the value (score) of the actual pixel along the calculated principal components. (orig.)

  11. Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors.

    Science.gov (United States)

    Lepore, N; Brun, C; Chou, Y Y; Chiang, M C; Dutton, R A; Hayashi, K M; Luders, E; Lopez, O L; Aizenstein, H J; Toga, A W; Becker, J T; Thompson, P M

    2008-01-01

    This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling's $T(2) test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulative p-value plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and large-scale studies of disease that use tensor-based morphometry.

  12. A multivariate statistical methodology for detection of degradation and failure trends using nuclear power plant operational data

    International Nuclear Information System (INIS)

    Samanta, P.K.; Teichmann, T.

    1990-01-01

    In this paper, a multivariate statistical method is presented and demonstrated as a means for analyzing nuclear power plant transients (or events) and safety system performance for detection of malfunctions and degradations within the course of the event based on operational data. The study provides the methodology and illustrative examples based on data gathered from simulation of nuclear power plant transients (due to lack of easily accessible operational data). Such an approach, once fully developed, can be used to detect failure trends and patterns and so can lead to prevention of conditions with serious safety implications

  13. Assessment of Surface Water Quality Using Multivariate Statistical Techniques in the Terengganu River Basin

    International Nuclear Information System (INIS)

    Aminu Ibrahim; Hafizan Juahir; Mohd Ekhwan Toriman; Mustapha, A.; Azman Azid; Isiyaka, H.A.

    2015-01-01

    Multivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA) rendered significant data reduction with 4 parameters (pH, NH 3 -NL, PO 4 and EC) and correct assignation of 95.80 %. The PCA/ FA applied to the data sets, yielded in five latent factors accounting 72.42 % of the total variance in the water quality data. The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste, industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important in environmental management. (author)

  14. Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

    OpenAIRE

    Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

    2014-01-01

    The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and C...

  15. Study on loss detection algorithms for tank monitoring data using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Suzuki, Mitsutoshi; Burr, Tom

    2009-01-01

    Evaluation of solution monitoring data to support material balance evaluation was proposed about a decade ago because of concerns regarding the large throughput planned at Rokkasho Reprocessing Plant (RRP). A numerical study using the simulation code (FACSIM) was done and significant increases in the detection probabilities (DP) for certain types of losses were shown. To be accepted internationally, it is very important to verify such claims using real solution monitoring data. However, a demonstrative study with real tank data has not been carried out due to the confidentiality of the tank data. This paper describes an experimental study that has been started using actual data from the Solution Measurement and Monitoring System (SMMS) in the Tokai Reprocessing Plant (TRP) and the Savannah River Site (SRS). Multivariate statistical methods, such as a vector cumulative sum and a multi-scale statistical analysis, have been applied to the real tank data that have superimposed simulated loss. Although quantitative conclusions have not been derived for the moment due to the difficulty of baseline evaluation, the multivariate statistical methods remain promising for abrupt and some types of protracted loss detection. (author)

  16. An Improvement of the Hotelling T2 Statistic in Monitoring Multivariate Quality Characteristics

    Directory of Open Access Journals (Sweden)

    Ashkan Shabbak

    2012-01-01

    Full Text Available The Hotelling T2 statistic is the most popular statistic used in multivariate control charts to monitor multiple qualities. However, this statistic is easily affected by the existence of more than one outlier in the data set. To rectify this problem, robust control charts, which are based on the minimum volume ellipsoid and the minimum covariance determinant, have been proposed. Most researchers assess the performance of multivariate control charts based on the number of signals without paying much attention to whether those signals are really outliers. With due respect, we propose to evaluate control charts not only based on the number of detected outliers but also with respect to their correct positions. In this paper, an Upper Control Limit based on the median and the median absolute deviation is also proposed. The results of this study signify that the proposed Upper Control Limit improves the detection of correct outliers but that it suffers from a swamping effect when the positions of outliers are not taken into consideration. Finally, a robust control chart based on the diagnostic robust generalised potential procedure is introduced to remedy this drawback.

  17. Application of multivariate statistical methods in analyzing expectation surveys in Central Bank of Nigeria

    OpenAIRE

    Raymond, Ogbuka Obinna

    2017-01-01

    In analyzing survey data, most researchers and analysts make use of statistical methods with straight forward statistical approaches. More common, is the use of one‐way, two‐way or multi‐way tables, and graphical displays such as bar charts, line charts, etc. A brief overview of these approaches and a good discussion on aspects needing attention during the data analysis process can be found in Wilson & Stern (2001). In most cases however, analysis procedures that go beyond simp...

  18. TOURISM SEGMENTATION BASED ON TOURISTS PREFERENCES: A MULTIVARIATE APPROACH

    Directory of Open Access Journals (Sweden)

    Sérgio Dominique Ferreira

    2010-11-01

    Full Text Available Over the last decades, tourism became one of the most important sectors of the international economy. Specifically in Portugal and Brazil, its contribution to Gross Domestic Product (GDP and job creation is quite relevant. In this sense, to follow a strong marketing approach on the management of tourism resources of a country comes to be paramount. Such an approach should be based on innovations which help unveil the preferences of tourists with accuracy, turning it into a competitive advantage. In this context, the main objective of the present study is to illustrate the importance and benefits associated with the use of multivariate methodologies for market segmentation. Another objective of this work is to illustrate on the importance of a post hoc segmentation. In this work, the authors applied a Cluster Analysis, with a hierarchical method followed by an  optimization method. The main results of this study allow the identification of five clusters that are distinguished by assigning special importance to certain tourism attributes at the moment of choosing a specific destination. Thus, the authors present the advantages of post hoc segmentation based on tourists’ preferences, in opposition to an a priori segmentation based on socio-demographic characteristics.

  19. Application of instrumental neutron activation analysis and multivariate statistical methods to archaeological Syrian ceramics

    International Nuclear Information System (INIS)

    Bakraji, E. H.; Othman, I.; Sarhil, A.; Al-Somel, N.

    2002-01-01

    Instrumental neutron activation analysis (INAA) has been utilized in the analysis of thirty-seven archaeological ceramics fragment samples collected from Tal AI-Wardiate site, Missiaf town, Hamma city, Syria. 36 chemical elements were determined. These elemental concentrations have been processed using two multivariate statistical methods, cluster and factor analysis in order to determine similarities and correlation between the various samples. Factor analysis confirms that samples were correctly classified by cluster analysis. The results showed that samples can be considered to be manufactured using three different sources of raw material. (author)

  20. Intermediate statistics a modern approach

    CERN Document Server

    Stevens, James P

    2007-01-01

    Written for those who use statistical techniques, this text focuses on a conceptual understanding of the material. It uses definitional formulas on small data sets to provide conceptual insight into what is being measured. It emphasizes the assumptions underlying each analysis, and shows how to test the critical assumptions using SPSS or SAS.

  1. Statistical sampling approaches for soil monitoring

    NARCIS (Netherlands)

    Brus, D.J.

    2014-01-01

    This paper describes three statistical sampling approaches for regional soil monitoring, a design-based, a model-based and a hybrid approach. In the model-based approach a space-time model is exploited to predict global statistical parameters of interest such as the space-time mean. In the hybrid

  2. Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

    Science.gov (United States)

    Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

    2018-03-01

    The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

  3. Quantitative Evaluation of Hybrid Aspen Xylem and Immunolabeling Patterns Using Image Analysis and Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    David Sandquist

    2015-06-01

    Full Text Available A new method is presented for quantitative evaluation of hybrid aspen genotype xylem morphology and immunolabeling micro-distribution. This method can be used as an aid in assessing differences in genotypes from classic tree breeding studies, as well as genetically engineered plants. The method is based on image analysis, multivariate statistical evaluation of light, and immunofluorescence microscopy images of wood xylem cross sections. The selected immunolabeling antibodies targeted five different epitopes present in aspen xylem cell walls. Twelve down-regulated hybrid aspen genotypes were included in the method development. The 12 knock-down genotypes were selected based on pre-screening by pyrolysis-IR of global chemical content. The multivariate statistical evaluations successfully identified comparative trends for modifications in the down-regulated genotypes compared to the unmodified control, even when no definitive conclusions could be drawn from individual studied variables alone. Of the 12 genotypes analyzed, three genotypes showed significant trends for modifications in both morphology and immunolabeling. Six genotypes showed significant trends for modifications in either morphology or immunocoverage. The remaining three genotypes did not show any significant trends for modification.

  4. Point defect characterization in HAADF-STEM images using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

    2011-01-01

    Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

  5. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

    Science.gov (United States)

    Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

  6. Lake Chini Water Quality Assessment Using Multivariate Approach

    International Nuclear Information System (INIS)

    Ahmad, A.K.; Shuhaimi, Othman M.; Lim, E.C.; Aziz, Z.A.

    2013-01-01

    An analysis was undertaken using the multivariate approach to determine the important water quality for shallow lake water quality assessment. Fourteen water quality parameters which includes biological, physical and chemical components were collected monthly over twelve month period. The data were analysed using factor analysis which involves identification of factor correlation, factor extraction and factor permutations. The first process involved the clustering of high correlation parameters into its respective factor and the removal of parameters that have more than one factor. Agglomerative hierarchy (HACA) and discriminant analysis (DA) were also used to exhibit the important factors that has significant influence on lake water quality. The analysis showed that Lake Chini water quality was determined by more than one factor. The results indicated that the biological and chemical (nutrients) components have significant influence in determining the lake water quality. The biological parameters namely BOD5, COD, chlorophyll a and chemical (nitrate and orthophosphate) are important parameters in Lake Chini. All analysis demonstrated the importance of biological and chemical water quality components in the determination of Lake Chini water quality. (author)

  7. Multivariate statistical analyses demonstrate unique host immune responses to single and dual lentiviral infection.

    Directory of Open Access Journals (Sweden)

    Sunando Roy

    2009-10-01

    Full Text Available Feline immunodeficiency virus (FIV and human immunodeficiency virus (HIV are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals, and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual

  8. Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

    Science.gov (United States)

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

    The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

  9. Statistical inference an integrated approach

    CERN Document Server

    Migon, Helio S; Louzada, Francisco

    2014-01-01

    Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...

  10. Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan

    International Nuclear Information System (INIS)

    Adam, Abdel Majid A.; Eltayeb, Mohamed Ahmed H.

    2012-01-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q–Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate. - Highlights: ► Multivariate statistical techniques were used to characterize radioactive data. ► U-238 in Uro phosphate shows higher degree of mobility (CV% = 82.6). ► U-238 in Kurun phosphate shows lower degree of mobility (CV% = 64.7). ► The radioactive variables did not follow a normal distribution. ► The ratio of Th

  11. A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance...

  12. Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

    OpenAIRE

    Bersimis, Sotiris; Panaretos, John; Psarakis, Stelios

    2005-01-01

    Woodall and Montgomery [35] in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC).This ...

  13. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  14. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

    2012-03-15

    To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. Water quality, Multivariate statistical techniques, submarine out fall, spatial variation, temporal variation

    International Nuclear Information System (INIS)

    Garcia, Francisco; Palacio, Carlos; Garcia, Uriel

    2012-01-01

    Multivariate statistical techniques were used to investigate the temporal and spatial variations of water quality at the Santa Marta coastal area where a submarine out fall that discharges 1 m3/s of domestic wastewater is located. Two-way analysis of variance (ANOVA), cluster and principal component analysis and Krigging interpolation were considered for this report. Temporal variation showed two heterogeneous periods. From December to April, and July, where the concentration of the water quality parameters is higher; the rest of the year (May, June, August-November) were significantly lower. The spatial variation reported two areas where the water quality is different, this difference is related to the proximity to the submarine out fall discharge.

  16. Multivariate statistical analysis of stream sediments for mineral resources from the Craig NTMS Quadrangle, Colorado

    International Nuclear Information System (INIS)

    Beyth, M.; McInteer, C.; Broxton, D.E.; Bolivar, S.L.; Luke, M.E.

    1980-06-01

    Multivariate statistical analyses were carried out on Hydrogeochemical and Stream Sediment Reconnaissance data from the Craig quadrangle, Colorado, to support the National Uranium Resource Evaluation and to evaluate strategic or other important commercial mineral resources. A few areas for favorable uranium mineralization are suggested for parts of the Wyoming Basin, Park Range, and Gore Range. Six potential source rocks for uranium are postulated based on factor score mapping. Vanadium in stream sediments is suggested as a pathfinder for carnotite-type mineralization. A probable northwest trend of lead-zinc-copper mineralization associated with Tertiary intrusions is suggested. A few locations are mapped where copper is associated with cobalt. Concentrations of placer sands containing rare earth elements, probably of commercial value, are indicated for parts of the Sand Wash Basin

  17. Forensic classification of counterfeit banknote paper by X-ray fluorescence and multivariate statistical methods.

    Science.gov (United States)

    Guo, Hongling; Yin, Baohua; Zhang, Jie; Quan, Yangke; Shi, Gaojun

    2016-09-01

    Counterfeiting of banknotes is a crime and seriously harmful to economy. Examination of the paper, ink and toners used to make counterfeit banknotes can provide useful information to classify and link different cases in which the suspects use the same raw materials. In this paper, 21 paper samples of counterfeit banknotes seized from 13 cases were analyzed by wavelength dispersive X-ray fluorescence. After measuring the elemental composition in paper semi-quantitatively, the normalized weight percentage data of 10 elements were processed by multivariate statistical methods of cluster analysis and principle component analysis. All these paper samples were mainly classified into 3 groups. Nine separate cases were successfully linked. It is demonstrated that elemental composition measured by XRF is a useful way to compare and classify papers used in different cases. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

    Directory of Open Access Journals (Sweden)

    Voza Danijela

    2015-12-01

    Full Text Available The aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA, Factor Analysis (FA and Cluster Analysis (CA in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

  19. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

    Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  20. Development of infill drilling recovery models for carbonates reservoirs using neural networks and multivariate statistical as a novel method

    International Nuclear Information System (INIS)

    Soto, R; Wu, Ch. H; Bubela, A M

    1999-01-01

    This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in west Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), oilfield intelligence (01), as an engineering tool to improve the characterization of oil and gas reservoirs. 01 integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial water flooding, number of wells for primary recovery, number of infill wells over the initial water flooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery

  1. Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

    Science.gov (United States)

    2012-01-01

    Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

  2. A statistical approach to root system classification.

    Directory of Open Access Journals (Sweden)

    Gernot eBodner

    2013-08-01

    Full Text Available Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for plant functional type identification in ecology can be applied to the classification of root systems. We demonstrate that combining principal component and cluster analysis yields a meaningful classification of rooting types based on morphological traits. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. Biplot inspection is used to determine key traits and to ensure stability in cluster based grouping. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Three rooting types emerged from measured data, distinguished by diameter/weight, density and spatial distribution respectively. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement

  3. Quantum Statistical Approach to Superconductivity

    Science.gov (United States)

    Nam, Eunsoo

    The Frohlich Hamiltonian representing an interaction between electron and phonon is derived. By exchanging a virtual phonon, a system of two electrons can lower the system's total energy if the difference of their kinetic energies is less than the energy of the phonon exchanged. This is shown by using quantum mechanical perturbation theory, which is fully developed. A general theory of superconductivity is developed, starting with a BCS Hamiltonian in which the interaction strengths (V_{11}, V_{22 }, V_{12}) among and between "electron" (1) and "hole" (2) Cooper pairs are differentiated. The supercondensate is shown to be composed of equal numbers of "electron" and "hole" ground (zero-momentum) Cooper pairs with charges mp 2e.. Based on the Hamiltonian, the normal-to-super phase transition is investigated, approaching the critical temperature T_{c} from the high temperature side. Non zero momentum Cooper pairs, that is, pairs of electrons (holes) with antiparallel spins and nearly opposite momenta above T_{c } in the bulk limit, are shown to move like independent bosons with the energy momentum relation varepsilon = (1/2)upsilon_ {F}p, where upsilon_ {F} represents the Fermi velocity. We have investigated the Bose-Einstein condensation of pairons. The system of free Cooper pairs in a 3D superconductors undergoes a phase transition of the second order with the critical temperature T_{c} given byk_{B}T_{c } = (1/2)(pi^2hbar^3v_sp {F}{3}n/1.20257)^{1over3 }where n is the number density of Cooper pairs. We calculate various properties associated with superconductivity at finite temperature. We derive general expressions for the energy gaps for both quasi electrons and pairons. Based on the independent pairon model, we explain the flux quantization, London's equation and the Josephson effects, stressing the importance of the macroscopic wave -function which represents the supercondensate in motion. We derived the basic equations governing the behavior of the

  4. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

    Science.gov (United States)

    Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

    2016-07-01

    A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  5. Permutation statistical methods an integrated approach

    CERN Document Server

    Berry, Kenneth J; Johnston, Janis E

    2016-01-01

    This research monograph provides a synthesis of a number of statistical tests and measures, which, at first consideration, appear disjoint and unrelated. Numerous comparisons of permutation and classical statistical methods are presented, and the two methods are compared via probability values and, where appropriate, measures of effect size. Permutation statistical methods, compared to classical statistical methods, do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This text takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field in statistics. This topic is new in that it took modern computing power to make permutation methods available to people working in the mainstream of research. This research monograph addresses a statistically-informed audience, and can also easily serve as a ...

  6. Statistical inference an integrated Bayesianlikelihood approach

    CERN Document Server

    Aitkin, Murray

    2010-01-01

    Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre

  7. A statistical approach to plasma profile analysis

    International Nuclear Information System (INIS)

    Kardaun, O.J.W.F.; McCarthy, P.J.; Lackner, K.; Riedel, K.S.

    1990-05-01

    A general statistical approach to the parameterisation and analysis of tokamak profiles is presented. The modelling of the profile dependence on both the radius and the plasma parameters is discussed, and pertinent, classical as well as robust, methods of estimation are reviewed. Special attention is given to statistical tests for discriminating between the various models, and to the construction of confidence intervals for the parameterised profiles and the associated global quantities. The statistical approach is shown to provide a rigorous approach to the empirical testing of plasma profile invariance. (orig.)

  8. multivariate approach to the study of aquatic species diversity

    African Journals Online (AJOL)

    User

    2016-12-02

    Dec 2, 2016 ... Eigen value of the three variables namely; Temperature, pH and Electrical Conductivity ... affect the stream macroinvertebrates (Fornaroli et al., 2016). ... relation to stream land use activities (Tinotenda et al., ... to rotate the multivariate data cloud and extract the ..... community modeling of species distribution.

  9. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mew, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-24

    Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

  10. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

    International Nuclear Information System (INIS)

    Chattopadhyay, Tanuka; Sharina, Margarita; Davoust, Emmanuel; De, Tuli; Chattopadhyay, Asis Kumar

    2012-01-01

    We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

  11. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Chattopadhyay, Tanuka [Department of Applied Mathematics, Calcutta University, 92 A.P.C. Road, Calcutta 700009 (India); Sharina, Margarita [Special Astrophysical Observatory, Russian Academy of Sciences, N. Arkhyz, KCh R 369167 (Russian Federation); Davoust, Emmanuel [IRAP, Universite de Toulouse, CNRS, 14 Avenue Edouard Belin, 31400 Toulouse (France); De, Tuli; Chattopadhyay, Asis Kumar, E-mail: tanuka@iucaa.ernet.in, E-mail: sme@sao.ru, E-mail: davoust@ast.obs-mip.fr, E-mail: akcstat@caluniv.ac.in [Department of Statistics, Calcutta University, 35 B.C. Road, Calcutta 700019 (India)

    2012-05-10

    We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

  12. Assessment of arsenic and heavy metal contents in cockles (Anadara granosa) using multivariate statistical techniques

    International Nuclear Information System (INIS)

    Abbas Alkarkhi, F.M.; Ismail, Norli; Easa, Azhar Mat

    2008-01-01

    Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers

  13. Functional integral approach to classical statistical dynamics

    International Nuclear Information System (INIS)

    Jensen, R.V.

    1980-04-01

    A functional integral method is developed for the statistical solution of nonlinear stochastic differential equations which arise in classical dynamics. The functional integral approach provides a very natural and elegant derivation of the statistical dynamical equations that have been derived using the operator formalism of Martin, Siggia, and Rose

  14. A statistical approach to instrument calibration

    Science.gov (United States)

    Robert R. Ziemer; David Strauss

    1978-01-01

    Summary - It has been found that two instruments will yield different numerical values when used to measure identical points. A statistical approach is presented that can be used to approximate the error associated with the calibration of instruments. Included are standard statistical tests that can be used to determine if a number of successive calibrations of the...

  15. Comparative Estimation of Russia’s Regions Investment Potential on the Base of the Multivariate Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Victor V. Nikitin

    2013-01-01

    Full Text Available The article introduces the algorithm of Russia’s regions investment potential estimation, developed by means of multivariate statistical methods, determines the factors, reflecting regions investment state. The integral indicator was developed on their basis, using statistical data. The article presents regions’ classification on the basis of the integral index

  16. Statistical mixture design and multivariate analysis of inkjet printed a-WO3/TiO2/WOX electrochromic films.

    Science.gov (United States)

    Wojcik, Pawel Jerzy; Pereira, Luís; Martins, Rodrigo; Fortunato, Elvira

    2014-01-13

    An efficient mathematical strategy in the field of solution processed electrochromic (EC) films is outlined as a combination of an experimental work, modeling, and information extraction from massive computational data via statistical software. Design of Experiment (DOE) was used for statistical multivariate analysis and prediction of mixtures through a multiple regression model, as well as the optimization of a five-component sol-gel precursor subjected to complex constraints. This approach significantly reduces the number of experiments to be realized, from 162 in the full factorial (L=3) and 72 in the extreme vertices (D=2) approach down to only 30 runs, while still maintaining a high accuracy of the analysis. By carrying out a finite number of experiments, the empirical modeling in this study shows reasonably good prediction ability in terms of the overall EC performance. An optimized ink formulation was employed in a prototype of a passive EC matrix fabricated in order to test and trial this optically active material system together with a solid-state electrolyte for the prospective application in EC displays. Coupling of DOE with chromogenic material formulation shows the potential to maximize the capabilities of these systems and ensures increased productivity in many potential solution-processed electrochemical applications.

  17. Approach to determine measurement uncertainty in complex nanosystems with multiparametric dependencies and multivariate output quantities

    Science.gov (United States)

    Hampel, B.; Liu, B.; Nording, F.; Ostermann, J.; Struszewski, P.; Langfahl-Klabes, J.; Bieler, M.; Bosse, H.; Güttler, B.; Lemmens, P.; Schilling, M.; Tutsch, R.

    2018-03-01

    In many cases, the determination of the measurement uncertainty of complex nanosystems provides unexpected challenges. This is in particular true for complex systems with many degrees of freedom, i.e. nanosystems with multiparametric dependencies and multivariate output quantities. The aim of this paper is to address specific questions arising during the uncertainty calculation of such systems. This includes the division of the measurement system into subsystems and the distinction between systematic and statistical influences. We demonstrate that, even if the physical systems under investigation are very different, the corresponding uncertainty calculation can always be realized in a similar manner. This is exemplarily shown in detail for two experiments, namely magnetic nanosensors and ultrafast electro-optical sampling of complex time-domain signals. For these examples the approach for uncertainty calculation following the guide to the expression of uncertainty in measurement (GUM) is explained, in which correlations between multivariate output quantities are captured. To illustate the versatility of the proposed approach, its application to other experiments, namely nanometrological instruments for terahertz microscopy, dimensional scanning probe microscopy, and measurement of concentration of molecules using surface enhanced Raman scattering, is shortly discussed in the appendix. We believe that the proposed approach provides a simple but comprehensive orientation for uncertainty calculation in the discussed measurement scenarios and can also be applied to similar or related situations.

  18. Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

    Energy Technology Data Exchange (ETDEWEB)

    Wallace, Jack, E-mail: jack.wallace@ce.queensu.ca [Department of Civil Engineering, Queen’s University, Ellis Hall, 58 University Avenue, Kingston, Ontario K7L 3N6 (Canada); Champagne, Pascale, E-mail: champagne@civil.queensu.ca [Department of Civil Engineering, Queen’s University, Ellis Hall, 58 University Avenue, Kingston, Ontario K7L 3N6 (Canada); Monnier, Anne-Charlotte, E-mail: anne-charlotte.monnier@insa-lyon.fr [National Institute for Applied Sciences – Lyon, 20 Avenue Albert Einstein, 69621 Villeurbanne Cedex (France)

    2015-01-15

    Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system, followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling

  19. Multivariate statistical assessment of heavy metal pollution sources of groundwater around a lead and zinc plant

    Directory of Open Access Journals (Sweden)

    Zamani Abbas Ali

    2012-12-01

    Full Text Available Abstract The contamination of groundwater by heavy metal ions around a lead and zinc plant has been studied. As a case study groundwater contamination in Bonab Industrial Estate (Zanjan-Iran for iron, cobalt, nickel, copper, zinc, cadmium and lead content was investigated using differential pulse polarography (DPP. Although, cobalt, copper and zinc were found correspondingly in 47.8%, 100.0%, and 100.0% of the samples, they did not contain these metals above their maximum contaminant levels (MCLs. Cadmium was detected in 65.2% of the samples and 17.4% of them were polluted by this metal. All samples contained detectable levels of lead and iron with 8.7% and 13.0% of the samples higher than their MCLs. Nickel was also found in 78.3% of the samples, out of which 8.7% were polluted. In general, the results revealed the contamination of groundwater sources in the studied zone. The higher health risks are related to lead, nickel, and cadmium ions. Multivariate statistical techniques were applied for interpreting the experimental data and giving a description for the sources. The data analysis showed correlations and similarities between investigated heavy metals and helps to classify these ion groups. Cluster analysis identified five clusters among the studied heavy metals. Cluster 1 consisted of Pb, Cu, and cluster 3 included Cd, Fe; also each of the elements Zn, Co and Ni was located in groups with single member. The same results were obtained by factor analysis. Statistical investigations revealed that anthropogenic factors and notably lead and zinc plant and pedo-geochemical pollution sources are influencing water quality in the studied area.

  20. Multivariate statistical process control in product quality review assessment - A case study.

    Science.gov (United States)

    Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A

    2017-11-01

    According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T 2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.

  1. Use of multivariate statistics to identify unreliable data obtained using CASA.

    Science.gov (United States)

    Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

    2013-06-01

    In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

  2. Multivariate statistical assessment of heavy metal pollution sources of groundwater around a lead and zinc plant.

    Science.gov (United States)

    Zamani, Abbas Ali; Yaftian, Mohammad Reza; Parizanganeh, Abdolhossein

    2012-12-17

    The contamination of groundwater by heavy metal ions around a lead and zinc plant has been studied. As a case study groundwater contamination in Bonab Industrial Estate (Zanjan-Iran) for iron, cobalt, nickel, copper, zinc, cadmium and lead content was investigated using differential pulse polarography (DPP). Although, cobalt, copper and zinc were found correspondingly in 47.8%, 100.0%, and 100.0% of the samples, they did not contain these metals above their maximum contaminant levels (MCLs). Cadmium was detected in 65.2% of the samples and 17.4% of them were polluted by this metal. All samples contained detectable levels of lead and iron with 8.7% and 13.0% of the samples higher than their MCLs. Nickel was also found in 78.3% of the samples, out of which 8.7% were polluted. In general, the results revealed the contamination of groundwater sources in the studied zone. The higher health risks are related to lead, nickel, and cadmium ions. Multivariate statistical techniques were applied for interpreting the experimental data and giving a description for the sources. The data analysis showed correlations and similarities between investigated heavy metals and helps to classify these ion groups. Cluster analysis identified five clusters among the studied heavy metals. Cluster 1 consisted of Pb, Cu, and cluster 3 included Cd, Fe; also each of the elements Zn, Co and Ni was located in groups with single member. The same results were obtained by factor analysis. Statistical investigations revealed that anthropogenic factors and notably lead and zinc plant and pedo-geochemical pollution sources are influencing water quality in the studied area.

  3. Multivariate statistical study of heavy metal enrichment in sediments of the Pearl River Estuary

    International Nuclear Information System (INIS)

    Liu, W.X.; Li, X.D.; Shen, Z.G.; Wang, D.C.; Wai, O.W.H.; Li, Y.S.

    2003-01-01

    Multivariate statistical analysis identified the heavy metal accumulation layers of sediment profiles and showed the various sources of metals in the estuary. - The concentrations and chemical partitioning of heavy metals in the sediment cores of the Pearl River Estuary were studied. Based on Pearson correlation coefficients and principal component analysis results, Al was selected as the concentration normalizer for Pb, while Fe was used as the normalizing element for Co, Cu, Ni and Zn. In each profile, sections with metal concentrations exceeding the upper 95% prediction interval of the linear regression model were regarded as metal enrichment layers. The heavy metal accumulation mainly occurred at sites in the western shallow water areas and east channel, which reflected the hydraulic conditions and influence from riparian anthropogenic activities. Heavy metals in the enrichment sections were evaluated by a sequential extraction method for possible chemical forms in sediments. Since the residual, Fe/Mn oxides and organic/sulfide fractions were dominant geochemical phases in the enriched sections, the bioavailability of heavy metals in sediments was generally low. The 206 Pb/ 207 Pb ratios in the metal-enriched sediment sections also revealed the influence of anthropogenic sources. The spatial distribution of cumulative heavy metals in the sediments suggested that the Zn and Cu mainly originated from point sources, while the Pb probably came from non-point sources in the estuary

  4. Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

    Science.gov (United States)

    Gürgey, K.; Canbolat, S.

    2017-11-01

    Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  5. APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS TO BIOMARKERS IN SE-TURKEY CRUDE OILS

    Directory of Open Access Journals (Sweden)

    K. Gürgey

    2017-11-01

    Full Text Available Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%, Stable Carbon Isotope, Gas Chromatography (GC, and Gas Chromatography-Mass Spectrometry (GC-MS data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  6. Determination of geographic provenance of cotton fibres using multi-isotope profiles and multivariate statistical analysis

    Science.gov (United States)

    Daeid, N. Nic; Meier-Augenstein, W.; Kemp, H. F.

    2012-04-01

    The analysis of cotton fibres can be particularly challenging within a forensic science context where discrimination of one fibre from another is of importance. Normally cotton fibre analysis examines the morphological structure of the recovered material and compares this with that of a known fibre from a particular source of interest. However, the conventional microscopic and chemical analysis of fibres and any associated dyes is generally unsuccessful because of the similar morphology of the fibres. Analysis of the dyes which may have been applied to the cotton fibre can also be undertaken though this can be difficult and unproductive in terms of discriminating one fibre from another. In the study presented here we have explored the potential for Isotope Ratio Mass Spectrometry (IRMS) to be utilised as an additional tool for cotton fibre analysis in an attempt to reveal further discriminatory information. This work has concentrated on un-dyed cotton fibres of known origin in order to expose the potential of the analytical technique. We report the results of a pilot study aimed at testing the hypothesis that multi-element stable isotope analysis of cotton fibres in conjunction with multivariate statistical analysis of the resulting isotopic abundance data using well established chemometric techniques permits sample provenancing based on the determination of where the cotton was grown and as such will facilitate sample discrimination. To date there is no recorded literature of this type of application of IRMS to cotton samples, which may be of forensic science relevance.

  7. Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

    Directory of Open Access Journals (Sweden)

    Qing Gu

    2016-03-01

    Full Text Available Qiandao Lake (Xin’an Jiang reservoir plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010–2013 were obtained for analysis. Cluster analysis (CA was applied to classify the 12 sampling sites into three groups (Groups A, B and C and the 12 monitoring months into two clusters (April-July, and the remaining months. Discriminant analysis (DA identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

  8. Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

    Science.gov (United States)

    Babamoradi, Hamid; van den Berg, Frans; Rinnan, Åsmund

    2016-02-18

    In Multivariate Statistical Process Control, when a fault is expected or detected in the process, contribution plots are essential for operators and optimization engineers in identifying those process variables that were affected by or might be the cause of the fault. The traditional way of interpreting a contribution plot is to examine the largest contributing process variables as the most probable faulty ones. This might result in false readings purely due to the differences in natural variation, measurement uncertainties, etc. It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs. Asymptotic methods cannot provide confidence limits for contribution plots, leaving re-sampling methods as the only option. We suggest bootstrap re-sampling to build confidence limits for all contribution plots in online PCA-based MSPC. The new strategy to estimate CLs is compared to the previously reported CLs for contribution plots. An industrial batch process dataset was used to illustrate the concepts. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

    Science.gov (United States)

    Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

    2018-01-01

    In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

  10. Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

    Directory of Open Access Journals (Sweden)

    Teck-Yee Ling

    2017-01-01

    Full Text Available The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA and principal component analysis (PCA. Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge.

  11. Combining Statistical Methodologies in Water Quality Monitoring in a Hydrological Basin - Space and Time Approaches

    OpenAIRE

    Costa, Marco; A. Manuela Gonçalves

    2012-01-01

    In this work are discussed some statistical approaches that combine multivariate statistical techniques and time series analysis in order to describe and model spatial patterns and temporal evolution by observing hydrological series of water quality variables recorded in time and space. These approaches are illustrated with a data set collected in the River Ave hydrological basin located in the Northwest region of Portugal.

  12. Flow prediction models using macroclimatic variables and multivariate statistical techniques in the Cauca River Valley

    International Nuclear Information System (INIS)

    Carvajal Escobar Yesid; Munoz, Flor Matilde

    2007-01-01

    The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.

  13. Risk assessment of transitional economies by multivariate and multicriteria approaches

    Directory of Open Access Journals (Sweden)

    Tomić-Plazibat Neli

    2010-01-01

    Full Text Available This article assesses country-risk of sixteen Central, Baltic and South-East European transition countries, for 2005 and 2007, using multivariate cluster analysis. It was aided by the appropriate ANOVA (analysis of variance testing and the multicriteria PROMETHEE method. The combination of methods makes for more accurate and efficient country-risk assessment. Country risk classifications and ratings involve evaluating the performance of countries while considering their economic and socio-political characteristics. The purpose of the article is to classify, and then find the comparative position of each individual country in the group of analyzed countries, in order to find out to which extent development of market economy and democratic society has been achieved.

  14. Energy and economic growth in the USA: a multivariate approach

    International Nuclear Information System (INIS)

    Stern, D.I.

    1993-01-01

    This paper examines the casual relationship between Gross Domestic Product and energy use for the period 1947-90 in the United States of America. The relationship between energy use and economic growth has been examined by both biophysical and neoclassical economists. In particular, several studies have tested for the presence of a causal relationships (in the Granger sense) between energy use and economic growth. However, these tests do not allow a direct test of the relative explanatory powers of the neoclassical and biophysical models. A multivariate adaptation of the test-vector autoregression (VAR) does allow such a test. A VAR of GDP, energy use, capital stock and employment is estimated and Granger tests for causal relationships between the variables are carried out. Although there is no evidence that gross energy use Granger causes GDP, a measure of final energy use adjusted for changing fuel composition does Granger cause GDP. (author)

  15. Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

    Science.gov (United States)

    Catelani, Tiago A; Santos, João Rodrigo; Páscoa, Ricardo N M J; Pezza, Leonardo; Pezza, Helena R; Lopes, João A

    2018-03-01

    This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T 2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques

    Directory of Open Access Journals (Sweden)

    Vujović Svetlana R.

    2013-01-01

    Full Text Available This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA/principal component analysis (PCA and cluster analysis (CA, were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA is an

  17. Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

    Science.gov (United States)

    Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

    2017-03-01

    A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

  18. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    Science.gov (United States)

    Djorgovski, S. George

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

  19. Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

    Science.gov (United States)

    Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

    2008-01-01

    Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

  20. Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

    Science.gov (United States)

    Djorgovski, S. G.

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has

  1. Correlation analysis of energy indicators for sustainable development using multivariate statistical techniques

    International Nuclear Information System (INIS)

    Carneiro, Alvaro Luiz Guimaraes; Santos, Francisco Carlos Barbosa dos

    2007-01-01

    Energy is an essential input for social development and economic growth. The production and use of energy cause environmental degradation at all levels, being local, regional and global such as, combustion of fossil fuels causing air pollution; hydropower often causes environmental damage due to the submergence of large areas of land; and global climate change associated with the increasing concentration of greenhouse gases in the atmosphere. As mentioned in chapter 9 of Agenda 21, the Energy is essential to economic and social development and improved quality of life. Much of the world's energy, however, is currently produced and consumed in ways that could not be sustained if technologies were remain constant and if overall quantities were to increase substantially. All energy sources will need to be used in ways that respect the atmosphere, human health, and the environment as a whole. The energy in the context of sustainable development needs a set of quantifiable parameters, called indicators, to measure and monitor important changes and significant progress towards the achievement of the objectives of sustainable development policies. The indicators are divided into four dimensions: social, economic, environmental and institutional. This paper shows a methodology of analysis using Multivariate Statistical Technique that provide the ability to analyse complex sets of data. The main goal of this study is to explore the correlation analysis among the indicators. The data used on this research work, is an excerpt of IBGE (Instituto Brasileiro de Geografia e Estatistica) data census. The core indicators used in this study follows The IAEA (International Atomic Energy Agency) framework: Energy Indicators for Sustainable Development. (author)

  2. Authigenic oxide Neodymium Isotopic composition as a proxy of seawater: applying multivariate statistical analyses.

    Science.gov (United States)

    McKinley, C. C.; Scudder, R.; Thomas, D. J.

    2016-12-01

    The Neodymium Isotopic composition (Nd IC) of oxide coatings has been applied as a tracer of water mass composition and used to address fundamental questions about past ocean conditions. The leached authigenic oxide coating from marine sediment is widely assumed to reflect the dissolved trace metal composition of the bottom water interacting with sediment at the seafloor. However, recent studies have shown that readily reducible sediment components, in addition to trace metal fluxes from the pore water, are incorporated into the bottom water, influencing the trace metal composition of leached oxide coatings. This challenges the prevailing application of the authigenic oxide Nd IC as a proxy of seawater composition. Therefore, it is important to identify the component end-members that create sediments of different lithology and determine if, or how they might contribute to the Nd IC of oxide coatings. To investigate lithologic influence on the results of sequential leaching, we selected two sites with complete bulk sediment statistical characterization. Site U1370 in the South Pacific Gyre, is predominantly composed of Rhyolite ( 60%) and has a distinguishable ( 10%) Fe-Mn Oxyhydroxide component (Dunlea et al., 2015). Site 1149 near the Izu-Bonin-Arc is predominantly composed of dispersed ash ( 20-50%) and eolian dust from Asia ( 50-80%) (Scudder et al., 2014). We perform a two-step leaching procedure: a 14 mL of 0.02 M hydroxylamine hydrochloride (HH) in 20% acetic acid buffered to a pH 4 for one hour, targeting metals bound to Fe- and Mn- oxides fractions, and a second HH leach for 12 hours, designed to remove any remaining oxides from the residual component. We analyze all three resulting fractions for a large suite of major, trace and rare earth elements, a sub-set of the samples are also analyzed for Nd IC. We use multivariate statistical analyses of the resulting geochemical data to identify how each component of the sediment partitions across the sequential

  3. Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

    Science.gov (United States)

    Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

    2018-04-26

    Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants

  4. Comparison of multivariate and univariate statistical process control and monitoring methods

    International Nuclear Information System (INIS)

    Leger, R.P.; Garland, WM.J.; Macgregor, J.F.

    1996-01-01

    Work in recent years has lead to the development of multivariate process monitoring schemes which use Principal Component Analysis (PCA). This research compares the performance of a univariate scheme and a multivariate PCA scheme used for monitoring a simple process with 11 measured variables. The multivariate PCA scheme was able to adequately represent the process using two principal components. This resulted in a PCA monitoring scheme which used two charts as opposed to 11 charts for the univariate scheme and therefore had distinct advantages in terms of both data representation, presentation, and fault diagnosis capabilities. (author)

  5. Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace elements occurrence in the Campania Plain, Southern Italy.

    Science.gov (United States)

    Busico, Gianluigi; Cuoco, Emilio; Kazakis, Nerantzis; Colombani, Nicolò; Mastrocicco, Micòl; Tedesco, Dario; Voudouris, Konstantinos

    2018-03-01

    Shallow aquifers are the most accessible reservoirs of potable groundwater; nevertheless, they are also prone to various sources of pollution and it is usually difficult to distinguish between human and natural sources at the watershed scale. The area chosen for this study (the Campania Plain) is characterized by high spatial heterogeneities both in geochemical features and in hydraulic properties. Groundwater mineralization is driven by many processes such as, geothermal activity, weathering of volcanic products and intense human activities. In such a landscape, multivariate statistical analysis has been used to differentiate among the main hydrochemical processes occurring in the area, using three different approaches of factor analysis: (i) major elements, (ii) trace elements, (iii) both major and trace elements. The elaboration of the factor analysis approaches has revealed seven distinct hydrogeochemical processes: i) Salinization (Cl - , Na + ); ii) Carbonate rocks dissolution; iii) Anthropogenic inputs (NO 3 - , SO 4 2- , U, V); iv) Reducing conditions (Fe 2+ , Mn 2+ ); v) Heavy metals contamination (Cr and Ni); vi) Geothermal fluids influence (Li + ); and vii) Volcanic products contribution (As, Rb). Results from this study highlight the need to separately apply factor analysis when a large data set of trace elements is available. In fact, the impact of geothermal fluids in the shallow aquifer was identified from the application of the factor analysis using only trace elements. This study also reveals that the factor analysis of major and trace elements can differentiate between anthropogenic and geogenic sources of pollution in intensively exploited aquifers. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses

    OpenAIRE

    Buttigieg, Pier Luigi; Ramette, Alban Nicolas

    2014-01-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynami...

  7. Trace elements of concern affecting urban agriculture in industrialized areas: A multivariate approach.

    Science.gov (United States)

    Boente, C; Matanzas, N; García-González, N; Rodríguez-Valdés, E; Gallego, J R

    2017-09-01

    The urban and peri-urban soils used for agriculture could be contaminated by atmospheric deposition or industrial releases, thus raising concerns about the potential risk to public health. Here we propose a method to evaluate potential soil pollution based on multivariate statistics, geostatistics (kriging), a novel soil pollution index, and bioavailability assessments. This approach was tested in two districts of a highly populated and industrialized city (Gijón, Spain). The soils showed anomalous content of several trace elements, such as As and Pb (up to 80 and 585 mg kg -1 respectively). In addition, factor analyses associated these elements with anthropogenic activity, whereas other elements were attributed to natural sources. Subsequent clustering also facilitated the differentiation between the northern area studied (only limited Pb pollution found) and the southern area (pattern of coal combustion, including simultaneous anomalies of trace elements and benzo(a)pyrene). A normalized soil pollution index (SPI) was calculated by kriging, using only the elements falling above threshold levels; therefore point-source polluted zones in the northern area and diffuse contamination in the south were identified. In addition, in the six mapping units with the highest SPIs of the fifty studied, we observed low bioavailability for most of the elements that surpassed the threshold levels. However, some anomalies of Pb contents and the pollution fingerprint in the central area of the southern grid call for further site-specific studies. On the whole, the combination of a multivariate (geo) statistic approach and a bioavailability assessment allowed us to efficiently identify sources of contamination and potential risks. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics : Method development, evaluation, and application to proteinuria

    NARCIS (Netherlands)

    Kemperman, Ramses F. J.; Horvatovich, Peter L.; Hoekman, Berend; Reijmers, Theo H.; Muskiet, Frits A. J.; Bischoff, Rainer

    2007-01-01

    We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit

  9. Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

    Science.gov (United States)

    The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

  10. Quantitative profiling of polar metabolites in herbal medicine injections for multivariate statistical evaluation based on independence principal component analysis.

    Directory of Open Access Journals (Sweden)

    Miaomiao Jiang

    Full Text Available Botanical primary metabolites extensively exist in herbal medicine injections (HMIs, but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (1H NMR profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI, was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative 1H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA, the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs. IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids of χ2 and Hotelling T2 control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.

  11. Helicopter Gas Turbine Engine Performance Analysis : A Multivariable Approach

    NARCIS (Netherlands)

    Arush, Ilan; Pavel, M.D.

    2017-01-01

    Helicopter performance relies heavily on the available output power of the engine(s) installed. A simplistic single-variable analysis approach is often used within the flight-testing community to reduce raw flight-test data in order to predict the available output power under different atmospheric

  12. Drought assessment in the Duero basin (Central Spain) by means of multivariate extreme value statistics

    Science.gov (United States)

    Kallache, M.

    2012-04-01

    Droughts cause important losses. On the Iberian Peninsula, for example, non-irrigated agriculture and the tourism sector are affected in regular intervals. The goal of this study is the description of droughts and their dependence in the Duero basin in Central Spain. To do so, daily or monthly precipitation data is used. Here cumulative precipitation deficits below a threshold define meteorological droughts. This drought indicator is similar to the commonly used standard precipitation index. However, here the focus lies on the modeling of severe droughts, which is done by applying multivariate extreme value theory (MEVT) to model extreme drought events. Data from several stations are assessed jointly, thus the uncertainty of the results is reduced. Droughts are a complex phenomenon, their severity, spatial extension and duration has to be taken into account. Our approach captures severity and spatial extension. In general we find a high correlation between deficit volumes and drought duration, thus the duration is not explicitely modeled. We apply a MEVT model with asymmetric logistic dependence function, which is capable to model asymptotic dependence and independence (cf. Ramos and Ledford, 2009). To summarize the information on the dependence in the joint tail of the extreme drought events, we utilise the fragility index (Geluk et al., 2007). Results show that droughts also occur frequently in winter. Moreover, it is very common for one site to suffer dry conditions, whilst neighboring areas experience normal or even humid conditions. Interpolation is thus difficult. Bivariate extremal dependence is present in the data. However, most stations are at least asymptotically independent. The according fragility indices are important information for risk calculations. The emerging spatial patterns for bivariate dependence are mostly influenced by topography. When looking at the dependence between more than two stations, it shows that joint extremes can occur more

  13. Source Identification of Heavy Metals in Soils Surrounding the Zanjan Zinc Town by Multivariate Statistical Techniques

    Directory of Open Access Journals (Sweden)

    M.A. Delavar

    2016-02-01

    Full Text Available Introduction: The accumulation of heavy metals (HMs in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. HMs may be considered as the most important soil pollutants, because they are not biodegradable and their physical movement through the soil profile is relatively limited. Therefore, root uptake process may provide a big chance for these pollutants to transfer from the surface soil to natural and cultivated plants, which may eventually steer them to human bodies. The general behavior of HMs in the environment, especially their bioavailability in the soil, is influenced by their origin. Hence, source apportionment of HMs may provide some essential information for better management of polluted soils to restrict the HMs entrance to the human food chain. This paper explores the applicability of multivariate statistical techniques in the identification of probable sources that can control the concentration and distribution of selected HMs in the soils surrounding the Zanjan Zinc Specialized Industrial Town (briefly Zinc Town. Materials and Methods: The area under investigation has a size of approximately 4000 ha.It is located around the Zinc Town, Zanjan province. A regular grid sampling pattern with an interval of 500 meters was applied to identify the sample location, and 184 topsoil samples (0-10 cm were collected. The soil samples were air-dried and sieved through a 2 mm polyethylene sieve and then, were digested using HNO3. The total concentrations of zinc (Zn, lead (Pb, cadmium (Cd, Nickel (Ni and copper (Cu in the soil solutions were determined via Atomic Absorption Spectroscopy (AAS. Data were statistically analyzed using the SPSS software version 17.0 for Windows. Correlation Matrix (CM, Principal Component Analyses (PCA and Factor Analyses (FA techniques were performed in order to identify the probable sources of HMs in the studied soils. Results and

  14. Antioxidant activity of Costa Rican propolis: a multivariate analysis approach

    International Nuclear Information System (INIS)

    Umana Rojas, Eduardo; Solado, Godofredo; Tamayo-Castillo, Giselle

    2013-01-01

    Propolis is produced by Apis mellifera bees from resins of plants that are found around the apiary. The chemical composition is highly variable and Costa Rica has reported without studies of characterization to define the types of propolis in the country. 119 samples were collected from beekeeping areas of the country. The spectrum of 1 H-NMR and its antioxidant activity against DPPH radical were measured. The spectra have been divided into 243 blocks of 0,04 ppm and processed with the Minitab software for multivariate analysis. 99 of the samples collected were used for construction of models for the valuation of the predictive ability of the model have been used coefficients of determination (R 2 ) of prediction by the software and the remaining 20 samples. The existence of three types of propolis with chemically different metabolomes were determined by principal component analysis (PCA). A prediction model was constructed by analysis of partial least squares (PLS). The prediction model has allowed to classify a propolis according to the level of antioxidant activity (AAO), high (type I and II) or low (type III) from the spectrum of 1 H-NMR. The R 2 has been 0.88 and R 2 prediction of 0, 718 for new samples. The nconiferyl benzoate of group I and nemorosone of the group II as two discriminated antioxidants among the groups I and II were isolated and high concentration levels of these compounds have been differentiated with respect to type III. This has allowed the construction of a linear discriminant model with a success rate of 100% for the samples used for formulation and 92,9 for the prediction of different samples. The classification systems could be applied to the standardization of the quality of propolis from Costa Rica for future medicinal or cosmetic applications that take advantage of its antioxidant properties. Also, the methylated derivative has isolated and identified of the nconiferyl benzoate thereof propolis than was obtained his counterpart

  15. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

    International Nuclear Information System (INIS)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-01-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component

  16. Assessment of roadside surface water quality of Savar, Dhaka, Bangladesh using GIS and multivariate statistical techniques

    Science.gov (United States)

    Ahmed, Fahad; Fakhruddin, A. N. M.; Imam, MD. Toufick; Khan, Nasima; Abdullah, Abu Tareq Mohammad; Khan, Tanzir Ahmed; Rahman, Md. Mahfuzur; Uddin, Mohammad Nashir

    2017-11-01

    In this study, multivariate statistical techniques in collaboration with GIS are used to assess the roadside surface water quality of Savar region. Nineteen water samples were collected in dry season and 15 water quality parameters including TSS, TDS, pH, DO, BOD, Cl-, F-, NO3 2-, NO2 -, SO4 2-, Ca, Mg, K, Zn and Pb were measured. The univariate overview of water quality parameters are TSS 25.154 ± 8.674 mg/l, TDS 840.400 ± 311.081 mg/l, pH 7.574 ± 0.256 pH unit, DO 4.544 ± 0.933 mg/l, BOD 0.758 ± 0.179 mg/l, Cl- 51.494 ± 28.095 mg/l, F- 0.771 ± 0.153 mg/l, NO3 2- 2.211 ± 0.878 mg/l, NO2 - 4.692 ± 5.971 mg/l, SO4 2- 69.545 ± 53.873 mg/l, Ca 48.458 ± 22.690 mg/l, Mg 19.676 ± 7.361 mg/l, K 12.874 ± 11.382 mg/l, Zn 0.027 ± 0.029 mg/l, Pb 0.096 ± 0.154 mg/l. The water quality data were subjected to R-mode PCA which resulted in five major components. PC1 explains 28% of total variance and indicates the roadside and brick field dust settle down (TDS, TSS) in the nearby water body. PC2 explains 22.123% of total variance and indicates the agricultural influence (K, Ca, and NO2 -). PC3 describes the contribution of nonpoint pollution from agricultural and soil erosion processes (SO4 2-, Cl-, and K). PC4 depicts heavy positively loaded by vehicle emission and diffusion from battery stores (Zn, Pb). PC5 depicts strong positive loading of BOD and strong negative loading of pH. Cluster analysis represents three major clusters for both water parameters and sampling sites. The site based on cluster showed similar grouping pattern of R-mode factor score map. The present work reveals a new scope to monitor the roadside water quality for future research in Bangladesh.

  17. Study of groundwater arsenic pollution in Lanyang Plain using multivariate statistical analysis

    Science.gov (United States)

    chan, S.

    2013-12-01

    The study area, Lanyang Plain in the eastern Taiwan, has highly developed agriculture and aquaculture, which consume over 70% of the water supplies. Groundwater is frequently considered as an alternative water source. However, the serious arsenic pollution of groundwater in Lanyan Plain should be well studied to ensure the safety of groundwater usage. In this study, 39 groundwater samples were collected. The results of hydrochemistry demonstrate two major trends in Piper diagram. The major trend with most of groundwater samples is determined with water type between Ca+Mg-HCO3 and Na+K-HCO3. This can be explained with cation exchange reaction. The minor trend is obviously corresponding to seawater intrusion, which has water type of Na+K-Cl, because the localities of these samples are all in the coastal area. The multivariate statistical analysis on hydrochemical data was conducted for further exploration on the mechanism of arsenic contamination. Two major factors can be extracted with factor analysis. The major factor includes Ca, Mg and Sr while the minor factor includes Na, K and As. This reconfirms that cation exchange reaction mainly control the groundwater hydrochemistry in the study area. It is worth to note that arsenic is positively related to Na and K. The result of cluster analysis shows that groundwater samples with high arsenic concentration can be grouped into that with high Na, K and HCO3. This supports that cation exchange would enhance the release of arsenic and exclude the effect of seawater intrusion. In other words, the water-rock reaction time is key to obtain higher arsenic content. In general, the major source of arsenic in sediments include exchangeable, reducible and oxidizable phases, which are adsorbed ions, Fe-Mn oxides and organic matters/pyrite, respectively. However, the results of factor analysis do not show apparent correlation between arsenic and Fe/Mn. This may exclude Fe-Mn oxides as a major source of arsenic. The other sources

  18. Hydrochemical analysis of groundwater using multivariate statistical methods - The Volta region, Ghana

    Science.gov (United States)

    Banoeng-Yakubo, B.; Yidana, S.M.; Nti, E.

    2009-01-01

    Q and R-mode multivariate statistical analyses were applied to groundwater chemical data from boreholes and wells in the northern section of the Volta region Ghana. The objective was to determine the processes that affect the hydrochemistry and the variation of these processes in space among the three main geological terrains: the Buem formation, Voltaian System and the Togo series that underlie the area. The analyses revealed three zones in the groundwater flow system: recharge, intermediate and discharge regions. All three zones are clearly different with respect to all the major chemical parameters, with concentrations increasing from the perceived recharge areas through the intermediate regions to the discharge areas. R-mode HCA and factor analysis (using varimax rotation and Kaiser Criterion) were then applied to determine the significant sources of variation in the hydrochemistry. This study finds that groundwater hydrochemistry in the area is controlled by the weathering of silicate and carbonate minerals, as well as the chemistry of infiltrating precipitation. This study finds that the ??D and ??18O data from the area fall along the Global Meteoric Water Line (GMWL). An equation of regression derived for the relationship between ??D and ??18O bears very close semblance to the equation which describes the GMWL. On the basis of this, groundwater in the study area is probably meteoric and fresh. The apparently low salinities and sodicities of the groundwater seem to support this interpretation. The suitability of groundwater for domestic and irrigation purposes is related to its source, which determines its constitution. A plot of the sodium adsorption ratio (SAR) and salinity (EC) data on a semilog axis, suggests that groundwater serves good irrigation quality in the area. Sixty percent (60%), 20% and 20% of the 67 data points used in this study fall within the medium salinity - low sodicity (C2-S1), low salinity -low sodicity (C1-S1) and high salinity - low

  19. Inverse statistical approach in heartbeat time series

    International Nuclear Information System (INIS)

    Ebadi, H; Shirazi, A H; Mani, Ali R; Jafari, G R

    2011-01-01

    We present an investigation on heart cycle time series, using inverse statistical analysis, a concept borrowed from studying turbulence. Using this approach, we studied the distribution of the exit times needed to achieve a predefined level of heart rate alteration. Such analysis uncovers the most likely waiting time needed to reach a certain change in the rate of heart beat. This analysis showed a significant difference between the raw data and shuffled data, when the heart rate accelerates or decelerates to a rare event. We also report that inverse statistical analysis can distinguish between the electrocardiograms taken from healthy volunteers and patients with heart failure

  20. A multivariate-utility approach for selection of energy sources

    International Nuclear Information System (INIS)

    Ahmed, S; Husseiny, A.A.

    1978-01-01

    A deterministic approach is devised to compare the safety features of various energy sources. The approach is based on multiattribute utility theory. The method is used in evaluating the safety aspects of alternative energy sources used for the production of electrical energy. Four alternative energy sources are chosen which could be considered for the production of electricity to meet the national energy demand. These are nuclear, coal, solar, and geothermal energy. For simplicity, a total electrical system is considered in each case. A computer code is developed to evaluate the overall utility function for each alternative from the utility patterns corresponding to 23 energy attributes, mostly related to safety. The model can accommodate other attributes assuming that these are independent. The technique is kept flexible so that virtually any decision problem with various attributes can be attacked and optimal decisions can be reached. The selected data resulted in preference of geothermal and nuclear energy over other sources, and the method is found viable in making decisions on energy uses based on quantified and subjective attributes. (author)

  1. Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

    Science.gov (United States)

    McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

    2017-12-01

    Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

  2. Paradigms and pragmatism: approaches to medical statistics.

    Science.gov (United States)

    Healy, M J

    2000-01-01

    Until recently, the dominant philosophy of science was that due to Karl Popper, with its doctrine that the proper task of science was the formulation of hypotheses followed by attempts at refuting them. In spite of the close analogy with significance testing, these ideas do not fit well with the practice of medical statistics. The same can be said of the later philosophy of Thomas Kuhn, who maintains that science proceeds by way of revolutionary upheavals separated by periods of relatively pedestrian research which are governed by what Kuhn refers to as paradigms. Through there have been paradigm shifts in the history of statistics, a degree of continuity can also be discerned. A current paradigm shift is embodied in the spread of Bayesian ideas. It may be that a future paradigm will emphasise the pragmatic approach to statistics that is associated with the name of Daniel Schwartz.

  3. Multivariate statistical study with a factor analysis of foraminiferal fauna from the Chilka Lake, India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    Harbour, En- gland: a reappraisal using multivariate tech- niques. J. Paleontol., 43 (3) : 660-675. Imbrie, J. and F.B. Phleger. 1963. Analisis por vectores de los foraminiferos bentonicos del area de San Diego, California. Soc. Geol. Mex., Bol., 26...

  4. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

    Science.gov (United States)

    Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome. Chave

    2014-01-01

    We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

  5. Understanding the whole city as landscape. A multivariate approach to urban landscape morphology

    Directory of Open Access Journals (Sweden)

    Richard Stiles

    2014-05-01

    Full Text Available The European Landscape Convention implies a requirement for signatory states to identify their urban landscapes which goes beyond the traditional focus on individual parks and green spaces and the links between them. Landscape ecological approaches can provide a useful model for identifying urban landscape types across a whole territory, but the variables relevant for urban landscapes are very different to those usually addressing rural areas. This paper presents an approach to classifying the urban landscape of Vienna that was developed in a research project funded by the Austrian Ministry for Transport, Innovation and Technology: ‘Urban Fabric and Microclimate Response’. Nine landscape types and a number of sub-types were defined, using a multivariate statistical approach which takes account of both morphological and urban climate related variables. Although the variables were selected to objectively reflect the factors that could best represent the urban climatic characteristics of the urban landscape, the results also provided a widely plausible representation of the structure of the city’s landscapes. Selected examples of the landscape types that were defined in this way were used both to simulate current microclimatic conditions and also to model the effects of possible climatic amelioration measures. Finally the paper looks forward to developing a more general-purpose urban landscape typology that allows investigating a much broader complex of urban landscape functions.

  6. Damage detection of engine bladed-disks using multivariate statistical analysis

    Science.gov (United States)

    Fang, X.; Tang, J.

    2006-03-01

    The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

  7. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

    Science.gov (United States)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-12-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

  8. Understanding gendered aspects of migration aspiration and motives of university students by multivariate statistical methods

    Directory of Open Access Journals (Sweden)

    Đula Borozan

    2014-03-01

    Full Text Available The paper deals with the application of multivariate analysis of variance and logistic regression in measuring, explaining and evaluating (i gender differences in expressing migration aspirations, and (ii a gender effect on migration motivation of university students in Croatia. The results supported the thesis that migration is a complex gendering process that assumes subjective assessment of the whole set of interrelated motives. According to logistic regression, gender is a significant predictor of migration aspirations among the selected demographic and socio-economic variables. A multivariate analysis of variance showed that gender and migration aspirations in interaction matter when it comes to migration motives, particularly related to the perceived importance of social networks. Females, and especially those who aspire to migrate, assessed these motives as more important than males.

  9. Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

    Science.gov (United States)

    Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

    2009-01-01

    Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

  10. A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

    Science.gov (United States)

    Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

    2016-01-13

    A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

  11. Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic Fingerprint

    OpenAIRE

    Xiong, Haoshu; Yu, Lawrence X.; Qu, Haibin

    2013-01-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many ...

  12. Statistical algebraic approach to quantum mechanics

    International Nuclear Information System (INIS)

    Slavnov, D.A.

    2001-01-01

    The scheme for plotting the quantum theory with application of the statistical algebraic approach is proposed. The noncommutative algebra elements (observed ones) and nonlinear functionals on this algebra (physical state) are used as the primary constituents. The latter ones are associated with the single-unit measurement results. Certain physical state groups are proposed to consider as quantum states of the standard quantum mechanics. It is shown that the mathematical apparatus of the standard quantum mechanics may be reproduced in such a scheme in full volume [ru

  13. Heavy metals in soils and sediments from Dongting Lake in China: occurrence, sources, and spatial distribution by multivariate statistical analysis.

    Science.gov (United States)

    Zhang, Yaxin; Tian, Ye; Shen, Maocai; Zeng, Guangming

    2018-03-03

    Heavy metal contamination in soils/sediments and its impact on human health and ecological environment have aroused wide concerns. Our study investigated 30 samples of soils and sediments around Dongting Lake to analyze the concentration of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn in the samples and to distinguish the natural and anthropogenic sources. Also, the relationship between heavy metals and the physicochemical properties of samples was studied by multivariate statistical analysis. Concentration of Cd at most sampling sites were more than five times that of national environmental quality standard for soil in China (GB 15618-1995), and Pb and Zn levels exceeded one to two times. Moreover, Cr in the soil was higher than the national environmental quality standards for one to two times while in sediment was lower than the national standard. The investigation revealed that the accumulations of As, Cd, Mn, and Pb in the soils, and sediments were affected apparently by anthropogenic activities; however, Cr, Fe, and Ni levels were impacted by parent materials. Human activities around Dongting Lake mainly consisted of industrial activities, mining and smelting, sewage discharges, fossil fuel combustion, and agricultural chemicals. The spatial distribution of heavy metal in soil followed the rule of geographical gradient, whereas in sediments, it was significantly affected by the river basins and human activities. The result of principal component analysis (PCA) demonstrated that heavy metals in soils were associated with pH and total phosphorus (TP), while in sediments, As, Cr, Fe, and Ni were closely associated with cation exchange capacity (CEC) and pH, where Pb, Zn, and Cd were associated with total nitrogen (TN), TP, total carbon (TC), moisture content (MC), soil organic matter (SOM), and ignition lost (IL). Our research provides comprehensive approaches to better understand the potential sources and the fate of contaminants in lakeshore soils and sediments.

  14. Fault detection of a spur gear using vibration signal with multivariable statistical parameters

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2014-10-01

    Full Text Available This paper presents a condition monitoring technique of a spur gear fault detection using vibration signal analysis based on time domain. Vibration signals were acquired from gearboxes and used to simulate various faults on spur gear tooth. In this study, vibration signals were applied to monitor a normal and various fault conditions of a spur gear such as normal, scuffing defect, crack defect and broken tooth. The statistical parameters of vibration signal were used to compare and evaluate the value of fault condition. This technique can be applied to set alarm limit of the signal condition based on statistical parameter such as variance, kurtosis, rms and crest factor. These parameters can be used to set as a boundary decision of signal condition. From the results, the vibration signal analysis with single statistical parameter is unclear to predict fault of the spur gears. The using at least two statistical parameters can be clearly used to separate in every case of fault detection. The boundary decision of statistical parameter with the 99.7% certainty ( 3   from 300 referenced dataset and detected the testing condition with 99.7% ( 3   accuracy and had an error of less than 0.3 % using 50 testing dataset.

  15. The outlier sample effects on multivariate statistical data processing geochemical stream sediment survey (Moghangegh region, North West of Iran)

    International Nuclear Information System (INIS)

    Ghanbari, Y.; Habibnia, A.; Memar, A.

    2009-01-01

    In geochemical stream sediment surveys in Moghangegh Region in north west of Iran, sheet 1:50,000, 152 samples were collected and after the analyze and processing of data, it revealed that Yb, Sc, Ni, Li, Eu, Cd, Co, as contents in one sample is far higher than other samples. After detecting this sample as an outlier sample, the effect of this sample on multivariate statistical data processing for destructive effects of outlier sample in geochemical exploration was investigated. Pearson and Spear man correlation coefficient methods and cluster analysis were used for multivariate studies and the scatter plot of some elements together the regression profiles are given in case of 152 and 151 samples and the results are compared. After investigation of multivariate statistical data processing results, it was realized that results of existence of outlier samples may appear as the following relations between elements: - true relation between two elements, which have no outlier frequency in the outlier sample. - false relation between two elements which one of them has outlier frequency in the outlier sample. - complete false relation between two elements which both have outlier frequency in the outlier sample

  16. Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

    Science.gov (United States)

    Xu, Qinzeng; Gao, Fei; Xu, Qiang; Yang, Hongsheng

    2014-11-01

    Fatty acids (FAs) provide energy and also can be used to trace trophic relationships among organisms. Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months. We examined fatty acid profiles in aestivated and non-aestivated A. japonicus using multivariate analyses (PERMANOVA, MDS, ANOSIM, and SIMPER). The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly. The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers. Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation, and long chain FAs such as eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) that produced from intestinal degradation, are digested during deep aestivation. Specific changes in the fatty acid composition of A. japonicus during aestivation needs more detailed study in the future.

  17. Multivariate statistical approximation of the in situ gamma-ray spectrometry of the State of Zacatecas, Mexico

    International Nuclear Information System (INIS)

    Lopez I, J. F.; Rios M, C.; Mireles G, F.; Saucedo A, S.; Davila R, I.; Pinedo, J.L.

    2017-09-01

    The environmental radioactivity evaluation is a key point in the assessment of the environmental quality. Through this, it can be found possible radioactive contamination, locate possible Uranium and Thorium deposits and evaluate the primordial isotopes concentration due to human activities. A radioactive map of the Zacatecas State, Mexico is under construction based on in situ gamma-ray spectrometry. The present work reports the results of the multivariate statistical approximation of the measured activity data. Based on Pearson correlation, the 228 Ac and 208 Tl activities are statistically significant, while the 214 Bi and 214 Pb activities are not statistically significant. These can be due to the existence or not of secular equilibrium in the Thorium and Uranium series. (Author)

  18. Multivariate statistical approximation of the in situ gamma-ray spectrometry of the State of Zacatecas, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Lopez I, J. F.; Rios M, C.; Mireles G, F.; Saucedo A, S.; Davila R, I.; Pinedo, J.L., E-mail: fernandolf498@gmail.com [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98060 Zacatecas, Zac. (Mexico)

    2017-09-15

    The environmental radioactivity evaluation is a key point in the assessment of the environmental quality. Through this, it can be found possible radioactive contamination, locate possible Uranium and Thorium deposits and evaluate the primordial isotopes concentration due to human activities. A radioactive map of the Zacatecas State, Mexico is under construction based on in situ gamma-ray spectrometry. The present work reports the results of the multivariate statistical approximation of the measured activity data. Based on Pearson correlation, the {sup 228}Ac and {sup 208}Tl activities are statistically significant, while the {sup 214}Bi and {sup 214}Pb activities are not statistically significant. These can be due to the existence or not of secular equilibrium in the Thorium and Uranium series. (Author)

  19. [Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

    Science.gov (United States)

    Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

    2017-10-01

    To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

  20. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

    Science.gov (United States)

    Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

    2009-05-30

    An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

  1. An Application of Multivariate Statistical Analysis for Query-Driven Visualization

    Energy Technology Data Exchange (ETDEWEB)

    Gosink, Luke J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Garth, Christoph [Univ. of California, Davis, CA (United States); Anderson, John C. [Univ. of California, Davis, CA (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joy, Kenneth I. [Univ. of California, Davis, CA (United States)

    2011-03-01

    Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.

  2. Introducing linear functions: an alternative statistical approach

    Science.gov (United States)

    Nolan, Caroline; Herbert, Sandra

    2015-12-01

    The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be `threshold concepts'. There is recognition that linear functions can be taught in context through the exploration of linear modelling examples, but this has its limitations. Currently, statistical data is easily attainable, and graphics or computer algebra system (CAS) calculators are common in many classrooms. The use of this technology provides ease of access to different representations of linear functions as well as the ability to fit a least-squares line for real-life data. This means these calculators could support a possible alternative approach to the introduction of linear functions. This study compares the results of an end-of-topic test for two classes of Australian middle secondary students at a regional school to determine if such an alternative approach is feasible. In this study, test questions were grouped by concept and subjected to concept by concept analysis of the means of test results of the two classes. This analysis revealed that the students following the alternative approach demonstrated greater competence with non-standard questions.

  3. Thermodynamics and statistical mechanics an integrated approach

    CERN Document Server

    Shell, M Scott

    2015-01-01

    Learn classical thermodynamics alongside statistical mechanics with this fresh approach to the subjects. Molecular and macroscopic principles are explained in an integrated, side-by-side manner to give students a deep, intuitive understanding of thermodynamics and equip them to tackle future research topics that focus on the nanoscale. Entropy is introduced from the get-go, providing a clear explanation of how the classical laws connect to the molecular principles, and closing the gap between the atomic world and thermodynamics. Notation is streamlined throughout, with a focus on general concepts and simple models, for building basic physical intuition and gaining confidence in problem analysis and model development. Well over 400 guided end-of-chapter problems are included, addressing conceptual, fundamental, and applied skill sets. Numerous worked examples are also provided together with handy shaded boxes to emphasize key concepts, making this the complete teaching package for students in chemical engineer...

  4. Robot Trajectories Comparison: A Statistical Approach

    Directory of Open Access Journals (Sweden)

    A. Ansuategui

    2014-01-01

    Full Text Available The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners.

  5. Robot Trajectories Comparison: A Statistical Approach

    Science.gov (United States)

    Ansuategui, A.; Arruti, A.; Susperregi, L.; Yurramendi, Y.; Jauregi, E.; Lazkano, E.; Sierra, B.

    2014-01-01

    The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners. PMID:25525618

  6. Elementary statistical thermodynamics a problems approach

    CERN Document Server

    Smith, Norman O

    1982-01-01

    This book is a sequel to my Chemical Thermodynamics: A Prob­ lems Approach published in 1967, which concerned classical thermodynamics almost exclusively. Most books on statistical thermodynamics now available are written either for the superior general chemistry student or for the specialist. The author has felt the need for a text which would bring the intermediate reader to the point where he could not only appreciate the roots of the subject but also have some facility in calculating thermodynamic quantities. Although statistical thermodynamics comprises an essential part of the college training of a chemist, its treatment in general physical chem­ istry texts is, of necessity, compressed to the point where the less competent student is unable to appreciate or comprehend its logic and beauty, and is reduced to memorizing a series of formulas. It has been my aim to fill this need by writing a logical account of the foundations and applications of the sub­ ject at a level which can be grasped by an under...

  7. Bootstrap-based confidence estimation in PCA and multivariate statistical process control

    DEFF Research Database (Denmark)

    Babamoradi, Hamid

    be used to detect outliers in the data since the outliers can distort the bootstrap estimates. Bootstrap-based confidence limits were suggested as alternative to the asymptotic limits for control charts and contribution plots in MSPC (Paper II). The results showed that in case of the Q-statistic......Traditional/Asymptotic confidence estimation has limited applicability since it needs statistical theories to estimate the confidences, which are not available for all indicators/parameters. Furthermore, in case the theories are available for a specific indicator/parameter, the theories are based....... The goal was to improve process monitoring by improving the quality of MSPC charts and contribution plots. Bootstrapping algorithm to build confidence limits was illustrated in a case study format (Paper I). The main steps in the algorithm were discussed where a set of sensible choices (plus...

  8. Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

    Science.gov (United States)

    Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

    2010-10-01

    We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

  9. DETERMINING INDICATORS OF URBAN HOUSEHOLD WATER CONSUMPTION THROUGH MULTIVARIATE STATISTICAL TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Gledsneli Maria Lima Lins

    2010-12-01

    Full Text Available Water has a decisive influence on populations’ life quality – specifically in areas like urban supply, drainage, and effluents treatment – due to its sound impact over public health. Water rational use constitutes the greatest challenge faced by water demand management, mainly with regard to urban household water consumption. This makes it important to develop researches to assist water managers and public policy-makers in planning and formulating water demand measures which may allow urban water rational use to be met. This work utilized the multivariate techniques Factor Analysis and Multiple Linear Regression Analysis – in order to determine the participation level of socioeconomic and climatic variables in monthly urban household consumption changes – applying them to two districts of Campina Grande city (State of Paraíba, Brazil. The districts were chosen based on socioeconomic criterion (income level so as to evaluate their water consumer’s behavior. A 9-year monthly data series (from year 2000 up to 2008 was utilized, comprising family income, water tariff, and quantity of household connections (economies – as socioeconomic variables – and average temperature and precipitation, as climatic variables. For both the selected districts of Campina Grande city, the obtained results point out the variables “water tariff” and “family income” as indicators of these district’s household consumption.

  10. Multivariate statistical techniques for the evaluation of surface water quality of the Himalayan foothills streams, Pakistan

    Science.gov (United States)

    Malik, Riffat Naseem; Hashmi, Muhammad Zaffar

    2017-10-01

    Himalayan foothills streams, Pakistan play an important role in living water supply and irrigation of farmlands; thus, the water quality is closely related to public health. Multivariate techniques were applied to check spatial and seasonal trends, and metals contamination sources of the Himalayan foothills streams, Pakistan. Grab surface water samples were collected from different sites (5-15 cm water depth) in pre-washed polyethylene containers. Fast Sequential Atomic Absorption Spectrophotometer (Varian FSAA-240) was used to measure the metals concentration. Concentrations of Ni, Cu, and Mn were high in pre-monsoon season than the post-monsoon season. Cluster analysis identified impaired, moderately impaired and least impaired clusters based on water parameters. Discriminant function analysis indicated spatial variability in water was due to temperature, electrical conductivity, nitrates, iron and lead whereas seasonal variations were correlated with 16 physicochemical parameters. Factor analysis identified municipal and poultry waste, automobile activities, surface runoff, and soil weathering as major sources of contamination. Levels of Mn, Cr, Fe, Pb, Cd, Zn and alkalinity were above the WHO and USEPA standards for surface water. The results of present study will help to higher authorities for the management of the Himalayan foothills streams.

  11. The iron bars from the ‘Gresham Ship’: employing multivariate statistics to further slag inclusion analysis of ferrous objects

    DEFF Research Database (Denmark)

    Birch, Thomas; Martinón-Torres, Marcos

    2015-01-01

    An assemblage of post-medieval iron bars was found with the Princes Channel wreck, salvaged from the Thames Estuary in 2003. They were recorded and studied, with a focus on metallography and slag inclusion analysis. The investigation provided an opportunity to explore the use of multivariate...... statistical techniques to analyse slag inclusion data. Cluster analysis supplemented by principal components analysis revealed two groups of iron, probably originating from different smelting systems, which were compared to those observed macroscopically and through metallography. The analyses reveal...

  12. A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2012-01-01

    Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

  13. Statistical approach to partial equilibrium analysis

    Science.gov (United States)

    Wang, Yougui; Stanley, H. E.

    2009-04-01

    A statistical approach to market equilibrium and efficiency analysis is proposed in this paper. One factor that governs the exchange decisions of traders in a market, named willingness price, is highlighted and constitutes the whole theory. The supply and demand functions are formulated as the distributions of corresponding willing exchange over the willingness price. The laws of supply and demand can be derived directly from these distributions. The characteristics of excess demand function are analyzed and the necessary conditions for the existence and uniqueness of equilibrium point of the market are specified. The rationing rates of buyers and sellers are introduced to describe the ratio of realized exchange to willing exchange, and their dependence on the market price is studied in the cases of shortage and surplus. The realized market surplus, which is the criterion of market efficiency, can be written as a function of the distributions of willing exchange and the rationing rates. With this approach we can strictly prove that a market is efficient in the state of equilibrium.

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

    Science.gov (United States)

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

    2012-01-01

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

  15. Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients

    Directory of Open Access Journals (Sweden)

    M. Agostini

    2013-01-01

    Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

  16. Multivariate Statistical Analysis of Water Chemistry in Evaluating the Origin of Contamination in Many Devils Wash, Shiprock, New Mexico

    International Nuclear Information System (INIS)

    2012-01-01

    This report evaluates the chemistry of seep water occurring in three desert drainages near Shiprock, New Mexico: Many Devils Wash, Salt Creek Wash, and Eagle Nest Arroyo. Through the use of geochemical plotting tools and multivariate statistical analysis techniques, analytical results of samples collected from the three drainages are compared with the groundwater chemistry at a former uranium mill in the Shiprock area (the Shiprock site), managed by the U.S. Department of Energy Office of Legacy Management. The objective of this study was to determine, based on the water chemistry of the samples, if statistically significant patterns or groupings are apparent between the sample populations and, if so, whether there are any reasonable explanations for those groupings.

  17. Multivariate Statistical Analysis of Water Chemistry in Evaluating the Origin of Contamination in Many Devils Wash, Shiprock, New Mexico

    Energy Technology Data Exchange (ETDEWEB)

    None, None

    2012-12-31

    This report evaluates the chemistry of seep water occurring in three desert drainages near Shiprock, New Mexico: Many Devils Wash, Salt Creek Wash, and Eagle Nest Arroyo. Through the use of geochemical plotting tools and multivariate statistical analysis techniques, analytical results of samples collected from the three drainages are compared with the groundwater chemistry at a former uranium mill in the Shiprock area (the Shiprock site), managed by the U.S. Department of Energy Office of Legacy Management. The objective of this study was to determine, based on the water chemistry of the samples, if statistically significant patterns or groupings are apparent between the sample populations and, if so, whether there are any reasonable explanations for those groupings.

  18. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    Science.gov (United States)

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

  19. Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

    Science.gov (United States)

    Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

    2014-12-01

    The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

  20. Using integrated multivariate statistics to assess the hydrochemistry of surface water quality, Lake Taihu basin, China

    Directory of Open Access Journals (Sweden)

    Xiangyu Mu

    2014-09-01

    Full Text Available Natural factors and anthropogenic activities both contribute dissolved chemical loads to  lakes and streams.  Mineral solubility,  geomorphology of the drainage basin, source strengths and climate all contribute to concentrations and their variability. Urbanization and agriculture waste-water particularly lead to aquatic environmental degradation. Major contaminant sources and controls on water quality can be asssessed by analyzing the variability in proportions of major and minor solutes in water coupled to mutivariate statistical methods.   The demand for freshwater needed for increasing crop production puulation and industrialization occurs almost everywhere in in China and these conflicting needs have led to widespread water contamination. Because of heavy nutrient loadings from all of these sources, Lake Taihu (eastern China notably suffers periodic hyper-eutrophication and drinking water deterioration, which has led to shortages of freshwater for the City of Wuxi and other nearby cities. This lake, the third largest freshwater body in China, has historically beeen considered a cultural treasure of China, and has supported long-term fisheries. The is increasing pressure to remediate the present contamination which compromises both aquiculture and the prior economic base centered on tourism.  However, remediation cannot be effectively done without first characterizing the broad nature of the non-point source pollution. To this end, we investigated the hydrochemical setting of Lake Taihu to determine how different land use types influence the variability of surface water chemistry in different water sources to the lake. We found that waters broadly show wide variability ranging from  calcium-magnesium-bicarbonate hydrochemical facies type to mixed sodium-sulfate-chloride type. Principal components analysis produced three principal components that explained 78% of the variance in the water quality and reflect three major types of water

  1. Circular codes revisited: a statistical approach.

    Science.gov (United States)

    Gonzalez, D L; Giannerini, S; Rosa, R

    2011-04-21

    In 1996 Arquès and Michel [1996. A complementary circular code in the protein coding genes. J. Theor. Biol. 182, 45-58] discovered the existence of a common circular code in eukaryote and prokaryote genomes. Since then, circular code theory has provoked great interest and underwent a rapid development. In this paper we discuss some theoretical issues related to the synchronization properties of coding sequences and circular codes with particular emphasis on the problem of retrieval and maintenance of the reading frame. Motivated by the theoretical discussion, we adopt a rigorous statistical approach in order to try to answer different questions. First, we investigate the covering capability of the whole class of 216 self-complementary, C(3) maximal codes with respect to a large set of coding sequences. The results indicate that, on average, the code proposed by Arquès and Michel has the best covering capability but, still, there exists a great variability among sequences. Second, we focus on such code and explore the role played by the proportion of the bases by means of a hierarchy of permutation tests. The results show the existence of a sort of optimization mechanism such that coding sequences are tailored as to maximize or minimize the coverage of circular codes on specific reading frames. Such optimization clearly relates the function of circular codes with reading frame synchronization. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Bio hydrogen production from cassava starch by anaerobic mixed cultures: Multivariate statistical modeling

    Science.gov (United States)

    Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim

    2017-09-01

    Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.

  3. Multivariate Regression Analysis and Statistical Modeling for Summer Extreme Precipitation over the Yangtze River Basin, China

    Directory of Open Access Journals (Sweden)

    Tao Gao

    2014-01-01

    Full Text Available Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF and latent heat flux (LHF, which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB, have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved.

  4. Hierarchical probabilistic regionalization of volcanism for Sengan region in Japan using multivariate statistical techniques and geostatistical interpolation techniques

    International Nuclear Information System (INIS)

    Park, Jinyong; Balasingham, P.; McKenna, Sean Andrew; Kulatilake, Pinnaduwa H. S. W.

    2004-01-01

    Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater

  5. Quality by design case study: an integrated multivariate approach to drug product and process development.

    Science.gov (United States)

    Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

    2009-12-01

    To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

  6. Fabrication of lipidic nanocarriers of loratadine for facilitated intestinal permeation using multivariate design approach.

    Science.gov (United States)

    Verma, Samridhi; Singh, Sandeep Kumar; Verma, Priya Ranjan Prasad

    2016-01-01

    In this investigation, multivariate design approach was employed to develop self-nanoemulsifying drug delivery system (SNEDDS) of loratadine and to exploit its potential for intestinal permeability. Drug solubility was determined in various vehicles and existence of self-nanoemulsifying region was evaluated by phase diagram studies. The influence of formulation variables X1 (Capmul MCM C8) and X2 (Solutol HS15) on SNEDDS was assessed for mean globule sizes in different media (Y1-Y3), emulsification time (Y4) and drug-release parameters (Y5-Y6), to improve quality attributes of SNEDDS. Significant models were generated, statistically analyzed by analysis of variance and validated using the residual and leverage plots. The interaction, contour and response plots explicitly demonstrated the influence of one factor on the other and displayed trend of factor-effect on responses. The pH-independent optimized formulation was obtained with appreciable global desirability (0.9266). The strenuous act of determining emulsification time is innovatively replaced by the use of oil-soluble dye to produce visibly distinct globules that otherwise may be deceiving. TEM images displayed non-aggregated state of spherical globules (size < 25 nm) and also revealed the structural transitions occurring during emulsification. Optimized formulation exhibited non-Newtonian flow justified by the model-fit and also presented the stability to dilution effects and thermodynamic stress testing. The ex vivo permeation study using confocal laser scanning microscopy indicate strong potential of rhodamine 123-loaded loratadine-SNEDDS to inhibit P-gp efflux and facilitate intestinal permeation. To conclude, the effectiveness of design yields a stable optimized SNEDDS with enhanced permeation potential, which is expected to improve oral bioavailability of loratadine.

  7. A new approach to spin and statistics

    International Nuclear Information System (INIS)

    Kuckert, B.

    1994-11-01

    We give an algebraic proof of the spin-statistics connection for the parabosonic and parafermionic quantum topological charges of a theory of local observables with a modular P 1 CT-symmetry. The argument avoids the use of the spinor calculus and also works in 1+2 dimensions. It is expected to be a progress towards a general spin-statistics theorem including also (1+2)-dimensional theories with braid group statistics. (orig.)

  8. A statistical mechanics approach to Granovetter theory

    Science.gov (United States)

    Barra, Adriano; Agliari, Elena

    2012-05-01

    In this paper we try to bridge breakthroughs in quantitative sociology/econometrics, pioneered during the last decades by Mac Fadden, Brock-Durlauf, Granovetter and Watts-Strogatz, by introducing a minimal model able to reproduce essentially all the features of social behavior highlighted by these authors. Our model relies on a pairwise Hamiltonian for decision-maker interactions which naturally extends the multi-populations approaches by shifting and biasing the pattern definitions of a Hopfield model of neural networks. Once introduced, the model is investigated through graph theory (to recover Granovetter and Watts-Strogatz results) and statistical mechanics (to recover Mac-Fadden and Brock-Durlauf results). Due to the internal symmetries of our model, the latter is obtained as the relaxation of a proper Markov process, allowing even to study its out-of-equilibrium properties. The method used to solve its equilibrium is an adaptation of the Hamilton-Jacobi technique recently introduced by Guerra in the spin-glass scenario and the picture obtained is the following: shifting the patterns from [-1,+1]→[0.+1] implies that the larger the amount of similarities among decision makers, the stronger their relative influence, and this is enough to explain both the different role of strong and weak ties in the social network as well as its small-world properties. As a result, imitative interaction strengths seem essentially a robust request (enough to break the gauge symmetry in the couplings), furthermore, this naturally leads to a discrete choice modelization when dealing with the external influences and to imitative behavior à la Curie-Weiss as the one introduced by Brock and Durlauf.

  9. Evaluation of strategies to promote learning using ICT: the case of a course on Topics of Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    Mario Miguel Ojeda Ramírez

    2017-01-01

    Full Text Available Currently some teachers implement different methods in order to promote education linked to reality, to provide more effective training and a meaningful learning. Activemethods aim to increase motivation and create scenarios in which student participation is central to achieve a more meaningful learning. This paper reports on the implementation of a process of educational innovation in the course of Topics of Multivariate Statistics offered in the degree in Statistical Sciences and Techniques at the Universidad Veracruzana (Mexico. The strategies used as sets for data collection, design and project development and realization of individual and group presentations are described. Information and communication technologies (ICT used are: EMINUS, distributed education platform of the Universidad Veracruzana, and managing files with Dropbox, plus communication via WhatsApp. The R software was used for statistical analysis and for making presentations in academic forums. To explore students' perceptions depth interviews were conducted and indicators for evaluating the student satisfaction were defined; the results show positive evidence, concluding that students were satisfied with the way that the course was designed and implemented. They also stated that they feel able to apply what they have learned. The opinions put that using these strategies they were feeling in preparation for their professional life. Finally, some suggestions for improving the course in future editions are included.

  10. Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Valdez, C. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanner, R. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Martinez, H. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-11-28

    Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

  11. Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

    Science.gov (United States)

    Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

    2016-05-01

    The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

  12. Study on sources of colored glaze of Xiyue Temple in Shanxi province by INAA and multivariable statistical analysis

    International Nuclear Information System (INIS)

    Cheng Lin; Feng Songlin

    2005-01-01

    The major, minor and trace elements in the bodies of ancient colored glazes which came from the site of Xiyue Temple and Lidipo kiln in Shanxi province, and were unearthed from the stratums of Song, Yuan, Ming, Early Qing and Late Qing dynasty were analyzed by instrumental neutron activation analysis (INAA). The results of multivariable statistical analyses show that the chemical compositions of the colored glaze bodies are steady from Song to Early Qing dynasty, but distinctly different from that in Late Qing. Probably, the sources of fired material of ancient colored glaze from Song to Early Qing came from the site of Xiyue Temple. The chemical compositions of three pieces of colored glazes in Ming dynasty and that in Late Qing are similar to that of Lidipo kiln. From this, authors could conclude that the sources of the materials of ancient coloured glazes of Xiyue Temple in Late Qing dynasty were fired in Lidipo kiln. (authors)

  13. [Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

    Science.gov (United States)

    Lifshits, A M

    1979-01-01

    General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

  14. Correlating phospholipid fatty acids (PLFA) in a landfill leachate polluted aquifer with biogeochemical factors by multivariate statistical methods

    DEFF Research Database (Denmark)

    Ludvigsen, Liselotte; Albrechtsen, Hans-Jørgen; Rootzén, Helle

    1997-01-01

    Different multivariate statistical analyses were applied to phospholipid fatty acids representing the biomass composition and to different biogeochemical parameters measured in 37 samples from a landfill contaminated aquifer at Grindsted Landfill (Denmark). Principal component analysis...... and correspondence analysis were used to identify groups of samples showing similar patterns with respect to biogeochemical variables and phospholipid fatty acid composition. The principal component analysis revealed that for the biogeochemical parameters the first principal component was linked to the pollution...... was used to allocate samples of phospholipid fatty acids into predefined classes. A large percentages of samples were classified correctly when discriminating samples into groups of dissolved organic carbon and specific conductivity, indicating that the biomass is highly influenced by the pollution...

  15. Multivariate statistical assessments of greenhouse-gas-induced climatic change and comparison with results from general circulation models

    International Nuclear Information System (INIS)

    Schoenwiese, C.D.

    1990-01-01

    Based on univariate correction and coherence analyses, including techniques moving in time, and taking account of the physical basis of the relationships, a simple multivariate concept is presented which correlates observational climatic time series simultaneously with solar, volcanic, ENSO (El Nino/Souther Oscillation) and anthropogenic greenhouse-gas forcing. The climatic elements considered are air temperature (near the ground and stratosphere), sea surface temperature, sea level and precipitation, and cover at least the period 1881-1980 (stratospheric temperature only since 1960). The climate signal assessments which may be hypothetically attributed to the observed CO 2 or equivalent CO 2 (implying additional greenhouse gases) increase are compared with those resulting from GCM experiments. In case of the Northern hemisphere air temperature these comparisons are performed not only in respect to hemispheric and global means, but also in respect to the regional and seasonal patterns. Autocorrelations and phase shifts of the climate response to natural and anthropogenic forcing complicate the statistical assessments

  16. Structural analysis and design of multivariable control systems: An algebraic approach

    Science.gov (United States)

    Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

    1988-01-01

    The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

  17. Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

    Science.gov (United States)

    Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

    2016-09-01

    Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. The large deviation approach to statistical mechanics

    International Nuclear Information System (INIS)

    Touchette, Hugo

    2009-01-01

    The theory of large deviations is concerned with the exponential decay of probabilities of large fluctuations in random systems. These probabilities are important in many fields of study, including statistics, finance, and engineering, as they often yield valuable information about the large fluctuations of a random system around its most probable state or trajectory. In the context of equilibrium statistical mechanics, the theory of large deviations provides exponential-order estimates of probabilities that refine and generalize Einstein's theory of fluctuations. This review explores this and other connections between large deviation theory and statistical mechanics, in an effort to show that the mathematical language of statistical mechanics is the language of large deviation theory. The first part of the review presents the basics of large deviation theory, and works out many of its classical applications related to sums of random variables and Markov processes. The second part goes through many problems and results of statistical mechanics, and shows how these can be formulated and derived within the context of large deviation theory. The problems and results treated cover a wide range of physical systems, including equilibrium many-particle systems, noise-perturbed dynamics, nonequilibrium systems, as well as multifractals, disordered systems, and chaotic systems. This review also covers many fundamental aspects of statistical mechanics, such as the derivation of variational principles characterizing equilibrium and nonequilibrium states, the breaking of the Legendre transform for nonconcave entropies, and the characterization of nonequilibrium fluctuations through fluctuation relations.

  19. The large deviation approach to statistical mechanics

    Science.gov (United States)

    Touchette, Hugo

    2009-07-01

    The theory of large deviations is concerned with the exponential decay of probabilities of large fluctuations in random systems. These probabilities are important in many fields of study, including statistics, finance, and engineering, as they often yield valuable information about the large fluctuations of a random system around its most probable state or trajectory. In the context of equilibrium statistical mechanics, the theory of large deviations provides exponential-order estimates of probabilities that refine and generalize Einstein’s theory of fluctuations. This review explores this and other connections between large deviation theory and statistical mechanics, in an effort to show that the mathematical language of statistical mechanics is the language of large deviation theory. The first part of the review presents the basics of large deviation theory, and works out many of its classical applications related to sums of random variables and Markov processes. The second part goes through many problems and results of statistical mechanics, and shows how these can be formulated and derived within the context of large deviation theory. The problems and results treated cover a wide range of physical systems, including equilibrium many-particle systems, noise-perturbed dynamics, nonequilibrium systems, as well as multifractals, disordered systems, and chaotic systems. This review also covers many fundamental aspects of statistical mechanics, such as the derivation of variational principles characterizing equilibrium and nonequilibrium states, the breaking of the Legendre transform for nonconcave entropies, and the characterization of nonequilibrium fluctuations through fluctuation relations.

  20. Defining critical habitats of threatened and endemic reef fishes with a multivariate approach.

    Science.gov (United States)

    Purcell, Steven W; Clarke, K Robert; Rushworth, Kelvin; Dalton, Steven J

    2014-12-01

    Understanding critical habitats of threatened and endemic animals is essential for mitigating extinction risks, developing recovery plans, and siting reserves, but assessment methods are generally lacking. We evaluated critical habitats of 8 threatened or endemic fish species on coral and rocky reefs of subtropical eastern Australia, by measuring physical and substratum-type variables of habitats at fish sightings. We used nonmetric and metric multidimensional scaling (nMDS, mMDS), Analysis of similarities (ANOSIM), similarity percentages analysis (SIMPER), permutational analysis of multivariate dispersions (PERMDISP), and other multivariate tools to distinguish critical habitats. Niche breadth was widest for 2 endemic wrasses, and reef inclination was important for several species, often found in relatively deep microhabitats. Critical habitats of mainland reef species included small caves or habitat-forming hosts such as gorgonian corals and black coral trees. Hard corals appeared important for reef fishes at Lord Howe Island, and red algae for mainland reef fishes. A wide range of habitat variables are required to assess critical habitats owing to varied affinities of species to different habitat features. We advocate assessments of critical habitats matched to the spatial scale used by the animals and a combination of multivariate methods. Our multivariate approach furnishes a general template for assessing the critical habitats of species, understanding how these vary among species, and determining differences in the degree of habitat specificity. © 2014 Society for Conservation Biology.

  1. Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

    Science.gov (United States)

    Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

    2016-04-01

    The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

  2. Factors controlling physico-chemical characteristics in the coastal waters off Mangalore - A multivariate approach

    Digital Repository Service at National Institute of Oceanography (India)

    Shirodkar, P.V.; Mesquita, A.; Pradhan, U.K.; Verlekar, X.N.; Babu, M.T.; Vethamony, P.

    in the south; Fig.1) using the RCM – 9 MK II current meters, manufactured by Aanderaa Co., Norway. The two locations (13 m and 15 m water depths respectively) were separated by a distance of approximately 10km. Current measurements were carried out for one... and salinity were obtained from different locations using a portable SBE 19 SEACAT Profiler, manufactured by Sea-Bird Electronic, Inc., USA. Vertical profiles were continued for the period 14 -19 April 2007. 2.4. Multivariate statistical analysis a...

  3. Structure formation from non-Gaussian initial conditions: Multivariate biasing, statistics, and comparison with N-body simulations

    International Nuclear Information System (INIS)

    Giannantonio, Tommaso; Porciani, Cristiano

    2010-01-01

    We study structure formation in the presence of primordial non-Gaussianity of the local type with parameters f NL and g NL . We show that the distribution of dark-matter halos is naturally described by a multivariate bias scheme where the halo overdensity depends not only on the underlying matter density fluctuation δ but also on the Gaussian part of the primordial gravitational potential φ. This corresponds to a non-local bias scheme in terms of δ only. We derive the coefficients of the bias expansion as a function of the halo mass by applying the peak-background split to common parametrizations for the halo mass function in the non-Gaussian scenario. We then compute the halo power spectrum and halo-matter cross spectrum in the framework of Eulerian perturbation theory up to third order. Comparing our results against N-body simulations, we find that our model accurately describes the numerical data for wave numbers k≤0.1-0.3h Mpc -1 depending on redshift and halo mass. In our multivariate approach, perturbations in the halo counts trace φ on large scales, and this explains why the halo and matter power spectra show different asymptotic trends for k→0. This strongly scale-dependent bias originates from terms at leading order in our expansion. This is different from what happens using the standard univariate local bias where the scale-dependent terms come from badly behaved higher-order corrections. On the other hand, our biasing scheme reduces to the usual local bias on smaller scales, where |φ| is typically much smaller than the density perturbations. We finally discuss the halo bispectrum in the context of multivariate biasing and show that, due to its strong scale and shape dependence, it is a powerful tool for the detection of primordial non-Gaussianity from future galaxy surveys.

  4. Mathematical and statistical approaches to AIDS epidemiology

    CERN Document Server

    1989-01-01

    The 18 research articles of this volume discuss the major themes that have emerged from mathematical and statistical research in the epidemiology of HIV. The opening paper reviews important recent contributions. Five sections follow: Statistical Methodology and Forecasting, Infectivity and the HIV, Heterogeneity and HIV Transmission Dynamics, Social Dynamics and AIDS, and The Immune System and The HIV. In each, leading experts in AIDS epidemiology present the recent results. Some address the role of variable infectivity, heterogeneous mixing, and long periods of infectiousness in the dynamics of HIV; others concentrate on parameter estimation and short-term forecasting. The last section looks at the interaction between the HIV and the immune system.

  5. Bayesian approach to inverse statistical mechanics

    Science.gov (United States)

    Habeck, Michael

    2014-05-01

    Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

  6. Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

    Science.gov (United States)

    Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

    2017-08-07

    A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan.

    Science.gov (United States)

    Muhammad, Said; Tahir Shah, M; Khan, Sardar

    2010-10-01

    The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

  8. Screening for collusion: a spatial statistics approach

    NARCIS (Netherlands)

    Heijnen, P.; Haan, M.A.; Soetevent, A.R.

    2012-01-01

    We develop a method to screen for local cartels. We first test whether there is statistical evidence of clustering of outlets that score high on some characteristic that is consistent with collusive behavior. If so, we determine in a second step the most suspicious regions where further antitrust

  9. Screening for collusion: a spatial statistics approach

    NARCIS (Netherlands)

    Heijnen, P.; Haan, M.A.; Soetevent, A.R.

    2015-01-01

    We develop a method to screen for local cartels. We first test whether there is statistical evidence of clustering of outlets that score high on some characteristic that is consistent with collusive behavior. If so, we determine in a second step the most suspicious regions where further antitrust

  10. Screening for collusion : A spatial statistics approach

    NARCIS (Netherlands)

    Heijnen, Pim; Haan, Marco A.; Soetevent, Adriaan R.

    2015-01-01

    We develop a method to screen for local cartels. We first test whether there is statistical evidence of clustering of outlets that score high on some characteristic that is consistent with collusive behavior. If so, we determine in a second step the most suspicious regions where further antitrust

  11. The fuzzy approach to statistical analysis

    NARCIS (Netherlands)

    Coppi, Renato; Gil, Maria A.; Kiers, Henk A. L.

    2006-01-01

    For the last decades, research studies have been developed in which a coalition of Fuzzy Sets Theory and Statistics has been established with different purposes. These namely are: (i) to introduce new data analysis problems in which the objective involves either fuzzy relationships or fuzzy terms;

  12. Estimation of age in forensic medicine using multivariate approach to image analysis

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Belyaev, Ivan; Fominykh, Sergey

    2009-01-01

    approach based on statistical analysis of grey-level co-occurrence matrix, fractal analysis, wavelet transformation and Angle Measure Technique. Projection on latent structures regression was chosen for calibration and prediction. The method has been applied to 70 male and 63 female individuals aged from...... 21 to 93 and results were compared with traditional approach. Some important questions and problems have been raised....

  13. One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

    Science.gov (United States)

    Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

    2015-04-01

    Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where

  14. Statistical approach to study of lithium magnesium metaborate glasses

    Directory of Open Access Journals (Sweden)

    Nedyalkova Miroslava

    2017-03-01

    Full Text Available Alkali borate glasses and alkaline earth borate glasses are commonly used materials in the field of optoelectronics. Infrared (FTIR and Raman spectroscopy are valuable tools for structural investigation of borate glass networks. The compositional and structural variety of lithium magnesium metaborate glasses is usually determined by traditional instrumental methods. In this study a data set is classified by structural and physicochemical parameters (FTIR, Raman spectra, glass transition temperature-Tg. Characterisation of magnesium containing metaborate glasses by multivariate statistics (hierarchical cluster analysis to reveal potential relationships (similarity or dissimilarity between the type of glasses included in the data set using specific structural features available in the literature is conducted. The clustering of the glass objects indicates a good separation of different magnesium containing borate glass compositions. The grouping of variables concerning Tg and structural data for BO3 and BO4 linkage confirms that BO4/BO3 ratios strongly affect Tg. Additionally, patterns of similarity could be detected not only between the glass composition but also between the features (variables describing the glasses. The proposed approach can be further used as an expert tool for glass properties prediction or fingerprinting (identification of unknown compositions.

  15. Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

    International Nuclear Information System (INIS)

    Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

    2008-01-01

    Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

  16. Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

    International Nuclear Information System (INIS)

    Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo

    2015-01-01

    Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

  17. Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

    Energy Technology Data Exchange (ETDEWEB)

    Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo [Kyung Hee University, Yongin (Korea, Republic of)

    2015-08-15

    Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

  18. A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

    Science.gov (United States)

    Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

    2012-09-01

    Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are

  19. Classical statistical mechanics approach to multipartite entanglement

    Energy Technology Data Exchange (ETDEWEB)

    Facchi, P [Dipartimento di Matematica, Universita di Bari, I-70125 Bari (Italy); Florio, G; Pascazio, S [Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Marzolino, U [Dipartimento di Fisica, Universita di Trieste, and Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34014 Trieste (Italy); Parisi, G [Dipartimento di Fisica, Universita di Roma ' La Sapienza' , Piazzale Aldo Moro 2, Centre for Statistical Mechanics and Complexity (SMC), CNR-INFM (Italy)

    2010-06-04

    We characterize the multipartite entanglement of a system of n qubits in terms of the distribution function of the bipartite purity over balanced bipartitions. We search for maximally multipartite entangled states, whose average purity is minimal, and recast this optimization problem into a problem of statistical mechanics, by introducing a cost function, a fictitious temperature and a partition function. By investigating the high-temperature expansion, we obtain the first three moments of the distribution. We find that the problem exhibits frustration.

  20. Classical statistical mechanics approach to multipartite entanglement

    Science.gov (United States)

    Facchi, P.; Florio, G.; Marzolino, U.; Parisi, G.; Pascazio, S.

    2010-06-01

    We characterize the multipartite entanglement of a system of n qubits in terms of the distribution function of the bipartite purity over balanced bipartitions. We search for maximally multipartite entangled states, whose average purity is minimal, and recast this optimization problem into a problem of statistical mechanics, by introducing a cost function, a fictitious temperature and a partition function. By investigating the high-temperature expansion, we obtain the first three moments of the distribution. We find that the problem exhibits frustration.

  1. Classical statistical mechanics approach to multipartite entanglement

    International Nuclear Information System (INIS)

    Facchi, P; Florio, G; Pascazio, S; Marzolino, U; Parisi, G

    2010-01-01

    We characterize the multipartite entanglement of a system of n qubits in terms of the distribution function of the bipartite purity over balanced bipartitions. We search for maximally multipartite entangled states, whose average purity is minimal, and recast this optimization problem into a problem of statistical mechanics, by introducing a cost function, a fictitious temperature and a partition function. By investigating the high-temperature expansion, we obtain the first three moments of the distribution. We find that the problem exhibits frustration.

  2. Working females : a modern statistical approach

    OpenAIRE

    Kuhlenkasper, Torben

    2010-01-01

    The thesis analyzes the changing employment and economic situation of females when they become mothers. Two major questions are focused in the thesis: First, when do mothers return to their previous employment after bearing a child? Secondly, what are the individual economic consequences after having returned to the labor market? Both questions are analyzed empirically with latest statistical methods. The first major part of the thesis, corresponding to the above first motivated question, ...

  3. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques--a case study

    International Nuclear Information System (INIS)

    Singh, Kunwar P.; Malik, Amrita; Sinha, Sarita

    2005-01-01

    Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the data set on water quality of the Gomti river (India), generated during three years (1999-2001) monitoring at eight different sites for 34 parameters (9792 observations). This study presents usefulness of multivariate statistical techniques for evaluation and interpretation of large complex water quality data sets and apportionment of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Three significant groups, upper catchments (UC), middle catchments (MC) and lower catchments (LC) of sampling sites were obtained through CA on the basis of similarity between them. FA/PCA applied to the data sets pertaining to three catchments regions of the river resulted in seven, seven and six latent factors, respectively responsible for the data structure, explaining 74.3, 73.6 and 81.4% of the total variance of the respective data sets. These included the trace metals group (leaching from soil and industrial waste disposal sites), organic pollution group (municipal and industrial effluents), nutrients group (agricultural runoff), alkalinity, hardness, EC and solids (soil leaching and runoff process). DA showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. It rendered five parameters (temperature, total alkalinity, Cl, Na and K) affording more than 94% right assignations in temporal analysis, while 10 parameters (river discharge, pH, BOD, Cl, F, PO 4 , NH 4 -N, NO 3 -N, TKN and Zn) to afford 97% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Further

  4. On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

    Science.gov (United States)

    Schaffrin, Burkhard; Felus, Yaron A.

    2008-06-01

    The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model ( Y- E Y = ( X- E X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E Y and E X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler-Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335-342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.

  5. Thermodynamics and statistical mechanics an integrated approach

    CERN Document Server

    Hardy, Robert J

    2014-01-01

    This textbook brings together the fundamentals of the macroscopic and microscopic aspects of thermal physics by presenting thermodynamics and statistical mechanics as complementary theories based on small numbers of postulates. The book is designed to give the instructor flexibility in structuring courses for advanced undergraduates and/or beginning graduate students and is written on the principle that a good text should also be a good reference. The presentation of thermodynamics follows the logic of Clausius and Kelvin while relating the concepts involved to familiar phenomena and the mod

  6. Statistical quality control a loss minimization approach

    CERN Document Server

    Trietsch, Dan

    1999-01-01

    While many books on quality espouse the Taguchi loss function, they do not examine its impact on statistical quality control (SQC). But using the Taguchi loss function sheds new light on questions relating to SQC and calls for some changes. This book covers SQC in a way that conforms with the need to minimize loss. Subjects often not covered elsewhere include: (i) measurements, (ii) determining how many points to sample to obtain reliable control charts (for which purpose a new graphic tool, diffidence charts, is introduced), (iii) the connection between process capability and tolerances, (iv)

  7. A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

    Science.gov (United States)

    Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

  8. A proposal for a multivariate quantitative approach to infer karyological relationships among taxa

    Directory of Open Access Journals (Sweden)

    Lorenzo Peruzzi

    2014-12-01

    Full Text Available Until now, basic karyological parameters have been used in different ways by researchers to infer karyological relationships among organisms. In the present study, we propose a standardized approach to this aim, integrating six different, not redundant, parameters in a multivariate PCoA analysis. These parameters are chromosome number, basic chromosome number, total haploid chromosome length, MCA (Mean Centromeric Asymmetry, CVCL (Coefficient of Variation of Chromosome Length and CVCI (Coefficient of Variation of Centromeric Index. The method is exemplified with the application to several plant taxa, and its significance and limits are discussed in the light of current phylogenetic knowledge of these groups.

  9. Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach

    International Nuclear Information System (INIS)

    Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji

    2010-01-01

    This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)

  10. Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach

    Energy Technology Data Exchange (ETDEWEB)

    Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji [Department of Economics, University of Ibadan, Ibadan (Nigeria)

    2010-01-15

    This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)

  11. Optimisation of Oil Spill Dispersants on Weathered Oils. A New Approach Using Experimental Design and Multivariate Data Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Brandvik, Per Johan

    1997-12-31

    This thesis describes how laboratory experiments combined with numerical modelling were used to predict weathering of an oil slick at different environmental conditions (temperature, wind etc.). It also applies laboratory test methods to screen dispersant effectiveness under different temperatures and salinities. A new approach is developed for dispersant optimization based on statistical design and multivariate analysis; this resulted in a new dispersant with low toxicity and high effectiveness on a broad selection of oil types. The thesis illustrates the potential of dispersant used as an operational response method on oil spills by discussing three different oil spill scenarios and compares the effect of using dispersants to using mechanical recovery and to doing nothing. Some recommendations that may increase the effectiveness of the Norwegian oil spill contingency are also given. 172 refs., 65 figs., 9 tabs.

  12. Statistical Physics Approaches to RNA Editing

    Science.gov (United States)

    Bundschuh, Ralf

    2012-02-01

    The central dogma of molecular Biology states that DNA is transcribed base by base into RNA which is in turn translated into proteins. However, some organisms edit their RNA before translation by inserting, deleting, or substituting individual or short stretches of bases. In many instances the mechanisms by which an organism recognizes the positions at which to edit or by which it performs the actual editing are unknown. One model system that stands out by its very high rate of on average one out of 25 bases being edited are the Myxomycetes, a class of slime molds. In this talk we will show how the computational methods and concepts from statistical Physics can be used to analyze DNA and protein sequence data to predict editing sites in these slime molds and to guide experiments that identified previously unknown types of editing as well as the complete set of editing events in the slime mold Physarum polycephalum.

  13. Statistical Physics Approaches to Microbial Ecology

    Science.gov (United States)

    Mehta, Pankaj

    The unprecedented ability to quantitatively measure and probe complex microbial communities has renewed interest in identifying the fundamental ecological principles governing community ecology in microbial ecosystems. Here, we present work from our group and others showing how ideas from statistical physics can help us uncover these ecological principles. Two major lessons emerge from this work. First, large, ecosystems with many species often display new, emergent ecological behaviors that are absent in small ecosystems with just a few species. To paraphrase Nobel laureate Phil Anderson, ''More is Different'', especially in community ecology. Second, the lack of trophic layer separation in microbial ecology fundamentally distinguishes microbial ecology from classical paradigms of community ecology and leads to qualitative different rules for community assembly in microbes. I illustrate these ideas using both theoretical modeling and novel new experiments on large microbial ecosystems performed by our collaborators (Joshua Goldford and Alvaro Sanchez). Work supported by Simons Investigator in MMLS and NIH R35 R35 GM119461.

  14. Aftershock Energy Distribution by Statistical Mechanics Approach

    Science.gov (United States)

    Daminelli, R.; Marcellini, A.

    2015-12-01

    The aim of our work is to research the most probable distribution of the energy of aftershocks. We started by applying one of the fundamental principles of statistical mechanics that, in case of aftershock sequences, it could be expressed as: the greater the number of different ways in which the energy of aftershocks can be arranged among the energy cells in phase space the more probable the distribution. We assume that each cell in phase space has the same possibility to be occupied, and that more than one cell in the phase space can have the same energy. Seeing that seismic energy is proportional to products of different parameters, a number of different combinations of parameters can produce different energies (e.g., different combination of stress drop and fault area can release the same seismic energy). Let us assume that there are gi cells in the aftershock phase space characterised by the same energy released ɛi. Therefore we can assume that the Maxwell-Boltzmann statistics can be applied to aftershock sequences with the proviso that the judgment on the validity of this hypothesis is the agreement with the data. The aftershock energy distribution can therefore be written as follow: n(ɛ)=Ag(ɛ)exp(-βɛ)where n(ɛ) is the number of aftershocks with energy, ɛ, A and β are constants. Considering the above hypothesis, we can assume g(ɛ) is proportional to ɛ. We selected and analysed different aftershock sequences (data extracted from Earthquake Catalogs of SCEC, of INGV-CNT and other institutions) with a minimum magnitude retained ML=2 (in some cases ML=2.6) and a time window of 35 days. The results of our model are in agreement with the data, except in the very low energy band, where our model resulted in a moderate overestimation.

  15. A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

    Directory of Open Access Journals (Sweden)

    MELEK ACAR BOYACIOĞLU

    2013-06-01

    Full Text Available Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and working environment of a bank are being evaluated in this context. In this study, a model has been developed in order to predict the financial strength rating of Turkish banks. The methodology of this study is as follows: Selecting variables to be used in the model, creating a data set, choosing the techniques to be used and the evaluation of classification success of the techniques. It is concluded that the artificial neural network system shows a better performance in terms of classification of financial strength rating in comparison to multivariate statistical methods in the raining set. On the other hand, there is no meaningful difference could be found in the validation set in which the prediction performances of the employed techniques are tested.

  16. [Retrospective statistical analysis of clinical factors of recurrence in chronic subdural hematoma: correlation between univariate and multivariate analysis].

    Science.gov (United States)

    Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu

    2012-10-01

    Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.

  17. Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

    Directory of Open Access Journals (Sweden)

    Jiabo Chen

    2016-10-01

    Full Text Available Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011 on water quality in the Liao River system (China. Cluster analysis (CA classified the 12 months of the year into three groups (May–October, February–April and November–January and the 66 sampling sites into three groups (groups A, B and C based on similarities in water quality characteristics. Discriminant analysis (DA determined that temperature, dissolved oxygen (DO, pH, chemical oxygen demand (CODMn, 5-day biochemical oxygen demand (BOD5, NH4+–N, total phosphorus (TP and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA and positive matrix factorization (PMF identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.

  18. Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

    Science.gov (United States)

    Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

    2014-10-15

    Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Application of multivariate statistical analysis in the pollution and health risk of traffic-related heavy metals.

    Science.gov (United States)

    Ebqa'ai, Mohammad; Ibrahim, Bashar

    2017-12-01

    This study aims to analyse the heavy metal pollutants in Jeddah, the second largest city in the Gulf Cooperation Council with a population exceeding 3.5 million, and many vehicles. Ninety-eight street dust samples were collected seasonally from the six major roads as well as the Jeddah Beach, and subsequently digested using modified Leeds Public Analyst method. The heavy metals (Fe, Zn, Mn, Cu, Cd, and Pb) were extracted from the ash using methyl isobutyl ketone as solvent extraction and eventually analysed by atomic absorption spectroscopy. Multivariate statistical techniques, principal component analysis (PCA), and hierarchical cluster analysis were applied to these data. Heavy metal concentrations were ranked according to the following descending order: Fe > Zn > Mn > Cu > Pb > Cd. In order to study the pollution and health risk from these heavy metals as well as estimating their effect on the environment, pollution indices, integrated pollution index, enrichment factor, daily dose average, hazard quotient, and hazard index were all analysed. The PCA showed high levels of Zn, Fe, and Cd in Al Kurnish road, while these elements were consistently detected on King Abdulaziz and Al Madina roads. The study indicates that high levels of Zn and Pb pollution were recorded for major roads in Jeddah. Six out of seven roads had high pollution indices. This study is the first step towards further investigations into current health problems in Jeddah, such as anaemia and asthma.

  20. Spatial and temporal variation of water quality of a segment of Marikina River using multivariate statistical methods.

    Science.gov (United States)

    Chounlamany, Vanseng; Tanchuling, Maria Antonia; Inoue, Takanobu

    2017-09-01

    Payatas landfill in Quezon City, Philippines, releases leachate to the Marikina River through a creek. Multivariate statistical techniques were applied to study temporal and spatial variations in water quality of a segment of the Marikina River. The data set included 12 physico-chemical parameters for five monitoring stations over a year. Cluster analysis grouped the monitoring stations into four clusters and identified January-May as dry season and June-September as wet season. Principal components analysis showed that three latent factors are responsible for the data set explaining 83% of its total variance. The chemical oxygen demand, biochemical oxygen demand, total dissolved solids, Cl - and PO 4 3- are influenced by anthropogenic impact/eutrophication pollution from point sources. Total suspended solids, turbidity and SO 4 2- are influenced by rain and soil erosion. The highest state of pollution is at the Payatas creek outfall from March to May, whereas at downstream stations it is in May. The current study indicates that the river monitoring requires only four stations, nine water quality parameters and testing over three specific months of the year. The findings of this study imply that Payatas landfill requires a proper leachate collection and treatment system to reduce its impact on the Marikina River.

  1. Hydrochemical Characteristics and Multivariate Statistical Analysis of Natural Water System: A Case Study in Kangding County, Southwestern China

    Directory of Open Access Journals (Sweden)

    Yunhui Zhang

    2018-01-01

    Full Text Available The utilization for water resource has been of great concern to human life. To assess the natural water system in Kangding County, the integrated methods of hydrochemical analysis, multivariate statistics and geochemical modelling were conducted on surface water, groundwater, and thermal water samples. Surface water and groundwater were dominated by Ca-HCO3 type, while thermal water belonged to Ca-HCO3 and Na-Cl-SO4 types. The analyzing results concluded the driving factors that affect hydrochemical components. Following the results of the combined assessments, hydrochemical process was controlled by the dissolution of carbonate and silicate minerals with slight influence from anthropogenic activity. The mixing model of groundwater and thermal water was calculated using silica-enthalpy method, yielding cold-water fraction of 0.56–0.79 and an estimated reservoir temperature of 130–199 °C, respectively. δD and δ18O isotopes suggested that surface water, groundwater and thermal springs were of meteoric origin. Thermal water should have deep circulation through the Xianshuihe fault zone, while groundwater flows through secondary fractures where it recharges with thermal water. Those analytical results were used to construct a hydrological conceptual model, providing a better understanding of the natural water system in Kangding County.

  2. Ultimate compression after impact load prediction in graphite/epoxy coupons using neural network and multivariate statistical analyses

    Science.gov (United States)

    Gregoire, Alexandre David

    2011-07-01

    The goal of this research was to accurately predict the ultimate compressive load of impact damaged graphite/epoxy coupons using a Kohonen self-organizing map (SOM) neural network and multivariate statistical regression analysis (MSRA). An optimized use of these data treatment tools allowed the generation of a simple, physically understandable equation that predicts the ultimate failure load of an impacted damaged coupon based uniquely on the acoustic emissions it emits at low proof loads. Acoustic emission (AE) data were collected using two 150 kHz resonant transducers which detected and recorded the AE activity given off during compression to failure of thirty-four impacted 24-ply bidirectional woven cloth laminate graphite/epoxy coupons. The AE quantification parameters duration, energy and amplitude for each AE hit were input to the Kohonen self-organizing map (SOM) neural network to accurately classify the material failure mechanisms present in the low proof load data. The number of failure mechanisms from the first 30% of the loading for twenty-four coupons were used to generate a linear prediction equation which yielded a worst case ultimate load prediction error of 16.17%, just outside of the +/-15% B-basis allowables, which was the goal for this research. Particular emphasis was placed upon the noise removal process which was largely responsible for the accuracy of the results.

  3. Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

    Science.gov (United States)

    Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

    2012-11-01

    Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. Fragments analysis of Marajoara pubic covers using a portable system of X-ray fluorescence and multivariate statistics

    International Nuclear Information System (INIS)

    Freitas, Renato; Rabello, Angela; Lima, Tania

    2011-01-01

    Full text: In this work it was characterized the elemental composition of 102 fragments of Marajoara pubic covers, belonging to the National Museum collection, using EDXRF and multivariate statistics analysis. The objective was to identify possible groups of samples that presented similar characteristics. This information will be useful in the development of a systematic classification of these artifacts. Provenance studies of ancient ceramics are based on the assumption that pottery produced from a specific clay will present a similar chemical composition, which will distinguish them from pottery produced from a different clay. In this way, the pottery is assigned to particular production groups, which are then correlated with their respective origins. EDXRF measurements were carried out with a portable system, developed in the Nuclear Instrumentation Laboratory, consisting of an X-ray tube Oxford TF3005 with tungsten (W) anode, operating at 25 kV and 100 μA, and a Si-PIN XR-100CR detector from Amptek. In each one of the 102 fragments, six points were analyzed (three in the front part and three in the reverse) with an acquisition time of 600 s and a beam collimation of 2 mm. The spectra were processed and analyzed using the software QXAS-AXIL from IAEA. PCA was applied to the XRF results revealing a clear cluster separation to the samples. (author)

  5. Labour status approach to labour statistics.

    OpenAIRE

    Standing G

    1983-01-01

    ILO pub-WEP pub. Working paper presenting a theoretical framework for an employment status approach to data collecting on labour force - discusses the definition of exploitation, underemployment, division of labour, occupation, skill, labour force, etc.; proposes a taxonomy of labour status including forced labour, feudalism, sharecropper, skilled worker, wages and family workers, peasant farmer, payment by result, apprentice, landowner and employer; and outlines forms of labour control. Refe...

  6. Parametric statistical inference basic theory and modern approaches

    CERN Document Server

    Zacks, Shelemyahu; Tsokos, C P

    1981-01-01

    Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt

  7. Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

    Science.gov (United States)

    Khan, Firdos; Pilz, Jürgen

    2016-04-01

    South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological

  8. A quantum information approach to statistical mechanics

    International Nuclear Information System (INIS)

    Cuevas, G.

    2011-01-01

    The field of quantum information and computation harnesses and exploits the properties of quantum mechanics to perform tasks more efficiently than their classical counterparts, or that may uniquely be possible in the quantum world. Its findings and techniques have been applied to a number of fields, such as the study of entanglement in strongly correlated systems, new simulation techniques for many-body physics or, generally, to quantum optics. This thesis aims at broadening the scope of quantum information theory by applying it to problems in statistical mechanics. We focus on classical spin models, which are toy models used in a variety of systems, ranging from magnetism, neural networks, to quantum gravity. We tackle these models using quantum information tools from three different angles. First, we show how the partition function of a class of widely different classical spin models (models in different dimensions, different types of many-body interactions, different symmetries, etc) can be mapped to the partition function of a single model. We prove this by first establishing a relation between partition functions and quantum states, and then transforming the corresponding quantum states to each other. Second, we give efficient quantum algorithms to estimate the partition function of various classical spin models, such as the Ising or the Potts model. The proof is based on a relation between partition functions and quantum circuits, which allows us to determine the quantum computational complexity of the partition function by studying the corresponding quantum circuit. Finally, we outline the possibility of applying quantum information concepts and tools to certain models of dis- crete quantum gravity. The latter provide a natural route to generalize our results, insofar as the central quantity has the form of a partition function, and as classical spin models are used as toy models of matter. (author)

  9. Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

    Science.gov (United States)

    Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

    2014-01-01

    Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers.

  10. Hidden Statistics Approach to Quantum Simulations

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    Recent advances in quantum information theory have inspired an explosion of interest in new quantum algorithms for solving hard computational (quantum and non-quantum) problems. The basic principle of quantum computation is that the quantum properties can be used to represent structure data, and that quantum mechanisms can be devised and built to perform operations with this data. Three basic non-classical properties of quantum mechanics superposition, entanglement, and direct-product decomposability were main reasons for optimism about capabilities of quantum computers that promised simultaneous processing of large massifs of highly correlated data. Unfortunately, these advantages of quantum mechanics came with a high price. One major problem is keeping the components of the computer in a coherent state, as the slightest interaction with the external world would cause the system to decohere. That is why the hardware implementation of a quantum computer is still unsolved. The basic idea of this work is to create a new kind of dynamical system that would preserve the main three properties of quantum physics superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods. In other words, such a system would reinforce the advantages and minimize limitations of both quantum and classical aspects. Based upon a concept of hidden statistics, a new kind of dynamical system for simulation of Schroedinger equation is proposed. The system represents a modified Madelung version of Schroedinger equation. It preserves superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods. Such an optimal combination of characteristics is a perfect match for simulating quantum systems. The model includes a transitional component of quantum potential (that has been overlooked in previous treatment of the Madelung equation). The role of the

  11. Statistical physics approaches to Alzheimer's disease

    Science.gov (United States)

    Peng, Shouyong

    Alzheimer's disease (AD) is the most common cause of late life dementia. In the brain of an AD patient, neurons are lost and spatial neuronal organizations (microcolumns) are disrupted. An adequate quantitative analysis of microcolumns requires that we automate the neuron recognition stage in the analysis of microscopic images of human brain tissue. We propose a recognition method based on statistical physics. Specifically, Monte Carlo simulations of an inhomogeneous Potts model are applied for image segmentation. Unlike most traditional methods, this method improves the recognition of overlapped neurons, and thus improves the overall recognition percentage. Although the exact causes of AD are unknown, as experimental advances have revealed the molecular origin of AD, they have continued to support the amyloid cascade hypothesis, which states that early stages of aggregation of amyloid beta (Abeta) peptides lead to neurodegeneration and death. X-ray diffraction studies reveal the common cross-beta structural features of the final stable aggregates-amyloid fibrils. Solid-state NMR studies also reveal structural features for some well-ordered fibrils. But currently there is no feasible experimental technique that can reveal the exact structure or the precise dynamics of assembly and thus help us understand the aggregation mechanism. Computer simulation offers a way to understand the aggregation mechanism on the molecular level. Because traditional all-atom continuous molecular dynamics simulations are not fast enough to investigate the whole aggregation process, we apply coarse-grained models and discrete molecular dynamics methods to increase the simulation speed. First we use a coarse-grained two-bead (two beads per amino acid) model. Simulations show that peptides can aggregate into multilayer beta-sheet structures, which agree with X-ray diffraction experiments. To better represent the secondary structure transition happening during aggregation, we refine the

  12. A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

    Science.gov (United States)

    Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

    2017-12-01

    Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared

  13. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems

    Directory of Open Access Journals (Sweden)

    Shashank Vyas

    2016-01-01

    Full Text Available Integration of solar photovoltaic (PV generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor (K-NN binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.

  14. Multivariate statistical analysis of the hydrogeochemical and isotopic composition of the groundwater resources in northeastern Peloponnesus (Greece).

    Science.gov (United States)

    Matiatos, Ioannis; Alexopoulos, Apostolos; Godelitsas, Athanasios

    2014-04-01

    The present study involves an integration of the hydrogeological, hydrochemical and isotopic (both stable and radiogenic) data of the groundwater samples taken from aquifers occurring in the region of northeastern Peloponnesus. Special emphasis has been given to health-related ions and isotopes in relation to the WHO and USEPA guidelines, to highlight the concentrations of compounds (e.g., As and Ba) exceeding the drinking water thresholds. Multivariate statistical analyses, i.e. two principal component analyses (PCA) and one discriminant analysis (DA), combined with conventional hydrochemical methodologies, were applied, with the aim to interpret the spatial variations in the groundwater quality and to identify the main hydrogeochemical factors and human activities responsible for the high ion concentrations and isotopic content in the groundwater analysed. The first PCA resulted in a three component model, which explained approximately 82% of the total variance of the data sets and enabled the identification of the hydrogeological processes responsible for the isotopic content i.e., δ(18)Ο, tritium and (222)Rn. The second PCA, involving the trace element presence in the water samples, revealed a four component model, which explained approximately 89% of the total variance of the data sets, giving more insight into the geochemical and anthropogenic controls on the groundwater composition (e.g., water-rock interaction, hydrothermal activity and agricultural activities). Using discriminant analysis, a four parameter (δ(18)O, (Ca+Mg)/(HCO3+SO4), EC and Cl) discriminant function concerning the (222)Rn content was derived, which favoured a classification of the samples according to the concentration of (222)Rn as (222)Rn-safe (11 Bq·L(-1)). The selection of radon builds on the fact that this radiogenic isotope has been generally related to increased health risk when consumed. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Weili Duan

    2016-01-01

    Full Text Available Multivariate statistical methods including cluster analysis (CA, discriminant analysis (DA and component analysis/factor analysis (PCA/FA, were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season and two regions (low pollution and high pollution, respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH4-N, and total nitrogen (TN were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD, TN, Fluoride (F, and Sulphide (S led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu, chromium (Cr and Zinc (Zn, fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.

  16. The assessment of processes controlling the spatial distribution of hydrogeochemical groundwater types in Mali using multivariate statistics

    Science.gov (United States)

    Keita, Souleymane; Zhonghua, Tang

    2017-10-01

    Sustainable management of groundwater resources is a major issue for developing countries, especially in Mali. The multiple uses of groundwater led countries to promote sound management policies for sustainable use of the groundwater resources. For this reason, each country needs data enabling it to monitor and predict the changes of the resources. Also given the importance of groundwater quality changes often marked by the recurrence of droughts; the potential impacts of regional and geological setting of groundwater resources requires careful study. Unfortunately, recent decades have seen a considerable reduction of national capacities to ensure the hydrogeological monitoring and production of qualit data for decision making. The purpose of this work is to use the groundwater data and translate into useful information that can improve water resources management capacity in Mali. In this paper, we used groundwater analytical data from accredited, laboratories in Mali to carry out a national scale assessment of the groundwater types and their distribution. We, adapted multivariate statistical methods to classify 2035 groundwater samples into seven main groundwater types and built a national scale map from the results. We used a two-level K-mean clustering technique to examine the hydro-geochemical records as percentages of the total concentrations of major ions, namely sodium (Na), magnesium (Mg), calcium (Ca), chloride (Cl), bicarbonate (HCO3), and sulphate (SO4). The first step of clustering formed 20 groups, and these groups were then re-clustered to produce the final seven groundwater types. The results were verified and confirmed using Principal Component Analysis (PCA) and RockWare (Aq.QA) software. We found that HCO3 was the most dominant anion throughout the country and that Cl and SO4 were only important in some local zones. The dominant cations were Na and Mg. Also, major ion ratios changed with geographical location and geological, and climatic

  17. Characterizing and locating air pollution sources in a complex industrial district using optical remote sensing technology and multivariate statistical modeling.

    Science.gov (United States)

    Chang, Pao-Erh Paul; Yang, Jen-Chih Rena; Den, Walter; Wu, Chang-Fu

    2014-09-01

    Emissions of volatile organic compounds (VOCs) are most frequent environmental nuisance complaints in urban areas, especially where industrial districts are nearby. Unfortunately, identifying the responsible emission sources of VOCs is essentially a difficult task. In this study, we proposed a dynamic approach to gradually confine the location of potential VOC emission sources in an industrial complex, by combining multi-path open-path Fourier transform infrared spectrometry (OP-FTIR) measurement and the statistical method of principal component analysis (PCA). Close-cell FTIR was further used to verify the VOC emission source by measuring emitted VOCs from selected exhaust stacks at factories in the confined areas. Multiple open-path monitoring lines were deployed during a 3-month monitoring campaign in a complex industrial district. The emission patterns were identified and locations of emissions were confined by the wind data collected simultaneously. N,N-Dimethyl formamide (DMF), 2-butanone, toluene, and ethyl acetate with mean concentrations of 80.0 ± 1.8, 34.5 ± 0.8, 103.7 ± 2.8, and 26.6 ± 0.7 ppbv, respectively, were identified as the major VOC mixture at all times of the day around the receptor site. As the toxic air pollutant, the concentrations of DMF in air samples were found exceeding the ambient standard despite the path-average effect of OP-FTIR upon concentration levels. The PCA data identified three major emission sources, including PU coating, chemical packaging, and lithographic printing industries. Applying instrumental measurement and statistical modeling, this study has established a systematic approach for locating emission sources. Statistical modeling (PCA) plays an important role in reducing dimensionality of a large measured dataset and identifying underlying emission sources. Instrumental measurement, however, helps verify the outcomes of the statistical modeling. The field study has demonstrated the feasibility of

  18. Determinants of Food Crop Diversity and Profitability in Southeastern Nigeria: A Multivariate Tobit Approach

    Directory of Open Access Journals (Sweden)

    Sanzidur Rahman

    2016-04-01

    Full Text Available The present study jointly determines the factors influencing decisions to diversify into multiple food crops (i.e., rice, yam and cassava vis-à-vis profitability of 400 farmers from Ebonyi and Anambra states of Southeastern Nigeria using a multivariate Tobit model. Model diagnostic reveals that the decisions to diversify into multiple crops and profits generated therefrom are significantly correlated, thereby justifying use of a multivariate approach. Results reveal that 68% of the farmers grew at least two food crops and profitability is highest for only rice producers followed by joint rice and yam producers, which are mainly for sale. Farm size is the most dominant determinant of crop diversity vis-à-vis profitability. A rise in the relative price of plowing significantly reduces profitability of yam and rice. High yield is the main motive for growing yam and cassava whereas ready market is for rice. Other determinants with varying level of influences are proximity to market and/or extension office, extension contact, training, agricultural credit, subsistence pressure and location. Policy recommendations include investments in market infrastructure and credit services, land and/or tenurial reform and input price stabilization to promote food crop diversity vis-à-vis profitability in Southeastern Nigeria.

  19. A Multivariate Approach to Dilepton Analyses in the Upgraded ALICE Detector at CERN-LHC

    CERN Document Server

    AUTHOR|(CDS)2242451; Weber, Michael

    ALICE, the dedicated heavy-ion experiment at CERN-LHC, will undergo a major upgrade in 2019/20. This work aims to assess the feasibility of conventional and multivariate analysis techniques for low-mass dielectron measurements in Pb-Pb collisions in a scenario involving the upgraded ALICE detector with a low magnetic field ($B=0.2~\\text{T}$). These electron-positron pairs are promising probes for the hot and dense medium, which is created in collisions of ultra-relativistic heavy nuclei, as they traverse the medium without significant final-state modifications. Due to their small signal-to-background ratio, high-purity dielectron samples are required. They can be provided by conventional analysis methods, which are based on sequential cuts, however at the price of low signal efficiency. This work shows that existing methods can be improved by employing multivariate approaches to reject different background sources of the dielectron invariant mass spectrum. The major background components are dielectrons from ...

  20. Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

    Directory of Open Access Journals (Sweden)

    Kin Keung Lai

    2012-04-01

    Full Text Available In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR. The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporatesthe mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark ExponentialWeighted Moving Average (EWMA and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

  1. A trust region approach with multivariate Padé model for optimal circuit design

    Science.gov (United States)

    Abdel-Malek, Hany L.; Ebid, Shaimaa E. K.; Mohamed, Ahmed S. A.

    2017-11-01

    Since the optimization process requires a significant number of consecutive function evaluations, it is recommended to replace the function by an easily evaluated approximation model during the optimization process. The model suggested in this article is based on a multivariate Padé approximation. This model is constructed using data points of ?, where ? is the number of parameters. The model is updated over a sequence of trust regions. This model avoids the slow convergence of linear models of ? and has features of quadratic models that need interpolation data points of ?. The proposed approach is tested by applying it to several benchmark problems. Yield optimization using such a direct method is applied to some practical circuit examples. Minimax solution leads to a suitable initial point to carry out the yield optimization process. The yield is optimized by the proposed derivative-free method for active and passive filter examples.

  2. Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

    Science.gov (United States)

    Gu, Huaying; Liu, Zhixue; Weng, Yingliang

    2017-04-01

    The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

  3. A statistical mechanical approach to restricted integer partition functions

    Science.gov (United States)

    Zhou, Chi-Chun; Dai, Wu-Sheng

    2018-05-01

    The main aim of this paper is twofold: (1) suggesting a statistical mechanical approach to the calculation of the generating function of restricted integer partition functions which count the number of partitions—a way of writing an integer as a sum of other integers under certain restrictions. In this approach, the generating function of restricted integer partition functions is constructed from the canonical partition functions of various quantum gases. (2) Introducing a new type of restricted integer partition functions corresponding to general statistics which is a generalization of Gentile statistics in statistical mechanics; many kinds of restricted integer partition functions are special cases of this restricted integer partition function. Moreover, with statistical mechanics as a bridge, we reveal a mathematical fact: the generating function of restricted integer partition function is just the symmetric function which is a class of functions being invariant under the action of permutation groups. Using this approach, we provide some expressions of restricted integer partition functions as examples.

  4. Inference of reactive transport model parameters using a Bayesian multivariate approach

    Science.gov (United States)

    Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick

    2014-08-01

    Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.

  5. AMBI indices and multivariate approach to assess the ecological health of Vellar-Coleroon estuarine system undergoing various human activities.

    Science.gov (United States)

    Sigamani, Sivaraj; Perumal, Murugesan; Arumugam, Silambarasan; Preetha Mini Jose, H M; Veeraiyan, Bharathidasan

    2015-11-15

    Estuaries receive a considerable amount of pollutants from various sources. Presently an attempt has been made to assess whether the aquaculture discharges and dredging activities alter the ecological conditions of Vellar-Coleroon estuarine complex. The European Water Framework Directive (WFD) established a framework for the protection of marine waters. In this commission, a variety of indices were used, among them, AMBI (AZTI Marine Biotic Index) indices along with multivariate statistical approach is unique, to assess the ecological status by using macrobenthic communities. Keeping this in view, stations VE-1 and VE-4 in Vellar; CE-6 and CE-7 in Coleroon estuaries showed moderately disturbed with the AMBI values ranging between 3.45 and 3.72. The above said stations were situated near the shrimp farm discharge point and sites of dredging activities. The present study proves that various statistical and biotic indices have great potential in assessing the nature of the ecosystem undergoing various human pressures. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

    Science.gov (United States)

    Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

    2012-07-01

    Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

  7. A Bayesian approach to estimating variance components within a multivariate generalizability theory framework.

    Science.gov (United States)

    Jiang, Zhehan; Skorupski, William

    2017-12-12

    In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.

  8. A Statistical Approach to Optimizing Concrete Mixture Design

    OpenAIRE

    Ahmad, Shamsad; Alghamdi, Saeid A.

    2014-01-01

    A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33). A total of 27 concrete mixtures with three replicate...

  9. Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Hao [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China); Lu, Xinwei, E-mail: luxinwei@snnu.edu.cn [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China); Li, Loretta Y., E-mail: lli@civil.ubc.ca [Department of Civil Engineering, University of British Columbia, Vancouver V6T 1Z4 (Canada); Gao, Tianning; Chang, Yuyu [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China)

    2014-06-01

    The concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V and Zn in campus dust from kindergartens, elementary schools, middle schools and universities of Xi'an, China were determined by X-ray fluorescence spectrometry. Correlation coefficient analysis, principal component analysis (PCA) and cluster analysis (CA) were used to analyze the data and to identify possible sources of these metals in the dust. The spatial distributions of metals in urban dust of Xi'an were analyzed based on the metal concentrations in campus dusts using the geostatistics method. The results indicate that dust samples from campuses have elevated metal concentrations, especially for Pb, Zn, Co, Cu, Cr and Ba, with the mean values of 7.1, 5.6, 3.7, 2.9, 2.5 and 1.9 times the background values for Shaanxi soil, respectively. The enrichment factor results indicate that Mn, Ni, V, As and Ba in the campus dust were deficiently to minimally enriched, mainly affected by nature and partly by anthropogenic sources, while Co, Cr, Cu, Pb and Zn in the campus dust and especially Pb and Zn were mostly affected by human activities. As and Cu, Mn and Ni, Ba and V, and Pb and Zn had similar distribution patterns. The southwest high-tech industrial area and south commercial and residential areas have relatively high levels of most metals. Three main sources were identified based on correlation coefficient analysis, PCA, CA, as well as spatial distribution characteristics. As, Ni, Cu, Mn, Pb, Zn and Cr have mixed sources — nature, traffic, as well as fossil fuel combustion and weathering of materials. Ba and V are mainly derived from nature, but partly also from industrial emissions, as well as construction sources, while Co principally originates from construction. - Highlights: • Metal content in dust from schools was determined by XRF. • Spatial distribution of metals in urban dust was focused on campus samples. • Multivariate statistic and spatial distribution were used to identify metal

  10. Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

    International Nuclear Information System (INIS)

    Chen, Hao; Lu, Xinwei; Li, Loretta Y.; Gao, Tianning; Chang, Yuyu

    2014-01-01

    The concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V and Zn in campus dust from kindergartens, elementary schools, middle schools and universities of Xi'an, China were determined by X-ray fluorescence spectrometry. Correlation coefficient analysis, principal component analysis (PCA) and cluster analysis (CA) were used to analyze the data and to identify possible sources of these metals in the dust. The spatial distributions of metals in urban dust of Xi'an were analyzed based on the metal concentrations in campus dusts using the geostatistics method. The results indicate that dust samples from campuses have elevated metal concentrations, especially for Pb, Zn, Co, Cu, Cr and Ba, with the mean values of 7.1, 5.6, 3.7, 2.9, 2.5 and 1.9 times the background values for Shaanxi soil, respectively. The enrichment factor results indicate that Mn, Ni, V, As and Ba in the campus dust were deficiently to minimally enriched, mainly affected by nature and partly by anthropogenic sources, while Co, Cr, Cu, Pb and Zn in the campus dust and especially Pb and Zn were mostly affected by human activities. As and Cu, Mn and Ni, Ba and V, and Pb and Zn had similar distribution patterns. The southwest high-tech industrial area and south commercial and residential areas have relatively high levels of most metals. Three main sources were identified based on correlation coefficient analysis, PCA, CA, as well as spatial distribution characteristics. As, Ni, Cu, Mn, Pb, Zn and Cr have mixed sources — nature, traffic, as well as fossil fuel combustion and weathering of materials. Ba and V are mainly derived from nature, but partly also from industrial emissions, as well as construction sources, while Co principally originates from construction. - Highlights: • Metal content in dust from schools was determined by XRF. • Spatial distribution of metals in urban dust was focused on campus samples. • Multivariate statistic and spatial distribution were used to identify metal sources.

  11. Nonparametric statistics a step-by-step approach

    CERN Document Server

    Corder, Gregory W

    2014-01-01

    "…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory.  It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca

  12. Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

    Science.gov (United States)

    Vasconcelos, A G; Almeida, R M; Nobre, F F

    2001-08-01

    This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

  13. Taking a comparative approach: analysing personality as a multivariate behavioural response across species.

    Directory of Open Access Journals (Sweden)

    Alecia J Carter

    Full Text Available Animal personality, repeatable behaviour through time and across contexts, is ecologically and evolutionarily important as it can account for the exhibition of sub-optimal behaviours. Interspecific comparisons have been suggested as important for understanding the evolution of animal personality; however, these are seldom accomplished due, in part, to the lack of statistical tools for quantifying differences and similarities in behaviour between groups of individuals. We used nine species of closely-related coral reef fishes to investigate the usefulness of ecological community analyses for the analysis of between-species behavioural differences and behavioural heterogeneity. We first documented behavioural carryover across species by observing the fishes' behaviour and measuring their response to a threatening stimulus to quantify boldness. Bold fish spent more time away from the reef and fed more than shy fish. We then used ecological community analysis tools (canonical variate analysis, multi-response permutation procedure, and permutational analysis of multivariate dispersion and identified four 'clusters' of behaviourally similar fishes, and found that the species differ in the behavioural variation expressed; some species are more behaviourally heterogeneous than others. We found that ecological community analysis tools are easily and fruitfully applied to comparative studies of personality and encourage their use by future studies.

  14. Statistical Data Processing with R – Metadata Driven Approach

    Directory of Open Access Journals (Sweden)

    Rudi SELJAK

    2016-06-01

    Full Text Available In recent years the Statistical Office of the Republic of Slovenia has put a lot of effort into re-designing its statistical process. We replaced the classical stove-pipe oriented production system with general software solutions, based on the metadata driven approach. This means that one general program code, which is parametrized with process metadata, is used for data processing for a particular survey. Currently, the general program code is entirely based on SAS macros, but in the future we would like to explore how successfully statistical software R can be used for this approach. Paper describes the metadata driven principle for data validation, generic software solution and main issues connected with the use of statistical software R for this approach.

  15. A Multivariate Quality Loss Function Approach for Optimization of Spinning Processes

    Science.gov (United States)

    Chakraborty, Shankar; Mitra, Ankan

    2018-05-01

    Recent advancements in textile industry have given rise to several spinning techniques, such as ring spinning, rotor spinning etc., which can be used to produce a wide variety of textile apparels so as to fulfil the end requirements of the customers. To achieve the best out of these processes, they should be utilized at their optimal parametric settings. However, in presence of multiple yarn characteristics which are often conflicting in nature, it becomes a challenging task for the spinning industry personnel to identify the best parametric mix which would simultaneously optimize all the responses. Hence, in this paper, the applicability of a new systematic approach in the form of multivariate quality loss function technique is explored for optimizing multiple quality characteristics of yarns while identifying the ideal settings of two spinning processes. It is observed that this approach performs well against the other multi-objective optimization techniques, such as desirability function, distance function and mean squared error methods. With slight modifications in the upper and lower specification limits of the considered quality characteristics, and constraints of the non-linear optimization problem, it can be successfully applied to other processes in textile industry to determine their optimal parametric settings.

  16. Propensity Score Analysis: An Alternative Statistical Approach for HRD Researchers

    Science.gov (United States)

    Keiffer, Greggory L.; Lane, Forrest C.

    2016-01-01

    Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…

  17. Measuring University Students' Approaches to Learning Statistics: An Invariance Study

    Science.gov (United States)

    Chiesi, Francesca; Primi, Caterina; Bilgin, Ayse Aysin; Lopez, Maria Virginia; del Carmen Fabrizio, Maria; Gozlu, Sitki; Tuan, Nguyen Minh

    2016-01-01

    The aim of the current study was to provide evidence that an abbreviated version of the Approaches and Study Skills Inventory for Students (ASSIST) was invariant across different languages and educational contexts in measuring university students' learning approaches to statistics. Data were collected on samples of university students attending…

  18. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  19. Understanding the groundwater dynamics in the Southern Rift Valley Lakes Basin (Ethiopia). Multivariate statistical analysis method, oxygen (δ 18O) and deuterium (δ 2H)

    International Nuclear Information System (INIS)

    Girum Admasu Nadew; Zebene Lakew Tefera

    2013-01-01

    Multivariate statistical analysis is very important to classify waters of different hydrochemical groups. Statistical techniques, such as cluster analysis, can provide a powerful tool for analyzing water chemistry data. This method is used to test water quality data and determine if samples can be grouped into distinct populations that may be significant in the geologic context, as well as from a statistical point of view. Multivariate statistical analysis method is applied to the geochemical data in combination with δ 18 O and δ 2 H isotopes with the objective to understand the dynamics of groundwater using hierarchical clustering and isotope analyses. The geochemical and isotope data of the central and southern rift valley lakes have been collected and analyzed from different works. Isotope analysis shows that most springs and boreholes are recharged by July and August rainfalls. The different hydrochemical groups that resulted from the multivariate analysis are described and correlated with the geology of the area and whether it has any interaction with a system or not. (author)

  20. Quantitative and statistical approaches to geography a practical manual

    CERN Document Server

    Matthews, John A

    2013-01-01

    Quantitative and Statistical Approaches to Geography: A Practical Manual is a practical introduction to some quantitative and statistical techniques of use to geographers and related scientists. This book is composed of 15 chapters, each begins with an outline of the purpose and necessary mechanics of a technique or group of techniques and is concluded with exercises and the particular approach adopted. These exercises aim to enhance student's ability to use the techniques as part of the process by which sound judgments are made according to scientific standards while tackling complex problems. After a brief introduction to the principles of quantitative and statistical geography, this book goes on dealing with the topics of measures of central tendency; probability statements and maps; the problem of time-dependence, time-series analysis, non-normality, and data transformations; and the elements of sampling methodology. Other chapters cover the confidence intervals and estimation from samples, statistical hy...

  1. A multivariate time series approach to forecasting daily attendances at hospital emergency department

    KAUST Repository

    Kadri, Farid

    2018-02-07

    Efficient management of patient demands in emergency departments (EDs) has recently received increasing attention by most healthcare administrations. Forecasting ED demands greatly helps ED\\'s managers to make suitable decisions by optimally allocating the available limited resources to efficiently handle patient attendances. Furthermore, it permits pre-emptive action(s) to mitigate and/or prevent overcrowding situations and to enhance the quality of care. In this work, we present a statistical approach based on a vector autoregressive moving average (VARMA) model for a short term forecasting of daily attendances at an ED. The VARMA model has been validated using an experimental data from the paediatric emergency department (PED) at Lille regional hospital centre, France. The results obtained indicate the effectiveness of the proposed approach in forecasting patient demands.

  2. A multivariate time series approach to modeling and forecasting demand in the emergency department.

    Science.gov (United States)

    Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

    2009-02-01

    The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

  3. A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

    Science.gov (United States)

    Sawall, Mathias; Kubis, Christoph; Börner, Armin; Selent, Detlef; Neymeyr, Klaus

    2015-09-03

    Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements. The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Examining carbon emissions economic growth nexus for India: A multivariate cointegration approach

    International Nuclear Information System (INIS)

    Ghosh, Sajal

    2010-01-01

    The study probes cointegration and causality between carbon emissions and economic growth for India using ARDL bounds testing approach complemented by Johansen-Juselius maximum likelihood procedure in a multivariate framework by incorporating energy supply, investment and employment for time span 1971-2006. The study fails to establish long-run equilibrium relationship and long term causality between carbon emissions and economic growth; however, there exists a bi-directional short-run causality between the two. Hence, in the short-run, any effort to reduce carbon emissions could lead to a fall in the national income. This study also establishes unidirectional short-run causality running from economic growth to energy supply and energy supply to carbon emissions. The absence of causality running from energy supply to economic growth implies that in India, energy conservation and energy efficiency measures can be implemented to minimize the wastage of energy across value chain. Such measures would narrow energy demand-supply gap. Absence of long-run causality between carbon emissions and economic growth implies that in the long-run, focus should be given on harnessing energy from clean sources to curb carbon emissions, which would not affect the country's economic growth.

  5. Behavioral investment strategy matters: a statistical arbitrage approach

    OpenAIRE

    Sun, David; Tsai, Shih-Chuan; Wang, Wei

    2011-01-01

    In this study, we employ a statistical arbitrage approach to demonstrate that momentum investment strategy tend to work better in periods longer than six months, a result different from findings in past literature. Compared with standard parametric tests, the statistical arbitrage method produces more clearly that momentum strategies work only in longer formation and holding periods. Also they yield positive significant returns in an up market, but negative yet insignificant returns in a down...

  6. Statistical analysis of management data

    CERN Document Server

    Gatignon, Hubert

    2013-01-01

    This book offers a comprehensive approach to multivariate statistical analyses. It provides theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications.

  7. MODEL APPLICATION MULTIVARIATE ANALYSIS OF STATISTICAL TECHNIQUES PCA AND HCA ASSESSMENT QUESTIONNAIRE ON CUSTOMER SATISFACTION: CASE STUDY IN A METALLURGICAL COMPANY OF METAL CONTAINERS

    Directory of Open Access Journals (Sweden)

    Cláudio Roberto Rosário

    2012-07-01

    Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.

  8. Human Exposure Risk Assessment Due to Heavy Metals in Groundwater by Pollution Index and Multivariate Statistical Methods: A Case Study from South Africa

    OpenAIRE

    Vetrimurugan Elumalai; K. Brindha; Elango Lakshmanan

    2017-01-01

    Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese ex...

  9. Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

    Science.gov (United States)

    Yuan, Ke-Hai

    2008-01-01

    In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

  10. Multivariate alteration detection (MAD) in multispectral, bi-temporal image data: A new approach to change detction studies

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut

    This paper introduces a new orthogonal transformation, the multivariate alteration detection (MAD) transformation, based on an established multivariate statistical technique canonical correlation analysis. The theory for canonical correlation analysis is sketched and a result necessary...... for the definition of the MAD transformation is proven. As opposed to traditional univariate change detection schemes our scheme transforms two sets of multivariate observations (e.g. two multispectral satellite images covering the same geographical area acquired at different points in time) into a difference...... between two linear combinations of the original variables explaining maximal change (i.e. the difference explaining maximal variance) in all variables simultaneously. The MAD transformation is invariant to linear scaling. The MAD transformation can be used iteratively. First, it can be used to detect...

  11. A statistical mechanics approach to mixing in stratified fluids

    OpenAIRE

    Venaille , Antoine; Gostiaux , Louis; Sommeria , Joël

    2016-01-01

    Accepted for the Journal of Fluid Mechanics; Predicting how much mixing occurs when a given amount of energy is injected into a Boussinesq fluid is a longstanding problem in stratified turbulence. The huge number of degrees of freedom involved in these processes renders extremely difficult a deterministic approach to the problem. Here we present a statistical mechanics approach yielding a prediction for a cumulative, global mixing efficiency as a function of a global Richard-son number and th...

  12. Interdependence of environmental parameters and sand dwelling benthic species abundance: a multivariate approach

    Digital Repository Service at National Institute of Oceanography (India)

    Harkantra, S.N.; Parulekar, A.H.

    Multivariate analysis showed dependence of distribution and abundance of sand dwelling fauna on more than one ecologically significant environmental parameters rather than one ecological master factor. Salinity, grain size, beach gradient, dissolved...

  13. A multivariate approach for the study of the environmental drivers of wine production structure

    Science.gov (United States)

    Lorenzetti, Romina; Costantini, Edoardo A. C.; Malorgio, Giulio

    2015-04-01

    Vitivinicultural "terroir" is a concept referring to an area in which the collective knowledge of the interactions between environment and vitivinicultural practices develops, providing distinctive characteristics to the products. The effect of the environment components over the terroir has been already widely demonstrated. What it has not been studied yet is their possible effect on the structure of wine production. Therefore, the aim of this work was to find if environmental drivers influence the wine production structure. This kind of investigation necessarily involves a change of scale towards wide territories. We used the Italian Denomination of Origin territories, which were grouped in Macro-areas (reference scale 1:500,000) with respect of geographic proximity, environmental features, viticultural affinity and tradition. The characterization of the structure of the wine transformation industry was based on the official data reported in the wine production declarations related to the year 2008. Statistics were taken into account about general quantitative variables of wine farms, presence of associative forms, degree of vertical integration of wineries, quality orientation of wine producers, and acreage of vineyard. The environmental variables climate, soil, and vegetation vigour were selected for their direct influence on the vine growing. A second set of variables was chosen to express the effect of land morphology on viticultural management. The third one was intended to discover the possible relationships between viticultural structures and land quality, such as the indexes of sensitivity to desertification, the soil resistance to water erosion, and land vulnerability. A PCA was carried out separately for the environmental and economic data to reduce the database dimensions. The new economic and environmental synthetic descriptors were involved in three multivariate analyses: i) the correlation between economic and environmental descriptors through the

  14. A public perspective on the adoption of microgeneration technologies in New Zealand: A multivariate probit approach

    International Nuclear Information System (INIS)

    Baskaran, Ramesh; Managi, Shunsuke; Bendig, Mirko

    2013-01-01

    The growing demand for electricity in New Zealand has led to the construction of new hydro-dams or power stations that have had environmental, social and cultural effects. These effects may drive increases in electricity prices, as such prices reflect the cost of running existing power stations as well as building new ones. This study uses Canterbury and Central Otago as case studies because both regions face similar issues in building new hydro-dams and ever-increasing electricity prices that will eventually prompt households to buy power at higher prices. One way for households to respond to these price changes is to generate their own electricity through microgeneration technologies (MGT). The objective of this study is to investigate public perception and preferences regarding MGT and to analyze the factors that influence people’s decision to adopt such new technologies in New Zealand. The study uses a multivariate probit approach to examine households’ willingness to adopt any one MGT system or a combination of the MGT systems. Our findings provide valuable information for policy makers and marketers who wish to promote effective microgeneration technologies. - Highlights: ► We examine New Zealand households’ awareness level for microgeneration technologies (MGT) and empirically explore the factors that determine people’s willingness to adopt for MGT. ► The households are interested and willing to adopt the MGT systems. ► Noticeable heterogeneity exists between groups of households in adopting the MGT. ► No significant regional difference exists in promoting solar hot water policies. ► Public and private sectors incentives are important in promoting the MGT

  15. Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

    Directory of Open Access Journals (Sweden)

    Jenicek Michal

    2018-03-01

    Full Text Available The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth. The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle. The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

  16. Experiential Approach to Teaching Statistics and Research Methods ...

    African Journals Online (AJOL)

    Statistics and research methods are among the more demanding topics for students of education to master at both the undergraduate and postgraduate levels. It is our conviction that teaching these topics should be combined with real practical experiences. We discuss an experiential teaching/ learning approach that ...

  17. Modelling diversity in building occupant behaviour: a novel statistical approach

    DEFF Research Database (Denmark)

    Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm

    2016-01-01

    We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...

  18. A statistical approach to traditional Vietnamese medical diagnoses standardization

    International Nuclear Information System (INIS)

    Nguyen Hoang Phuong; Nguyen Quang Hoa; Le Dinh Long

    1990-12-01

    In this paper the first results of the statistical approach for Cold-Heat diagnosis standardization as a first work in the ''eight rules diagnoses'' standardization of Traditional Vietnamese Medicine are briefly described. Some conclusions and suggestions for further work are given. 3 refs, 2 tabs

  19. General renormalized statistical approach with finite cross-field correlations

    International Nuclear Information System (INIS)

    Vakulenko, M.O.

    1992-01-01

    The renormalized statistical approach is proposed, accounting for finite correlations of potential and magnetic fluctuations. It may be used for analysis of a wide class of nonlinear model equations describing the cross-correlated plasma states. The influence of a cross spectrum on stationary potential and magnetic ones is investigated. 10 refs. (author)

  20. Statistical mechanics of learning: A variational approach for real data

    International Nuclear Information System (INIS)

    Malzahn, Doerthe; Opper, Manfred

    2002-01-01

    Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone

  1. A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages

    DEFF Research Database (Denmark)

    Malzahn, Dorthe; Opper, Manfred

    2003-01-01

    We apply the replica method of Statistical Physics combined with a variational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our approach on regression with Gaussian processes and compare our results with averages...

  2. Six sigma for organizational excellence a statistical approach

    CERN Document Server

    Muralidharan, K

    2015-01-01

    This book discusses the integrated concepts of statistical quality engineering and management tools. It will help readers to understand and apply the concepts of quality through project management and technical analysis, using statistical methods. Prepared in a ready-to-use form, the text will equip practitioners to implement the Six Sigma principles in projects. The concepts discussed are all critically assessed and explained, allowing them to be practically applied in managerial decision-making, and in each chapter, the objectives and connections to the rest of the work are clearly illustrated. To aid in understanding, the book includes a wealth of tables, graphs, descriptions and checklists, as well as charts and plots, worked-out examples and exercises. Perhaps the most unique feature of the book is its approach, using statistical tools, to explain the science behind Six Sigma project management and integrated in engineering concepts. The material on quality engineering and statistical management tools of...

  3. 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.

  4. Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

    Science.gov (United States)

    Cannon, Alex

    2017-04-01

    Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a

  5. An analytical statistical approach to the 3D reconstruction problem

    Energy Technology Data Exchange (ETDEWEB)

    Cierniak, Robert [Czestochowa Univ. of Technology (Poland). Inst. of Computer Engineering

    2011-07-01

    The presented here approach is concerned with the reconstruction problem for 3D spiral X-ray tomography. The reconstruction problem is formulated taking into considerations the statistical properties of signals obtained in X-ray CT. Additinally, image processing performed in our approach is involved in analytical methodology. This conception significantly improves quality of the obtained after reconstruction images and decreases the complexity of the reconstruction problem in comparison with other approaches. Computer simulations proved that schematically described here reconstruction algorithm outperforms conventional analytical methods in obtained image quality. (orig.)

  6. Multivariate pattern dependence.

    Directory of Open Access Journals (Sweden)

    Stefano Anzellotti

    2017-11-01

    Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

  7. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study

    Directory of Open Access Journals (Sweden)

    Tania Dehesh

    2015-01-01

    Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  8. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

    Science.gov (United States)

    Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

    2015-01-01

    Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  9. Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

    Science.gov (United States)

    D'Amico, E J; Neilands, T B; Zambarano, R

    2001-11-01

    Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

  10. A κ-generalized statistical mechanics approach to income analysis

    International Nuclear Information System (INIS)

    Clementi, F; Gallegati, M; Kaniadakis, G

    2009-01-01

    This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low–middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful

  11. A κ-generalized statistical mechanics approach to income analysis

    Science.gov (United States)

    Clementi, F.; Gallegati, M.; Kaniadakis, G.

    2009-02-01

    This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.

  12. Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India

    Science.gov (United States)

    Herojeet, Rajkumar; Rishi, Madhuri S.; Lata, Renu; Dolma, Konchok

    2017-09-01

    Sirsa River flows through the central part of the Nalagarh valley, belongs to the rapid industrial belt of Baddi, Barotiwala and Nalagarh (BBN). The appraisal of surface water quality to ascertain its utility in such ecologically sensitive areas is need of the hour. The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies and its evolution processes of water types in Nalagarh valley, India. The quality assessment is made by estimating pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness, major ions (Na+, K+, Ca2+, Mg2+, HCO3 -, Cl-, SO4 2-, NO3 - and PO4 3-), dissolved oxygen (DO), biological oxygen demand (BOD) and total coliform (TC) to determine its suitability for drinking and domestic purposes. The parameters like pH, TDS, TH, Ca2+, HCO3 -, Cl-, SO4 2-, NO3 - are within the desirable limit as per Bureau of Indian Standards (Indian Standard Drinking Water Specification (Second Edition) IS:10500. Indian Standard Institute, New Delhi, pp 1-18, 2012). Mg2+, Na+ and K+ ions for pre monsoon and EC during pre and post monsoon at few sites and approx 40% samples of BOD and TC for both seasons exceeds the permissible limits indicate organic contamination from human activities. Water quality classification for designated use indicates that maximum surface water samples are not suitable for drinking water source without conventional treatment. The result of piper trillinear and Chadha's diagram classified majority of surface water samples for both seasons fall in the fields of Ca2+-Mg2+-HCO3 - water type indicating temporary hardness. PCA and CA reveal that the surface water chemistry is influenced by natural factors such as weathering of minerals, ion exchange processes and anthropogenic factors. Thus, the present paper illustrates the importance of

  13. The mass transfer approach to multivariate discrete first order stochastic dominance

    DEFF Research Database (Denmark)

    Østerdal, Lars Peter Raahave

    2010-01-01

    A fundamental result in the theory of stochastic dominance tells that first order dominance between two finite multivariate distributions is equivalent to the property that the one can be obtained from the other by shifting probability mass from one outcome to another that is worse a finite numbe...

  14. Statistical approach for selection of biologically informative genes.

    Science.gov (United States)

    Das, Samarendra; Rai, Anil; Mishra, D C; Rai, Shesh N

    2018-05-20

    Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes

  15. A statistical approach to the prediction of pressure tube fracture toughness

    International Nuclear Information System (INIS)

    Pandey, M.D.; Radford, D.D.

    2008-01-01

    The fracture toughness of the zirconium alloy (Zr-2.5Nb) is an important parameter in determining the flaw tolerance for operation of pressure tubes in a nuclear reactor. Fracture toughness data have been generated by performing rising pressure burst tests on sections of pressure tubes removed from operating reactors. The test data were used to generate a lower-bound fracture toughness curve, which is used in defining the operational limits of pressure tubes. The paper presents a comprehensive statistical analysis of burst test data and develops a multivariate statistical model to relate toughness with material chemistry, mechanical properties, and operational history. The proposed model can be useful in predicting fracture toughness of specific in-service pressure tubes, thereby minimizing conservatism associated with a generic lower-bound approach

  16. Daniel Goodman’s empirical approach to Bayesian statistics

    Science.gov (United States)

    Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

    2016-01-01

    Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

  17. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

    Directory of Open Access Journals (Sweden)

    Jay Krishna Thakur

    2015-08-01

    Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

  18. Mineralization model for Chahar Gonbad copper-gold deposit (Sirjan, using mineralogical, alteration and geochemical data and multivariate statistical methods

    Directory of Open Access Journals (Sweden)

    Seayed Jaber Yousefi

    2012-04-01

    Full Text Available The study area is located in southeastern Iran, about 110 km southwest of Kerman. Geologically, the area is composed of ophiolitic rocks, volcanic rocks, intrusive bodies and sedimentary rocks. Vein mineralization within andesite, andesitic basalt, andesitic tuffs occurred along the Chahar Gonbad fault. Sulfide mineralization in the ore deposit has taken place as dissemination, veins and veinlets in which pyrite and chalcopyrite are the most important ores. In this area, argillic, phyllic and propylitic alteration types are observed. Such elements as Au, Bi, Cu, S and Se are more enriched than others and the enrichment factors for these elements in comparison with background concentration are 321, 503, 393, 703 and 208, and with respect to Clark concentration are 401, 222, 532, 101 and 156, respectively. According to multivariate analysis, three major mineralization phases are recognized in the deposit. During the first phase, hydrothermal calcite veins are enriched in As, Cd, Pb, Zn and Ca, the second phase is manifested by the enrichment of sulfide veins in Cu, Au, Ag, Bi, Fe and S and the third phase mineralization includes Ni, Mn, Se and Sb as an intermediate level between the two previous phases.

  19. Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

    Science.gov (United States)

    Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

    2016-01-01

    A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. A statistical approach for water movement in the unsaturated zone

    International Nuclear Information System (INIS)

    Tielin Zang.

    1991-01-01

    This thesis presents a statistical approach for estimating and analyzing the downward transport pattern and distribution of soil water by the use of pattern analysis of space-time correlation structures. This approach, called the Space-time-Correlation Field, is mainly based on the analyses of correlation functions simultaneously in the space and time domain. The overall purpose of this work is to derive an alternative statistical procedure in soil moisture analysis without involving detailed information on hydraulic parameters and to visualize the dynamics of soil water variability in the space and time domains. A numerical model using method of characteristics is employed to provide hypothetical time series to use in the statistical method, which is, after the verification and calibration, applied to the field measured time series. The results of the application show that the space-time correlation fields reveal effects of soil layers with different hydraulic properties and boundaries between them. It is concluded that the approach poses special advantages when visualizing time and space dependent properties simultaneously. It can be used to investigate the hydrological response of soil water dynamics and characteristics in different dimensions (space and time) and scales. This approach can be used to identify the dominant component in unsaturated flow systems. It is possible to estimate the pattern and the propagation rate downwards of moisture movement in the soil profile. Small-scale soil heterogeneities can be identified by the correlation field. Since the correlation field technique give a statistical measure of the dependent property that varies within the space-time field, it is possible to interpolate the fields to points where observations are not available, estimating spatial or temporal averages from discrete observations. (au)

  1. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  2. Use of multivariate statistical tool for data processing in the analysis of Cu, Cr, Fe, Pb, Mo and Mg in lubricating oil by LIBS

    International Nuclear Information System (INIS)

    Alves, Luana F.N.; Sarkis, Jorge E.S.; Bordon, Isabela C.A.C.

    2015-01-01

    Analysis of industrial lubricants is widely used for monitoring and predicting maintenance requirements in a broad range of mechanical systems. Laser induced breakdown spectroscopy has been used to evaluate the potentiality of the technique for the determination of metals in lubricating oils. Prior to quantitative analysis, the LIBS system was calibrated using standard samples containing the elements investigated (Cu, Cr, Fe, Pb, Mo and Mg). This study presents the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets in order to get more information about concentration of metals in oils lubricants is related to engine wear. (author)

  3. Application of multivariate statistical methods to classify archaeological pottery from Tel-Alramad site, Syria, based on x-ray fluorescence analysis

    International Nuclear Information System (INIS)

    Bakraji, E. H.

    2007-01-01

    Radioisotopic x-ray fluorescence (XRF) analysis has been utilized to determine the elemental composition of 55 archaeological pottery samples by the determination of 17 chemical elements. Fifty-four of them came from the Tel-Alramad Site in Katana town, near Damascus city, Syria, and one sample came from Brazil. The XRF results have been processed using two multivariate statistical methods, cluster and factor analysis, in order to determine similarities and correlation between the selected samples based on their elemental composition. The methodology successfully separates the samples where four distinct chemical groups were identified. (author)

  4. Modeling the geochemical distribution of rare earth elements (REEs using multivariate statistics in the eastern part of Marvast placer, the Yazd province

    Directory of Open Access Journals (Sweden)

    Amin Hossein Morshedy

    2017-07-01

    is to maximize both the similarity within each cluster and the difference between clusters, and finally find the structure in the data. Nowadays, cluster analysis is applied in many disciplines: biology, botany, medicine, psychology, geography, marketing, image processing, psychiatry, archaeology, etc. (Everitt et al., 2011. To execute a partitioning algorithm, the principal components analysis (PCA algorithm is applied for feature selection, feature extraction and dimension reduction. Hierarchical clustering can be utilized to provide a nested sequence of partitions with bottom-up or top-down methods based on similarity. The single linkage and complete linkage are the most popular hierarchical algorithms (Jain et al., 1999; Ji et al., 2007. Results and discussion The REE chondrite-normalized pattern for the eastern area in the Marvast placer represents a high match to the standard pattern of monazite. This pattern shows the positive anomaly of Ce and the negative anomaly of Eu. To determine the distribution of REEs concentration, 2D interpolation maps were plotted in three groups of light, middle, and heavy REEs (LREE, MREE, and HREE, which were indicated in the geochemical anomaly at the south and south-west of the area. The relative ratios of (LREE/HREE and (Ce/Eu exposed the high proportion of LREEs to HREEs. In the next section, the hierarchical clustering algorithm was employed to partition the data in the feature and sample levels. The elements portioning demonstrated four separated groups, which can be related to atomic and chemical structures. The studied region was divided into four zones by the clustering approach. The fourth zone confine coincided with the REE anomaly area. Finally, PCA was applied as the multivariate statistical tool to this dataset. Hence three principal components modeled over 90% of the variance. For the first component, the distribution map of load factor has a good agreement with anomaly area. References Alipour-Asll, M., Mirnejad

  5. Statistical energy as a tool for binning-free, multivariate goodness-of-fit tests, two-sample comparison and unfolding

    International Nuclear Information System (INIS)

    Aslan, B.; Zech, G.

    2005-01-01

    We introduce the novel concept of statistical energy as a statistical tool. We define statistical energy of statistical distributions in a similar way as for electric charge distributions. Charges of opposite sign are in a state of minimum energy if they are equally distributed. This property is used to check whether two samples belong to the same parent distribution, to define goodness-of-fit tests and to unfold distributions distorted by measurement. The approach is binning-free and especially powerful in multidimensional applications

  6. Using the expected detection delay to assess the performance of different multivariate statistical process monitoring methods for multiplicative and drift faults.

    Science.gov (United States)

    Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Peng, Kaixiang

    2017-03-01

    Using the expected detection delay (EDD) index to measure the performance of multivariate statistical process monitoring (MSPM) methods for constant additive faults have been recently developed. This paper, based on a statistical investigation of the T 2 - and Q-test statistics, extends the EDD index to the multiplicative and drift fault cases. As well, it is used to assess the performance of common MSPM methods that adopt these two test statistics. Based on how to use the measurement space, these methods can be divided into two groups, those which consider the complete measurement space, for example, principal component analysis-based methods, and those which only consider some subspace that reflects changes in key performance indicators, such as partial least squares-based methods. Furthermore, a generic form for them to use T 2 - and Q-test statistics are given. With the extended EDD index, the performance of these methods to detect drift and multiplicative faults is assessed using both numerical simulations and the Tennessee Eastman process. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Transfer of drug dissolution testing by statistical approaches: Case study

    Science.gov (United States)

    AL-Kamarany, Mohammed Amood; EL Karbane, Miloud; Ridouan, Khadija; Alanazi, Fars K.; Hubert, Philippe; Cherrah, Yahia; Bouklouze, Abdelaziz

    2011-01-01

    The analytical transfer is a complete process that consists in transferring an analytical procedure from a sending laboratory to a receiving laboratory. After having experimentally demonstrated that also masters the procedure in order to avoid problems in the future. Method of transfers is now commonplace during the life cycle of analytical method in the pharmaceutical industry. No official guideline exists for a transfer methodology in pharmaceutical analysis and the regulatory word of transfer is more ambiguous than for validation. Therefore, in this study, Gauge repeatability and reproducibility (R&R) studies associated with other multivariate statistics appropriates were successfully applied for the transfer of the dissolution test of diclofenac sodium as a case study from a sending laboratory A (accredited laboratory) to a receiving laboratory B. The HPLC method for the determination of the percent release of diclofenac sodium in solid pharmaceutical forms (one is the discovered product and another generic) was validated using accuracy profile (total error) in the sender laboratory A. The results showed that the receiver laboratory B masters the test dissolution process, using the same HPLC analytical procedure developed in laboratory A. In conclusion, if the sender used the total error to validate its analytical method, dissolution test can be successfully transferred without mastering the analytical method validation by receiving laboratory B and the pharmaceutical analysis method state should be maintained to ensure the same reliable results in the receiving laboratory. PMID:24109204

  8. INNOVATIVE APPROACH TO EDUCATION AND TEACHING OF STATISTICS

    Directory of Open Access Journals (Sweden)

    Andrea Jindrová

    2010-06-01

    Full Text Available Educational and tutorial programs are being developed together, with the changing world of information technology it is a necessary course to adapt to and accept new possibilities and needs. Use of online learning tools can amplify our teaching resources and create new types of learning opportunities that did not exist in the pre-Internet age. The world is full of information, which needs to be constantly updated. Virtualisation of studying materials enables us to update and manage them quickly and easily. As an advantage, we see an asynchronous approach towards learning materials that can be tailored for the students´ needs and adjusted according to their time and availability. The specificness of statistical learning lies in various statistical programs. The high technical demands of these programs require tutorials (instructional presentations, which can help students to learn how to use them efficiently. Instructional presentation may be understood as a demonstration of how the statistical software program works. This is one of the options that students may use to simplify the utilization of control and navigation through the statistical system. Thanks to instructional presentations, students will be able to transfer their theoretical statistical knowledge into practical situation and real life and, therefore, improve their personal development process. The goal of this tutorial is to show an innovative approach for learning of statistics in the Czech University of Life Sciences. The use of presentations and their benefits for students was evaluated according to results obtained from a questionnaire survey completed by students of the 4th grade of the Faculty of Economics and Management. The aim of this pilot survey was to evaluate the benefits of these instructional presentations, and the students interest in using them. The information obtained was used as essential data for the evaluation of the efficiency of this new approach. Firstly

  9. Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques

    International Nuclear Information System (INIS)

    El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

    2017-01-01

    Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock–water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. - Highlights: • Hydrochemical investigations were carried out in Dhurma aquifer in Saudi Arabia. • The factors controlling potential groundwater pollution in an arid region were studied. • Chemical and statistical analyses are integrated to assess these factors. • Five main factors were extracted, which explain >77% of the total data variance. • The chemical characteristics of the groundwater were influenced by rock–water interactions

  10. Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

    Science.gov (United States)

    Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

    2017-02-01

    Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. A statistical approach to optimizing concrete mixture design.

    Science.gov (United States)

    Ahmad, Shamsad; Alghamdi, Saeid A

    2014-01-01

    A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (3(3)). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m(3)), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

  12. A Statistical Approach to Optimizing Concrete Mixture Design

    Directory of Open Access Journals (Sweden)

    Shamsad Ahmad

    2014-01-01

    Full Text Available A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33. A total of 27 concrete mixtures with three replicates (81 specimens were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48, cementitious materials content (350, 375, and 400 kg/m3, and fine/total aggregate ratio (0.35, 0.40, and 0.45. The experimental data were utilized to carry out analysis of variance (ANOVA and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

  13. Statistical approaches in published ophthalmic clinical science papers: a comparison to statistical practice two decades ago.

    Science.gov (United States)

    Zhang, Harrison G; Ying, Gui-Shuang

    2018-02-09

    The aim of this study is to evaluate the current practice of statistical analysis of eye data in clinical science papers published in British Journal of Ophthalmology ( BJO ) and to determine whether the practice of statistical analysis has improved in the past two decades. All clinical science papers (n=125) published in BJO in January-June 2017 were reviewed for their statistical analysis approaches for analysing primary ocular measure. We compared our findings to the results from a previous paper that reviewed BJO papers in 1995. Of 112 papers eligible for analysis, half of the studies analysed the data at an individual level because of the nature of observation, 16 (14%) studies analysed data from one eye only, 36 (32%) studies analysed data from both eyes at ocular level, one study (1%) analysed the overall summary of ocular finding per individual and three (3%) studies used the paired comparison. Among studies with data available from both eyes, 50 (89%) of 56 papers in 2017 did not analyse data from both eyes or ignored the intereye correlation, as compared with in 60 (90%) of 67 papers in 1995 (P=0.96). Among studies that analysed data from both eyes at an ocular level, 33 (92%) of 36 studies completely ignored the intereye correlation in 2017, as compared with in 16 (89%) of 18 studies in 1995 (P=0.40). A majority of studies did not analyse the data properly when data from both eyes were available. The practice of statistical analysis did not improve in the past two decades. Collaborative efforts should be made in the vision research community to improve the practice of statistical analysis for ocular data. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  14. A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring.

    Science.gov (United States)

    Möltgen, C-V; Herdling, T; Reich, G

    2013-11-01

    This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

    Science.gov (United States)

    Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

    2013-01-01

    This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

  16. Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

    OpenAIRE

    González Martínez, Jose María; Ferrer Riquelme, Alberto José; Westerhuis, Johan A.

    2011-01-01

    This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)—or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al.'s approach [1] will be introduced to ach...

  17. Statistical Approaches to Assess Biosimilarity from Analytical Data.

    Science.gov (United States)

    Burdick, Richard; Coffey, Todd; Gutka, Hiten; Gratzl, Gyöngyi; Conlon, Hugh D; Huang, Chi-Ting; Boyne, Michael; Kuehne, Henriette

    2017-01-01

    Protein therapeutics have unique critical quality attributes (CQAs) that define their purity, potency, and safety. The analytical methods used to assess CQAs must be able to distinguish clinically meaningful differences in comparator products, and the most important CQAs should be evaluated with the most statistical rigor. High-risk CQA measurements assess the most important attributes that directly impact the clinical mechanism of action or have known implications for safety, while the moderate- to low-risk characteristics may have a lower direct impact and thereby may have a broader range to establish similarity. Statistical equivalence testing is applied for high-risk CQA measurements to establish the degree of similarity (e.g., highly similar fingerprint, highly similar, or similar) of selected attributes. Notably, some high-risk CQAs (e.g., primary sequence or disulfide bonding) are qualitative (e.g., the same as the originator or not the same) and therefore not amenable to equivalence testing. For biosimilars, an important step is the acquisition of a sufficient number of unique originator drug product lots to measure the variability in the originator drug manufacturing process and provide sufficient statistical power for the analytical data comparisons. Together, these analytical evaluations, along with PK/PD and safety data (immunogenicity), provide the data necessary to determine if the totality of the evidence warrants a designation of biosimilarity and subsequent licensure for marketing in the USA. In this paper, a case study approach is used to provide examples of analytical similarity exercises and the appropriateness of statistical approaches for the example data.

  18. Data mining using multivariate statistical analysis: The case of heavy metals in sediments of the Msimbazi Creek mangrove wetland

    Directory of Open Access Journals (Sweden)

    A. Mrutu

    2013-12-01

    Full Text Available Mangrove wetlands are important biological systems that usually filter out organic and inorganic contaminants from the wastewaters before entering the ocean. Our previous work showed that sediments of the Msimbazi Creek wetland are contaminated with heavy metals and the amounts decreased with increasing depth. However, the hidden relationships between the heavy metals and clay particles were not fully understood based on the numerical data. Therefore this work used the data from literature and the Statistical Package for Social Sciences (SPSS software to study how significant the relationships are and predict the sources of heavy metals and clays. The results showed that Cd is the only metal that showed insignificant correlations with other heavy metals (with Pb and Zn while the rest of heavy metals exhibited significant positive correlation (except Pb vs. Ni. Cluster analysis classified the heavy metals based on the concentration and the first 50 cm cores (0-50 cm had higher heavy metals and % clay than the second 50 cm cores (51-100 cm. The results from the factor analysis suggests that Pb, Cd, Ni, and clay owe their source mostly from anthropogenic activities while Fe, Co, Cr, Zn and sand come from both anthropogenic and natural sources. These results support our previous suggestions that heavy metals and clays found in this wetland have mostly anthropogenic origin. However, we recommend isotopic tracing studies in order to accurately identify the origins of the heavy metals and clays in sediments of Msimbazi Creek mangrove wetland.

  19. Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

    Science.gov (United States)

    El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

    2017-10-01

    Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock-water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Structures and algorithms for post-processing large data sets and multi-variate functions in spatio-temporal statistics

    KAUST Repository

    Litvinenko, Alexander

    2017-12-10

    Matrices began in the 2nd century BC with the Chinese. One can find traces, which go to the 4th century BC to the Babylonians. The text ``Nine Chapters of the Mathematical Art\\'\\' written during the Han Dynasty in China gave the first known example of matrix methods. They were used to solve simultaneous linear equations (more in http://math.nie.edu.sg/bwjyeo/it/MathsOnline_AM/livemath/the/IT3AMMatricesHistory.html). The first ideas of the maximum likelihood estimation (MLE) was introduces by Laplace (1749-1827), by Gauss (1777-1855), the Likelihood was defined by Thiele Thorvald (1838-1910). Why we still use matrices? The matrix data format is more than 2200 years old. Our world is multi-dimensional! Why not to introduce a more appropriate data format and why not to reformulate the MLE method for it? In this work we are utilizing the low-rank tensor formats for multi-dimansional functions, which appear in spatial statistics.

  1. Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach

    DEFF Research Database (Denmark)

    Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale

    2011-01-01

    deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... showed that it was possible to differentiate between the different batches depending on their history and that some batches were more oxidised than others. Furthermore, based on the results from the data analysis, critical control points in the entire production chain will be identified and strategies...

  2. Extracting climate signals from large hydrological data cubes using multivariate statistics - an example for the Mediterranean basin

    Science.gov (United States)

    Kauer, Agnes; Dorigo, Wouter; Bauer-Marschallinger, Bernhard

    2017-04-01

    Global warming is expected to change ocean-atmosphere oscillation patterns, e.g. the El Nino Southern Oscillation, and may thus have a substantial impact on water resources over land. Yet, the link between climate oscillations and terrestrial hydrology has large uncertainties. In particular, the climate in the Mediterranean basin is expected to be sensitive to global warming as it may increase insufficient and irregular water supply and lead to more frequent and intense droughts and heavy precipitation events. The ever increasing need for water in tourism and agriculture reinforce the problem. Therefore, the monitoring and better understanding of the hydrological cycle are crucial for this area. This study seeks to quantify the effect of regional climate modes, e.g. the Northern Atlantic Oscillation (NAO) on the hydrological cycle in the Mediterranean. We apply Empirical Orthogonal Functions (EOF) to a wide range of hydrological datasets to extract the major modes of variation over the study period. We use more than ten datasets describing precipitation, soil moisture, evapotranspiration, and changes in water mass with study periods ranging from one to three decades depending on the dataset. The resulting EOFs are then examined for correlations with regional climate modes using Spearman rank correlation analysis. This is done for the entire time span of the EOFs and for monthly and seasonally sampled data. We find relationships between the hydrological datasets and the climate modes NAO, Arctic Oscillation (AO), Eastern Atlantic (EA), and Tropical Northern Atlantic (TNA). Analyses of monthly and seasonally sampled data reveal high correlations especially in the winter months. However, the spatial extent of the data cube considered for the analyses have a large impact on the results. Our statistical analyses suggest an impact of regional climate modes on the hydrological cycle in the Mediterranean area and may provide valuable input for evaluating process

  3. Metal contamination in urban, suburban, and country park soils of Hong Kong: A study based on GIS and multivariate statistics

    International Nuclear Information System (INIS)

    Lee, Celine Siu-lan; Li Xiangdong; Shi Wenzhong; Cheung, Sharon Ching-nga; Thornton, Iain

    2006-01-01

    The urban environment quality is of vital importance as the majority of people now live in cities. Due to the continuous urbanisation and industrialisation in many parts of the world, metals are continuously emitted into the terrestrial environment and pose a great threat on human health. An extensive survey was conducted in the highly urbanised and commercialised Hong Kong Island area (80.3 km 2 ) of Hong Kong using a systematic sampling strategy of five soil samples per km 2 in urban areas and two samples per km 2 in the suburban and country park sites (0-15 cm). The analytical results indicated that the surface soils in urban and suburban areas are enriched with metals, such as Cu, Pb, and Zn. The Pb concentration in the urban soils was found to exceed the Dutch target value. The statistical analyses using principal component analysis (PCA) and cluster analysis (CA) showed distinctly different associations among trace metals and the major elements (Al, Ca, Fe, Mg, Mn) in the urban, suburban, and country park soils. Soil pollution maps of trace metals (Cd, Co, Cr, Cu, Ni, Pb, and Zn) in the surface soils were produced based on geographical information system (GIS) technology. The hot-spot areas of metal contamination were mainly concentrated in the northern and western parts of Hong Kong Island, and closely related to high traffic conditions. The Pb isotopic composition of the urban, suburban, and country park soils showed that vehicular emissions were the major anthropogenic sources for Pb. The 206 Pb/ 207 Pb and 208 Pb/ 207 Pb ratios in soils decreased as Pb concentrations increased in a polynomial line (degree = 2)

  4. Statistical distance and the approach to KNO scaling

    International Nuclear Information System (INIS)

    Diosi, L.; Hegyi, S.; Krasznovszky, S.

    1990-05-01

    A new method is proposed for characterizing the approach to KNO scaling. The essence of our method lies in the concept of statistical distance between nearby KNO distributions which reflects their distinguishability in spite of multiplicity fluctuations. It is shown that the geometry induced by the distance function defines a natural metric on the parameter space of a certain family of KNO distributions. Some examples are given in which the energy dependences of distinguishability of neighbouring KNO distributions are compared in nondiffractive hadron-hadron collisions and electron-positron annihilation. (author) 19 refs.; 4 figs

  5. Statistical approach of weakly nonlinear ablative Rayleigh-Taylor instability

    International Nuclear Information System (INIS)

    Garnier, J.; Masse, L.

    2005-01-01

    A weakly nonlinear model is proposed for the Rayleigh-Taylor instability in presence of ablation and thermal transport. The nonlinear effects for a single-mode disturbance are computed, included the nonlinear correction to the exponential growth of the fundamental modulation. Mode coupling in the spectrum of a multimode disturbance is thoroughly analyzed by a statistical approach. The exponential growth of the linear regime is shown to be reduced by the nonlinear mode coupling. The saturation amplitude is around 0.1λ for long wavelengths, but higher for short instable wavelengths in the ablative regime

  6. From inverse problems to learning: a Statistical Mechanics approach

    Science.gov (United States)

    Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo

    2018-01-01

    We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.

  7. Statistical approach to LHCD modeling using the wave kinetic equation

    International Nuclear Information System (INIS)

    Kupfer, K.; Moreau, D.; Litaudon, X.

    1993-04-01

    Recent work has shown that for parameter regimes typical of many present day current drive experiments, the orbits of the launched LH rays are chaotic (in the Hamiltonian sense), so that wave energy diffuses through the stochastic layer and fills the spectral gap. We have analyzed this problem using a statistical approach, by solving the wave kinetic equation for the coarse-grained spectral energy density. An interesting result is that the LH absorption profile is essentially independent of both the total injected power and the level of wave stochastic diffusion

  8. Statistical margin to DNB safety analysis approach for LOFT

    International Nuclear Information System (INIS)

    Atkinson, S.A.

    1982-01-01

    A method was developed and used for LOFT thermal safety analysis to estimate the statistical margin to DNB for the hot rod, and to base safety analysis on desired DNB probability limits. This method is an advanced approach using response surface analysis methods, a very efficient experimental design, and a 2nd-order response surface equation with a 2nd-order error propagation analysis to define the MDNBR probability density function. Calculations for limiting transients were used in the response surface analysis thereby including transient interactions and trip uncertainties in the MDNBR probability density

  9. The Precautionary Principle and statistical approaches to uncertainty

    DEFF Research Database (Denmark)

    Keiding, Niels; Budtz-Jørgensen, Esben

    2004-01-01

    is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures...... for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model...

  10. Spatial zonation of zooplankton in the northwestern Arabian Sea: A multivariate approach

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.

    Latitudinal variation in abundance, diversity, dominance pattern and zonation of the major groups of zooplankton was studied n the coastal waters of northwestern Arabian Sea, between 25°44' N and 10°44' N. Maxwell Boltzmann Statistic...

  11. Using Statistical Multivariable Models to Understand the Relationship Between Interplanetary Coronal Mass Ejecta and Magnetic Flux Ropes

    Science.gov (United States)

    Riley, P.; Richardson, I. G.

    2012-01-01

    In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and

  12. A Statistical Approach to Exoplanetary Molecular Spectroscopy Using Spitzer Eclipses

    Science.gov (United States)

    Deming, Drake; Garhart, Emily; Burrows, Adam; Fortney, Jonathan; Knutson, Heather; Todorov, Kamen

    2018-01-01

    Secondary eclipses of exoplanets observed using the Spitzer Space Telescope measure the total emission emergent from exoplanetary atmospheres integrated over broad photometric bands. Spitzer photometry is excellent for measuring day side temperatures, but is less well suited to the detection of molecular absorption or emission features. Even for very hot exoplanets, it can be difficult to attain the accuracy on eclipse depth that is needed to unambiguously interpret the Spitzer results in terms of molecular absorption or emission. However, a statistical approach, wherein we seek deviations from a simple blackbody planet as a function of the planet's equilibrium temperature, shows promise for defining the nature and strength of molecular absorption in ensembles of planets. In this paper, we explore such an approach using secondary eclipses observed for tens of hot exoplanets during Spitzer's Cycles 10, 12, and 13. We focus on the possibility that the hottest planets exhibit molecular features in emission, due to temperature inversions.

  13. New advances in the statistical parton distributions approach*

    Directory of Open Access Journals (Sweden)

    Soffer Jacques

    2016-01-01

    Full Text Available The quantum statistical parton distributions approach proposed more than one decade ago is revisited by considering a larger set of recent and accurate Deep Inelastic Scattering experimental results. It enables us to improve the description of the data by means of a new determination of the parton distributions. This global next-to-leading order QCD analysis leads to a good description of several structure functions, involving unpolarized parton distributions and helicity distributions, in terms of a rather small number of free parameters. There are many serious challenging issues. The predictions of this theoretical approach will be tested for single-jet production and charge asymmetry in W± production in p̄p and pp collisions up to LHC energies, using recent data and also for forthcoming experimental results.

  14. Learning the Language of Statistics: Challenges and Teaching Approaches

    Science.gov (United States)

    Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine

    2016-01-01

    Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…

  15. Assessment of Groundwater Quality of Udayagiri area, Nellore District, Andhra Pradesh, South India Using Multivariate Statistical Techniques

    Directory of Open Access Journals (Sweden)

    Arveti Nagaraju

    2016-10-01

    Full Text Available Hydrogeochemical studies were carried out in and around Udayagiri area of Andhra Pradesh in order to assess the chemistry of the groundwater and to identify the dominant hydrogeochemical processes and mechanisms responsible for the evolution of the chemical composition of the groundwater. Descriptive statistics, correlation matrices, principal component analysis (PCA, together with cluster analysis (CA were used to gain an understanding of the hydrogeochemical processes in the study area. PCA has identified 4 main processes influencing the groundwater chemistry viz., mineral precipitation and dissolution, seawater intrusion, cation exchange, and carbonate balance. Further, three clusters C1, C2 and C3 were obtained. Samples from C1 contain high level of Cl− and may be due to the intensive evaporation and contamination from landfill leachate. Most of the samples from C2 are located closer to the sea and the high level of Na+ +K+ in these samples may be attributed to seawater intrusion. The geochemistry of water samples in C3 are more likely to originate from rock weathering. This has been supported by Gibbs diagram. The groundwater geochemistry in the study area is mostly of natural origin, but is influenced to some degree by human activity.    Evaluación de la calidad del agua subterránea a través de técnicas estadísticas multivariadas en el área Udayagiri, distrito Nellore, Andhra Pradesh, en el sur de India Resumen Se realizaron estudios hidrogeoquímicos en y alrededor del área Udayagiri de Andhra Pradesh para evaluar la química del agua subterránea e identificar los procesos hidrogeoquímicos dominantes y los mecanismos responsables de la evolución en la composición química del agua subterránea. Se utilizaron estadísticas descriptivas, matrices de correlación, análisis de componentes principales, al igual que análisis de grupos, para obtener y entender los procesos hidrogeoquímicos en el área de estudio. Los an

  16. Search for Heavy Stable Charged Particles at $\\sqrt{s}$ = 13 TeV Utilizing a Multivariate Approach

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00375809

    Heavy stable charged particles (HSCPs) have been searched for at the Large Hadron Collider since its initial data taking in 2010. The search for heavy stable charged particles provide a means of directly probing the new physics realm, as they produce a detector signature unlike any particle discovered to date. The goal of this research is to investigate an idea that was introduced in the later stages of 2010-2012 data taking period. Rather than utilizing the current tight selection on the calculated particle mass the hypothesis is that by incorporating a multivariate approach, specif- ically an artificial neural network, the remaining selection criteria could be loosened allowing for a greater signal acceptance while maintaining acceptable background rejection via the multivariate discriminator from the artificial neural network. The increase in signal acceptance and retention or increase in background rejection increases the discovery potential for HSCPs and as a secondary objective calculates improved limit...

  17. Multivariate analysis with LISREL

    CERN Document Server

    Jöreskog, Karl G; Y Wallentin, Fan

    2016-01-01

    This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

  18. A New Approach to Monte Carlo Simulations in Statistical Physics

    Science.gov (United States)

    Landau, David P.

    2002-08-01

    Monte Carlo simulations [1] have become a powerful tool for the study of diverse problems in statistical/condensed matter physics. Standard methods sample the probability distribution for the states of the system, most often in the canonical ensemble, and over the past several decades enormous improvements have been made in performance. Nonetheless, difficulties arise near phase transitions-due to critical slowing down near 2nd order transitions and to metastability near 1st order transitions, and these complications limit the applicability of the method. We shall describe a new Monte Carlo approach [2] that uses a random walk in energy space to determine the density of states directly. Once the density of states is known, all thermodynamic properties can be calculated. This approach can be extended to multi-dimensional parameter spaces and should be effective for systems with complex energy landscapes, e.g., spin glasses, protein folding models, etc. Generalizations should produce a broadly applicable optimization tool. 1. A Guide to Monte Carlo Simulations in Statistical Physics, D. P. Landau and K. Binder (Cambridge U. Press, Cambridge, 2000). 2. Fugao Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001); Phys. Rev. E64, 056101-1 (2001).

  19. Statistical physics approach to earthquake occurrence and forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Arcangelis, Lucilla de [Department of Industrial and Information Engineering, Second University of Naples, Aversa (CE) (Italy); Godano, Cataldo [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy); Grasso, Jean Robert [ISTerre, IRD-CNRS-OSUG, University of Grenoble, Saint Martin d’Héres (France); Lippiello, Eugenio, E-mail: eugenio.lippiello@unina2.it [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy)

    2016-04-25

    There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we first present a description of the phenomenological laws of earthquake occurrence which represent the frame of reference for a variety of statistical mechanical models, ranging from the spring-block to more complex fault models. Next, we discuss the problem of seismic forecasting in the general framework of stochastic processes, where seismic occurrence can be described as a branching process implementing space–time-energy correlations between earthquakes. In this context we show how correlations originate from dynamical scaling relations between time and energy, able to account for universality and provide a unifying description for the phenomenological power laws. Then we discuss how branching models can be implemented to forecast the temporal evolution of the earthquake occurrence probability and allow to discriminate among different physical mechanisms responsible for earthquake triggering. In particular, the forecasting problem will be presented in a rigorous mathematical framework, discussing the relevance of the processes acting at different temporal scales for

  20. CAUSAL RELATIONSHIP BETWEEN FOSSIL FUEL CONSUMPTION AND ECONOMIC GROWTH IN JAPAN: A MULTIVARIATE APPROACH

    Directory of Open Access Journals (Sweden)

    Hazuki Ishida

    2013-01-01

    Full Text Available This paper explores whether Japanese economy can continue to grow without extensive dependence on fossil fuels. The paper conducts time series analysis using a multivariate model of fossil fuels, non-fossil energy, labor, stock and GDP to investigate the relationship between fossil fuel consumption and economic growth in Japan. The results of cointegration tests indicate long-run relationships among the variables. Using a vector error-correction model, the study reveals bidirectional causality between fossil fuels and GDP. The results also show that there is no causal relationship between non-fossil energy and GDP. The results of cointegration analysis, Granger causality tests, and variance decomposition analysis imply that non-fossil energy may not necessarily be able to play the role of fossil fuels. Japan cannot seem to realize both continuous economic growth and the departure from dependence on fossil fuels. Hence, growth-oriented macroeconomic policies should be re-examined.

  1. Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis.

    Science.gov (United States)

    Whist, A C; Liland, K H; Jonsson, M E; Sæbø, S; Sviland, S; Østerås, O; Norström, M; Hopp, P

    2014-11-01

    Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T(2) statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds

  2. Behavioral event occurrence differs between behavioral states in Sotalia guianensis (Cetarctiodactyla: Delphinidae dolphins: a multivariate approach

    Directory of Open Access Journals (Sweden)

    Rodrigo H. Tardin

    2014-02-01

    Full Text Available Difficulties in quantifying behavioral events can cause loss of information about cetacean behavior, especially behaviors whose functions are still debated. The lack of knowledge is greater for South American species such as Sotalia guianensis (Van Benédén, 1864. Our objective was to contextualize the behavioral events inside behavioral states using a Permutational Multivariate Analysis of Variance (MANOVA. Three events occurred in the Feeding, Socio-Sexual and Travelling states (Porpoising, Side flop, Tail out dive, and five events occurred in the Feeding and Travelling states (Back flop, Horizontal jump, Lobtail, Spy-hop, Partial flop ahead. Three events (Belly exposure, Club, and Heading occurred exclusively in the Socio-sexual state. Partial Back flop and Head flop occurred exclusively in the Feeding state. For the events that occurred in multiple states, we observed that some events occurred more frequently in one of the states (p < 0.001, such as Lobtail, Tail out dive horizontal Jump, Partial flop ahead and Side flop. Our multivariate analysis, which separated Socio-sexual behavior from Feeding and Travelling, showed that the abundance of behavioral events differs between states. This differentiation indicates that some events are associated with specific behavioral states. Almost 40% of the events observed were exclusively performed in one state, which indicates a high specialization for some events. Proper discrimination and contextualization of behavioral events may be efficient tools to better understand dolphin behaviors. Similar studies in other habitats and with other species, will help build a broader scenario to aid our understanding of the functions of dolphin behavioral events.

  3. Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

    Science.gov (United States)

    Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

    2017-05-01

    The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for

  4. Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

    Science.gov (United States)

    Velasco-Tapia, Fernando

    2014-01-01

    Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

  5. Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt

    Directory of Open Access Journals (Sweden)

    Fernando Velasco-Tapia

    2014-01-01

    Full Text Available Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC volcanic range (Mexican Volcanic Belt. In this locality, the volcanic activity (3.7 to 0.5 Ma was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward’s linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas in the comingled lavas (binary mixtures.

  6. Inferring the origin of rare fruit distillates from compositional data using multivariate statistical analyses and the identification of new flavour constituents.

    Science.gov (United States)

    Mihajilov-Krstev, Tatjana M; Denić, Marija S; Zlatković, Bojan K; Stankov-Jovanović, Vesna P; Mitić, Violeta D; Stojanović, Gordana S; Radulović, Niko S

    2015-04-01

    In Serbia, delicatessen fruit alcoholic drinks are produced from autochthonous fruit-bearing species such as cornelian cherry, blackberry, elderberry, wild strawberry, European wild apple, European blueberry and blackthorn fruits. There are no chemical data on many of these and herein we analysed volatile minor constituents of these rare fruit distillates. Our second goal was to determine possible chemical markers of these distillates through a statistical/multivariate treatment of the herein obtained and previously reported data. Detailed chemical analyses revealed a complex volatile profile of all studied fruit distillates with 371 identified compounds. A number of constituents were recognised as marker compounds for a particular distillate. Moreover, 33 of them represent newly detected flavour constituents in alcoholic beverages or, in general, in foodstuffs. With the aid of multivariate analyses, these volatile profiles were successfully exploited to infer the origin of raw materials used in the production of these spirits. It was also shown that all fruit distillates possessed weak antimicrobial properties. It seems that the aroma of these highly esteemed wild-fruit spirits depends on the subtle balance of various minor volatile compounds, whereby some of them are specific to a certain type of fruit distillate and enable their mutual distinction. © 2014 Society of Chemical Industry.

  7. A utilização da avaliação tipo "teste" on-line como apoio ao ensino presencial: uma abordagem quantitativa sobre a sua contribuição no ensino de ferramentas estatística multivariadas Use of on-line evaluation type "test" as support for presential teaching: a quantitative approach on its contribution to the teaching of multivariate statistics tools

    Directory of Open Access Journals (Sweden)

    Erica Ferreira Marques

    2011-07-01

    ância do uso dessa ferramenta de avaliação como apoio ao ensino presencial e a sua contribuição para o processo de ensino-aprendizagem.This work aims to show the important role an online assessment test can play as a tool developed to support the presential teaching of multivariate statistical resources to Business Management undergraduate students at FEARP/USP enrolled in Applied Statistics to Business Management II. This study is part of a project named LaVie, a virtual environment of teaching-learning of Statistics, applied and developed to support presential teaching in this field. Based on the importance of an assessment tool, LaVie created content, interaction and "test your knowledge" tools. This assessment tool was developed based on online quizzes having three adaptation levels: basic (I, intermediate (II, and advanced (III to each module of the discipline. The methodology used for checking the efficiency of this online test tool was based on a quantitative assessment according to the students' (users' opinions. Four assumptions were investigated in this study. Data were collected in two distinct occasions: second semester of 2005, as a pilot project, and second semester of 2006, thus enabling a comparative analysis of the system by the users. This survey was conducted in class where students completed two questionnaires, one before the presential assessment and the other immediately after it. The study shows the importance of this tool as a support to presential teaching and its contribution to the teaching-learning process.

  8. Discrimination of source reactor type by multivariate statistical analysis of uranium and plutonium isotopic concentrations in unknown irradiated nuclear fuel material.

    Science.gov (United States)

    Robel, Martin; Kristo, Michael J

    2008-11-01

    The problem of identifying the provenance of unknown nuclear material in the environment by multivariate statistical analysis of its uranium and/or plutonium isotopic composition is considered. Such material can be introduced into the environment as a result of nuclear accidents, inadvertent processing losses, illegal dumping of waste, or deliberate trafficking in nuclear materials. Various combinations of reactor type and fuel composition were analyzed using Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLSDA) of the concentrations of nine U and Pu isotopes in fuel as a function of burnup. Real-world variation in the concentrations of (234)U and (236)U in the fresh (unirradiated) fuel was incorporated. The U and Pu were also analyzed separately, with results that suggest that, even after reprocessing or environmental fractionation, Pu isotopes can be used to determine both the source reactor type and the initial fuel composition with good discrimination.

  9. Urban pavement surface temperature. Comparison of numerical and statistical approach

    Science.gov (United States)

    Marchetti, Mario; Khalifa, Abderrahmen; Bues, Michel; Bouilloud, Ludovic; Martin, Eric; Chancibaut, Katia

    2015-04-01

    The forecast of pavement surface temperature is very specific in the context of urban winter maintenance. to manage snow plowing and salting of roads. Such forecast mainly relies on numerical models based on a description of the energy balance between the atmosphere, the buildings and the pavement, with a canyon configuration. Nevertheless, there is a specific need in the physical description and the numerical implementation of the traffic in the energy flux balance. This traffic was originally considered as a constant. Many changes were performed in a numerical model to describe as accurately as possible the traffic effects on this urban energy balance, such as tires friction, pavement-air exchange coefficient, and infrared flux neat balance. Some experiments based on infrared thermography and radiometry were then conducted to quantify the effect fo traffic on urban pavement surface. Based on meteorological data, corresponding pavement temperature forecast were calculated and were compared with fiels measurements. Results indicated a good agreement between the forecast from the numerical model based on this energy balance approach. A complementary forecast approach based on principal component analysis (PCA) and partial least-square regression (PLS) was also developed, with data from thermal mapping usng infrared radiometry. The forecast of pavement surface temperature with air temperature was obtained in the specific case of urban configurtation, and considering traffic into measurements used for the statistical analysis. A comparison between results from the numerical model based on energy balance, and PCA/PLS was then conducted, indicating the advantages and limits of each approach.

  10. A Multidisciplinary Approach for Teaching Statistics and Probability

    Science.gov (United States)

    Rao, C. Radhakrishna

    1971-01-01

    The author presents a syllabus for an introductory (first year after high school) course in statistics and probability and some methods of teaching statistical techniques. The description comes basically from the procedures used at the Indian Statistical Institute, Calcutta. (JG)

  11. Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

    Science.gov (United States)

    Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

    2015-05-01

    The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

  12. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

    Science.gov (United States)

    Matiatos, Ioannis

    2016-01-15

    Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

  13. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece)

    International Nuclear Information System (INIS)

    Matiatos, Ioannis

    2016-01-01

    Nitrate (NO_3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ"1"5N–NO_3 and δ"1"8O–NO_3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO_3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC). - Highlights: • More enriched N-isotope values were observed in the industrial/urban areas. • A Bayesian isotope mixing model was applied in a multiple land-use area. • A 3-component model explained the factors controlling nitrate content in groundwater. • Industrial/urban nitrogen source was

  14. BioIMAX: A Web 2.0 approach for easy exploratory and collaborative access to multivariate bioimage data

    Directory of Open Access Journals (Sweden)

    Khan Michael

    2011-07-01

    Full Text Available Abstract Background Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places. Results In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application. Conclusions BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec.uni-bielefeld.de/BioIMAX with the username "test" and the password "test1" for testing purposes.

  15. Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

    Science.gov (United States)

    Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

    2018-02-09

    The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

  16. A multivariate approach to oil hydrocarbon fingerprinting and spill source identification

    DEFF Research Database (Denmark)

    Christensen, Jan H.; Tomasi, Giorgio

    2016-01-01

    statistical methods, as well as data evaluation and visualization tools have been tested. IMOF is exemplified using parallel factor analysis of fluorescence excitation-emission spectra, and pixel-based analysis of gas chromatography - mass spectrometry selected ion chromatograms (GC-MS SICs). Its application...... to other data types such as GC-flame ionization detection, liquid chromatography-MS, and two-dimensional GC and LC are briefly discussed....

  17. Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery

    Directory of Open Access Journals (Sweden)

    Fan Liang

    2013-02-01

    Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.

  18. Assessing heavy metal sources in sugarcane Brazilian soils: an approach using multivariate analysis.

    Science.gov (United States)

    da Silva, Fernando Bruno Vieira; do Nascimento, Clístenes Williams Araújo; Araújo, Paula Renata Muniz; da Silva, Luiz Henrique Vieira; da Silva, Roberto Felipe

    2016-08-01

    Brazil is the world's largest sugarcane producer and soils in the northeastern part of the country have been cultivated with the crop for over 450 years. However, so far, there has been no study on the status of heavy metal accumulation in these long-history cultivated soils. To fill the gap, we collect soil samples from 60 sugarcane fields in order to determine the contents of Cd, Cr, Cu, Ni, Pb, and Zn. We used multivariate analysis to distinguish between natural and anthropogenic sources of these metals in soils. Analytical determinations were performed in ICP-OES after microwave acid solution digestion. Mean concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 1.9, 18.8, 6.4, 4.9, 11.2, and 16.2 mg kg(-1), respectively. The principal component one was associated with lithogenic origin and comprised the metals Cr, Cu, Ni, and Zn. Cluster analysis confirmed that 68 % of the evaluated sites have soil heavy metal concentrations close to the natural background. The Cd concentration (principal component two) was clearly associated with anthropogenic sources with P fertilization being the most likely source of Cd to soils. On the other hand, the third component (Pb concentration) indicates a mixed origin for this metal (natural and anthropogenic); hence, Pb concentrations are probably related not only to the soil parent material but also to industrial emissions and urbanization in the vicinity of the agricultural areas.

  19. Statistical approach for uncertainty quantification of experimental modal model parameters

    DEFF Research Database (Denmark)

    Luczak, M.; Peeters, B.; Kahsin, M.

    2014-01-01

    Composite materials are widely used in manufacture of aerospace and wind energy structural components. These load carrying structures are subjected to dynamic time-varying loading conditions. Robust structural dynamics identification procedure impose tight constraints on the quality of modal models...... represent different complexity levels ranging from coupon, through sub-component up to fully assembled aerospace and wind energy structural components made of composite materials. The proposed method is demonstrated on two application cases of a small and large wind turbine blade........ This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...

  20. A Multivariant Stream Analysis Approach to Detect and Mitigate DDoS Attacks in Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Raenu Kolandaisamy

    2018-01-01

    Full Text Available Vehicular Ad Hoc Networks (VANETs are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.

  1. Flow Equation Approach to the Statistics of Nonlinear Dynamical Systems

    Science.gov (United States)

    Marston, J. B.; Hastings, M. B.

    2005-03-01

    The probability distribution function of non-linear dynamical systems is governed by a linear framework that resembles quantum many-body theory, in which stochastic forcing and/or averaging over initial conditions play the role of non-zero . Besides the well-known Fokker-Planck approach, there is a related Hopf functional methodootnotetextUriel Frisch, Turbulence: The Legacy of A. N. Kolmogorov (Cambridge University Press, 1995) chapter 9.5.; in both formalisms, zero modes of linear operators describe the stationary non-equilibrium statistics. To access the statistics, we investigate the method of continuous unitary transformationsootnotetextS. D. Glazek and K. G. Wilson, Phys. Rev. D 48, 5863 (1993); Phys. Rev. D 49, 4214 (1994). (also known as the flow equation approachootnotetextF. Wegner, Ann. Phys. 3, 77 (1994).), suitably generalized to the diagonalization of non-Hermitian matrices. Comparison to the more traditional cumulant expansion method is illustrated with low-dimensional attractors. The treatment of high-dimensional dynamical systems is also discussed.

  2. MASKED AREAS IN SHEAR PEAK STATISTICS: A FORWARD MODELING APPROACH

    International Nuclear Information System (INIS)

    Bard, D.; Kratochvil, J. M.; Dawson, W.

    2016-01-01

    The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology in forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc., which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impacts of masking and other survey artifacts are accounted for in the theoretical prediction of cosmological parameters, rather than correcting survey data to remove them. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ≈1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and evaluate how much small survey areas are impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance

  3. Physiological and biochemical responses to severe drought stress of nine Eucalyptus globulus clones: a multivariate approach.

    Science.gov (United States)

    Granda, Víctor; Delatorre, Carolina; Cuesta, Candela; Centeno, María L; Fernández, Belén; Rodríguez, Ana; Feito, Isabel

    2014-07-01

    Seasonal drought, typical of temperate and Mediterranean environments, creates problems in establishing plantations and affects development and yield, and it has been widely studied in numerous species. Forestry fast-growing species such as Eucalyptus spp. are an important resource in such environments, selected clones being generally used for production purposes in plantations in these areas. However, use of mono-specific plantations increases risk of plant loss due to abiotic stresses, making it essential to understand differences in an individual clone's physiological responses to drought stress. In order to study clonal differences in drought responses, nine Eucalyptus globulus (Labill.) clones (C14, C46, C97, C120, C222, C371, C405, C491 and C601) were gradually subjected to severe drought stress (<14% of field capacity). A total of 31 parameters, physiological (e.g., photosynthesis, gas exchange), biochemical (e.g., chlorophyll content) and hormonal (abscisic acid [ABA] content), were analysed by classic and multivariate techniques. Relationships between parameters were established, allowing related measurements to be grouped into functional units (pigment, growth, water and ABA). Differences in these units showed that there were two distinct groups of E. globulus clones on the basis of their different strategies when faced with drought stress. The C14 group (C14, C120, C405, C491 and C601) clones behave as water savers, maintaining high water content and showing high stomatal adjustment, and reducing their aerial growth to a great extent. The C46 group (C46, C97, C222 and C371) clones behave as water spenders, reducing their water content drastically and presenting osmotic adjustment. The latter maintains the highest growth rate under the conditions tested. The method presented here can be used to identify appropriate E. globulus clones for drought environments, facilitating the selection of material for production and repopulation environments. © The

  4. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  5. Robust multivariate analysis

    CERN Document Server

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  6. Statistical Approaches for Spatiotemporal Prediction of Low Flows

    Science.gov (United States)

    Fangmann, A.; Haberlandt, U.

    2017-12-01

    An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be

  7. A statistical approach to nuclear fuel design and performance

    Science.gov (United States)

    Cunning, Travis Andrew

    As CANDU fuel failures can have significant economic and operational consequences on the Canadian nuclear power industry, it is essential that factors impacting fuel performance are adequately understood. Current industrial practice relies on deterministic safety analysis and the highly conservative "limit of operating envelope" approach, where all parameters are assumed to be at their limits simultaneously. This results in a conservative prediction of event consequences with little consideration given to the high quality and precision of current manufacturing processes. This study employs a novel approach to the prediction of CANDU fuel reliability. Probability distributions are fitted to actual fuel manufacturing datasets provided by Cameco Fuel Manufacturing, Inc. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case---a hypothesized 80% reactor outlet header break loss of coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of less than 0.3% when predicting the first statistical moment, and a relative discrepancy of less than 5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance

  8. Dating and classification of Syrian excavated pottery from Tell Saka Site, by means of thermoluminescence analysis, and multivariate statistical methods, based on PIXE analysis

    International Nuclear Information System (INIS)

    Bakraji, E.H.; Ahmad, M.; Salman, N.; Haloum, D.; Boutros, N.; Abboud, R.

    2011-01-01

    Thermoluminescence (TL) dating and Proton Induced X-ray Emission (PIXE) techniques have been utilized for the study of archaeological pottery fragment samples from Tell Saka Site, which is located at 25 km south east of Damascus city, Syria. Four samples were chosen randomly from the site, two from third level and two from fourth level for dating using TL technique and the results were in good agreement with the date assigned by archaeologists. Twenty-eight sherds were analyzed using PIXE technique in order to identify and characterize the elemental composition of pottery excavated from third and fourth levels, using 3 MV tandem accelerator in Damascus. The analysis provided almost 20 elements (Na, Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb). However, only 14 elements as follows: K, Ca, Ti, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb were chosen for statistical analysis and have been processed using two multivariate statistical methods, Cluster and Factor analysis. The studied pottery were classify into two well defined groups. (author)

  9. Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

    Science.gov (United States)

    Kalegowda, Yogesh; Harmer, Sarah L

    2012-03-20

    Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

  10. Inference of reactive transport model parameters using a Bayesian multivariate approach

    NARCIS (Netherlands)

    Carniato, L.; Schoups, G.H.W.; Van de Giesen, N.C.

    2014-01-01

    Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least

  11. Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

    Science.gov (United States)

    Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

    2015-03-15

    Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate

  12. Influence of microclimatic ammonia levels on productive performance of different broilers' breeds estimated with univariate and multivariate approaches.

    Science.gov (United States)

    Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A

    2017-08-01

    Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

  13. Recent trends in application of multivariate curve resolution approaches for improving gas chromatography-mass spectrometry analysis of essential oils.

    Science.gov (United States)

    Jalali-Heravi, Mehdi; Parastar, Hadi

    2011-08-15

    Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing challenges and are able to handle these problems. This review focuses on the application of MCR techniques for improving GC-MS analysis of EOs published between January 2000 and December 2010. In the first part, the importance of EOs in human life and their relevance in analytical chemistry is discussed. In the second part, an insight into some basics needed to understand prospects and limitations of the MCR techniques are given. In the third part, the significance of the combination of the MCR approaches with GC-MS analysis of EOs is highlighted. Furthermore, the commonly used algorithms for preprocessing, chemical rank determination, local rank analysis and multivariate resolution in the field of EOs analysis are reviewed. Copyright © 2011 Elsevier B.V. All rights reserved.

  14. DEVELOPING MARKETING STRATEGY OF POULTRY MEAT SUPPLY IN EU- 28 COUNTRIES: MULTIVARIATE ANALYSIS APPROACH

    Directory of Open Access Journals (Sweden)

    Miro Simonič

    2016-03-01

    Full Text Available To create a concept of the marketing strategy, it is necessary to analyse the factors affecting the purchasing decisions of consumers. For the variables: production, import, export, and manufacturer's price we examine their impact on the marketing of poultry meat in the EU-28 in 2009 and 2011. Countries are grouped into clusters, their properties are analysed in relation to the mentioned variables. With multiple regression analysis, we find that there is a statistical correlation between high production and de-pending on the variable, and between the imports and exports as the independent vari-ables. Based on the analysed data in the researched countries, we conclude that the qualitative development of the production of poultry meat required implementing sophis-ticated agricultural policy with low inputs prices and exploit all available spare re-sources.

  15. Multivariate GARCH models and Black-Litterman approach for tracking error constrained portfolios: an empirical analysis

    OpenAIRE

    Giulio PALOMBA

    2006-01-01

    In a typical tactical asset allocation set up managers generally make their investment decisions by inserting private information in an optimisation mechanism used to beat a benchmark portfolio; in this context the sole approach a' la Markowitz (1959) does not use all the available information about expected excess return and especially it does not take two main factors into account: first, asset returns often show changes in volatility, and second, the manager's private information plays no ...

  16. Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals.

    Science.gov (United States)

    Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

    2018-02-09

    Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels. In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed. The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T 2 -statistic and Q 2 -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

  17. On the analysis of line profile variations: A statistical approach

    International Nuclear Information System (INIS)

    McCandliss, S.R.

    1988-01-01

    This study is concerned with the empirical characterization of the line profile variations (LPV), which occur in many of and Wolf-Rayet stars. The goal of the analysis is to gain insight into the physical mechanisms producing the variations. The analytic approach uses a statistical method to quantify the significance of the LPV and to identify those regions in the line profile which are undergoing statistically significant variations. Line positions and flux variations are then measured and subject to temporal and correlative analysis. Previous studies of LPV have for the most part been restricted to observations of a single line. Important information concerning the range and amplitude of the physical mechanisms involved can be obtained by simultaneously observing spectral features formed over a range of depths in the extended mass losing atmospheres of massive, luminous stars. Time series of a Wolf-Rayet and two of stars with nearly complete spectral coverage from 3940 angstrom to 6610 angstrom and with spectral resolution of R = 10,000 are analyzed here. These three stars exhibit a wide range of both spectral and temporal line profile variations. The HeII Pickering lines of HD 191765 show a monotonic increase in the peak rms variation amplitude with lines formed at progressively larger radii in the Wolf-Rayet star wind. Two times scales of variation have been identified in this star: a less than one day variation associated with small scale flickering in the peaks of the line profiles and a greater than one day variation associated with large scale asymmetric changes in the overall line profile shapes. However, no convincing period phenomena are evident at those periods which are well sampled in this time series

  18. Evaluating an Active Learning Approach to Teaching Introductory Statistics: A Classroom Workbook Approach

    Science.gov (United States)

    Carlson, Kieth A.; Winquist, Jennifer R.

    2011-01-01

    The study evaluates a semester-long workbook curriculum approach to teaching a college level introductory statistics course. The workbook curriculum required students to read content before and during class and then work in groups to complete problems and answer conceptual questions pertaining to the material they read. Instructors spent class…

  19. The statistical-inference approach to generalized thermodynamics

    International Nuclear Information System (INIS)

    Lavenda, B.H.; Scherer, C.

    1987-01-01

    Limit theorems, such as the central-limit theorem and the weak law of large numbers, are applicable to statistical thermodynamics for sufficiently large sample size of indipendent and identically distributed observations performed on extensive thermodynamic (chance) variables. The estimation of the intensive thermodynamic quantities is a problem in parametric statistical estimation. The normal approximation to the Gibbs' distribution is justified by the analysis of large deviations. Statistical thermodynamics is generalized to include the statistical estimation of variance as well as mean values

  20. A statistical state dynamics approach to wall turbulence.

    Science.gov (United States)

    Farrell, B F; Gayme, D F; Ioannou, P J

    2017-03-13

    This paper reviews results obtained using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD approach used in this work employs a second-order closure that retains only the interaction between the streamwise mean flow and the streamwise mean perturbation covariance. This closure restricts nonlinearity in the SSD to that explicitly retained in the streamwise constant mean flow together with nonlinear interactions between the mean flow and the perturbation covariance. This dynamical restriction, in which explicit perturbation-perturbation nonlinearity is removed from the perturbation equation, results in a simplified dynamics referred to as the restricted nonlinear (RNL) dynamics. RNL systems, in which a finite ensemble of realizations of the perturbation equation share the same mean flow, provide tractable approximations to the SSD, which is equivalent to an infinite ensemble RNL system. This infinite ensemble system, referred to as the stochastic structural stability theory system, introduces new analysis tools for studying turbulence. RNL systems provide computationally efficient means to approximate the SSD and produce self-sustaining turbulence exhibiting qualitative features similar to those observed in direct numerical simulations despite greatly simplified dynamics. The results presented show that RNL turbulence can be supported by as few as a single streamwise varying component interacting with the streamwise constant mean flow and that judicious selection of this truncated support or 'band-limiting' can be used to improve quantitative accuracy of RNL turbulence. These results suggest that the SSD approach provides new analytical and computational tools that allow new insights into wall turbulence.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

  1. A statistical approach to evaluate flood risk at the regional level: an application to Italy

    Science.gov (United States)

    Rossi, Mauro; Marchesini, Ivan; Salvati, Paola; Donnini, Marco; Guzzetti, Fausto; Sterlacchini, Simone; Zazzeri, Marco; Bonazzi, Alessandro; Carlesi, Andrea

    2016-04-01

    Floods are frequent and widespread in Italy, causing every year multiple fatalities and extensive damages to public and private structures. A pre-requisite for the development of mitigation schemes, including financial instruments such as insurance, is the ability to quantify their costs starting from the estimation of the underlying flood hazard. However, comprehensive and coherent information on flood prone areas, and estimates on the frequency and intensity of flood events, are not often available at scales appropriate for risk pooling and diversification. In Italy, River Basins Hydrogeological Plans (PAI), prepared by basin administrations, are the basic descriptive, regulatory, technical and operational tools for environmental planning in flood prone areas. Nevertheless, such plans do not cover the entire Italian territory, having significant gaps along the minor hydrographic network and in ungauged basins. Several process-based modelling approaches have been used by different basin administrations for the flood hazard assessment, resulting in an inhomogeneous hazard zonation of the territory. As a result, flood hazard assessments expected and damage estimations across the different Italian basin administrations are not always coherent. To overcome these limitations, we propose a simplified multivariate statistical approach for the regional flood hazard zonation coupled with a flood impact model. This modelling approach has been applied in different Italian basin administrations, allowing a preliminary but coherent and comparable estimation of the flood hazard and the relative impact. Model performances are evaluated comparing the predicted flood prone areas with the corresponding PAI zonation. The proposed approach will provide standardized information (following the EU Floods Directive specifications) on flood risk at a regional level which can in turn be more readily applied to assess flood economic impacts. Furthermore, in the assumption of an appropriate

  2. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece)

    Energy Technology Data Exchange (ETDEWEB)

    Matiatos, Ioannis, E-mail: i.matiatos@iaea.org

    2016-01-15

    Nitrate (NO{sub 3}) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ{sup 15}N–NO{sub 3} and δ{sup 18}O–NO{sub 3}) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO{sub 3} sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC). - Highlights: • More enriched N-isotope values were observed in the industrial/urban areas. • A Bayesian isotope mixing model was applied in a multiple land-use area. • A 3-component model explained the factors controlling nitrate content in groundwater. • Industrial

  3. Graphene growth process modeling: a physical-statistical approach

    Science.gov (United States)

    Wu, Jian; Huang, Qiang

    2014-09-01

    As a zero-band semiconductor, graphene is an attractive material for a wide variety of applications such as optoelectronics. Among various techniques developed for graphene synthesis, chemical vapor deposition on copper foils shows high potential for producing few-layer and large-area graphene. Since fabrication of high-quality graphene sheets requires the understanding of growth mechanisms, and methods of characterization and control of grain size of graphene flakes, analytical modeling of graphene growth process is therefore essential for controlled fabrication. The graphene growth process starts with randomly nucleated islands that gradually develop into complex shapes, grow in size, and eventually connect together to cover the copper foil. To model this complex process, we develop a physical-statistical approach under the assumption of self-similarity during graphene growth. The growth kinetics is uncovered by separating island shapes from area growth rate. We propose to characterize the area growth velocity using a confined exponential model, which not only has clear physical explanation, but also fits the real data well. For the shape modeling, we develop a parametric shape model which can be well explained by the angular-dependent growth rate. This work can provide useful information for the control and optimization of graphene growth process on Cu foil.

  4. Infrared spectroscopy with multivariate analysis to interrogate endometrial tissue: a novel and objective diagnostic approach.

    Science.gov (United States)

    Taylor, S E; Cheung, K T; Patel, I I; Trevisan, J; Stringfellow, H F; Ashton, K M; Wood, N J; Keating, P J; Martin-Hirsch, P L; Martin, F L

    2011-03-01

    Endometrial cancer is the most common gynaecological malignancy in the United Kingdom. Diagnosis currently involves subjective expert interpretation of highly processed tissue, primarily using microscopy. Previous work has shown that infrared (IR) spectroscopy can be used to distinguish between benign and malignant cells in a variety of tissue types. Tissue was obtained from 76 patients undergoing hysterectomy, 36 had endometrial cancer. Slivers of endometrial tissue (tumour and tumour-adjacent tissue if present) were dissected and placed in fixative solution. Before analysis, tissues were thinly sliced, washed, mounted on low-E slides and desiccated; 10 IR spectra were obtained per slice by attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. Derived data was subjected to principal component analysis followed by linear discriminant analysis. Post-spectroscopy analyses, tissue sections were haematoxylin and eosin-stained to provide histological verification. Using this approach, it is possible to distinguish benign from malignant endometrial tissue, and various subtypes of both. Cluster vector plots of benign (verified post-spectroscopy to be free of identifiable pathology) vs malignant tissue indicate the importance of the lipid and secondary protein structure (Amide I and Amide II) regions of the spectrum. These findings point towards the possibility of a simple objective test for endometrial cancer using ATR-FTIR spectroscopy. This would facilitate earlier diagnosis and so reduce the morbidity and mortality associated with this disease.

  5. A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability.

    Science.gov (United States)

    Atzori, A S; Tedeschi, L O; Cannas, A

    2013-05-01

    The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21

  6. Framework for determining airport daily departure and arrival delay thresholds: statistical modelling approach.

    Science.gov (United States)

    Wesonga, Ronald; Nabugoomu, Fabian

    2016-01-01

    The study derives a framework for assessing airport efficiency through evaluating optimal arrival and departure delay thresholds. Assumptions of airport efficiency measurements, though based upon minimum numeric values such as 15 min of turnaround time, cannot be extrapolated to determine proportions of delay-days of an airport. This study explored the concept of delay threshold to determine the proportion of delay-days as an expansion of the theory of delay and our previous work. Data-driven approach using statistical modelling was employed to a limited set of determinants of daily delay at an airport. For the purpose of testing the efficacy of the threshold levels, operational data for Entebbe International Airport were used as a case study. Findings show differences in the proportions of delay at departure (μ = 0.499; 95 % CI = 0.023) and arrival (μ = 0.363; 95 % CI = 0.022). Multivariate logistic model confirmed an optimal daily departure and arrival delay threshold of 60 % for the airport given the four probable thresholds {50, 60, 70, 80}. The decision for the threshold value was based on the number of significant determinants, the goodness of fit statistics based on the Wald test and the area under the receiver operating curves. These findings propose a modelling framework to generate relevant information for the Air Traffic Management relevant in planning and measurement of airport operational efficiency.

  7. Study of Syrian archaeological pottery by the combined application of thermoluminescence (TL) dating, X-ray fluorescence analysis and statistical multivariate analysis

    International Nuclear Information System (INIS)

    Bakraji, E.H.

    2012-01-01

    X-ray fluorescence method and the technique of thermoluminescence (TL) dating have been utilized for the study of archaeological pottery fragment samples, fairly representative of Romanian period between 1 st century B.C. and 4th century A.D, from Judaidet Yabous site, which is located north-west of Damascus city, Syria. Four samples were chosen randomly among the forty six samples for dating using thermoluminescence technique and the results were in good agreement with the date assigned by archaeologists. The samples were irradiated for 1000 s live time twice, first using a Mo X-ray Tube and second using a 109 Cd radioactive source. Fifteen elements (K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, and Pb) were determined. The elemental concentrations have been processed using two multivariate statistical methods. The purpose of the study was to characterize by means of elements contents the pottery paste from Judaidet Yabous archaeological site and providing new data to the Syrian databases for future studies. From an archaeological point of view the results indicated that most of the potteries, were locally produced. (author)

  8. PIXE multivariate statistics and OSL investigation for the classification and dating of archaeological pottery excavated at Tell Al-Rawda site, Syria

    Energy Technology Data Exchange (ETDEWEB)

    Bakraji, E.H., E-mail: cscientificl@aec.org.sy [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic); Rihawy, M.S. [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic); Castel, C. [CNRS – Maison de l’Orient et de la Méditerranée, Laboratoire “Archéorient”, CNRS/Université Lumière-Lyon 2 (France); Abboud, R. [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic)

    2015-03-15

    Highlights: •PIXE and OSL methods were used to classify and date pottery from Tell Al-Rawda site. •Three groups were classified using PIXE, which suggest different sources of the clay. •OSL was used for dating the site and the date found was consistent with typology. -- Abstract: Particle Induced X-ray Emission (PIXE) technique has been utilised to study 48 Syrian ancient pottery fragments taken from excavations at Tell Al-Rawda site. Eighteen elements (Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Ni, Zn, As, Br, Rb, Sr, Y, and Pb) were determined. The elements concentrations have been processed using two multivariate statistical methods, to classify the pottery where one main group and other two small groups were defined. In addition, four samples from different places on the site were subjected to optically stimulated luminescence (OSL) dating. The average age obtained using a single aliquot regeneration (SAR) protocol was found to be 4350 ± 240 year.

  9. Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

    Science.gov (United States)

    Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

    2018-02-01

    Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

  10. Comparing Relationships among Yield and Its Related Traits in Mycorrhizal and Nonmycorrhizal Inoculated Wheat Cultivars under Different Water Regimes Using Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    Armin Saed-Moucheshi

    2013-01-01

    Full Text Available Multivariate statistical techniques were used to compare the relationship between yield and its related traits under noninoculated and inoculated cultivars with mycorrhizal fungus (Glomus intraradices; each one consisted of three wheat cultivars and four water regimes. Results showed that, under inoculation conditions, spike weight per plant and total chlorophyll content of the flag leaf were the most important variables contributing to wheat grain yield variation, while, under noninoculated condition, in addition to two mentioned traits, grain weight per spike and leaf area were also important variables accounting for wheat grain yield variation. Therefore, spike weight per plant and chlorophyll content of flag leaf can be used as selection criteria in breeding programs for both inoculated and noninoculated wheat cultivars under different water regimes, and also grain weight per spike and leaf area can be considered for noninoculated condition. Furthermore, inoculation of wheat cultivars showed higher value in the most measured traits, and the results indicated that inoculation treatment could change the relationship among morphological traits of wheat cultivars under drought stress. Also, it seems that the results of stepwise regression as a selecting method together with principal component and factor analysis are stronger methods to be applied in breeding programs for screening important traits.

  11. A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

    Science.gov (United States)

    Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

    2013-02-01

    Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

  12. Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

    Science.gov (United States)

    Liu, Zechang; Wang, Liping; Liu, Yumei

    2018-01-18

    Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

  13. Estrogenic compound profiles in an urbanized industry-impacted coastal bay and potential risk assessment by pollution indices and multivariative statistical methods.

    Science.gov (United States)

    Wang, Zaosheng; Li, Rui; Wu, Fengchang; Feng, Chenglian; Ye, Chun; Yan, Changzhou

    2017-01-15

    The occurrence and distribution of target estrogenic compounds in a highly urbanized industry-impacted coastal bay were investigated, and contamination profiles were evaluated by estimating total estradiol equivalents (∑EEQs) and risk quotients (RQs). Phenolic compounds were the most abundant xenoestrogens, but seldom showed contribution to the ∑EEQs. The diethylstilbestrol (DES) and 17α-ethinylestradiol (EE2) were the major contributors followed by 17β-estradiol (E2) in comparison with a slight contribution from estrone (E1) and estriol (E3). Both ∑EEQs and RQs indicated likely adverse effects posed on resident organisms. Further, multivariate statistical method comprehensively revealed pollution status by visualized factor scores and identified multiple "hotspots" of estrogenic sources, demonstrating the presence of complex pollution risk gradients inside and particularly outside of bay area. Overall, this study favors the integrative utilization of pollution indices and factor analysis as powerful tool to scientifically diagnose the pollution characterization of human-derived chemicals for better management decisions in aquatic environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

    Science.gov (United States)

    Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

    2016-04-01

    Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Human Exposure Risk Assessment Due to Heavy Metals in Groundwater by Pollution Index and Multivariate Statistical Methods: A Case Study from South Africa

    Directory of Open Access Journals (Sweden)

    Vetrimurugan Elumalai

    2017-04-01

    Full Text Available Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese exceeded the limit at few locations. Heavy metal pollution index based on ten heavy metals indicated that 85% of the area had good quality water, but 15% was unsuitable. Human exposure dose through the drinking water pathway indicated no risk due to boron, nickel and zinc, moderate risk due to cadmium and lithium and high risk due to silver, copper, manganese and lead. Hazard quotients were high in all sampling locations for humans of all age groups, indicating that groundwater is unsuitable for drinking purposes. Highly polluted areas were located near the coast, close to industrial operations and at a landfill site representing human-induced pollution. Factor analysis identified the four major pollution sources as: (1 industries; (2 mining and related activities; (3 mixed sources- geogenic and anthropogenic and (4 fertilizer application.

  16. Elemental characterization of herbal medicines used in Ghana by instrumental neutron activation analysis and atomic absorption spectrometry and multivariate statistical analysis

    International Nuclear Information System (INIS)

    Ayivor, J.E.; Nyarko, B.J.B.; Dampare, S.B.; Okine, L.K.

    2010-01-01

    k 0 instrumental neutron activation analysis and atomic absorption spectrometry were applied to determine multi elements in thirteen Ghanaian herbal medicines used for the management of various diseases. Concentrations of AI, Cu, Mg, Mn and Na were determined. As, Br, K, CI, and Na were determined by short and medium irradiations at a thermal neutron flux of 5x10ncm -2 s -1 . Fe, Cr, Pb, Co, Ni, Sn, Ca, Ba, Li and Sb were determined using atomic absorption spectrometry. Ba, Cu, Li and V were present at trace levels whereas AI, CI, Na, Ca were present at major levels. K, Br, Mg, Mn, Co, Ni, Fe and Sb were also present at minor levels. The precision and accuracy of the method using real samples and standard reference materials were within ±10% of the reported value. Multivariate analytical techniques, such as cluster analysis and principal component analysis (PCA)/factor analysis (FA), have been applied to evaluate the chemical variations in the herbal medicine dataset. All the 13 samples may be grouped into two statistically significant clusters, reflecting the different chemical compositions. The concentrations of elements were within the recommended daily allowances or maximum permissible levels posing no adverse effects on human health.

  17. Metabolomic profiling of the phytomedicinal constituents of Carica papaya L. leaves and seeds by 1H NMR spectroscopy and multivariate statistical analysis.

    Science.gov (United States)

    Gogna, Navdeep; Hamid, Neda; Dorai, Kavita

    2015-11-10

    Extracts from the Carica papaya L. plant are widely reported to contain metabolites with antibacterial, antioxidant and anticancer activity. This study aims to analyze the metabolic profiles of papaya leaves and seeds in order to gain insights into their phytomedicinal constituents. We performed metabolite fingerprinting using 1D and 2D 1H NMR experiments and used multivariate statistical analysis to identify those plant parts that contain the most concentrations of metabolites of phytomedicinal value. Secondary metabolites such as phenyl propanoids, including flavonoids, were found in greater concentrations in the leaves as compared to the seeds. UPLC-ESI-MS verified the presence of significant metabolites in the papaya extracts suggested by the NMR analysis. Interestingly, the concentration of eleven secondary metabolites namely caffeic, cinnamic, chlorogenic, quinic, coumaric, vanillic, and protocatechuic acids, naringenin, hesperidin, rutin, and kaempferol, were higher in young as compared to old papaya leaves. The results of the NMR analysis were corroborated by estimating the total phenolic and flavonoid content of the extracts. Estimation of antioxidant activity in leaves and seed extracts by DPPH and ABTS in-vitro assays and antioxidant capacity in C2C12 cell line also showed that papaya extracts exhibit high antioxidant activity. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. PIXE multivariate statistics and OSL investigation for the classification and dating of archaeological pottery excavated at Tell Al-Rawda site, Syria

    International Nuclear Information System (INIS)

    Bakraji, E.H.; Rihawy, M.S.; Castel, C.; Abboud, R.

    2015-01-01

    Highlights: •PIXE and OSL methods were used to classify and date pottery from Tell Al-Rawda site. •Three groups were classified using PIXE, which suggest different sources of the clay. •OSL was used for dating the site and the date found was consistent with typology. -- Abstract: Particle Induced X-ray Emission (PIXE) technique has been utilised to study 48 Syrian ancient pottery fragments taken from excavations at Tell Al-Rawda site. Eighteen elements (Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Ni, Zn, As, Br, Rb, Sr, Y, and Pb) were determined. The elements concentrations have been processed using two multivariate statistical methods, to classify the pottery where one main group and other two small groups were defined. In addition, four samples from different places on the site were subjected to optically stimulated luminescence (OSL) dating. The average age obtained using a single aliquot regeneration (SAR) protocol was found to be 4350 ± 240 year

  19. An introduction to statistical computing a simulation-based approach

    CERN Document Server

    Voss, Jochen

    2014-01-01

    A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems.  Sampling-based simulation techniques are now an invaluable tool for exploring statistical models.  This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods.  It also includes some advanced met

  20. Influence of microclimatic ammonia levels on productive performance of different broilers’ breeds estimated with univariate and multivariate approaches

    Science.gov (United States)

    Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.

    2017-01-01

    Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

  1. A Statistical Approach to Retrieving Historical Manuscript Images without Recognition

    National Research Council Canada - National Science Library

    Rath, Toni M; Lavrenko, Victor; Manmatha, R

    2003-01-01

    ...), and word spotting -- an image matching approach (computationally expensive). In this work, the authors present a novel retrieval approach for historical document collections that does not require recognition...

  2. Statistics

    CERN Document Server

    Hayslett, H T

    1991-01-01

    Statistics covers the basic principles of Statistics. The book starts by tackling the importance and the two kinds of statistics; the presentation of sample data; the definition, illustration and explanation of several measures of location; and the measures of variation. The text then discusses elementary probability, the normal distribution and the normal approximation to the binomial. Testing of statistical hypotheses and tests of hypotheses about the theoretical proportion of successes in a binomial population and about the theoretical mean of a normal population are explained. The text the

  3. Multivariate data analysis

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  4. Statistics

    Science.gov (United States)

    Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.

  5. Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Shujuan Xue

    2018-02-01

    Full Text Available Radix Rehmanniae (RR is a kind of herb which is widely used in the clinical and food processing industry. There are four forms of RR used in traditional Chinese medicine practice, which include fresh RR (FRR, raw RR (RRR, processed RR (PRR, and another processed RR (APRR, in which the APRR was processed by nine cycles of repeated steaming and drying. There are a large number of saccharides in RR. However, the differences in content were shown by different processing methods. In this study, an effective method using high-performance liquid chromatography (HPLC and high-performance liquid chromatography-mass spectrometry (LC-MS coupled with multivariate statistical analysis to rapidly distinguish different RR samples and validate the proposed chemical conversion mechanism. The datasets of the content of saccharides were subjected to principal component analysis (PCA and one-way analysis of variance. The results showed that there different changes occurred in the contents of saccharides corresponding to the different processing methods, in which the contents of monosaccharides—namely arabinose, glucose, mannose, and galactose—had an increasing trend or remained relatively stable. However, the contents of fructose and oligosaccharides, including manninotriose, melibiose, sucrose, and raffinose, first increased and then reduced, or gradually decreased, yet the content of stachyose gradually decreased. The MSn data indicated that manninotriose, melibiose, and some monosaccharides were produced by the hydrolysis of oligosaccharides. In addition, the fragmentation pathways of 1-phenyl-3-methyl-5-pyrazolone (PMP derivatization of monosaccharides were also found that its glycosidic bond was first broken and subsequently its inside ring broke, and the characteristic fragment ions were produced at m/z 511.22, 493.20, 373.16, and 175.08 in the PMP derivatization of monosaccharides. In conclusion, this study illustrates the change and chemical conversion

  6. Basin Characterisation by Means of Joint Inversion of Electromagnetic Geophysical Data, Borehole Data and Multivariate Statistical Methods: The Loop Head Peninsula, Western Ireland, Case Study

    Science.gov (United States)

    Campanya, J. L.; Ogaya, X.; Jones, A. G.; Rath, V.; McConnell, B.; Haughton, P.; Prada, M.

    2016-12-01

    The Science Foundation Ireland funded project IRECCSEM project (www.ireccsem.ie) aims to evaluate Ireland's potential for onshore carbon sequestration in saline aquifers by integrating new electromagnetic geophysical data with existing geophysical and geological data. One of the objectives of this component of IRECCSEM is to characterise the subsurface beneath the Loop Head Peninsula (part of Clare Basin, Co. Clare, Ireland), and identify major electrical resistivity structures that can guide an interpretation of the carbon sequestration potential of this area. During the summer of 2014, a magnetotelluric (MT) survey was carried out on the Loop Head Peninsula, and data from a total of 140 sites were acquired, including audio-magnetotelluric (AMT), and broadband magnetotelluric (BBMT). The dataset was used to generate shallow three-dimensional (3-D) electrical resistivity models constraining the subsurface to depths of up to 3.5 km. The three-dimensional (3-D) joint inversions were performed using three different types of electromagnetic data: MT impedance tensor (Z), geomagnetic transfer functions (T), and inter-station horizontal magnetic transfer-functions (H). The interpretation of the results was complemented with second-derivative models of the resulting electrical resistivity models, and a quantitative comparison with borehole data using multivariate statistical methods. Second-derivative models were used to define the main interfaces between the geoelectrical structures, facilitating superior comparison with geological and seismic results, and also reducing the influence of the colour scale when interpreting the results. Specific analysis was performed to compare the extant borehole data with the electrical resistivity model, identifying those structures that are better characterised by the resistivity model. Finally, the electrical resistivity model was also used to propagate some of the physical properties measured in the borehole, when a good relation was

  7. Influence of physical and chemical characteristics of diesel fuels and exhaust emissions on biological effects of particle extracts: a multivariate statistical analysis of ten diesel fuels.

    Science.gov (United States)

    Sjögren, M; Li, H; Banner, C; Rafter, J; Westerholm, R; Rannug, U

    1996-01-01

    The emission of diesel exhaust particulates is associated with potentially severe biological effects, e.g., cancer. The aim of the present study was to apply multivariate statistical methods to identify factors that affect the biological potency of these exhausts. Ten diesel fuels were analyzed regarding physical and chemical characteristics. Particulate exhaust emissions were sampled after combustion of these fuels on two makes of heavy duty diesel engines. Particle extracts were chemically analyzed and tested for mutagenicity in the Ames test. Also, the potency of the extracts to competitively inhibit the binding of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) to the Ah receptor was assessed. Relationships between fuel characteristics and biological effects of the extracts were studied, using partial least squares regression (PLS). The most influential chemical fuel parameters included the contents of sulfur, certain polycyclic aromatic compounds (PAC), and naphthenes. Density and flash point were positively correlated with genotoxic potency. Cetane number and upper distillation curve points were negatively correlated with both mutagenicity and Ah receptor affinity. Between 61% and 70% of the biological response data could be explained by the measured chemical and physical factors of the fuels. By PLS modeling of extract data versus the biological response data, 66% of the genotoxicity could be explained, by 41% of the chemical variation. The most important variables, associated with both mutagenicity and Ah receptor affinity, included 1-nitropyrene, particle bound nitrate, indeno[1,2,3-cd]pyrene, and emitted mass of particles. S9-requiring mutagenicity was highly correlated with certain PAC, whereas S9-independent mutagenicity was better correlated with nitrates and 1-nitropyrene. The emission of sulfates also showed a correlation both with the emission of particles and with the biological effects. The results indicate that fuels with biologically less hazardous

  8. A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

    Directory of Open Access Journals (Sweden)

    Annamaria Castrignanò

    2017-12-01

    Full Text Available To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion is not at all a naive problem and novel and powerful methods need to be developed.

  9. Diversity trends in bread wheat in Italy during the 20th century assessed by traditional and multivariate approaches.

    Science.gov (United States)

    Ormoli, Leonardo; Costa, Corrado; Negri, Stefano; Perenzin, Maurizio; Vaccino, Patrizia

    2015-02-25

    A collection of 157 Triticum aestivum accessions, representative of wheat breeding in Italy during the 20(th) century, was assembled to describe the evolutionary trends of cultivated varieties throughout this period. The lines were cultivated in Italy, in two locations, over two growing seasons, and evaluated for several agronomical, morphological and qualitative traits. Analyses were conducted using the most common univariate approach on individual plant traits coupled with a correspondance multivariate approach. ANOVA showed a clear trend from old to new varieties, leading towards earliness, plant height reduction and denser spikes with smaller seeds. The average protein content gradually decreased over time; however this trend did not affect bread-making quality, because it was counterbalanced by a gradual increase of SDS sedimentation volume, achieved by the incorporation of favourable alleles into recent cultivars. Correspondence analysis allowed an overall view of the breeding activity. A clear-cut separation was observed between ancient lines and all the others, matched with a two-step gradient, the first, corresponding roughly to the period 1920-1940, which can be ascribed mostly to genetics, the second, from the 40s onward, which can be ascribed also to the farming practice innovations, such as improvement of mechanical devices and optimised use of fertilizers.

  10. Estimating Potential GDP for the Romanian Economy and Assessing the Sustainability of Economic Growth: A Multivariate Filter Approach

    Directory of Open Access Journals (Sweden)

    Dan Armeanu

    2015-03-01

    Full Text Available In the current context of economic recovery and rebalancing, the necessity of modelling and estimating the potential output and output gap emerges in order to assess the quality and sustainability of economic growth, the monetary and fiscal policies, as well as the impact of business cycles. Despite the importance of potential GDP and the output gap, there are difficulties in reliably estimating them, as many of the models proposed in the economic literature are calibrated for developed economies and are based on complex macroeconomic relationships and a long history of robust data, while emerging economies exhibit high volatility. The object of this study is to develop a model in order to estimate the potential GDP and output gap and to assess the sustainability of projected growth using a multivariate filter approach. This trend estimation technique is the newest approach proposed by the economic literature and has gained wide acceptance with researchers and practitioners alike, while also being used by the IMF for Romania. The paper will be structured as follows. We first discuss the theoretical background of the model. The second section focuses on an analysis of the Romanian economy for the 1995–2013 time frame, while also providing a forecast for 2014–2017 and an assessment of the sustainability of Romania’s economic growth. The third section sums up the results and concludes.

  11. A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

    Science.gov (United States)

    Castrignanò, Annamaria; Buttafuoco, Gabriele; Quarto, Ruggiero; Vitti, Carolina; Langella, Giuliano; Terribile, Fabio; Venezia, Accursio

    2017-12-03

    To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0-1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

  12. Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

    Science.gov (United States)

    Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

    2018-06-01

    The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

  13. How many taxa can be recognized within the complex Tillandsia capillaris (Bromeliaceae, Tillandsioideae? Analysis of the available classifications using a multivariate approach

    Directory of Open Access Journals (Sweden)

    Lucía Castello

    2013-05-01

    Full Text Available Tillandsia capillaris Ruiz & Pav., which belongs to the subgenus Diaphoranthema is distributed in Ecuador, Peru, Bolivia, northern and central Argentina, and Chile, and includes forms that are difficult to circumscribe, thus considered to form a complex. The entities of this complex are predominantly small-sized epiphytes, adapted to xeric environments. The most widely used classification defines 5 forms for this complex based on few morphological reproductive traits: T. capillaris Ruiz & Pav. f. capillaris, T. capillaris f. incana (Mez L.B. Sm., T. capillaris f. cordobensis (Hieron. L.B. Sm., T. capillaris f. hieronymi (Mez L.B. Sm. and T. capillaris f. virescens (Ruiz & Pav. L.B. Sm. In this study, 35 floral and vegetative characters were analyzed with a multivariate approach in order to assess and discuss different proposals for classification of the T. capillaris complex, which presents morphotypes that co-occur in central and northern Argentina. To accomplish this, data of quantitative and categorical morphological characters of flowers and leaves were collected from herbarium specimens and field collections and were analyzed with statistical multivariate techniques. The results suggest that the last classification for the complex seems more comprehensive and three taxa were delimited: T. capillaris (=T. capillaris f. incana-hieronymi, T. virescens s. str. (=T. capillaris f. cordobensis and T. virescens s. l. (=T. capillaris f. virescens. While T. capillaris and T. virescens s. str. co-occur, T. virescens s. l. is restricted to altitudes above 2000 m in Argentina. Characters previously used for taxa delimitation showed continuous variation and therefore were not useful. New diagnostic characters are proposed and a key is provided for delimiting these three taxa within the complex.

  14. How many taxa can be recognized within the complex Tillandsia capillaris (Bromeliaceae, Tillandsioideae)? Analysis of the available classifications using a multivariate approach.

    Science.gov (United States)

    Castello, Lucía V; Galetto, Leonardo

    2013-01-01

    Tillandsia capillaris Ruiz & Pav., which belongs to the subgenus Diaphoranthema is distributed in Ecuador, Peru, Bolivia, northern and central Argentina, and Chile, and includes forms that are difficult to circumscribe, thus considered to form a complex. The entities of this complex are predominantly small-sized epiphytes, adapted to xeric environments. The most widely used classification defines 5 forms for this complex based on few morphological reproductive traits: Tillandsia capillaris Ruiz & Pav. f. capillaris, Tillandsia capillaris f. incana (Mez) L.B. Sm., Tillandsia capillaris f. cordobensis (Hieron.) L.B. Sm., Tillandsia capillaris f. hieronymi (Mez) L.B. Sm. and Tillandsia capillaris f. virescens (Ruiz & Pav.) L.B. Sm. In this study, 35 floral and vegetative characters were analyzed with a multivariate approach in order to assess and discuss different proposals for classification of the Tillandsia capillaris complex, which presents morphotypes that co-occur in central and northern Argentina. To accomplish this, data of quantitative and categorical morphological characters of flowers and leaves were collected from herbarium specimens and field collections and were analyzed with statistical multivariate techniques. The results suggest that the last classification for the complex seems more comprehensive and three taxa were delimited: Tillandsia capillaris (=Tillandsia capillaris f. incana-hieronymi), Tillandsia virescens s. str. (=Tillandsia capillaris f. cordobensis) and Tillandsia virescens s. l. (=Tillandsia capillaris f. virescens). While Tillandsia capillaris and Tillandsia virescens s. str. co-occur, Tillandsia virescens s. l. is restricted to altitudes above 2000 m in Argentina. Characters previously used for taxa delimitation showed continuous variation and therefore were not useful. New diagnostic characters are proposed and a key is provided for delimiting these three taxa within the complex.

  15. TOPSIS with statistical distances: A new approach to MADM

    Directory of Open Access Journals (Sweden)

    Vijaya Babu Vommi

    2017-01-01

    Full Text Available Multiple attribute decision making (MADM methods are very useful in choosing the best alternative among the available finite but conflicting alternatives. TOPSIS is one of the MADM methods, which is simple in its methodology and logic. In TOPSIS, Euclidean distances of each alternative from the positive and negative ideal solutions are utilized to find the best alternative. In literature, apart from Euclidean distances, the city block distances have also been tried to find the separations measures. In general, the attribute data are distributed with unequal ranges and also possess moderate to high correlations. Hence, in the present paper, use of statistical distances is proposed in place of Euclidean distances. Procedures to find the best alternatives are developed using statistical and weighted statistical distances respectively. The proposed methods are illustrated with some industrial problems taken from literature. Results show that the proposed methods can be used as new alternatives in MADM for choosing the best solutions.

  16. Heuristic versus statistical physics approach to optimization problems

    International Nuclear Information System (INIS)

    Jedrzejek, C.; Cieplinski, L.

    1995-01-01

    Optimization is a crucial ingredient of many calculation schemes in science and engineering. In this paper we assess several classes of methods: heuristic algorithms, methods directly relying on statistical physics such as the mean-field method and simulated annealing; and Hopfield-type neural networks and genetic algorithms partly related to statistical physics. We perform the analysis for three types of problems: (1) the Travelling Salesman Problem, (2) vector quantization, and (3) traffic control problem in multistage interconnection network. In general, heuristic algorithms perform better (except for genetic algorithms) and much faster but have to be specific for every problem. The key to improving the performance could be to include heuristic features into general purpose statistical physics methods. (author)

  17. Statistics

    International Nuclear Information System (INIS)

    2005-01-01

    For the years 2004 and 2005 the figures shown in the tables of Energy Review are partly preliminary. The annual statistics published in Energy Review are presented in more detail in a publication called Energy Statistics that comes out yearly. Energy Statistics also includes historical time-series over a longer period of time (see e.g. Energy Statistics, Statistics Finland, Helsinki 2004.) The applied energy units and conversion coefficients are shown in the back cover of the Review. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in GDP, energy consumption and electricity consumption, Carbon dioxide emissions from fossile fuels use, Coal consumption, Consumption of natural gas, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices in heat production, Fuel prices in electricity production, Price of electricity by type of consumer, Average monthly spot prices at the Nord pool power exchange, Total energy consumption by source and CO 2 -emissions, Supplies and total consumption of electricity GWh, Energy imports by country of origin in January-June 2003, Energy exports by recipient country in January-June 2003, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Price of natural gas by type of consumer, Price of electricity by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes, precautionary stock fees and oil pollution fees

  18. Statistical approach for collaborative tests, reference material certification procedures

    International Nuclear Information System (INIS)

    Fangmeyer, H.; Haemers, L.; Larisse, J.

    1977-01-01

    The first part introduces the different aspects in organizing and executing intercomparison tests of chemical or physical quantities. It follows a description of a statistical procedure to handle the data collected in a circular analysis. Finally, an example demonstrates how the tool can be applied and which conclusion can be drawn of the results obtained

  19. Effective viscosity of dispersions approached by a statistical continuum method

    NARCIS (Netherlands)

    Mellema, J.; Willemse, M.W.M.

    1983-01-01

    The problem of the determination of the effective viscosity of disperse systems (emulsions, suspensions) is considered. On the basis of the formal solution of the equations governing creeping flow in a statistically homogeneous dispersion, the effective viscosity is expressed in a series expansion

  20. Generalized statistical mechanics approaches to earthquakes and tectonics

    Science.gov (United States)

    Papadakis, Giorgos; Michas, Georgios

    2016-01-01

    Despite the extreme complexity that characterizes the mechanism of the earthquake generation process, simple empirical scaling relations apply to the collective properties of earthquakes and faults in a variety of tectonic environments and scales. The physical characterization of those properties and the scaling relations that describe them attract a wide scientific interest and are incorporated in the probabilistic forecasting of seismicity in local, regional and planetary scales. Considerable progress has been made in the analysis of the statistical mechanics of earthquakes, which, based on the principle of entropy, can provide a physical rationale to the macroscopic properties frequently observed. The scale-invariant properties, the (multi) fractal structures and the long-range interactions that have been found to characterize fault and earthquake populations have recently led to the consideration of non-extensive statistical mechanics (NESM) as a consistent statistical mechanics framework for the description of seismicity. The consistency between NESM and observations has been demonstrated in a series of publications on seismicity, faulting, rock physics and other fields of geosciences. The aim of this review is to present in a concise manner the fundamental macroscopic properties of earthquakes and faulting and how these can be derived by using the notions of statistical mechanics and NESM, providing further insights into earthquake physics and fault growth processes. PMID:28119548