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Sample records for mining heterogeneous multivariate

  1. A Pattern Mining Approach for Classifying Multivariate Temporal Data.

    PubMed

    Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F; Hauskrecht, Milos

    2011-11-12

    We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the minimal predictive temporal patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.

  2. Multivariate characterization of white matter heterogeneity in autism spectrum disorder.

    PubMed

    Dean, D C; Lange, N; Travers, B G; Prigge, M B; Matsunami, N; Kellett, K A; Freeman, A; Kane, K L; Adluru, N; Tromp, D P M; Destiche, D J; Samsin, D; Zielinski, B A; Fletcher, P T; Anderson, J S; Froehlich, A L; Leppert, M F; Bigler, E D; Lainhart, J E; Alexander, A L

    2017-01-01

    The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying

  3. Socioeconomic Heterogeneity of Mining-Dependent Counties.

    ERIC Educational Resources Information Center

    Nord, Mark; Luloff, A. E.

    1993-01-01

    Although the socioeconomic well-being of all U.S. mining-dependent counties was slightly above the national average in 1990, disaggregation reveals substantial effects of region and mining subsector. In particular, southern and Great Lakes coal-mining counties had significantly lower high school graduation rates and higher poverty and unemployment…

  4. Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

    PubMed Central

    Lehermeier, Christina; Schön, Chris-Carolin; de los Campos, Gustavo

    2015-01-01

    Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to “correct” for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP. PMID:26122758

  5. Mining Large Heterogeneous Graphs using Cray s Urika

    SciTech Connect

    Sukumar, Sreenivas R; Bond, Nathaniel A

    2013-01-01

    Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

  6. Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

    NASA Astrophysics Data System (ADS)

    Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

    2014-12-01

    Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

  7. Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model

    PubMed Central

    Cheng, Qing; Lu, Xin; Wu, Joseph T.; Liu, Zhong; Huang, Jincai

    2016-01-01

    Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression. PMID:27666657

  8. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data.

    PubMed

    Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

    2016-01-01

    This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.

  9. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

    PubMed Central

    Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

    2015-01-01

    This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

  10. Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

    ERIC Educational Resources Information Center

    Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

    2001-01-01

    Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

  11. A review of heterogeneous data mining for brain disorder identification.

    PubMed

    Cao, Bokai; Kong, Xiangnan; Yu, Philip S

    2015-12-01

    With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.

  12. Mining Heterogeneous Social Networks for Egocentric Information Abstraction

    NASA Astrophysics Data System (ADS)

    Li, Cheng-Te; Lin, Shou-De

    Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify combination of relations as features and compute statistical dependencies as feature values. These features, either linear or eyelie, intend to capture the semantic information in the surrounding environment of the ego. Then we design three abstraction measures to distill representative and important information to construct the abstracted graphs for visual presentation. The evaluations conducted on a real world movie datasct and an artificial crime dataset demonstrate that the abstractions can indeed retain significant information and facilitate more accurate and efficient human analysis.

  13. Interval Estimates of Multivariate Effect Sizes: Coverage and Interval Width Estimates under Variance Heterogeneity and Nonnormality

    ERIC Educational Resources Information Center

    Hess, Melinda R.; Hogarty, Kristine Y.; Ferron, John M.; Kromrey, Jeffrey D.

    2007-01-01

    Monte Carlo methods were used to examine techniques for constructing confidence intervals around multivariate effect sizes. Using interval inversion and bootstrapping methods, confidence intervals were constructed around the standard estimate of Mahalanobis distance (D[superscript 2]), two bias-adjusted estimates of D[superscript 2], and Huberty's…

  14. A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors.

    PubMed

    Prendes, Jorge; Chabert, Marie; Pascal, Frederic; Giros, Alain; Tourneret, Jean-Yves

    2015-03-01

    Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.

  15. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

    PubMed Central

    Li, Jun; Zhao, Patrick X.

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  16. Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness

    PubMed Central

    Liu, Dungang; Liu, Regina; Xie, Minge

    2014-01-01

    Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis. The exclusion of part of the studies can lead to a non-negligible loss of information. This paper introduces a metaanalysis for heterogeneous studies by combining the confidence density functions derived from the summary statistics of individual studies, hence referred to as the CD approach. It includes all the studies in the analysis and makes use of all information, direct as well as indirect. Under a general likelihood inference framework, this new approach is shown to have several desirable properties, including: i) it is asymptotically as efficient as the maximum likelihood approach using individual participant data (IPD) from all studies; ii) unlike the IPD analysis, it suffices to use summary statistics to carry out the CD approach. Individual-level data are not required; and iii) it is robust against misspecification of the working covariance structure of the parameter estimates. Besides its own theoretical significance, the last property also substantially broadens the applicability of the CD approach. All the properties of the CD approach are further confirmed by data simulated from a randomized clinical trials setting as well as by real data on aircraft landing performance. Overall, one obtains an unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases. PMID:26190875

  17. Application of Multivariate Analysis in Understanding Anions in Soils Close to an Abandoned Manganese Oxide Ore Mine

    NASA Astrophysics Data System (ADS)

    Ekosse, Georges-Ivo E.

    Multivariate Analysis (MVA) was used in elucidating on the relationships and environmental implications of anions (particularly chloride, sulphate and carbonate which generally affect bioavailability of soil nutrients) in soils within the proximity of an abandoned Mn oxide ore mine in Southeastern Botswana. Four hundred soil samples were obtained from a 4 km2 area close to the abandoned mine and analysed for their anionic contents and pH. The Statistical Package for Social Sciences (SPSS) software was used for data processing. Anion concentrations in the soil samples were: chloride = 0.2 to 11.9 mg kg-1, with a mean of 7.63 mg kg-1, sulphate = 2.1 to 47.5 mg kg-1, with a mean of 19.36 mg kg-1 and carbonate = 5.1 g kg-1 to 59.1 g kg-1, with a mean of 40.98 g kg-1. Correlation coefficients depicted strong positive associations. Two clusters were produced: cluster one had the three anions with SO42¯ being the most important; and cluster two equally had all three anions but with negative t-statistic values. The anions have been continuously displaced as result of their very high mobility as reflected in lower concentrations than those from the control site.

  18. Effects of selection and drift on G matrix evolution in a heterogeneous environment: a multivariate Qst-Fst Test with the freshwater snail Galba truncatula.

    PubMed

    Chapuis, Elodie; Martin, Guillaume; Goudet, Jérôme

    2008-12-01

    Unraveling the effect of selection vs. drift on the evolution of quantitative traits is commonly achieved by one of two methods. Either one contrasts population differentiation estimates for genetic markers and quantitative traits (the Q(st)-F(st) contrast) or multivariate methods are used to study the covariance between sets of traits. In particular, many studies have focused on the genetic variance-covariance matrix (the G matrix). However, both drift and selection can cause changes in G. To understand their joint effects, we recently combined the two methods into a single test (accompanying article by Martin et al.), which we apply here to a network of 16 natural populations of the freshwater snail Galba truncatula. Using this new neutrality test, extended to hierarchical population structures, we studied the multivariate equivalent of the Q(st)-F(st) contrast for several life-history traits of G. truncatula. We found strong evidence of selection acting on multivariate phenotypes. Selection was homogeneous among populations within each habitat and heterogeneous between habitats. We found that the G matrices were relatively stable within each habitat, with proportionality between the among-populations (D) and the within-populations (G) covariance matrices. The effect of habitat heterogeneity is to break this proportionality because of selection for habitat-dependent optima. Individual-based simulations mimicking our empirical system confirmed that these patterns are expected under the selective regime inferred. We show that homogenizing selection can mimic some effect of drift on the G matrix (G and D almost proportional), but that incorporating information from molecular markers (multivariate Q(st)-F(st)) allows disentangling the two effects.

  19. An Integrated Framework to Access and Mine Distributed Heterogeneous Data Streams with Uncertainty

    DTIC Science & Technology

    2015-05-13

    International Conference on Knowledge Discovery & Data Mining. 12-AUG-12, . : , Guan Wang, Yuchen Zhao, Xiaoxiao Shi , Philip S. Yu. Magnet Community...Identification on Social Networks, the 18th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 12-AUG-12, . : , jing Peng, Kun...Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), Gold Coast, Australia, April 2013. 51. X. Shi, and P.S. Yu, "Dimensionality

  20. Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

    PubMed

    Bagur, M G; Morales, S; López-Chicano, M

    2009-11-15

    Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

  1. Determination of source parameters and full moment tensors of seismic events in a very heterogeneous mining environment

    NASA Astrophysics Data System (ADS)

    Vavrycuk, V.; Kuehn, D.

    2012-04-01

    Using seismic data from 5 blasts and 5 induced events recorded in the Pyhasalmi ore mine, Finland, we propose and test a strategy for the inversion of moment tensors from waveforms in a very heterogeneous mining environment. The heterogeneities are caused not only by presence of the ore body in the host rock, but especially by presence of a system of tunnels and by large excavation areas in the mines. We show that the moment tensor inversion is feasible even in such a complex velocity model. First, locations of events needed in the inversion can be determined using the eikonal solver, provided a detailed geometry of the tunnels and the cavities is well documented and the velocities of rocks are known with a good accuracy. The solver takes into account refractions and diffractions and it is applicable even in strongly heterogeneous media where ray tracing may be problematic. Second, the Green's functions needed for the waveform moment tensor inversion can be calculated by the full waveform modelling capable to reproduce complex interactions of waves with the structure. We use the 3-D finite difference viscoelastic code and run it on a model specified using the spatial grid of 2 m and with the sampling frequency of 10 kHz. The computational time is reduced using the reciprocity principle. Third, the moment tensor inversion is performed in the time domain using the generalized linear inversion. Compared to the computation of the Green's functions, the inversion is computationally undemanding. To suppress the sensitivity of the inversion to inaccuracies in the locations and in the velocity model, we analyse data in the frequency range from 30 to 80 Hz. The analysis of 5 blasts and 5 induced microseismic events proved that the moment tensor inversion was successful. As expected the blasts display high percentage of the positive ISO components attaining values from 60 to 80%. However, we cannot exclude that some minor shear faulting was triggered during the blasting. On

  2. NeuroVis: combining dimensional stacking and pixelization to visually explore, analyze, and mine multidimensional multivariate data

    NASA Astrophysics Data System (ADS)

    Langton, John T.; Prinz, Astrid A.; Hickey, Timothy J.

    2007-01-01

    The combination of pixelization and dimensional stacking uniquely facilitates the visualization and analysis of large, multidimensional databases. Pixelization is the mapping of each data point in some set to a pixel in a 2D image. Dimensional stacking is a layout method where N dimensions are projected onto the axes of an information display. We have combined and expanded upon both methods in an application named NeuroVis that supports interactive, visual data mining. Users can spontaneously perform ad hoc queries, cluster the results through dimension reordering, and execute analyses on selected pixels. While NeuroVis is not intrinsically restricted to any particular database, it is named after its original function: the examination of a vast neuroscience database. Images produced from its approaches have now appeared in the Journal of Neurophysiology and NeuroVis itself is being used for educational purposes in neuroscience classes at Emory University. In this paper we detail the theoretical foundations of NeuroVis, the interaction techniques it supports, an informal evaluation of how it has been used in neuroscience investigations, and a generalization of its utility and limitations in other domains.

  3. Advances in cheminformatics methodologies and infrastructure to support the data mining of large, heterogeneous chemical datasets.

    PubMed

    Guha, Rajarshi; Gilbert, Kevin; Fox, Geoffrey; Pierce, Marlon; Wild, David; Yuan, Huapeng

    2010-03-01

    In recent years, there has been an explosion in the availability of publicly accessible chemical information, including chemical structures of small molecules, structure-derived properties and associated biological activities in a variety of assays. These data sources present us with a significant opportunity to develop and apply computational tools to extract and understand the underlying structure-activity relationships. Furthermore, by integrating chemical data sources with biological information (protein structure, gene expression and so on), we can attempt to build up a holistic view of the effects of small molecules in biological systems. Equally important is the ability for non-experts to access and utilize state of the art cheminformatics method and models. In this review we present recent developments in cheminformatics methodologies and infrastructure that provide a robust, distributed approach to mining large and complex chemical datasets. In the area of methodology development, we highlight recent work on characterizing structure-activity landscapes, Quantitative Structure Activity Relationship (QSAR) model domain applicability and the use of chemical similarity in text mining. In the area of infrastructure, we discuss a distributed web services framework that allows easy deployment and uniform access to computational (statistics, cheminformatics and computational chemistry) methods, data and models. We also discuss the development of PubChem derived databases and highlight techniques that allow us to scale the infrastructure to extremely large compound collections, by use of distributed processing on Grids. Given that the above work is applicable to arbitrary types of cheminformatics problems, we also present some case studies related to virtual screening for anti-malarials and predictions of anti-cancer activity.

  4. A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

    PubMed

    Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

    2015-10-01

    Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

  5. The 3-dimentional Distribution through Physical-Chemical-Mineralogical Characteristics of Subsurface Heterogeneity in Tailings of Guryoung Mine, Korea

    NASA Astrophysics Data System (ADS)

    Moon, Y.; Park, E.; Song, Y.; Moon, H.

    2005-12-01

    To build an efficient way of handling potential risk associated with a closed mining site with abandoned waste rock and finely crushed mill tailing, detailed characterization on the site is crucial. Among the items to be characterized, mineral characteristics, particle distribution and surface/groundwater flow relationship, shape existence of heavy metal and discriminating the sulfide mineral oxidation and second minerals formation due to the rain or surface water environment may reveal very important aspects of the contaminated site such as kinds and level of contamination, contaminant generation in the event of precipitations, contaminant reaction/transport in the vadose zone, etc. Guryoung mine site in Korea, which is consistent with the purpose of this research, was selected as a research area. In order to illustrate and describe a geological heterogeneity, three-dimensional Coupled Markov chain (CMC) was used. The sampling site was constructed as elemental data using GPS. The sample's analytic data of physical-chemical-mineralogical aspects and cation exchange capacity were built as the database. The result is following: In oxidation zone (0.43 to 1.00 m), the particle contained more than 10 percent of clayey due to the weathering, and the pH was below 3 by oxidation of pyrite. The presence of jarosite, gypsum and goethite was confirmed. The cation exchange capacity was analyzed greater than 10 meq per 100 mg. In transition zone, sandy contained greater percentage, and its pH was 3 to 5. Metals and heavy metals extracted using 0.1 N HCl had the highest concentration, and pyrite took the greatest percentage. In primary zone, sandy contained the greatest percentage at the top but silt took over as going down. The pH was neutral, 5 to 8. Also calcite was identified as second mineral due to the reduction of primary zone. These results were built as database, and applying CMC, the three-dimensional distribution of tailing was confirmed.

  6. Mining Input Data for Multivariate Probabilistic Modeling of Rainfall-Induced Landslide Hazard in the Lake ATITLÁN Watershed in Guatemala

    NASA Astrophysics Data System (ADS)

    Cobin, P. F.; Oommen, T.; Gierke, J. S.

    2013-12-01

    The Lake Atitlán watershed is home to approximately 200,000 people and is located in the western highlands of Guatemala. Steep slopes, highly susceptible to landslides during the rainy season, characterize the region. Typically these landslides occur during high-intensity precipitation events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. Different datasets of landslide and non-landslide points across the watershed were used to compare model success at a small scale and regional scale. This study used data from multiple attributes: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The open source software Weka was used for the data mining. Several attribute selection methods were applied to the data to predetermine the potential landslide causative influence. Different multivariate algorithms were then evaluated for their ability to predict landslide occurrence. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The attribute combinations of the most successful models were compared to the attribute evaluator results. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points for the regions selected in the watershed. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

  7. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

    NASA Astrophysics Data System (ADS)

    Hong, Haoyuan; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat

    2016-04-01

    Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua

  8. Scaling behavior and the effects of heterogeneity on shallow seismic imaging of mineral deposits: A case study from Brunswick No. 6 mining area, Canada

    NASA Astrophysics Data System (ADS)

    Cheraghi, Saeid; Malehmir, Alireza; Bellefleur, Gilles; Bongajum, Emmanuel; Bastani, Mehrdad

    2013-03-01

    We have studied the scaling behavior of compressional-wave velocity and density logs from an exploration borehole that extends down to about 700 m depth in the Brunswick No. 6 mining area, Bathurst Mining Camp, Canada. Using statistical methods, vertical and horizontal scale lengths of heterogeneity were estimated. Vertical scale length estimates from the velocity, density and calculated acoustic impedance are 14 m, 33 m, and about 20 m, respectively. Although the estimated scale length for the acoustic impedance implies a weak scattering environment, elastic finite difference modeling of seismic wave propagation in 2D heterogeneous media demonstrates that even this weak scattering medium can mask seismic signals from small, but yet economically feasible, massive sulfide deposits. Further analysis of the synthetic seismic data suggests that in the presence of heterogeneity, lenticular-shaped targets may only exhibit incomplete diffraction signals whereby the down-dip tails of these diffractions are mainly visible on the stacked sections. Therefore, identification of orebody generated diffractions is much easier on the unmigrated stacked sections than on migrated stacked sections. The numerical seismic modeling in 2D heterogeneous media indicates that in the presence of large horizontal, but small vertical scale lengths (structural anisotropy), identification of massive sulfide deposits is possible, but their delineation at depth requires detailed velocity modeling and processing algorithms which can handle the anisotropy.

  9. Combining light microscopy, dielectric spectroscopy, MALDI intact cell mass spectrometry, FTIR spectromicroscopy and multivariate data mining for morphological and physiological bioprocess characterization of filamentous organisms.

    PubMed

    Posch, Andreas E; Koch, Cosima; Helmel, Michaela; Marchetti-Deschmann, Martina; Macfelda, Karin; Lendl, Bernhard; Allmaier, Günter; Herwig, Christoph

    2013-02-01

    Along with productivity and physiology, morphological growth behavior is the key parameter in bioprocess design for filamentous fungi. Lacking tools for fast, reliable and efficient analysis however, fungal morphology is still commonly tackled by empirical trial-and-error techniques during strain selection and process development procedures. Bridging the gap, this work presents a comprehensive analytical approach for morphological analysis combining automated high-throughput microscopy, multi-frequency dielectric spectroscopy, MALDI intact cell mass spectrometry and FTIR spectromicroscopy. Industrial fed-batch production processes were investigated in fully instrumented, automated bioreactors using the model system Penicillium chrysogenum. Physiological process characterization was based on the determination of specific conversion rates as scale-independent parameters. Conventional light microscopic morphological analysis was based on holistic determination of time series for more than 30 morphological parameters and their frequency distributions over the respective parameter range by automated high-throughput light microscopy. Characteristic protein patterns enriched in specific morphological and physiological states were further obtained by MALDI intact cell mass spectrometry. Spatial resolution of molecular biomass composition was facilitated by FTIR spectromicroscopy. Real-time in situ monitoring of morphological process behavior was achieved by linking multi-frequency dielectric spectroscopy with above outlined off-line methods. Data integration of complementing orthogonal techniques for morphological and physiological analysis together with multivariate modeling of interdependencies between morphology, physiology and process parameters facilitated complete bioprocess characterization. The suggested approach will thus help understanding morphological and physiological behavior and, in turn, allow to control and optimize those complex processes.

  10. Multivariate normality

    NASA Technical Reports Server (NTRS)

    Crutcher, H. L.; Falls, L. W.

    1976-01-01

    Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

  11. pH gradient-induced heterogeneity of Fe(III)-reducing microorganisms in coal mining-associated lake sediments.

    PubMed

    Blöthe, Marco; Akob, Denise M; Kostka, Joel E; Göschel, Kathrin; Drake, Harold L; Küsel, Kirsten

    2008-02-01

    Lakes formed because of coal mining are characterized by low pH and high concentrations of Fe(II) and sulfate. The anoxic sediment is often separated into an upper acidic zone (pH 3; zone I) with large amounts of reactive iron and a deeper slightly acidic zone (pH 5.5; zone III) with smaller amounts of iron. In this study, the impact of pH on the Fe(III)-reducing activities in both of these sediment zones was investigated, and molecular analyses that elucidated the sediment microbial diversity were performed. Fe(II) was formed in zone I and III sediment microcosms at rates that were approximately 710 and 895 nmol cm(-3) day(-1), respectively. A shift to pH 5.3 conditions increased Fe(II) formation in zone I by a factor of 2. A shift to pH 3 conditions inhibited Fe(II) formation in zone III. Clone libraries revealed that the majority of the clones from both zones (approximately 44%) belonged to the Acidobacteria phylum. Since Acidobacterium capsulatum reduced Fe oxides at pHs ranging from 2 to 5, Acidobacteria might be involved in the cycling of iron [corrected]. PCR products specific for species related to Acidiphilium revealed that there were higher numbers of phylotypes related to cultured Acidiphilium or Acidisphaera species in zone III than in zone I. From the PCR products obtained for bioleaching-associated bacteria, only one phylotype with a level of similarity to Acidithiobacillus ferrooxidans of 99% was obtained. Using primer sets specific for Geobacteraceae, PCR products were obtained in higher DNA dilutions from zone III than from zone I. Phylogenetic analysis of clone libraries obtained from Fe(III)-reducing enrichment cultures grown at pH 5.5 revealed that the majority of clones were closely related to members of the Betaproteobacteria, primarily species of Thiomonas. Our results demonstrated that the upper acidic sediment was inhabited by acidophiles or moderate acidophiles which can also reduce Fe(III) under slightly acidic conditions. The majority of

  12. Introduction to multivariate discrimination

    NASA Astrophysics Data System (ADS)

    Kégl, Balázs

    2013-07-01

    Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

  13. Multivariate image analysis in biomedicine.

    PubMed

    Nattkemper, Tim W

    2004-10-01

    In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

  14. Multivariate Voronoi Outlier Detection for Time Series.

    PubMed

    Zwilling, Chris E; Wang, Michelle Yongmei

    2014-10-01

    Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

  15. Multivariable Control Systems

    DTIC Science & Technology

    1968-01-01

    one). Examples abound of systems with numerous controlled variables, and the modern tendency is toward ever greater utilization of systems and plants of this kind. We call them multivariable control systems (MCS).

  16. Multivariate bubbles and antibubbles

    NASA Astrophysics Data System (ADS)

    Fry, John

    2014-08-01

    In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.

  17. Multivariate Data EXplorer (MDX)

    SciTech Connect

    Steed, Chad Allen

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

  18. pH Gradient-Induced Heterogeneity of Fe(III)-Reducing Microorganisms in Coal Mining-Associated Lake Sediments▿ †

    PubMed Central

    Blöthe, Marco; Akob, Denise M.; Kostka, Joel E.; Göschel, Kathrin; Drake, Harold L.; Küsel, Kirsten

    2008-01-01

    Lakes formed because of coal mining are characterized by low pH and high concentrations of Fe(II) and sulfate. The anoxic sediment is often separated into an upper acidic zone (pH 3; zone I) with large amounts of reactive iron and a deeper slightly acidic zone (pH 5.5; zone III) with smaller amounts of iron. In this study, the impact of pH on the Fe(III)-reducing activities in both of these sediment zones was investigated, and molecular analyses that elucidated the sediment microbial diversity were performed. Fe(II) was formed in zone I and III sediment microcosms at rates that were approximately 710 and 895 nmol cm−3 day−1, respectively. A shift to pH 5.3 conditions increased Fe(II) formation in zone I by a factor of 2. A shift to pH 3 conditions inhibited Fe(II) formation in zone III. Clone libraries revealed that the majority of the clones from both zones (approximately 44%) belonged to the Acidobacteria phylum. Since moderately acidophilic Acidobacteria species have the ability to oxidize Fe(II) and since Acidobacterium capsulatum reduced Fe oxides at pHs ranging from 2 to 5, this group appeared to be involved in the cycling of iron. PCR products specific for species related to Acidiphilium revealed that there were higher numbers of phylotypes related to cultured Acidiphilium or Acidisphaera species in zone III than in zone I. From the PCR products obtained for bioleaching-associated bacteria, only one phylotype with a level of similarity to Acidithiobacillus ferrooxidans of 99% was obtained. Using primer sets specific for Geobacteraceae, PCR products were obtained in higher DNA dilutions from zone III than from zone I. Phylogenetic analysis of clone libraries obtained from Fe(III)-reducing enrichment cultures grown at pH 5.5 revealed that the majority of clones were closely related to members of the Betaproteobacteria, primarily species of Thiomonas. Our results demonstrated that the upper acidic sediment was inhabited by acidophiles or moderate

  19. Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA

    USGS Publications Warehouse

    Naftz, David L.; Walton-Day, Katherine

    2016-01-01

    During 2012, approximately 404,000 ha of Federal Land in northern Arizona was withdrawn from consideration of mineral extraction for a 20-year period to protect the Grand Canyon watershed from potentially adverse effects of U mineral exploration and development. The development, operation, and reclamation of the Canyon Mine during the withdrawal period provide an excellent field site to understand and document off-site migration of radionuclides within the withdrawal area. As part of the Department of Interior's (DOI's) study plan for the exclusion area, the objective of our study is to utilize pre-defined decision units (DUs) in areas within and surrounding the Canyon Mine to demonstrate how newly established incremental sampling methodologies (ISM) combined with multivariate statistical methods can be used to document a repeatable and statistically defensible measure of pre-mining baseline conditions in surface soils and stream sediment samples prior to ore extraction. During the survey in June 2013, the highest pre-mining 95% upper confidence level (UCL) concentrations with respect to As, Mo, U, and V were found in the triplicate samples collected from surface soils in the mine site DU designated as M1. Gamma activities were slightly elevated in soils within the M1 DU (up to 28 μR/h); however, off-site gamma activities in soil and stream-sediment samples were lower (< 6 to 12 μR/h). Hierarchical cluster analysis (HCA) was applied to 33 chemical constituents contained in the multivariate data generated from the analysis of triplicate samples collected in the soil and stream sediment DUs within and surrounding Canyon Mine. Most of the triplicate samples from individual DUs were grouped in the same dendrogram cluster when using a similarity value (SV) of 0.70 (unitless). Different group membership of triplicate samples from two of the four haul road DUs was likely the result of heterogeneity induced by non-native soil material introduced from the gravel road base

  20. Transient multivariable sensor evaluation

    DOEpatents

    Vilim, Richard B.; Heifetz, Alexander

    2017-02-21

    A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

  1. Multivariate Quantitative Chemical Analysis

    NASA Technical Reports Server (NTRS)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

    Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

  2. Longwall mining

    SciTech Connect

    1995-03-14

    As part of EIA`s program to provide information on coal, this report, Longwall-Mining, describes longwall mining and compares it with other underground mining methods. Using data from EIA and private sector surveys, the report describes major changes in the geologic, technological, and operating characteristics of longwall mining over the past decade. Most important, the report shows how these changes led to dramatic improvements in longwall mining productivity. For readers interested in the history of longwall mining and greater detail on recent developments affecting longwall mining, the report includes a bibliography.

  3. Clinical and pharmacogenomic data mining: 2. A simple method for the combination of information from associations and multivariances to facilitate analysis, decision, and design in clinical research and practice.

    PubMed

    Robson, Barry; Mushlin, Richard

    2004-01-01

    The physician and researcher must ultimately be able to combine qualitative and quantitative features from a variety of combinations of observations on data of many component items (i.e., many dimensions), and hence reach simple conclusions about interpretation, rational courses of action, and design. In the first paper of this series, it was noted that such needs are challenging the classical means of using statistics. Hence, the paper proposed the use of a Generalized Theory of Expected Information or "Zeta Theory". The conjoint event [a,b,c,..] is seen as a rule of association for a,b,c,.. associated with a rule strength I(a;b;c;...) = xi(s,o[a,b,c,..]) - xi (s,e[a,b,c,...]), where xi is the incomplete Zeta Function. Here, o[a,b,c,...] is the observed, and e[a,b,c,..] the expected, frequency of occurrence of conjoint event [a,b,c,...]. The present paper explores how output from this approach might be assembled in a form better suited for decision support. Related to this is the difficulty that the treatment of covariance and multivariance was previously rendered as a "fuzzy association" so that the output would fall into a similar form as the true associations, but this was a somewhat ad hoc approach in which only the final I( ) had any meaning. Users at clinical research sites had subsequently requested an alternative approach in which "effective frequencies" o[ ] and e[ ] calculated from the above variances and used to evaluate I( ) give some intuitive feeling analogous to the association treatment, and this is explored here. Though the present paper is theoretical, real examples are used to illustrate application. One clinical-genomic example illustrates experimental design by identifying data which is, or is not, statistically germane to the study. We also report on some impressions based on applying these techniques in studies of real, extensive patient record data which are now emerging, as well as on molecular design data originally studied in part to

  4. Topics in Multivariate Approximation Theory.

    DTIC Science & Technology

    1982-05-01

    include tensor products, multivariate polynomial interpolation , esp. Kergin Interpolation , and the recent developments of multivariate B-splines. t1...AMS (MOS) Subject Classifications: 41-02, 41A05, 41A10, 41A15, 41A63, 41A65 Key Words: multivariate, B-splines, Kergin interpolation , linear projectors...splines and in multivariate polynomial interpolation . These developments may well provide the theoretical foundation for efficient methods of

  5. Multivariate volume rendering

    SciTech Connect

    Crawfis, R.A.

    1996-03-01

    This paper presents a new technique for representing multivalued data sets defined on an integer lattice. It extends the state-of-the-art in volume rendering to include nonhomogeneous volume representations. That is, volume rendering of materials with very fine detail (e.g. translucent granite) within a voxel. Multivariate volume rendering is achieved by introducing controlled amounts of noise within the volume representation. Varying the local amount of noise within the volume is used to represent a separate scalar variable. The technique can also be used in image synthesis to create more realistic clouds and fog.

  6. Heterogeneous Catalysis.

    ERIC Educational Resources Information Center

    Miranda, R.

    1989-01-01

    Described is a heterogeneous catalysis course which has elements of materials processing embedded in the classical format of catalytic mechanisms and surface chemistry. A course outline and list of examples of recent review papers written by students are provided. (MVL)

  7. Heterogeneous catalysis.

    PubMed

    Schlögl, Robert

    2015-03-09

    A heterogeneous catalyst is a functional material that continually creates active sites with its reactants under reaction conditions. These sites change the rates of chemical reactions of the reactants localized on them without changing the thermodynamic equilibrium between the materials.

  8. Web Mining

    NASA Astrophysics Data System (ADS)

    Fürnkranz, Johannes

    The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. This chapter provides a brief overview of web mining techniques and research areas, most notably hypertext classification, wrapper induction, recommender systems and web usage mining.

  9. Data Mining.

    ERIC Educational Resources Information Center

    Benoit, Gerald

    2002-01-01

    Discusses data mining (DM) and knowledge discovery in databases (KDD), taking the view that KDD is the larger view of the entire process, with DM emphasizing the cleaning, warehousing, mining, and visualization of knowledge discovery in databases. Highlights include algorithms; users; the Internet; text mining; and information extraction.…

  10. Mining in chemometrics.

    PubMed

    Mutihac, Lucia; Mutihac, Radu

    2008-03-31

    Some of the increasingly spread data mining methods in chemometrics like exploratory data analysis, artificial neural networks, pattern recognition, and digital image processing with their highs and lows along with some of their representative applications are discussed. The development of more complex analytical instruments and the need to cope with larger experimental data sets have demanded for new approaches in data analysis, which have led to advanced methods in experimental design and data processing. Hypothesis-driven methods typified by inferential statistics have been gradually complemented or even replaced by data-driven model-free methods that seek for structure in data without reference to the experimental protocol or prior hypotheses. The emphasis is put on the ability of data mining methods to solve multivariate-multiresponse problems on the basis of experimental data and minimal statistical assumptions only, in contrast to classical methods, which require predefined priors to be tested against some null-hypothesis.

  11. Angles of multivariable root loci

    NASA Technical Reports Server (NTRS)

    Thompson, P. M.; Stein, G.; Laub, A. J.

    1982-01-01

    A generalized eigenvalue problem is demonstrated to be useful for computing the multivariable root locus, particularly when obtaining the arrival angles to finite transmission zeros. The multivariable root loci are found for a linear, time-invariant output feedback problem. The problem is then employed to compute a closed-loop eigenstructure. The method of computing angles on the root locus is demonstrated, and the method is extended to a multivariable optimal root locus.

  12. Scales of mantle heterogeneity

    NASA Astrophysics Data System (ADS)

    Moore, J. C.; Akber-Knutson, S.; Konter, J.; Kellogg, J.; Hart, S.; Kellogg, L. H.; Romanowicz, B.

    2004-12-01

    A long-standing question in mantle dynamics concerns the scale of heterogeneity in the mantle. Mantle convection tends to both destroy (through stirring) and create (through melt extraction and subduction) heterogeneity in bulk and trace element composition. Over time, these competing processes create variations in geochemical composition along mid-oceanic ridges and among oceanic islands, spanning a range of scales from extremely long wavelength (for example, the DUPAL anomaly) to very small scale (for example, variations amongst melt inclusions). While geochemical data and seismic observations can be used to constrain the length scales of mantle heterogeneity, dynamical mixing calculations can illustrate the processes and timescales involved in stirring and mixing. At the Summer 2004 CIDER workshop on Relating Geochemical and Seismological Heterogeneity in the Earth's Mantle, an interdisciplinary group evaluated scales of heterogeneity in the Earth's mantle using a combined analysis of geochemical data, seismological data and results of numerical models of mixing. We mined the PetDB database for isotopic data from glass and whole rock analyses for the Mid-Atlantic Ridge (MAR) and the East Pacific Rise (EPR), projecting them along the ridge length. We examined Sr isotope variability along the East Pacific rise by looking at the difference in Sr ratio between adjacent samples as a function of distance between the samples. The East Pacific Rise exhibits an overall bowl shape of normal MORB characteristics, with higher values in the higher latitudes (there is, however, an unfortunate gap in sampling, roughly 2000 km long). These background characteristics are punctuated with spikes in values at various locations, some, but not all of which are associated with off-axis volcanism. A Lomb-Scargle periodogram for unevenly spaced data was utilized to construct a power spectrum of the scale lengths of heterogeneity along both ridges. Using the same isotopic systems (Sr, Nd

  13. Implications of Emerging Data Mining

    NASA Astrophysics Data System (ADS)

    Kulathuramaiyer, Narayanan; Maurer, Hermann

    Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

  14. Association Analysis for Visual Exploration of Multivariate Scientific Data Sets.

    PubMed

    Liu, Xiaotong; Shen, Han-Wei

    2016-01-01

    The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

  15. Modular multivariable control improves hydrocracking

    SciTech Connect

    Chia, T.L.; Lefkowitz, I.; Tamas, P.D.

    1996-10-01

    Modular multivariable control (MMC), a system of interconnected, single process variable controllers, can be a user-friendly, reliable and cost-effective alternative to centralized, large-scale multivariable control packages. MMC properties and features derive directly from the properties of the coordinated controller which, in turn, is based on internal model control technology. MMC was applied to a hydrocracking unit involving two process variables and three controller outputs. The paper describes modular multivariable control, MMC properties, tuning considerations, application at the DCS level, constraints handling, and process application and results.

  16. Multivariate Visual Explanation for High Dimensional Datasets

    PubMed Central

    Barlowe, Scott; Zhang, Tianyi; Liu, Yujie; Yang, Jing; Jacobs, Donald

    2010-01-01

    Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. PMID:20694164

  17. Network structure of multivariate time series

    PubMed Central

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

  18. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  19. Visual cues for data mining

    NASA Astrophysics Data System (ADS)

    Rogowitz, Bernice E.; Rabenhorst, David A.; Gerth, John A.; Kalin, Edward B.

    1996-04-01

    This paper describes a set of visual techniques, based on principles of human perception and cognition, which can help users analyze and develop intuitions about tabular data. Collections of tabular data are widely available, including, for example, multivariate time series data, customer satisfaction data, stock market performance data, multivariate profiles of companies and individuals, and scientific measurements. In our approach, we show how visual cues can help users perform a number of data mining tasks, including identifying correlations and interaction effects, finding clusters and understanding the semantics of cluster membership, identifying anomalies and outliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies on perceptual organization, visual search, perceptual scaling, and color perception. These visual techniques are presented as a complement to the statistical and algorithmic methods more commonly associated with these tasks, and provide an interactive interface for the human analyst.

  20. African mining

    SciTech Connect

    Not Available

    1987-01-01

    This book contains papers presented at a conference addressing the development of the minerals industry in Africa. Topics covered include: A review - past, present and future - of Zimbabwe's mining industry; Geomorphological processes and related mineralization in Tanzania; and Rock mechanics investigations at Mufulira mine, Zambia.

  1. Multivariate analysis in thoracic research.

    PubMed

    Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

    2015-03-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

  2. Multivariate analysis in thoracic research

    PubMed Central

    Mengual-Macenlle, Noemí; Marcos, Pedro J.; Golpe, Rafael

    2015-01-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. PMID:25922743

  3. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    Dunson, David B.

    2013-01-01

    Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563

  4. Compound Data Mining for Drug Discovery.

    PubMed

    Bajorath, Jürgen

    2017-01-01

    In recent years, there has been unprecedented growth in compound activity data in the public domain. These compound data provide an indispensable resource for drug discovery in academic environments as well as in the pharmaceutical industry. To handle large volumes of heterogeneous and complex compound data and extract discovery-relevant knowledge from these data, advanced computational mining approaches are required. Herein, major public compound data repositories are introduced, data confidence criteria reviewed, and selected data mining approaches discussed.

  5. Multivariate Analog of Hays Omega-Squared.

    ERIC Educational Resources Information Center

    Sachdeva, Darshan

    The multivariate analog of Hays omega-squared for estimating the strength of the relationship in the multivariate analysis of variance has been proposed in this paper. The multivariate omega-squared is obtained through the use of Wilks' lambda test criterion. Application of multivariate omega-squared to a numerical example has been provided so as…

  6. Optimizing functional network representation of multivariate time series.

    PubMed

    Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; del Pozo, Francisco; Menasalvas, Ernestina; Boccaletti, Stefano

    2012-01-01

    By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

  7. Optimizing Functional Network Representation of Multivariate Time Series

    NASA Astrophysics Data System (ADS)

    Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano

    2012-09-01

    By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

  8. Parameter Sensitivity in Multivariate Methods

    ERIC Educational Resources Information Center

    Green, Bert F., Jr.

    1977-01-01

    Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)

  9. Multivariate Model of Infant Competence.

    ERIC Educational Resources Information Center

    Kierscht, Marcia Selland; Vietze, Peter M.

    This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…

  10. Asteroid mining

    NASA Technical Reports Server (NTRS)

    Gertsch, Richard E.

    1992-01-01

    The earliest studies of asteroid mining proposed retrieving a main belt asteroid. Because of the very long travel times to the main asteroid belt, attention has shifted to the asteroids whose orbits bring them fairly close to the Earth. In these schemes, the asteroids would be bagged and then processed during the return trip, with the asteroid itself providing the reaction mass to propel the mission homeward. A mission to one of these near-Earth asteroids would be shorter, involve less weight, and require a somewhat lower change in velocity. Since these asteroids apparently contain a wide range of potentially useful materials, our study group considered only them. The topics covered include asteroid materials and properties, asteroid mission selection, manned versus automated missions, mining in zero gravity, and a conceptual mining method.

  11. Multivariable PID control by decoupling

    NASA Astrophysics Data System (ADS)

    Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

    2016-04-01

    This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

  12. Information extraction from multivariate images

    NASA Technical Reports Server (NTRS)

    Park, S. K.; Kegley, K. A.; Schiess, J. R.

    1986-01-01

    An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

  13. Multivariate Bioclimatic Ecosystem Change Approaches

    DTIC Science & Technology

    2015-02-06

    Headquarters, US Army Corps of Engineers Washington, DC 20314-1000 ERDC/CERL TR-15-2 ii Abstract Changes in climatic parameters are important in that they... climatic changes on specific installations. To support this need, the research tested and evaluated the application of six multivariate approach...techniques to predict climatic changes on a specific Army installation, Fort Benning, GA. The six approaches were tested for their ability to identify

  14. Clines Arc through Multivariate Morphospace.

    PubMed

    Lohman, Brian K; Berner, Daniel; Bolnick, Daniel I

    2017-04-01

    Evolutionary biologists typically represent clines as spatial gradients in a univariate character (or a principal-component axis) whose mean changes as a function of location along a transect spanning an environmental gradient or ecotone. This univariate approach may obscure the multivariate nature of phenotypic evolution across a landscape. Clines might instead be plotted as a series of vectors in multidimensional morphospace, connecting sequential geographic sites. We present a model showing that clines may trace nonlinear paths that arc through morphospace rather than elongating along a single major trajectory. Arcing clines arise because different characters diverge at different rates or locations along a geographic transect. We empirically confirm that some clines arc through morphospace, using morphological data from threespine stickleback sampled along eight independent transects from lakes down their respective outlet streams. In all eight clines, successive vectors of lake-stream divergence fluctuate in direction and magnitude in trait space, rather than pointing along a single phenotypic axis. Most clines exhibit surprisingly irregular directions of divergence as one moves downstream, although a few clines exhibit more directional arcs through morphospace. Our results highlight the multivariate complexity of clines that cannot be captured with the traditional graphical framework. We discuss hypotheses regarding the causes, and implications, of such arcing multivariate clines.

  15. Planning the Mine and Mining the Plan

    NASA Astrophysics Data System (ADS)

    Boucher, D. S.; Chen, N.

    2016-11-01

    Overview of best practices used in the terrestrial mining industry when developing a mine site towards production. The intent is to guide planners towards an effective and well constructed roadmap for the development of ISRU mining activities. A strawman scenario is presented as an illustration for lunar mining of water ice.

  16. Multivariate Strategies in Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Hansen, Lars Kai

    2007-01-01

    We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

  17. Visualizing frequent patterns in large multivariate time series

    NASA Astrophysics Data System (ADS)

    Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

    2011-01-01

    The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

  18. Multivariate residues and maximal unitarity

    NASA Astrophysics Data System (ADS)

    Søgaard, Mads; Zhang, Yang

    2013-12-01

    We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.

  19. Software For Multivariate Bayesian Classification

    NASA Technical Reports Server (NTRS)

    Saul, Ronald; Laird, Philip; Shelton, Robert

    1996-01-01

    PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.

  20. The Mechanization of Mining.

    ERIC Educational Resources Information Center

    Marovelli, Robert L.; Karhnak, John M.

    1982-01-01

    Mechanization of mining is explained in terms of its effect on the mining of coal, focusing on, among others, types of mining, productivity, machinery, benefits to retired miners, fatality rate in underground coal mines, and output of U.S. mining industry. (Author/JN)

  1. Northern Trust Mines

    EPA Pesticide Factsheets

    The United States and the Navajo Nation entered into settlement agreements that provide funds to conduct investigations and any needed cleanup at 16 of the 46 priority mines, including six mines in the Northern Abandoned Uranium Mine Region.

  2. Exploration and Mining Roadmap

    SciTech Connect

    none,

    2002-09-01

    This Exploration and Mining Technology Roadmap represents the third roadmap for the Mining Industry of the Future. It is based upon the results of the Exploration and Mining Roadmap Workshop held May 10 ñ 11, 2001.

  3. Method of multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2004-01-06

    A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

  4. Mining review

    USGS Publications Warehouse

    McCartan, L.; Morse, D.E.; Plunkert, P.A.; Sibley, S.F.

    2004-01-01

    The average annual growth rate of real gross domestic product (GDP) from the third quarter of 2001 through the second quarter of 2003 in the United States was about 2.6 percent. GDP growth rates in the third and fourth quarters of 2003 were about 8 percent and 4 percent, respectively. The upward trends in many sectors of the U.S. economy in 2003, however, were shared by few of the mineral materials industries. Annual output declined in most nonfuel mining and mineral processing industries, although there was an upward turn toward yearend as prices began to increase.

  5. Surface mining

    SciTech Connect

    Not Available

    1989-06-01

    This paper reports on a GAO study of attorney and expert witness fees awarded as a result of litigation brought under the Surface Mining Control and Reclamation Act. As of March 24, 1989, a total of about $1.4 million had been awarded in attorney fees and expenses - about $1.3 subject to the provisions of the Employee Retirement Income Security Act, a comparison of its features with provisions of ERISA showed that the plan differed from ERISA provisions in areas such as eligibility, funding, and contribution limits.

  6. Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

    PubMed

    Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

    2017-03-01

    Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page.

  7. Wikipedia Mining

    NASA Astrophysics Data System (ADS)

    Nakayama, Kotaro; Ito, Masahiro; Erdmann, Maike; Shirakawa, Masumi; Michishita, Tomoyuki; Hara, Takahiro; Nishio, Shojiro

    Wikipedia, a collaborative Wiki-based encyclopedia, has become a huge phenomenon among Internet users. It covers a huge number of concepts of various fields such as arts, geography, history, science, sports and games. As a corpus for knowledge extraction, Wikipedia's impressive characteristics are not limited to the scale, but also include the dense link structure, URL based word sense disambiguation, and brief anchor texts. Because of these characteristics, Wikipedia has become a promising corpus and a new frontier for research. In the past few years, a considerable number of researches have been conducted in various areas such as semantic relatedness measurement, bilingual dictionary construction, and ontology construction. Extracting machine understandable knowledge from Wikipedia to enhance the intelligence on computational systems is the main goal of "Wikipedia Mining," a project on CREP (Challenge for Realizing Early Profits) in JSAI. In this paper, we take a comprehensive, panoramic view of Wikipedia Mining research and the current status of our challenge. After that, we will discuss about the future vision of this challenge.

  8. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

  9. Detrended fluctuation analysis of multivariate time series

    NASA Astrophysics Data System (ADS)

    Xiong, Hui; Shang, P.

    2017-01-01

    In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.

  10. Multivariate meta-analysis: potential and promise.

    PubMed

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-09-10

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.

  11. Unsupervised classification of multivariate geostatistical data: Two algorithms

    NASA Astrophysics Data System (ADS)

    Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

    2015-12-01

    With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

  12. Mardia's Multivariate Kurtosis with Missing Data

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.

    2004-01-01

    Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…

  13. Multivariate Density Estimation and Remote Sensing

    NASA Technical Reports Server (NTRS)

    Scott, D. W.

    1983-01-01

    Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

  14. Multivariate pluvial flood damage models

    SciTech Connect

    Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom

    2015-09-15

    Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.

  15. Impact of uranium mines closure and abandonment on groundwater quality.

    PubMed

    Rapantova, Nada; Licbinska, Monika; Babka, Ondrej; Grmela, Arnost; Pospisil, Pavel

    2013-11-01

    The aim of the study is to assess the evolving mine water quality of closed uranium mines (abandoned between 1958 and 1992) in the Czech Republic. This paper focuses on the changes in mine water quality over time and spatial variability. In 2010, systematic monitoring of mine water quality was performed at all available locations of previous uranium exploitation. Gravity flow discharges (mine adits, uncontrolled discharges) or shafts (in dynamic state or stagnating) were sampled. Since the quality of mine water results from multiple conditions-geology, type of sample, sampling depth, time since mine flooding, an assessment of mine water quality evolution was done taking into account all these conditions. Multivariate analyses were applied in order to identify the groups of samples based on their similarity. Evaluation of hydrogeochemical equilibrium and evolution of mine waters was done using the Geochemist's Workbench and PHREEQC software. The sampling proved that uranium concentrations in mine waters did not predominantly exceed 0.45 mg/L. In case of discharges from old adits abandoned more than 40 years ago, uranium concentrations were below the MCL of US Environmental Protection Agency for uranium in drinking water (0.03 mg/L). Higher concentrations, up to 1.23 mg/L of U, were found only at active dewatered mines. Activity concentration of 226Ra varied from 0.03 up to 1.85 Bq/L except for two sites with increased background values due to rock formation (granites). Radium has a typically increasing trend after mine abandonment with a large variability. Concerning metals in mine water, Al, Co and Ni exceeded legislative limits on two sites with low pH waters. The mine water quality changes with a focus on uranium mobility were described from recently dewatered mines to shafts with water level maintained in order to prevent outflows to surface water and finally to stagnating shafts and discharges of mine water from old adits. The results were in good agreement

  16. German mining equipment

    SciTech Connect

    Not Available

    1993-10-01

    The German mining equipment industry developed to supply machines and services to the local mining industry, i.e., coal, lignite, salt, potash, ore mining, industrial minerals, and quarrying. The sophistication and reliability of its technology also won it worldwide export markets -- which is just as well since former major domestic mining sectors such as coal and potash have declined precipitously, and others such as ore mining have all but disappeared. Today, German mining equipment suppliers focus strongly on export sales, and formerly unique German mining technologies such as continuous mining with bucket wheel excavators and conveyors for open pits, or plowing of underground coal longwalls are widely used abroad. The status of the German mining equipment industry is reviewed.

  17. 4. OVERALL VIEW OF MINE SITE, SHOWING MINE CAR TRACKS, ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    4. OVERALL VIEW OF MINE SITE, SHOWING MINE CAR TRACKS, SNOWSHEDS AND TIPPLE (LEFT BACKGROUND). VIEW TO EAST. - Park Utah Mining Company: Keetley Mine Complex, 1 mile East of U.S. 40 at Keetley, Heber City, Wasatch County, UT

  18. Multivariate normative comparisons using an aggregated database.

    PubMed

    Agelink van Rentergem, Joost A; Murre, Jaap M J; Huizenga, Hilde M

    2017-01-01

    In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.

  19. Multivariate normative comparisons using an aggregated database

    PubMed Central

    Murre, Jaap M. J.; Huizenga, Hilde M.

    2017-01-01

    In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

  20. Landfill mining: Giving garbage a second chance

    SciTech Connect

    Cobb, C.C.; Ruckstuhl, K. )

    1988-08-01

    Some communities face the problems of lack of landfill space and lack of landfill cover dirt. In some cases, existing landfills may be mined to reclaim dirt for use as cover material and to recover space for reuse. Such mining also has the potential of recovering recyclables and incinerator fuels. Machinery to reclaim refuse deposits, and their heterogeneous composted ingredients, was successfully tested at two Florida landfills in June 1987. One of the Florida mining tests, at the Collier County landfill near the city of Naples, had goals of demonstrating an economical mechanical system to separate the depository's ingredients into usable or redisposable components, and to see if the method could enable the county to avoid the expenses associated with permanent closure of a full landfill. This paper describes the history of the Collier County landfill, the equipment and feasibility test, economics, the monitoring of odors, landfill gas, and heavy metals, and results of the test.

  1. Exposures from mining and mine tailings

    NASA Astrophysics Data System (ADS)

    Chambers, Douglas B.; Cassaday, Valerie J.; Lowe, Leo M.

    The mining, milling and tailings management of uranium ores results in environmental radiation exposures. This paper describes the sources of radioactive emissions to the environment associated with these activities, reviews the basic approach used to estimate the resultant radiation exposures and presents examples of typical uranium mind and mill facilities. Similar concepts apply to radiation exposures associated with the mining of non-radioactive ores although the magnitudes of the exposures would normally be smaller than those associated with uranium mining.

  2. Phenotypically heterogeneous populations in spatially heterogeneous environments

    NASA Astrophysics Data System (ADS)

    Patra, Pintu; Klumpp, Stefan

    2014-03-01

    The spatial expansion of a population in a nonuniform environment may benefit from phenotypic heterogeneity with interconverting subpopulations using different survival strategies. We analyze the crossing of an antibiotic-containing environment by a bacterial population consisting of rapidly growing normal cells and slow-growing, but antibiotic-tolerant persister cells. The dynamics of crossing is characterized by mean first arrival times and is found to be surprisingly complex. It displays three distinct regimes with different scaling behavior that can be understood based on an analytical approximation. Our results suggest that a phenotypically heterogeneous population has a fitness advantage in nonuniform environments and can spread more rapidly than a homogeneous population.

  3. Mining lease handbook

    SciTech Connect

    Not Available

    1992-01-01

    Mining leases and similar agreements are some of the most common documents encountered by mining attorneys. The mining Lease Handbook contains a collection of mining lease clauses which have been organized and assembled for over 25 years. The clauses in this book have been coordinated and cross-referenced to enable the Handbook user to create a mining lease having a logical structure with consistent terminology throughout. In many cases, alternative clauses are included. The accompanying commentary provides insight into the use of the various clauses while pointing our pitfalls to be avoided. This Handbook is devoted primarily to mining leases, several chapters cover the subjects of options, subleases, and ancillary documents.

  4. Multivariate Behavior Genetic Analyses of Aggressive Behavior Subtypes

    PubMed Central

    Yeh, Michelle T.; Coccaro, Emil F.; Jacobson, Kristen C.

    2012-01-01

    This study examined the genetic and environmental architecture underlying aggressive behavior measured by the Life History of Aggression Questionnaire (LHA; Coccaro et al. 1997a). Following preliminary phenotypic factor analysis procedures, multivariate behavioral genetics models were fit to responses from 2,925 adult twins from the PennTwins cohort on five LHA items assessing lifetime frequency of temper tantrums, indirect aggression, verbal aggression, fighting, and physical assault. The best-fitting model was a 2-factor common pathway model, indicating that these five aggressive behaviors are underpinned by two distinct etiological factors with different genetic and nonshared environmental influences. Although there was evidence of significant sex differences, the structure of the two factors appeared to be quite similar in males and females, where General Aggression and Physical Aggression factors emerged. Heritability of these factors ranged from .37 to .57, and nonshared environmental effects ranged from .43 to .63. The results of this study highlight the heterogeneous nature of the aggression construct and the need to consider differences in genetic and environmental influences on individual aggressive behaviors in a multivariate context. PMID:20432061

  5. Patterns of Emphysema Heterogeneity

    PubMed Central

    Valipour, Arschang; Shah, Pallav L.; Gesierich, Wolfgang; Eberhardt, Ralf; Snell, Greg; Strange, Charlie; Barry, Robert; Gupta, Avina; Henne, Erik; Bandyopadhyay, Sourish; Raffy, Philippe; Yin, Youbing; Tschirren, Juerg; Herth, Felix J.F.

    2016-01-01

    Background Although lobar patterns of emphysema heterogeneity are indicative of optimal target sites for lung volume reduction (LVR) strategies, the presence of segmental, or sublobar, heterogeneity is often underappreciated. Objective The aim of this study was to understand lobar and segmental patterns of emphysema heterogeneity, which may more precisely indicate optimal target sites for LVR procedures. Methods Patterns of emphysema heterogeneity were evaluated in a representative cohort of 150 severe (GOLD stage III/IV) chronic obstructive pulmonary disease (COPD) patients from the COPDGene study. High-resolution computerized tomography analysis software was used to measure tissue destruction throughout the lungs to compute heterogeneity (≥ 15% difference in tissue destruction) between (inter-) and within (intra-) lobes for each patient. Emphysema tissue destruction was characterized segmentally to define patterns of heterogeneity. Results Segmental tissue destruction revealed interlobar heterogeneity in the left lung (57%) and right lung (52%). Intralobar heterogeneity was observed in at least one lobe of all patients. No patient presented true homogeneity at a segmental level. There was true homogeneity across both lungs in 3% of the cohort when defining heterogeneity as ≥ 30% difference in tissue destruction. Conclusion Many LVR technologies for treatment of emphysema have focused on interlobar heterogeneity and target an entire lobe per procedure. Our observations suggest that a high proportion of patients with emphysema are affected by interlobar as well as intralobar heterogeneity. These findings prompt the need for a segmental approach to LVR in the majority of patients to treat only the most diseased segments and preserve healthier ones. PMID:26430783

  6. Accumulation of heavy metals by vegetables grown in mine wastes

    SciTech Connect

    Cobb, G.P.; Sands, K.; Waters, M.; Wixson, B.G.; Dorward-King, E.

    2000-03-01

    Lead, cadmium, arsenic, and zinc were quantified in mine wastes and in soils mixed with mine wastes. Metal concentrations were found to be heterogeneous in the wastes. Iceberg lettuce, Cherry Belle radishes, Roma bush beans, and Better Boy tomatoes were cultivated in mine wastes and in waste-amended soils. Lettuce and radishes had 100% survival in the 100% mine waste treatments compared to 0% and 25% survival for tomatoes and beans, respectively. Metal concentrations were determined in plant tissues to determine uptake and distribution of metals in the edible plant parts. Individual soil samples were collected beneath each plant to assess metal content in the immediate plant environment. This analysis verified heterogeneous metal content of the mine wastes. The four plant species effectively accumulated and translocated lead, cadmium, arsenic, and zinc. Tomato and bean plants contained the four metals mainly in the roots and little was translocated to the fruits. Radish roots accumulated less metals compared to the leaves, whereas lettuce roots and leaves accumulated similar concentrations of the four metals. Lettuce leaves and radish roots accumulated significantly more metals than bean and tomato fruits. This accumulation pattern suggests that consumption of lettuce leaves or radish roots from plants grown in mine wastes would pose greater risks to humans and wildlife than would consumption of beans or tomatoes grown in the same area. The potential risk may be mitigated somewhat in humans, as vegetables grown in mine wastes exhibited stunted growth and chlorosis.

  7. Multivariate Statistical Mapping of Spectroscopic Imaging Data

    PubMed Central

    Young, K.; Govind, V.; Sharma, K.; Studholme, C.; Maudsley, A.A; Schuff, N.

    2010-01-01

    For magnetic resonance spectroscopic imaging (MRSI) studies of the brain it is important to measure the distribution of metabolites in a regionally unbiased way - that is without restrictions to apriori defined regions of interest (ROI). Since MRSI provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of MRSI for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues but it has not been previously employed for SI studies of brain. The aims of this study were to: 1) develop and validate multivariate voxel based statistical mapping for MRSI and 2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons. PMID:19953514

  8. Tumour Cell Heterogeneity

    PubMed Central

    Gay, Laura; Baker, Ann-Marie; Graham, Trevor A.

    2016-01-01

    The population of cells that make up a cancer are manifestly heterogeneous at the genetic, epigenetic, and phenotypic levels. In this mini-review, we summarise the extent of intra-tumour heterogeneity (ITH) across human malignancies, review the mechanisms that are responsible for generating and maintaining ITH, and discuss the ramifications and opportunities that ITH presents for cancer prognostication and treatment. PMID:26973786

  9. Abandoned Mine Lands

    EPA Pesticide Factsheets

    Abandoned Mine Lands are those lands, waters, and surrounding watersheds where extraction, beneficiation, or processing of ores and minerals (excluding coal) has occurred. These lands also include areas where mining or processing activity is inactive.

  10. A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity.

    PubMed

    Zeng, Qiang; Wen, Huiying; Huang, Helai; Pei, Xin; Wong, S C

    2017-02-01

    In this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002-2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R(2), while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors' effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis.

  11. Analysis of Mining Terrain Deformation Characteristics with Deformation Information System

    NASA Astrophysics Data System (ADS)

    Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

    2014-05-01

    Mapping and prediction of mining related deformations of the earth surface is an important measure for minimising threat to surface infrastructure, human population, the environment and safety of the mining operation itself arising from underground extraction of useful minerals. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and increasing with the development of geographical information technologies. These include for example: terrestrial geodetic measurements, global positioning systems, remote sensing, spatial interpolation, finite element method modelling, GIS based modelling, geological modelling, empirical modelling using the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The aim of this paper is to introduce the concept of an integrated Deformation Information System (DIS) developed in geographic information systems environment for analysis and modelling of various spatial data related to mining activity and demonstrate its applications for mapping and visualising, as well as identifying possible mining terrain deformation areas with various spatial modelling methods. The DIS concept is based on connected modules that include: the spatial database - the core of the system, the spatial data collection module formed by: terrestrial, satellite and remote sensing measurements of the ground changes, the spatial data mining module for data discovery and extraction, the geological modelling module, the spatial data modeling module with data processing algorithms for spatio-temporal analysis and mapping of mining deformations and their characteristics (e.g. deformation parameters: tilt, curvature and horizontal strain), the multivariate spatial data classification module and the visualization module allowing two-dimensional interactive and static mapping and three-dimensional visualizations of mining ground characteristics. The Systems's functionality has been presented on

  12. Mountaintop mining update

    SciTech Connect

    Buchsbaum, L.

    2006-07-15

    In a bad year for the US mining industry's safety record and public image, Morehead State University hosted a public meeting titled 'Mountaintop mining, health and safety forum'. This was a balanced event, with representatives from the mining industry as well as activists from the environmental community. A full account is given of the presentations and debate at the forum. 6 photos.

  13. Data Mining for CRM

    NASA Astrophysics Data System (ADS)

    Thearling, Kurt

    Data Mining technology allows marketing organizations to better understand their customers and respond to their needs. This chapter describes how Data Mining can be combined with customer relationship management to help drive improved interactions with customers. An example showing how to use Data Mining to drive customer acquisition activities is presented.

  14. Combined mining: discovering informative knowledge in complex data.

    PubMed

    Cao, Longbing; Zhang, Huaifeng; Zhao, Yanchang; Luo, Dan; Zhang, Chengqi

    2011-06-01

    Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

  15. Heterogeneous Atmospheric Chemistry

    NASA Astrophysics Data System (ADS)

    Schryer, David R.

    In the past few years it has become increasingly clear that heterogeneous, or multiphase, processes play an important role in the atmosphere. Unfortunately the literature on the subject, although now fairly extensive, is still rather dispersed. Furthermore, much of the expertise regarding heterogeneous processes lies in fields not directly related to atmospheric science. Therefore, it seemed desirable to bring together for an exchange of ideas, information, and methodologies the various atmospheric scientists who are actively studying heterogeneous processes as well as other researchers studying similar processes in the context of other fields.

  16. Data mining in radiology.

    PubMed

    Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

    2014-04-01

    Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining.

  17. Towards heterogeneous distributed debugging

    SciTech Connect

    Damodaran-Kamal, S.K.

    1995-04-01

    Several years of research and development in parallel debugger design have given up several techniques, though implemented in a wide range of tools for an equally wide range of systems. This paper is an evaluation of these myriad techniques as applied to the design of a heterogeneous distributed debugger. The evaluation is based on what features users perceive as useful, as well as the ease of implementation of the features using the available technology. A preliminary architecture for such a heterogeneous tool is proposed. Our effort in this paper is significantly different from the other efforts at creating portable and heterogeneous distributed debuggers in that we concentrate on support for all the important issues in parallel debugging, instead of simply concentrating on portability and heterogeneity.

  18. Heterogeneous atmospheric chemistry

    NASA Technical Reports Server (NTRS)

    Schryer, D. R.

    1982-01-01

    The present conference on heterogeneous atmospheric chemistry considers such topics concerning clusters, particles and microparticles as common problems in nucleation and growth, chemical kinetics, and catalysis, chemical reactions with aerosols, electron beam studies of natural and anthropogenic microparticles, and structural studies employing molecular beam techniques, as well as such gas-solid interaction topics as photoassisted reactions, catalyzed photolysis, and heterogeneous catalysis. Also discussed are sulfur dioxide absorption, oxidation, and oxidation inhibition in falling drops, sulfur dioxide/water equilibria, the evidence for heterogeneous catalysis in the atmosphere, the importance of heterogeneous processes to tropospheric chemistry, soot-catalyzed atmospheric reactions, and the concentrations and mechanisms of formation of sulfate in the atmospheric boundary layer.

  19. Heterogeneous basic catalysis

    SciTech Connect

    Hattori, Hideshi

    1995-05-01

    Heterogeneous acid catalysis attracted much attention primarily because heterogeneous acidic catalysts act as catalysts in petroleum refinery and are known as a main catalyst in the cracking process which is the largest process among the industrial chemical processes. In contrast to these extensive studies of heterogeneous acidic catalysts, fewer efforts have been given to the study of heterogeneous basic catalysts. The types of heterogeneous basic catalysts are listed in Table 1. Except for non-oxide catalysts, the basic sites are believed to be surface O atoms. The studies of heterogeneous catalysis have been continuous and progressed steadily. They have never been reviewed in the chemical Reviews before. It is more useful and informative to describe the studies of heterogeneous basic catalysis performed for a long period. In the present article, therefore, the cited papers are not restricted to those published recently, but include those published for the last 25 years. The paper first describes the generation of basic sites before describing methods used in the characterization of basic surfaces. These are indicator methods, temperature programmed desorption (TPD) of CO{sub 2}, UV absorption and luminescence spectroscopies, TPD of H{sub 2}, XPS, IR of CO{sub 2}, IR of pyrrole, and oxygen exchange between CO{sub 2} and the surface. The paper then discusses studies on the catalysis by heterogeneous basic catalysts. Some of these reactions are dehydration, dehydrogenation, hydrogenation, amination, alkylation, ring transformation, and reactions of organosilanes. Catalysts discussed are single component metal oxides, zeolites, non-oxide types, and superbasic catalysts. 141 refs.

  20. A multivariable control scheme for robot manipulators

    NASA Technical Reports Server (NTRS)

    Tarokh, M.; Seraji, H.

    1991-01-01

    The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.

  1. Schmidt decomposition and multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.

    2016-12-01

    The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.

  2. Multivariate analysis: A statistical approach for computations

    NASA Astrophysics Data System (ADS)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  3. Classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.

    2002-01-01

    An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

  4. Surface coal mining influences on macroinvertebrate assemblages in streams of the Canadian Rocky Mountains.

    PubMed

    Kuchapski, Kathryn A; Rasmussen, Joseph B

    2015-09-01

    To determine the region-specific impacts of surface coal mines on macroinvertebrate community health, chemical and physical stream characteristics and macroinvertebrate family and community metrics were measured in surface coal mine-affected and reference streams in the Canadian Rocky Mountains. Water chemistry was significantly altered in mine-affected streams, which had elevated conductivity, alkalinity, and selenium and ion concentrations compared with reference conditions. Multivariate redundancy analysis (RDA) indicated alterations in macroinvertebrate communities downstream of mine sites. In RDA ordination, Ephemeroptera family densities, family richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) richness, and % Ephemeroptera declined, whereas densities of Capniidae stoneflies increased along environmental gradients defined by variables associated with mine influence including waterborne Se concentration, alkalinity, substrate embeddedness, and interstitial material size. Shifts in macroinvertebrate assemblages may have been the result of multiple region-specific stressors related to mining influences including selenium toxicity, ionic toxicity, or stream substrate modifications.

  5. Heterogeneous recurrence monitoring and control of nonlinear stochastic processes

    SciTech Connect

    Yang, Hui Chen, Yun

    2014-03-15

    Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., values and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system.

  6. Characterization of Paper Heterogeneity

    NASA Astrophysics Data System (ADS)

    Considine, John M.

    Paper and paperboard are the most widely-used green materials in the world because they are renewable, recyclable, reusable, and compostable. Continued and expanded use of these materials and their potential use in new products requires a comprehensive understanding of the variability of their mechanical properties. This work develops new methods to characterize the mechanical properties of heterogeneous materials through a combination of techniques in experimental mechanics, materials science and numerical analysis. Current methods to analyze heterogeneous materials focus on crystalline materials or polymer-crystalline composites, where material boundaries are usually distinct. This work creates a methodology to analyze small, continuously-varying stiffness gradients in 100% polymer systems and is especially relevant to paper materials where factors influencing heterogeneity include local mass, fiber orientation, individual pulp fiber properties, local density, and drying restraint. A unique approach was used to understand the effect of heterogeneity on paper tensile strength. Additional variation was intentionally introduced, in the form of different size holes, and their effect on strength was measured. By modifying two strength criteria, an estimate of strength in the absence of heterogeneity was determined. In order to characterize stiffness heterogeneity, a novel load fixture was developed to excite full-field normal and shear strains for anisotropic stiffness determination. Surface strains were measured with digital image correlation and were analyzed with the VFM (Virtual Fields Method). This approach led to VFM-identified stiffnesses that were similar to values determined by conventional tests. The load fixture and VFM analyses were used to measure local stiffness and local stiffness variation on heterogeneous anisotropic materials. The approach was validated on simulated heterogeneous materials and was applied experimentally to three different paperboards

  7. New multivariate test for linkage, with application to pleiotropy: fuzzy Haseman-Elston.

    PubMed

    Kaabi, Belhassen; Elston, Robert C

    2003-05-01

    We propose a new method of linkage analysis based on using the grade of membership scores resulting from fuzzy clustering procedures to define new dependent variables for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means of data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (H=0.1, 0.2, and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with three components), it is more powerful than the principal component method and more powerful than the joint linkage test proposed by Mangin et al. ([1998] Biometrics 54:88-99) when there is pleiotropy.

  8. Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

    PubMed Central

    Chang, Qingliang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application. PMID:25258737

  9. Implementation of paste backfill mining technology in Chinese coal mines.

    PubMed

    Chang, Qingliang; Chen, Jianhang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application.

  10. Using Matlab in a Multivariable Calculus Course.

    ERIC Educational Resources Information Center

    Schlatter, Mark D.

    The benefits of high-level mathematics packages such as Matlab include both a computer algebra system and the ability to provide students with concrete visual examples. This paper discusses how both capabilities of Matlab were used in a multivariate calculus class. Graphical user interfaces which display three-dimensional surfaces, contour plots,…

  11. Multivariate Granger causality and generalized variance

    NASA Astrophysics Data System (ADS)

    Barrett, Adam B.; Barnett, Lionel; Seth, Anil K.

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  12. DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

    EPA Science Inventory

    Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

  13. Multivariate Granger causality and generalized variance.

    PubMed

    Barrett, Adam B; Barnett, Lionel; Seth, Anil K

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  14. Multivariable Control System Design for a Submarine,

    DTIC Science & Technology

    1984-05-01

    Open Loop Singular Values for the 5 and 1S Knot Linear Modelo *~~* b % % V’ , * % ~ .%~ C 9 ~ V. --.- V. V.-.--.--46..- S. 77’ Model S20R5 20- 10- -0...Control, Addison-Wesley, 1976, pp 65-86. 14. Kevin Boettcher, Analysis of Multivariable Control Systems with Structured Uncertainty, Area Examination

  15. Multivariate analysis: greater insights into complex systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

  16. Multivariate analysis of longitudinal rates of change.

    PubMed

    Bryan, Matthew; Heagerty, Patrick J

    2016-12-10

    Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd.

  17. Land Mines (Landminen)

    DTIC Science & Technology

    1978-02-02

    making contact with the safety pin of the pull fuze 42. Two locking bolts held the upper and lower case in position during transport, so that there... safety pin out of the extended striker, thus releasing it. These mines were filled with 200 g of explosives. This type of mine was the model for the...by inserting the detonator slide. However, the mine is not fully armed until the safety pin is removed and reinserted until it makes contact with the

  18. Mining agreements III

    SciTech Connect

    Not Available

    1991-01-01

    This book cover the following: Forms of mining agreements; Preliminary letter agreements; Acquisition of mineral interests involving securities; Partnership tax treatment in mining agreements; Non-tax consequences of partnerships under state law; Protection against joint venturers' liabilities; Joint venture decision making; Mining royalties; Commingling and unitization provisions; Indemnification and insurance provisions; Area of interest provision; Dispute resolution; and Non-participation and default provisions.

  19. A baseline lunar mine

    NASA Technical Reports Server (NTRS)

    Gertsch, Richard E.

    1992-01-01

    A models lunar mining method is proposed that illustrates the problems to be expected in lunar mining and how they might be solved. While the method is quite feasible, it is, more importantly, a useful baseline system against which to test other, possible better, methods. Our study group proposed the slusher to stimulate discussion of how a lunar mining operation might be successfully accomplished. Critics of the slusher system were invited to propose better methods. The group noted that while nonterrestrial mining has been a vital part of past space manufacturing proposals, no one has proposed a lunar mining system in any real detail. The group considered it essential that the design of actual, workable, and specific lunar mining methods begin immediately. Based on an earlier proposal, the method is a three-drum slusher, also known as a cable-operated drag scraper. Its terrestrial application is quite limited, as it is relatively inefficient and inflexible. The method usually finds use in underwater mining from the shore and in moving small amounts of ore underground. When lunar mining scales up, the lunarized slusher will be replaced by more efficient, high-volume methods. Other aspects of lunar mining are discussed.

  20. Mine waste technology program

    SciTech Connect

    Wilmoth, R.C.; Powers, T.J.

    1995-10-01

    The Mine Waste Technology Program (MWTP) was initiated to address mining waste generated by active and inactive mining production facilities. In June 1991, an Interagency Agreement was signed between the U.S. Environmental Protection Agency and the Department of Energy which outlined the following activities: To identify and prioritize treatment technologies as candidates for demonstration projects; To propose and conduct large pilot-/field-scale demonstration projects of several innovative technologies that show promise for cost effectively remediating local, regional, and national mine waste problems.

  1. Data mining support systems

    NASA Astrophysics Data System (ADS)

    Zhao, Yinliang; Yao, JingTao; Yao, Yiyu

    2004-04-01

    The main stream of research in data mining (or knowledge discovery in databases) focuses on algorithms and automatic or semi-automatic processes for discovering knowledge hidden in data. In this paper, we adopt a more general and goal oriented view of data mining. Data mining is regarded as a field of study covering the theories, methodologies, techniques, and activities with the goal of discovering new and useful knowledge. One of its objectives is to design and implement data mining systems. A miner solves problems of data mining manually, or semi-automatically by using such systems. However, there is a lack of studies on how to assist a miner in solving data mining problems. From the experiences and lessons of decision support systems, we introduce the concept of data mining support systems (DMSS). We draw an analogy between the field of decision-making and the field of data mining, and between the role of a manager and the role of a data miner. A DMSS is an active and highly interactive computer system that assists data mining activities. The needs and the basic features of DMSS are discussed.

  2. Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R.

    PubMed

    Asar, Ozgür; Ilk, Ozlem

    2014-07-01

    Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.

  3. Spatial heterogeneity in medulloblastoma.

    PubMed

    Morrissy, A Sorana; Cavalli, Florence M G; Remke, Marc; Ramaswamy, Vijay; Shih, David J H; Holgado, Borja L; Farooq, Hamza; Donovan, Laura K; Garzia, Livia; Agnihotri, Sameer; Kiehna, Erin N; Mercier, Eloi; Mayoh, Chelsea; Papillon-Cavanagh, Simon; Nikbakht, Hamid; Gayden, Tenzin; Torchia, Jonathon; Picard, Daniel; Merino, Diana M; Vladoiu, Maria; Luu, Betty; Wu, Xiaochong; Daniels, Craig; Horswell, Stuart; Thompson, Yuan Yao; Hovestadt, Volker; Northcott, Paul A; Jones, David T W; Peacock, John; Wang, Xin; Mack, Stephen C; Reimand, Jüri; Albrecht, Steffen; Fontebasso, Adam M; Thiessen, Nina; Li, Yisu; Schein, Jacqueline E; Lee, Darlene; Carlsen, Rebecca; Mayo, Michael; Tse, Kane; Tam, Angela; Dhalla, Noreen; Ally, Adrian; Chuah, Eric; Cheng, Young; Plettner, Patrick; Li, Haiyan I; Corbett, Richard D; Wong, Tina; Long, William; Loukides, James; Buczkowicz, Pawel; Hawkins, Cynthia E; Tabori, Uri; Rood, Brian R; Myseros, John S; Packer, Roger J; Korshunov, Andrey; Lichter, Peter; Kool, Marcel; Pfister, Stefan M; Schüller, Ulrich; Dirks, Peter; Huang, Annie; Bouffet, Eric; Rutka, James T; Bader, Gary D; Swanton, Charles; Ma, Yusanne; Moore, Richard A; Mungall, Andrew J; Majewski, Jacek; Jones, Steven J M; Das, Sunit; Malkin, David; Jabado, Nada; Marra, Marco A; Taylor, Michael D

    2017-04-10

    Spatial heterogeneity of transcriptional and genetic markers between physically isolated biopsies of a single tumor poses major barriers to the identification of biomarkers and the development of targeted therapies that will be effective against the entire tumor. We analyzed the spatial heterogeneity of multiregional biopsies from 35 patients, using a combination of transcriptomic and genomic profiles. Medulloblastomas (MBs), but not high-grade gliomas (HGGs), demonstrated spatially homogeneous transcriptomes, which allowed for accurate subgrouping of tumors from a single biopsy. Conversely, somatic mutations that affect genes suitable for targeted therapeutics demonstrated high levels of spatial heterogeneity in MB, malignant glioma, and renal cell carcinoma (RCC). Actionable targets found in a single MB biopsy were seldom clonal across the entire tumor, which brings the efficacy of monotherapies against a single target into question. Clinical trials of targeted therapies for MB should first ensure the spatially ubiquitous nature of the target mutation.

  4. Mining knowledge in astrophysical massive data sets

    NASA Astrophysics Data System (ADS)

    Brescia, Massimo; Longo, Giuseppe; Pasian, Fabio

    2010-11-01

    Modern scientific data mainly consist of huge data sets gathered by a very large number of techniques and stored in much diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic “virtual organizations” formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data-rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists of the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such an effort is that once the infrastructure is complete, it will allow a new type of multi-wavelength, multi-epoch science, which can only be barely imagined. Data mining, or knowledge discovery in databases, while being the main methodology to extract the scientific information contained in such Massive Data Sets (MDS), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced data mining methodologies in the case of the DAME (DAta Mining and Exploration) project.

  5. Cancer heterogeneity and imaging.

    PubMed

    O'Connor, James P B

    2016-10-04

    There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use.

  6. Mining and Integration of Environmental Data

    NASA Astrophysics Data System (ADS)

    Tran, V.; Hluchy, L.; Habala, O.; Ciglan, M.

    2009-04-01

    The project ADMIRE (Advanced Data Mining and Integration Research for Europe) is a 7th FP EU ICT project aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. The project is motivated by the difficulty of extracting meaningful information by data mining combinations of data from multiple heterogeneous and distributed resources. It will also provide an abstract view of data mining and integration, which will give users and developers the power to cope with complexity and heterogeneity of services, data and processes. The data sets describing phenomena from domains like business, society, and environment often contain spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions (e.g. size of the spatial grid) and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. Thus, to integrate heterogeneous spatio-temporal data from distinct source, transformation of one or more data sets is necessary. Following transformation operation are required: • transformation to common spatial and temporal representation - (e.g. transformation to common coordinate system), • spatial and/or temporal aggregation - data from detailed data source are aggregated to match the resolution of other resources involved in the integration process, • spatial and/or temporal record decomposition - records from source with lower resolution data are decomposed to match the granularity of the other data source. This operation decreases data quality (e.g. transformation of data from 50km grid to 10 km grid) - data from lower resolution data set in the integrated schema are imprecise, but it allows us to preserve higher resolution data. We can decompose the

  7. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

    PubMed

    Neupane, Binod; Beyene, Joseph

    2015-01-01

    In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data

  8. Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

    Cancer.gov

    The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

  9. mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.

    PubMed

    Asar, Özgür; İlk, Özlem

    2013-12-01

    Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.

  10. Heterogeneous Uncertainty Management

    DTIC Science & Technology

    2008-03-08

    probabilistic ( HTP ) agents, the concept of probabilistic version of XML and RDF, and probabilistic methods to reason about collections of moving objects. S...heterogeneous temporal probabilistic ( HTP ) agents, the concept of probabilistic version of XML and RDF, and probabilistic methods to reason about...temporal probabilistic ( HTP ) agent. HTP agents can build temporal probabilistic reasoning capabilities on top of multiple databases and software

  11. Heterogeneous waste processing

    DOEpatents

    Vanderberg, Laura A.; Sauer, Nancy N.; Brainard, James R.; Foreman, Trudi M.; Hanners, John L.

    2000-01-01

    A combination of treatment methods are provided for treatment of heterogeneous waste including: (1) treatment for any organic compounds present; (2) removal of metals from the waste; and, (3) bulk volume reduction, with at least two of the three treatment methods employed and all three treatment methods emplyed where suitable.

  12. Data Mining: The Art of Automated Knowledge Extraction

    NASA Astrophysics Data System (ADS)

    Karimabadi, H.; Sipes, T.

    2012-12-01

    Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

  13. Underground Coal Mining

    NASA Technical Reports Server (NTRS)

    Hill, G. M.

    1980-01-01

    Computer program models coal-mining production, equipment failure and equipment repair. Underground mine is represented as collection of work stations requiring service by production and repair crews alternately. Model projects equipment availability and productivity, and indicates proper balance of labor and equipment. Program is in FORTRAN IV for batch execution; it has been implemented on UNIVAC 1108.

  14. Biotreatment of mine drainage

    SciTech Connect

    Bender, J.; Phillips, R.

    1996-12-31

    Several experiments and field tests of microbial mats are described. One study determined the removal rate of Uranium 238 and metals from groundwater by microbial mats. Free floating mats, immobilized mats, excised mats, and pond treatment were examined. Field tests of acid coal mine drainage and precious metal mine drainage are also summarized. The mechanisms of metal removal are briefly described.

  15. Mining outlook in Indonesia

    SciTech Connect

    Not Available

    1984-06-01

    The outlook for mining in Indonesia is presented. Coal appears to be the most promising growth area for Indonesian mining interests, with production slated to reach 1.5 million t/yr by 1985, up from 0.5 million ton in 1983. Also discussed production and trends, aluminum, copper, nickel, silver, gold, tin and iron sands in Indonesia.

  16. Design of multivariable controllers for robot manipulators

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1986-01-01

    The paper presents a simple method for the design of linear multivariable controllers for multi-link robot manipulators. The control scheme consists of multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and achieves pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. The two controllers are independent of each other and are designed separately based on the linearized robot model and then integrated in the overall control scheme. The proposed scheme is simple and can be implemented for real-time control of robot manipulators.

  17. Multivariate temporal dictionary learning for EEG.

    PubMed

    Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

    2013-04-30

    This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

  18. Multivariate Approaches to Classification in Extragalactic Astronomy

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, Didier; Thuillard, Marc; Chattopadhyay, Asis Kumar

    2015-08-01

    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.

  19. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2004-03-23

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  20. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2002-01-01

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  1. PRB mines mature

    SciTech Connect

    Buchsbaum, L.

    2007-08-15

    Already seeing the results of reclamation efforts, America's largest surface mines advance as engineers prepare for the future. 30 years after the signing of the Surface Mining Control and Reclamation Act by Jimmy Carter, western strip mines in the USA, especially in the Powder River Basin, are producing more coal than ever. The article describes the construction and installation of a $38.5 million near-pit crusher and overland belt conveyor system at Foundation Coal West's (FCW) Belle Ayr surface mine in Wyoming, one of the earliest PRB mines. It goes on to describe the development by Rio Tinto of an elk conservatory, the Rochelle Hill Conservation Easement, on reclaimed land at Jacobs Ranch, adjacent to the Rochelle Hills. 4 photos.

  2. Mining Deployment Optimization

    NASA Astrophysics Data System (ADS)

    Čech, Jozef

    2016-09-01

    The deployment problem, researched primarily in the military sector, is emerging in some other industries, mining included. The principal decision is how to deploy some activities in space and time to achieve desired outcome while complying with certain requirements or limits. Requirements and limits are on the side constraints, while minimizing costs or maximizing some benefits are on the side of objectives. A model with application to mining of polymetallic deposit is presented. To obtain quick and immediate decision solutions for a mining engineer with experimental possibilities is the main intention of a computer-based tool. The task is to determine strategic deployment of mining activities on a deposit, meeting planned output from the mine and at the same time complying with limited reserves and haulage capacities. Priorities and benefits can be formulated by the planner.

  3. The Evolution of Multivariate Maternal Effects

    PubMed Central

    Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart

    2014-01-01

    There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346

  4. Multivariable PID Controller For Robotic Manipulator

    NASA Technical Reports Server (NTRS)

    Seraji, Homayoun; Tarokh, Mahmoud

    1990-01-01

    Gains updated during operation to cope with changes in characteristics and loads. Conceptual multivariable controller for robotic manipulator includes proportional/derivative (PD) controller in inner feedback loop, and proportional/integral/derivative (PID) controller in outer feedback loop. PD controller places poles of transfer function (in Laplace-transform space) of control system for linearized mathematical model of dynamics of robot. PID controller tracks trajectory and decouples input and output.

  5. Preliminary Multivariable Cost Model for Space Telescopes

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip

    2010-01-01

    Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

  6. Multivariable root loci on the real axis

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.; Levy, B. C.

    1982-01-01

    Some methods for determining the number of branches of multivariable root loci which are located on the real axis at a given point are obtained by using frequency domain methods. An equation for the number of branches is given for the general case, and simpler results for the special cases when the transfer function G(s) has size 2 x 2, and when G(s) is symmetric, are also presented.

  7. Multi-Variable Analysis and Design Techniques.

    DTIC Science & Technology

    1981-09-01

    by A.G.J.MacFarlane 2 MULTIVARIABLE DESIGN TECHNIQUES BASED ON SINGULAR VALUE GENERALIZATIONS OF CLASSICAL CONTROL by J.C. Doyle 3 LIMITATIONS ON...prototypes to complex mathematical representations. All of these assemblages of information or information generators can loosely be termed "models...non linearities (e.g., control saturation) I neglect of high frequency dynamics. T hese approximations are well understood and in general their impact

  8. Compressive tracking with incremental multivariate Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Li, Dongdong; Wen, Gongjian; Zhu, Gao; Zeng, Qiaoling

    2016-09-01

    Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.

  9. Control of wastewater using multivariate control chart

    NASA Astrophysics Data System (ADS)

    Nugraha, Jaka; Fatimah, Is; Prabowo, Rino Galang

    2017-03-01

    Wastewater treatment is a crucial process in industry cause untreated or improper treatment of wastewater may leads some problems affecting to the other parts of environmental aspects. For many kinds of wastewater treatments, the parameters of Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and the Total Suspend Solid (TSS) are usual parameters to be controlled as a standard. In this paper, the application of multivariate Hotteling T2 Individual was reported to control wastewater treatment. By using wastewater treatment data from PT. ICBP, east Java branch, while the fulfillment of quality standards are based on East Java Governor Regulation No. 72 Year 2013 on Standards of Quality of Waste Water Industry and / or Other Business Activities. The obtained results are COD and TSS has a correlation with BOD values with the correlation coefficient higher than 50%, and it is is also found that influence of the COD and TSS to BOD values are 82% and 1.9% respectively. Based on Multivariate control chart Individual T2 Hotteling, it is found that BOD-COD and BOD-TSS are each one subgroup that are outside the control limits. Thus, it can be said there is a process that is not multivariate controlled, but univariately the variables of BOD, COD and TSS are within specification (standard quality) that has been determined.

  10. Query-Based Outlier Detection in Heterogeneous Information Networks.

    PubMed

    Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

    2015-03-01

    Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.

  11. Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

    PubMed Central

    Nanni, Loris; Brahnam, Sheryl; Ghidoni, Stefano; Lumini, Alessandra

    2015-01-01

    We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset. PMID:26413089

  12. Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification.

    PubMed

    Nanni, Loris; Brahnam, Sheryl; Ghidoni, Stefano; Lumini, Alessandra

    2015-01-01

    We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset.

  13. Analysis of worldwide earthquake mortality using multivariate demographic and seismic data.

    PubMed

    Gutiérrez, E; Taucer, F; De Groeve, T; Al-Khudhairy, D H A; Zaldivar, J M

    2005-06-15

    In this paper, mortality in the immediate aftermath of an earthquake is studied on a worldwide scale using multivariate analysis. A statistical method is presented that analyzes reported earthquake fatalities as a function of a heterogeneous set of parameters selected on the basis of their presumed influence on earthquake mortality. The ensemble was compiled from demographic, seismic, and reported fatality data culled from available records of past earthquakes organized in a geographic information system. The authors consider the statistical relation between earthquake mortality and the available data ensemble, analyze the validity of the results in view of the parametric uncertainties, and propose a multivariate mortality analysis prediction method. The analysis reveals that, although the highest mortality rates are expected in poorly developed rural areas, high fatality counts can result from a wide range of mortality ratios that depend on the effective population size.

  14. Hidden Markov latent variable models with multivariate longitudinal data.

    PubMed

    Song, Xinyuan; Xia, Yemao; Zhu, Hongtu

    2017-03-01

    Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.

  15. 2. EMPIRE STATE MINE. VIEW OF COLLAPSED BUILDINGS AT MINE ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    2. EMPIRE STATE MINE. VIEW OF COLLAPSED BUILDINGS AT MINE WITH TAILINGS ON RIGHT. CAMERA POINTED SOUTHWEST. COLLAPSED ADIT APPROXIMATELY 25 YARDS UPHILL TO THE LEFT OF FAR BUILDING. TIP TOP AND ONTARIO ARE LOCATED OUT OF THE PICTURE TO THE RIGHT. - Florida Mountain Mining Sites, Empire State Mine, West side of Florida Mountain, Silver City, Owyhee County, ID

  16. Land Mines Removal

    NASA Technical Reports Server (NTRS)

    1999-01-01

    The same rocket fuel that helps power the Space Shuttle as it thunders into orbit will now be taking on a new role, with the potential to benefit millions of people worldwide. Leftover rocket fuel from NASA is being used to make a flare that destroys land mines where they were buried, without using explosives. The flare is safe to handle and easy to use. People working to deactivate the mines simply place the flare next to the uncovered land mine and ignite it from a safe distance using a battery-triggered electric match. The flare burns a hole in the land mine's case and ignites its explosive contents. The explosive burns away, disabling the mine and rendering it harmless. Using leftover rocket fuel to help destroy land mines incurs no additional costs to taxpayers. To ensure enough propellant is available for each Shuttle mission, NASA allows for a small percentage of extra propellant in each batch. Once mixed, surplus fuel solidifies and carnot be saved for use in another launch. In its solid form, it is an ideal ingredient for new the flare. The flare was developed by Thiokol Propulsion in Brigham City, Utah, the NASA contractor that designs and builds rocket motors for the Solid Rocket Booster Space Shuttle. An estimated 80 million or more active land mines are scattered around the world in at least 70 countries, and kill or maim 26,000 people a year. Worldwide, there is one casualty every 22 minutes.

  17. Land Mines Removal

    NASA Technical Reports Server (NTRS)

    1999-01-01

    The same rocket fuel that helps power the Space Shuttle as it thunders into orbit will now be taking on a new role, with the potential to benefit millions of people worldwide. Leftover rocket fuel from NASA is being used to make a flare that destroys land mines where they were buried, without using explosives. The flare is safe to handle and easy to use. People working to deactivate the mines simply place the flare next to the uncovered land mine and ignite it from a safe distance using a battery-triggered electric match. The flare burns a hole in the land mine's case and ignites its explosive contents. The explosive burns away, disabling the mine and rendering it harmless. Using leftover rocket fuel to help destroy land mines incurs no additional costs to taxpayers. To ensure enough propellant is available for each Shuttle mission, NASA allows for a small percentage of extra propellant in each batch. Once mixed, surplus fuel solidifies and carnot be saved for use in another launch. In its solid form, it is an ideal ingredient for the new flare. The flare was developed by Thiokol Propulsion in Brigham City, Utah, the NASA contractor that designs and builds rocket motors for the Solid Rocket Booster Space Shuttle. An estimated 80 million or more active land mines are scattered around the world in at least 70 countries, and kill or maim 26,000 people a year. Worldwide, there is one casualty every 22 minutes

  18. Concepts in Heterogeneous Catalysis

    DTIC Science & Technology

    1974-06-01

    OxIdlaing Species In Heterogeneous Catalytic Oxidation. In the history of the study of heterogeneioum oxidation catalysis, reaction mechanisms have’ been...for sonti timie but recent workŔ’ onl the lplatitnum-rtothe~idina alloy systemn semi- s quite promising 10) lvad ito at bettr understanding. 1t wait...chemical nature of the catalyst, its previous history , and on the courac of the catalytic reaction itself. The energy spectrum of the active surface

  19. Land reclamation beautifies coal mines

    SciTech Connect

    Coblentz, B.

    2009-07-15

    The article explains how the Mississippi Agricultural and Forestry Experiments station, MAFES, has helped prepare land exploited by strip mining at North American Coal Corporation's Red Hills Mine. The 5,800 acre lignite mine is over 200 ft deep and uncovers six layers of coal. About 100 acres of land a year is mined and reclaimed, mostly as pine plantations. 5 photos.

  20. Data Mining in Child Welfare.

    ERIC Educational Resources Information Center

    Schoech, Dick; Quinn, Andrew; Rycraft, Joan R.

    2000-01-01

    Examines the historical and larger context of data mining and describes data mining processes, techniques, and tools. Illustrates these using a child welfare dataset concerning the employee turnover that is mined, using logistic regression and a Bayesian neural network. Discusses the data mining process, the resulting models, their predictive…

  1. Heterogeneities in granular dynamics

    PubMed Central

    Mehta, A.; Barker, G. C.; Luck, J. M.

    2008-01-01

    The absence of Brownian motion in granular media is a source of much complexity, including the prevalence of heterogeneity, whether static or dynamic, within a given system. Such strong heterogeneities can exist as a function of depth in a box of grains; this is the system we study here. First, we present results from three-dimensional, cooperative and stochastic Monte Carlo shaking simulations of spheres on heterogeneous density fluctuations. Next, we juxtapose these with results obtained from a theoretical model of a column of grains under gravity; frustration via competing local fields is included in our model, whereas the effect of gravity is to slow down the dynamics of successively deeper layers. The combined conclusions suggest that the dynamics of a real granular column can be divided into different phases—ballistic, logarithmic, activated, and glassy—as a function of depth. The nature of the ground states and their retrieval (under zero-temperature dynamics) is analyzed; the glassy phase shows clear evidence of its intrinsic (“crystalline”) states, which lie below a band of approximately degenerate ground states. In the other three phases, by contrast, the system jams into a state chosen randomly from this upper band of metastable states. PMID:18541918

  2. Controlling coal mine bumps

    SciTech Connect

    Goode, C.A.; Campoli, A.A.; Zona, A.

    1984-10-01

    A coal bump or burst is defined as the instantaneous violent failure of a coal pillar(s) from overstress. The causes of coal bumps are not well understood, even though minor disturbances are a daily occurrence in bump prone seams. Lack of knowledge about coal bumps coupled with questionable mining practices can create disastrous consequences. Much of the early work on bumps was documented by US Bureau of Mines (BOM) researchers and operators of mines prone to bumps. In 1954 the BOM published Bulletin 535, This study compares recent events with those findings and suggests measures that can be taken to minimize the potential occurrence and severity of coal bumps.

  3. Closedure - Mine Closure Technologies Resource

    NASA Astrophysics Data System (ADS)

    Kauppila, Päivi; Kauppila, Tommi; Pasanen, Antti; Backnäs, Soile; Liisa Räisänen, Marja; Turunen, Kaisa; Karlsson, Teemu; Solismaa, Lauri; Hentinen, Kimmo

    2015-04-01

    Closure of mining operations is an essential part of the development of eco-efficient mining and the Green Mining concept in Finland to reduce the environmental footprint of mining. Closedure is a 2-year joint research project between Geological Survey of Finland and Technical Research Centre of Finland that aims at developing accessible tools and resources for planning, executing and monitoring mine closure. The main outcome of the Closedure project is an updatable wiki technology-based internet platform (http://mineclosure.gtk.fi) in which comprehensive guidance on the mine closure is provided and main methods and technologies related to mine closure are evaluated. Closedure also provides new data on the key issues of mine closure, such as performance of passive water treatment in Finland, applicability of test methods for evaluating cover structures for mining wastes, prediction of water effluents from mine wastes, and isotopic and geophysical methods to recognize contaminant transport paths in crystalline bedrock.

  4. Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

    ERIC Educational Resources Information Center

    Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

    2013-01-01

    Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

  5. Multivariate Models for Normal and Binary Responses in Intervention Studies

    ERIC Educational Resources Information Center

    Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

    2016-01-01

    Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

  6. Time varying, multivariate volume data reduction

    SciTech Connect

    Ahrens, James P; Fout, Nathaniel; Ma, Kwan - Liu

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  7. Multivariate Analysis of Genotype–Phenotype Association

    PubMed Central

    Mitteroecker, Philipp; Cheverud, James M.; Pavlicev, Mihaela

    2016-01-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype–phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype–phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype–phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype–phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the

  8. Multivariate Analysis of Genotype-Phenotype Association.

    PubMed

    Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

    2016-04-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

  9. Multivariate postprocessing techniques for probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2016-04-01

    Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power

  10. New multivariable capabilities of the INCA program

    NASA Technical Reports Server (NTRS)

    Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

    1989-01-01

    The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

  11. Multivariate curve-fitting in GAUSS

    USGS Publications Warehouse

    Bunck, C.M.; Pendleton, G.W.

    1988-01-01

    Multivariate curve-fitting techniques for repeated measures have been developed and an interactive program has been written in GAUSS. The program implements not only the one-factor design described in Morrison (1967) but also includes pairwise comparisons of curves and rates, a two-factor design, and other options. Strategies for selecting the appropriate degree for the polynomial are provided. The methods and program are illustrated with data from studies of the effects of environmental contaminants on ducklings, nesting kestrels and quail.

  12. Multivariate Lipschitz optimization: Survey and computational comparison

    SciTech Connect

    Hansen, P.; Gourdin, E.; Jaumard, B.

    1994-12-31

    Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.

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

    PubMed

    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.

  14. A "Tail" Of Two Mines: Determining The Sources Of Lead In Mine Waters Using Pb Isotopes

    NASA Astrophysics Data System (ADS)

    Cousens, B. L.; Allen, D. M.; Lepitre, M. E.; Mortensen, J. K.; Gabites, J. E.; Nugent, M.; Fortin, D.

    2004-12-01

    Acid mine drainage can be a significant environmental problem in regions where mine tailings are exposed to surface water and shallow groundwater flow. Whereas high metal concentrations in surface waters and groundwaters indicate that metals are being mobilized, these data do not uniquely identify the source of the contamination. The isotopic composition of Pb in mine waters is a superb tracer of Pb sources, because the isotopic composition of ore Pb is usually significantly different from that of host rocks, other surficial deposits, and aerosols. We have investigated metal mobility at two abandoned Pb-Zn mines in different geological settings: the sediment-hosted Sullivan Mine in southeastern British Columbia, and the New Calumet Mine of western Quebec that is hosted in metamorphic rocks of the Grenville Province. Ores from both mines have homogeneous Pb isotopic compositions that are much less radiogenic than surrounding host rocks. At Sullivan, the Pb isotopic compositions of water samples define a mixing line between Sullivan ore and at least one other more radiogenic end-member. Water samples with high Pb concentrations (0.002 to 0.3 mg/L) generally are acidic and have Pb isotope ratios equal to Sullivan ore, whereas waters with low Pb contents have near-neutral pH and have variably more radiogenic Pb isotope ratios. Thus not all the waters collected in the study area originate from Sullivan ore or mining operations, as previously thought. The dominant source of ore Pb in mine waters are the waste rock dumps. Based on their isotopic compositions, host shales or aerosols from the local Pb smelter are potential sources of non-Sullivan ore Pb; local glacial tills are an unlikely source due to their heterogeneous Pb isotopic composition. Similarly, at the New Calumet mine, water samples collected in direct contact with either ore at the surface or tailings have high Pb concentrations (up to 0.02 mg/L) and Pb isotope ratios equal to New Calumet Pb-Zn ore. However

  15. Acid mine drainage

    USGS Publications Warehouse

    Bigham, Jerry M.; Cravotta, Charles A.

    2016-01-01

    Acid mine drainage (AMD) consists of metal-laden solutions produced by the oxidative dissolution of iron sulfide minerals exposed to air, moisture, and acidophilic microbes during the mining of coal and metal deposits. The pH of AMD is usually in the range of 2–6, but mine-impacted waters at circumneutral pH (5–8) are also common. Mine drainage usually contains elevated concentrations of sulfate, iron, aluminum, and other potentially toxic metals leached from rock that hydrolyze and coprecipitate to form rust-colored encrustations or sediments. When AMD is discharged into surface waters or groundwaters, degradation of water quality, injury to aquatic life, and corrosion or encrustation of engineered structures can occur for substantial distances. Prevention and remediation strategies should consider the biogeochemical complexity of the system, the longevity of AMD pollution, the predictive power of geochemical modeling, and the full range of available field technologies for problem mitigation.

  16. Upper Animas Mining District

    EPA Pesticide Factsheets

    Web page provides narrative of What's New?, Site Description, Site Risk, Cleanup Progress, Community Involvement, Next Steps, Site Documents, FAQ, Contacts and LInks for the Upper Animas Mining District site in San Juan County, Colorado.

  17. Indonesian coal mining

    SciTech Connect

    2008-11-15

    The article examines the opportunities and challenges facing the Indonesian coal mining industry and how the coal producers, government and wider Indonesian society are working to overcome them. 2 figs., 1 tab.

  18. F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

    NASA Technical Reports Server (NTRS)

    Soeder, J. F.

    1983-01-01

    As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

  19. Data Stream Mining

    NASA Astrophysics Data System (ADS)

    Gaber, Mohamed Medhat; Zaslavsky, Arkady; Krishnaswamy, Shonali

    Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).

  20. Mining Specifications: A Roadmap

    NASA Astrophysics Data System (ADS)

    Zeller, Andreas

    Recent advances in software validation and verification make it possible to widely automate whether a specification is satisfied. This progress is hampered, though, by the persistent difficulty of writing specifications. Are we facing a “specification crisis”? In this paper, I show how to alleviate the burden of writing specifications by reusing and extending specifications as mined from existing software and give an overview on the state of the art in specification mining, its origins, and its potential.

  1. Managing Heterogeneous Information Systems through Discovery and Retrieval of Generic Concepts.

    ERIC Educational Resources Information Center

    Srinivasan, Uma; Ngu, Anne H. H.; Gedeon, Tom

    2000-01-01

    Introduces a conceptual integration approach to heterogeneous databases or information systems that exploits the similarity in metalevel information and performs metadata mining on database objects to discover a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. Presents results of…

  2. Final Technical Report - Investigation into the Relationship between Heterogeneity and Heavy-Tailed Solute Transport

    SciTech Connect

    Weissmann, Gary S

    2013-12-06

    The objective of this project was to characterize the influence that naturally complex geologic media has on anomalous dispersion and to determine if the nature of dispersion can be estimated from the underlying heterogeneous media. The UNM portion of this project was to provide detailed representations of aquifer heterogeneity through producing highly-resolved models of outcrop analogs to aquifer materials. This project combined outcrop-scale heterogeneity characterization (conducted at the University of New Mexico), laboratory experiments (conducted at Sandia National Laboratory), and numerical simulations (conducted at Sandia National Laboratory and Colorado School of Mines). The study was designed to test whether established dispersion theory accurately predicts the behavior of solute transport through heterogeneous media and to investigate the relationship between heterogeneity and the parameters that populate these models. The dispersion theory tested by this work was based upon the fractional advection-dispersion equation (fADE) model. Unlike most dispersion studies that develop a solute transport model by fitting the solute transport breakthrough curve, this project explored the nature of the heterogeneous media to better understand the connection between the model parameters and the aquifer heterogeneity. We also evaluated methods for simulating the heterogeneity to see whether these approaches (e.g., geostatistical) could reasonably replicate realistic heterogeneity. The UNM portion of this study focused on capturing realistic geologic heterogeneity of aquifer analogs using advanced outcrop mapping methods.

  3. Morenci Mine, AZ

    NASA Technical Reports Server (NTRS)

    2007-01-01

    The Morenci open-pit copper mine in southeast Arizona is North America's leading producer of copper. In the 1860s, prospectors arrived looking for gold; instead they found copper. Underground mining began in the 1870s, and the first pit was opened in 1939. Phelps Dodge employs over 200 people in the mining and refining operations. Around-the-clock removal of 700,000 tons of rock per day results in production of 382 thousand tons of copper per year. Phelps Dodge is now developing the Safford Mine, about 12 km southwest of Morenci. It will be the first new copper mine in the US in more than 30 years. When production starts in 2008, the Safford Mine will produce 109 thousand tons of copper. This ASTER image uses shortwavelength infrared bands to highlight in bright pink the altered rocks in the Morenci pit associated with copper mineralization.

    The image covers an area of 21 x 16.9 km, was acquired on July 14, 2007, and is centered near 33.1 degrees north latitude, 109.5 degrees west longitude.

    The U.S. science team is located at NASA's Jet Propulsion Laboratory, Pasadena, Calif. The Terra mission is part of NASA's Science Mission Directorate.

  4. An integrated multivariable artificial pancreas control system.

    PubMed

    Turksoy, Kamuran; Quinn, Lauretta T; Littlejohn, Elizabeth; Cinar, Ali

    2014-05-01

    The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.

  5. Multichannel hierarchical image classification using multivariate copulas

    NASA Astrophysics Data System (ADS)

    Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane

    2012-03-01

    This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

  6. Adaptable Multivariate Calibration Models for Spectral Applications

    SciTech Connect

    THOMAS,EDWARD V.

    1999-12-20

    Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.

  7. A semiparametric multivariate and multisite weather generator

    NASA Astrophysics Data System (ADS)

    Apipattanavis, Somkiat; Podestá, Guillermo; Rajagopalan, Balaji; Katz, Richard W.

    2007-11-01

    We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k-nearest neighbor (k-NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k-NN bootstrap captures the distributional and lag-dependence statistics of the weather variables. Traditional k-NN generators tend to under-simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k-NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.

  8. Deriving ocean climatologies with multivariate coupling

    NASA Astrophysics Data System (ADS)

    Barth, Alexander; Alvera Azcarate, Aida; Beckers, Jean-Marie

    2016-04-01

    In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and chemical parameters where the number of data is, by an order of magnitude, smaller than for physical parameters. However, physical and biogeochemical parameters are related through the ocean dynamics. In particular fronts visible in physical parameters are often related to gradients in biogeochemical parameters. Ocean climatologies are generally derived for different variables independently. For biogeochemical parameters, only the very large-scale variability can be derived for poorly sampled areas. Here we present a method to derive multivariate analysis taking the relationship between physical and biogeochemical variables into account. The benefit of this procedure is showed by using model data for salinity, nitrate and phosphate of the Mediterranean Sea. The model fields are sampled at the locations of true observations (extracted from the World Ocean Database 2013) and the analysed fields are compared to the original model fields. The multivariate analysis result in a reduction of the RMS error and to a better representation of the gradients.

  9. Multivariate intralocus sexual conflict in seed beetles.

    PubMed

    Berger, David; Berg, Elena C; Widegren, William; Arnqvist, Göran; Maklakov, Alexei A

    2014-12-01

    Intralocus sexual conflict (IaSC) is pervasive because males and females experience differences in selection but share much of the same genome. Traits with integrated genetic architecture should be reservoirs of sexually antagonistic genetic variation for fitness, but explorations of multivariate IaSC are scarce. Previously, we showed that upward artificial selection on male life span decreased male fitness but increased female fitness compared with downward selection in the seed beetle Callosobruchus maculatus. Here, we use these selection lines to investigate sex-specific evolution of four functionally integrated traits (metabolic rate, locomotor activity, body mass, and life span) that collectively define a sexually dimorphic life-history syndrome in many species. Male-limited selection for short life span led to correlated evolution in females toward a more male-like multivariate phenotype. Conversely, males selected for long life span became more female-like, implying that IaSC results from genetic integration of this suite of traits. However, while life span, metabolism, and body mass showed correlated evolution in the sexes, activity did not evolve in males but, surprisingly, did so in females. This led to sexual monomorphism in locomotor activity in short-life lines associated with detrimental effects in females. Our results thus support the general tenet that widespread pleiotropy generates IaSC despite sex-specific genetic architecture.

  10. Augmented classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2004-02-03

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  11. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-07-26

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  12. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-01-11

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  13. Fast Multivariate Search on Large Aviation Datasets

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

    2010-01-01

    Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

  14. A multivariate Bayesian model for embryonic growth.

    PubMed

    Willemsen, Sten P; Eilers, Paul H C; Steegers-Theunissen, Régine P M; Lesaffre, Emmanuel

    2015-04-15

    Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.

  15. A multivariate Baltic Sea environmental index.

    PubMed

    Dippner, Joachim W; Kornilovs, Georgs; Junker, Karin

    2012-11-01

    Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

  16. Unravelling mononuclear phagocyte heterogeneity

    PubMed Central

    Geissmann, Frédéric; Gordon, Siamon; Hume, David A.; Mowat, Allan M.; Randolph, Gwendalyn J.

    2011-01-01

    When Ralph Steinman and Zanvil Cohn first described dendritic cells (DCs) in 1973 it took many years to convince the immunology community that these cells were truly distinct from macrophages. Almost four decades later, the DC is regarded as the key initiator of adaptive immune responses; however, distinguishing DCs from macrophages still leads to confusion and debate in the field. Here, Nature Reviews Immunology asks five experts to discuss the issue of heterogeneity in the mononuclear phagocyte system and to give their opinion on the importance of defining these cells for future research. PMID:20467425

  17. Underground at Black Diamond Mines

    SciTech Connect

    Higgins, C.T.

    1989-10-01

    Although California is noted for its mining history and annually leads the nation in total monetary value of minerals produced, there a few opportunities for the public to tour underground mines. One reason is that nearly all mining in the state today is done above ground in open pits. Another reason is that active underground mines are not commonly favorable to public tours. There is one place, Black Diamond Mines Regional Preserve, where the public can safely tour a formerly active underground mine. Black Diamond Mines Regional Preserve is a 3,600-acre parkland about 5 miles southwest of Antioch in Contra Costa County. The Preserve was established in the early 1970s and is administered by the East Bay Regional Park District. Black Diamond Mines Preserve is noteworthy for its mining history as well as its natural history, both of which are briefly described here.

  18. Physics Mining of Multi-Source Data Sets

    NASA Technical Reports Server (NTRS)

    Helly, John; Karimabadi, Homa; Sipes, Tamara

    2012-01-01

    Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

  19. Harnessing agent technologies for data mining and knowledge discovery

    NASA Astrophysics Data System (ADS)

    McCormack, Jenifer S.; Wohlschlaeger, Brian

    2000-04-01

    Data mining and knowledge discovery in databases are providing means to analyze and discover new knowledge from large datasets. The growth of the Internet has provided the average user with the ability to more easily access and gather data. Many of the existing data mining tools require users to have advanced knowledge. New graphical-based tools are needed to allow the average user to easily and quickly discover new patterns and trends from heterogenous data. SAIC is developing an agent-based data mining tool called AgentMinertm as part of an internal research project. AgentMinertm will allow the user to perform advanced information retrieval and data mining to discover patterns and relationships across multiple distributed, heterogeneous data sources. The current system prototype utilizes an ontology to define common concepts and data elements that are contained in the distributed data sources. AgentMinertm can access data from relational databases, structured text, web pages, and open text sources. It is a Java-based application that contains a suite of graphical tools such as the Mission Manager, Graphical Ontology Builder (GOB), and Qualified English Interpreter (QEI). In addition, AgentMinertm provides the capability to support both 2-D and 3-D data visualization, including animation across a selected independent variable.

  20. The flyby anomaly: a multivariate analysis approach

    NASA Astrophysics Data System (ADS)

    Acedo, L.

    2017-02-01

    The flyby anomaly is the unexpected variation of the asymptotic post-encounter velocity of a spacecraft with respect to the pre-encounter velocity as it performs a slingshot manoeuvre. This effect has been detected in, at least, six flybys of the Earth but it has not appeared in other recent flybys. In order to find a pattern in these, apparently contradictory, data several phenomenological formulas have been proposed but all have failed to predict a new result in agreement with the observations. In this paper we use a multivariate dimensional analysis approach to propose a fitting of the data in terms of the local parameters at perigee, as it would occur if this anomaly comes from an unknown fifth force with latitude dependence. Under this assumption, we estimate the range of this force around 300 km.

  1. MM Algorithms for Some Discrete Multivariate Distributions.

    PubMed

    Zhou, Hua; Lange, Kenneth

    2010-09-01

    The MM (minorization-maximization) principle is a versatile tool for constructing optimization algorithms. Every EM algorithm is an MM algorithm but not vice versa. This article derives MM algorithms for maximum likelihood estimation with discrete multivariate distributions such as the Dirichlet-multinomial and Connor-Mosimann distributions, the Neerchal-Morel distribution, the negative-multinomial distribution, certain distributions on partitions, and zero-truncated and zero-inflated distributions. These MM algorithms increase the likelihood at each iteration and reliably converge to the maximum from well-chosen initial values. Because they involve no matrix inversion, the algorithms are especially pertinent to high-dimensional problems. To illustrate the performance of the MM algorithms, we compare them to Newton's method on data used to classify handwritten digits.

  2. Response Surface Modeling Using Multivariate Orthogonal Functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.; DeLoach, Richard

    2001-01-01

    A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

  3. Shape Control in Multivariate Barycentric Rational Interpolation

    NASA Astrophysics Data System (ADS)

    Nguyen, Hoa Thang; Cuyt, Annie; Celis, Oliver Salazar

    2010-09-01

    The most stable formula for a rational interpolant for use on a finite interval is the barycentric form [1, 2]. A simple choice of the barycentric weights ensures the absence of (unwanted) poles on the real line [3]. In [4] we indicate that a more refined choice of the weights in barycentric rational interpolation can guarantee comonotonicity and coconvexity of the rational interpolant in addition to a polefree region of interest. In this presentation we generalize the above to the multivariate case. We use a product-like form of univariate barycentric rational interpolants and indicate how the location of the poles and the shape of the function can be controlled. This functionality is of importance in the construction of mathematical models that need to express a certain trend, such as in probability distributions, economics, population dynamics, tumor growth models etc.

  4. Compensator improvement for multivariable control systems

    NASA Technical Reports Server (NTRS)

    Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

    1977-01-01

    A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

  5. Multivariate Markov chain modeling for stock markets

    NASA Astrophysics Data System (ADS)

    Maskawa, Jun-ichi

    2003-06-01

    We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

  6. Multivariable Harmonic Balance for Central Pattern Generators.

    PubMed

    Iwasaki, Tetsuya

    2008-12-01

    The central pattern generator (CPG) is a nonlinear oscillator formed by a group of neurons, providing a fundamental control mechanism underlying rhythmic movements in animal locomotion. We consider a class of CPGs modeled by a set of interconnected identical neurons. Based on the idea of multivariable harmonic balance, we show how the oscillation profile is related to the connectivity matrix that specifies the architecture and strengths of the interconnections. Specifically, the frequency, amplitudes, and phases are essentially encoded in terms of a pair of eigenvalue and eigenvector. This basic principle is used to estimate the oscillation profile of a given CPG model. Moreover, a systematic method is proposed for designing a CPG-based nonlinear oscillator that achieves a prescribed oscillation profile.

  7. Regionalization in geology by multivariate classification

    USGS Publications Warehouse

    Harff, Jan; Davis, J.C.

    1990-01-01

    The concept of multivariate classification of "geological objects" can be combined with the concept of regionalized variables to yield a procedure for typification of geological objects, such as rock units, well records, or samples. Numerical classification is followed by subdivision of the area of investigation, and culminates in a regionalization or mapping of the classification onto the plane. Regions are subdivisions of the map area which are spatially contiguous and relatively homogeneous in their geological properties. The probability of correct classification of each point within a region as being part of that region can be assessed in terms of Bayesian probability as a space-dependent function. The procedure is applied to subsurface data from western Kansas. The geologic properties used are quantitative variables, and relationships are expressed by Mahalanobis' distances. These functions could be replaced by other metrics if qualitative or binary data derived from geological descriptions or appraisals were included in the analysis. ?? 1990 International Association for Mathematical Geology.

  8. Design of feedforward controllers for multivariable plants

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1987-01-01

    Simple methods for the design of feedforward controllers to achieve steady-state disturbance rejection and command tracking in stable multivariable plants are developed in this paper. The controllers are represented by simple and low-order transfer functions and are not based on reconstruction of the states of the commands and disturbances. For unstable plants, it is shown that the present method can be applied directly when an additional feedback controller is employed to stabilize the plant. The feedback and feedforward controllers do not affect each other and can be designed independently based on the open-loop plant to achieve stability, disturbance rejection and command tracking, respectivley. Numerical examples are given for illustration.

  9. Multivariate analysis applied to tomato hybrid production.

    PubMed

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  10. Classification of adulterated honeys by multivariate analysis.

    PubMed

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

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

  11. Exploration of new multivariate spectral calibration algorithms.

    SciTech Connect

    Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.

    2004-03-01

    A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.

  12. Tailored multivariate analysis for modulated enhanced diffraction

    DOE PAGES

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

  13. Tailored multivariate analysis for modulated enhanced diffraction

    SciTech Connect

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

  14. Spatiotemporal patterns of acoustic emission (AE) activity in salt mine

    NASA Astrophysics Data System (ADS)

    Maghsoudi, S.; Cesca, S.; Hainzl, S.; Kaiser, D.; Dahm, T.

    2012-04-01

    Assessing the magnitude of completeness (Mc) is essential for the correct interpretation of earthquake catalogs. Knowledge on the spatiotemporal variation of Mc allows the mapping of other seismicity parameters, such as b-values. Spatial and temporal variations of b-values can indicate structural heterogeneities, stress perturbations and time-dependent fracturing processes. In order to precisely estimate Mc in strongly heterogeneous media, we propose a 3D development of the probabilistic magnitude of completeness (PMC) method, which relies on the analysis of network detection capabilities, to study spatial distribution of the Mc and b-value estimations for mining networks. We used a large dataset including more than 1 million acoustic emissions (AE), recorded at the Morsleben salt mine, Germany. Our study shows that the PMC estimations strongly depend on the source-receiver direction, and cannot be correctly accounted using a standard approach. The comparison between Mc using the 3D PMC method and Gutenberg-Richter methods show agreements for two reference depth ranges. Following our approach, we estimate Mc ranging between 1.25 (AE ,relative acoustic magnitude), at the center of the network, and 3.5, at further distances outside the network. Our method provides small-scale details about the capability of sensors to detect an AE event, and spatial distributions of Mc and b-value, which can be linked to the presence of structural heterogeneities or cavities in specific directions. Effects of heterogeneities on detection analysis are confirmed by synthetic tests using waveform modeling in heterogeneous media. This work has been funded by the German BMBF "Geotechnologien" project MINE (BMBF03G0737A).

  15. Disordered hyperuniform heterogeneous materials

    NASA Astrophysics Data System (ADS)

    Torquato, Salvatore

    2016-10-01

    Disordered hyperuniform many-body systems are distinguishable states of matter that lie between a crystal and liquid: they are like perfect crystals in the way they suppress large-scale density fluctuations and yet are like liquids or glasses in that they are statistically isotropic with no Bragg peaks. These systems play a vital role in a number of fundamental and applied problems: glass formation, jamming, rigidity, photonic and electronic band structure, localization of waves and excitations, self-organization, fluid dynamics, quantum systems, and pure mathematics. Much of what we know theoretically about disordered hyperuniform states of matter involves many-particle systems. In this paper, we derive new rigorous criteria that disordered hyperuniform two-phase heterogeneous materials must obey and explore their consequences. Two-phase heterogeneous media are ubiquitous; examples include composites and porous media, biological media, foams, polymer blends, granular media, cellular solids, and colloids. We begin by obtaining some results that apply to hyperuniform two-phase media in which one phase is a sphere packing in d-dimensional Euclidean space {{{R}}d} . Among other results, we rigorously establish the requirements for packings of spheres of different sizes to be ‘multihyperuniform’. We then consider hyperuniformity for general two-phase media in {{{R}}d} . Here we apply realizability conditions for an autocovariance function and its associated spectral density of a two-phase medium, and then incorporate hyperuniformity as a constraint in order to derive new conditions. We show that some functional forms can immediately be eliminated from consideration and identify other forms that are allowable. Specific examples and counterexamples are described. Contact is made with well-known microstructural models (e.g. overlapping spheres and checkerboards) as well as irregular phase-separation and Turing-type patterns. We also ascertain a family of

  16. Disordered hyperuniform heterogeneous materials.

    PubMed

    Torquato, Salvatore

    2016-10-19

    Disordered hyperuniform many-body systems are distinguishable states of matter that lie between a crystal and liquid: they are like perfect crystals in the way they suppress large-scale density fluctuations and yet are like liquids or glasses in that they are statistically isotropic with no Bragg peaks. These systems play a vital role in a number of fundamental and applied problems: glass formation, jamming, rigidity, photonic and electronic band structure, localization of waves and excitations, self-organization, fluid dynamics, quantum systems, and pure mathematics. Much of what we know theoretically about disordered hyperuniform states of matter involves many-particle systems. In this paper, we derive new rigorous criteria that disordered hyperuniform two-phase heterogeneous materials must obey and explore their consequences. Two-phase heterogeneous media are ubiquitous; examples include composites and porous media, biological media, foams, polymer blends, granular media, cellular solids, and colloids. We begin by obtaining some results that apply to hyperuniform two-phase media in which one phase is a sphere packing in d-dimensional Euclidean space [Formula: see text]. Among other results, we rigorously establish the requirements for packings of spheres of different sizes to be 'multihyperuniform'. We then consider hyperuniformity for general two-phase media in [Formula: see text]. Here we apply realizability conditions for an autocovariance function and its associated spectral density of a two-phase medium, and then incorporate hyperuniformity as a constraint in order to derive new conditions. We show that some functional forms can immediately be eliminated from consideration and identify other forms that are allowable. Specific examples and counterexamples are described. Contact is made with well-known microstructural models (e.g. overlapping spheres and checkerboards) as well as irregular phase-separation and Turing-type patterns. We also ascertain a family

  17. String Mining in Bioinformatics

    NASA Astrophysics Data System (ADS)

    Abouelhoda, Mohamed; Ghanem, Moustafa

    Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra- and inter-molecular similarities. Identifying intra-molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter-molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string- or pattern mining specific to biological strings. For a long time, this point of view, however, has not been explicitly embraced neither in the data mining nor in the sequence analysis text books, which may be attributed to the co-evolution of the two apparently independent fields. In other words, although the word "data-mining" is almost missing in the sequence analysis literature, its basic concepts have been implicitly applied. Interestingly, recent research in biological sequence analysis introduced efficient solutions to many problems in data mining, such as querying and analyzing time series [49,53], extracting information from web pages [20], fighting spam mails [50], detecting plagiarism [22], and spotting duplications in software systems [14].

  18. Mining the earth

    SciTech Connect

    Young, J.E.

    1992-01-01

    Substances extracted from the earth - stone, iron, bronze - have been so critical to human development that historians name the ages of our past after them. But while scholars have carefully tracked human use of minerals, they have never accounted for the vast environmental damage incurred in mineral production. Few people would guess that a copper mining operation has removed a piece of Utah seven times the weight of all the material dug for the Panama Canal. Few would dream that mines and smelters take up to a tenth of all the energy used each year, or that the waste left by mining measures in the billions of tons - dwarfing the world's total accumulation of more familiar kinds of waste, such as municipal garbage. Indeed, more material is now stripped from the earth by mining than by all the natural erosion of the earth's rivers. The effects of mining operations on the environment are discussed under the following topics: minerals in the global economy, laying waste, at what cost cleaning up, and dipping out. It is concluded that in the long run, the most effective strategy for minimizing new damage is not merely to make mineral extraction cleaner, but to reduce the rich nations needs for virgin (non-recycled) minerals.

  19. Heterogeneity in Waardenburg syndrome.

    PubMed Central

    Hageman, M J; Delleman, J W

    1977-01-01

    Heterogeneity of Waardenburg syndrome is demonstrated in a review of 1,285 patients from the literature and 34 previously unreported patients in five families in the Netherlands. The syndrome seems to consist of two genetically distinct entities that can be differentiated clinically: type I, Waardenburg syndrome with dystopia canthorum; and type II, Waardenburg syndrome without dystopia canthorum. Both types have an autosomal dominant mode of inheritance. The incidence of bilateral deafness in the two types of the syndrome was found in one-fourth with type I and about half of the patients with type II. This difference has important consequences for genetic counseling. Images Fig. 7 Fig. 8 Fig. 9 PMID:331943

  20. Interconnecting heterogeneous database management systems

    NASA Technical Reports Server (NTRS)

    Gligor, V. D.; Luckenbaugh, G. L.

    1984-01-01

    It is pointed out that there is still a great need for the development of improved communication between remote, heterogeneous database management systems (DBMS). Problems regarding the effective communication between distributed DBMSs are primarily related to significant differences between local data managers, local data models and representations, and local transaction managers. A system of interconnected DBMSs which exhibit such differences is called a network of distributed, heterogeneous DBMSs. In order to achieve effective interconnection of remote, heterogeneous DBMSs, the users must have uniform, integrated access to the different DBMs. The present investigation is mainly concerned with an analysis of the existing approaches to interconnecting heterogeneous DBMSs, taking into account four experimental DBMS projects.

  1. Longwall mining system

    SciTech Connect

    Guay, P.J.; Ludlow, J.E.; Peake, C.V.

    1983-05-10

    A longwall mining system includes a bidirectional shearer and a roof supporting structure. The shearer includes a pair of angled floor drums, a pivotable roof drum and a loading conveyor. Each drum has a plurality of picks disposed about the drum surface for cutting a material to be mined and a plurality of vanes disposed on the drum surface for carrying the cut material to the loading conveyor. The roof supporting structure includes a load carrying shield which is braced by a pair of supports. The supports are located under the shield in a position between the shearer and a face conveyor. The face conveyor, which is fed by the loading conveyor, carries the mined material to main conveyor for haulage to the outside.

  2. Gravity in a Mine Shaft.

    ERIC Educational Resources Information Center

    Hall, Peter M.; Hall, David J.

    1995-01-01

    Discusses the effects of gravity, local density compared to the density of the earth, the mine shaft, centrifugal force, and air buoyancy on the weight of an object at the top and at the bottom of a mine shaft. (JRH)

  3. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

    PubMed Central

    Li, Yanming; Zhu, Ji

    2015-01-01

    Summary We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. PMID:25732839

  4. Pneumatic stowing seals mines

    SciTech Connect

    Brezovec, D.

    1983-11-01

    A mechanized technique to seal abandoned mines has been used successfully to close 13 openings at Duquesne Light Co.'s mined-out Warwick No. 2 mine, near Greensboro, Pa. The mechanized system, which uses a pneumatic stower and crushed limestone, closed the entries more economically and in less time than it would have taken to install traditional concrete block stopping and clay plug seals, according to John C. Draper. Draper, a mining engineer with Duquesne Light's coal department, was in charge of installing the Warwick seals in a Bureau of Mines-sponsored field test on the pneumatic sealing technique. The lowest estimated cost for installing conventional stopping and plug closures for the 13 Warwick openings was $225,000, says Draper, while the openings were closed using the mechanized system for $245,000. Draper says the newer stopping cost more in the instance because work was stopped often to gather information for the experiment. The experimental closures were installed in 38 days. The job would have taken at least 149 days if traditional closures were being installed, Draper say. To install a traditional concrete block/clay plug closure, the mine opening must be cleaned thoroughly and the roof must be supported for some 3 ft from the outside. Then a solid wall or stopping must be built 25 ft from the surface and the entry must be packed with clay to the surface. Much of this job requires workers to remain underground. In pneumatic stowing, 1 1/2-in. crushed limestone with fines is conveyed through a pipeline and into the mine opening under low air pressure. Watertight seals can be installed by blowing about 10 ft of rock into the opening against the top to act as roof support. Safety posts are installed and about 10 or 15 ft of mine entry is cleaned. About 2 in. of raw cement or bentonite is placed on the floor and limestone mixed with dry cement or bentonite is blown into the opening.

  5. Granger causality and information flow in multivariate processes.

    PubMed

    Blinowska, Katarzyna J; Kuś, Rafał; Kamiński, Maciej

    2004-11-01

    The multivariate versus bivariate measures of Granger causality were considered. Granger causality in the application to multivariate physiological time series has the meaning of the information flow between channels. It was shown by means of simulations and by the example of experimental electroencephalogram signals that bivariate estimates of directionality in case of mutually interdependent channels give erroneous results, therefore multivariate measures such as directed transfer function should be used for determination of the information flow.

  6. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.

    PubMed

    Kambeitz, Joseph; Kambeitz-Ilankovic, Lana; Leucht, Stefan; Wood, Stephen; Davatzikos, Christos; Malchow, Berend; Falkai, Peter; Koutsouleris, Nikolaos

    2015-06-01

    Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity.

  7. Abandoned Mine Lands: Site Information

    EPA Pesticide Factsheets

    A catalogue of mining sites proposed for and listed on the NPL as well as mining sites being cleaned up using the Superfund Alternative Approach. Also mine sites not on the NPL but that have had removal or emergency response cleanup actions.

  8. REMOTE SENSING AND MOUNTAINTOP MINING

    EPA Science Inventory

    Coal mining is Appalachia has undergone dramatic changes in the past decade. Modem mining practices know as Mountaintop Mining (MTM) and Valley Fills (VF) are at the center of an environmental and legal controversy that has spawned lawsuits and major environmental investigations....

  9. Humanitarian Consequences of Land Mines.

    ERIC Educational Resources Information Center

    Rutherford, Ken

    1997-01-01

    Investigates the human and economic consequences of the continuing use and abandonment of land mines. Discusses the reasons for the worldwide proliferation (over 85 million uncleared mines in at least 62 countries) and the legal complexities in curtailing their use. Includes a brief account by a land-mine victim. (MJP)

  10. Mine-Mouth Geyser Problem.

    ERIC Educational Resources Information Center

    de Nevers, Noel

    1982-01-01

    An oilwell drilling rig accidentally drilled into an underground salt mine, draining a lake and filling the mine, with water jetting out of the mine 400 feet into the air. An explanation of the jetting phenomenon is offered in terms of the laws of fluid dynamics, with supporting diagrams and calculations. (Author/JN)

  11. Under-mining health: environmental justice and mining in India.

    PubMed

    Saha, Shubhayu; Pattanayak, Subhrendu K; Sills, Erin O; Singha, Ashok K

    2011-01-01

    Despite the potential for economic growth, extractive mineral industries can impose negative health externalities in mining communities. We estimate the size of these externalities by combining household interviews with mine location and estimating statistical functions of respiratory illness and malaria among villagers living along a gradient of proximity to iron-ore mines in rural India. Two-stage regression modeling with cluster corrections suggests that villagers living closer to mines had higher respiratory illness and malaria-related workday loss, but the evidence for mine workers is mixed. These findings contribute to the thin empirical literature on environmental justice and public health in developing countries.

  12. 76 FR 70075 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-10

    ... Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health Administration, Labor. ACTION... addressing Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines. This... Continuous Mining Machines in Underground Coal Mines. MSHA conducted hearings on October 18, October...

  13. Modeling Spatial Dependencies and Semantic Concepts in Data Mining

    SciTech Connect

    Vatsavai, Raju

    2012-01-01

    Data mining is the process of discovering new patterns and relationships in large datasets. However, several studies have shown that general data mining techniques often fail to extract meaningful patterns and relationships from the spatial data owing to the violation of fundamental geospatial principles. In this tutorial, we introduce basic principles behind explicit modeling of spatial and semantic concepts in data mining. In particular, we focus on modeling these concepts in the widely used classification, clustering, and prediction algorithms. Classification is the process of learning a structure or model (from user given inputs) and applying the known model to the new data. Clustering is the process of discovering groups and structures in the data that are ``similar,'' without applying any known structures in the data. Prediction is the process of finding a function that models (explains) the data with least error. One common assumption among all these methods is that the data is independent and identically distributed. Such assumptions do not hold well in spatial data, where spatial dependency and spatial heterogeneity are a norm. In addition, spatial semantics are often ignored by the data mining algorithms. In this tutorial we cover recent advances in explicitly modeling of spatial dependencies and semantic concepts in data mining.

  14. Etiologic heterogeneity in alcoholism.

    PubMed

    Gilligan, S B; Reich, T; Cloninger, C R

    1987-01-01

    Etiologic heterogeneity in alcohol abuse was evaluated in 195 extended pedigrees, comprising 288 nuclear families of 140 male and 55 female Caucasian American hospitalized alcoholics. Previous adoption studies in Sweden demonstrated differential heritability of two patterns of alcohol abuse in men: type-2 alcoholism exhibited early onset of abuse associated with criminal behavior, while type-1 abuse began at a later age, uncomplicated by antisocial traits. Alcohol abuse in female Swedish adoptees was relatively homogeneous and similar to the late-onset, type-1 abuse. The notion of etiologic heterogeneity, as suggested by the Stockholm Adoption Studies, was examined in the American pedigrees by contrasting the models of familial transmission of susceptibility to alcoholism obtained via segregation analyses of families of male versus female probands. Families of male probands demonstrated significant familial resemblance, accounted for by a multifactorial-polygenic background in addition to a major (gene) effect. In contrast, familial resemblance in the pedigrees of female probands was attributed solely to a multifactorial-polygenic effect. We considered whether some families of male alcoholics were similar to families of female probands, who expressed type-1 abuse predominantly. Pedigrees of male probands were separated in two groups: (1) "female-like" families had a better likelihood for the model obtained for families of female probands than the one for families of all male probands, (2) "male-like" families had a better likelihood for the model of familial transmission describing families of all male probands. A statistically significant difference in the pattern of familial transmission was observed between the "male-like" and "female-like" groups. Discriminant function analysis of alcohol-related symptoms showed that the familial subtypes differed in clinical features as well. Alcohol abuse by male relatives in "male-like" families was characterized by the

  15. Data manipulation in heterogeneous databases

    SciTech Connect

    Chatterjee, A.; Segev, A.

    1991-10-01

    Many important information systems applications require access to data stored in multiple heterogeneous databases. This paper examines a problem in inter-database data manipulation within a heterogeneous environment, where conventional techniques are no longer useful. To solve the problem, a broader definition for join operator is proposed. Also, a method to probabilistically estimate the accuracy of the join is discussed.

  16. Interference Management in Heterogeneous Networks

    DTIC Science & Technology

    2013-06-01

    INTERFERENCE MANAGEMENT IN HETEROGENEOUS NETWORKS UNIVERSITY OF MARYLAND JUNE 2013 FINAL TECHNICAL REPORT APPROVED...3. DATES COVERED (From - To) AUG 2011 – FEB 2013 4. TITLE AND SUBTITLE INTERFERENCE MANAGEMENT IN HETEROGENEOUS NETWORKS 5a. CONTRACT NUMBER...However, such deployments require efficient frequency allocation schemes for managing interference from the pico- and macro base stations that are

  17. A multivariate exploration of basic symptoms.

    PubMed

    Rubino, I Alex; Ciani, Nicola

    2002-01-01

    Little is known about the relationship between the different categories of basic symptoms (BS). Researchers of the Bonn School have accurately described the progression from second-level BS (relatively characteristic BS) to first-rank Schneiderian symptoms. Using a multiple regression model, the present study tried to investigate which kind of dynamic deficiencies (DDs; uncharacteristic first-level BS) mostly lead to each type of second-level BS. A group of 108 patients with a DSM-III-R diagnosis of schizophrenia completed an inventory on BS, with all items in strict accordance with those of the Bonn Scale. Five dependent variables (cognitive thought disorders, cognitive perception disorders, cognitive action disorders, increased impressionability, cenesthesias) and four independent variables (DDs with direct negative symptoms, DDs with indirect negative symptoms, affective DDs, relational DDs) were considered. Among the significant findings, a widespread contribution of DDs with indirect negative symptoms to most of the dependent variables, and the special role of DDs with direct negative symptoms as a predictor of cognitive thought disorders, must be emphasized. Suggestions for further multivariate studies in the field of BS are presented.

  18. Multivariate semiparametric spatial methods for imaging data.

    PubMed

    Chen, Huaihou; Cao, Guanqun; Cohen, Ronald A

    2017-04-01

    Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions. The proposed method is applicable to both cross-sectional and longitudinal region-level imaging data. We show the asymptotic rates for the bias and covariance functions of the proposed estimator and its asymptotic normality. Our simulation studies demonstrate that by borrowing information from similar regions, the proposed spatial similarity method improves the efficiency remarkably. We apply the proposed method to two neuroimaging data examples. The results reveal that accounting for the spatial similarity leads to more accurate estimators and better functional clustering results for visualizing brain atrophy pattern.Functional clustering; Longitudinal magnetic resonance imaging (MRI); Penalized B-splines; Region of interest (ROI); Spatial penalty.

  19. The multivariate statistical structure of DRASTIC model

    NASA Astrophysics Data System (ADS)

    Pacheco, Fernando A. L.; Sanches Fernandes, Luís F.

    2013-01-01

    SummaryAn assessment of aquifer intrinsic vulnerability was conducted in the Sordo river basin, a small watershed located in the Northeast of Portugal that drains to a lake used as public resource of drinking water. The method adopted to calculate intrinsic vulnerability was the DRASTIC model, which hinges on a weighted addition of seven hydrogeologic features, but was combined with a pioneering approach for feature reduction and adjustment of feature weights to local settings, based on a multivariate statistical method. Basically, with the adopted statistical technique-Correspondence Analysis-one identified and minimized redundancy between DRASTIC features, allowing for the calculation of a composite index based on just three of them: topography, recharge and aquifer material. The combined algorithm was coined vector-DRASTIC and proved to describe more realistically intrinsic vulnerability than DRASTC. The proof resulted from a validation of DRASTIC and vector-DRASTIC by the results of a groundwater pollution risk assessment standing on the spatial distribution of land uses and nitrate concentrations in groundwater, referred to as [NO3-]-DRASTIC method. Vector-DRASTIC and [NO3-]-DRASTIC portray the Sordo river basin as an environment with a self-capability to neutralize contaminants, preventing its propagation downstream. This observation was confirmed by long-standing low nitrate concentrations in the lake water and constitutes additional validation of vector-DRASTIC results. Nevertheless, some general recommendations are proposed in regard to agriculture management practices for water quality protection, as part of an overall watershed approach.

  20. A Gibbs sampler for multivariate linear regression

    NASA Astrophysics Data System (ADS)

    Mantz, Adam B.

    2016-04-01

    Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.

  1. Multivariate volume visualization through dynamic projections

    SciTech Connect

    Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer -Timo; Pascucci, Valerio

    2014-11-01

    We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.

  2. Multivariate sensitivity to voice during auditory categorization.

    PubMed

    Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-09-01

    Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex.

  3. Multivariate Models of Adult Pacific Salmon Returns

    PubMed Central

    Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa

    2013-01-01

    Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586

  4. Apparatus and system for multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2003-06-24

    An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

  5. Geographical heterogeneity and influenza infection within households

    PubMed Central

    2014-01-01

    Background Although it has been suggested that schoolchildren vaccination reduces influenza morbidity and mortality in the community, it is unknown whether geographical heterogeneity would affect vaccine effectiveness. Methods A 3-year prospective, non-randomized sero-epidemiological study was conducted during 2008–2011 by recruiting schoolchildren from both urban and rural areas. Respective totals of 124, 206, and 176 households were recruited and their household contacts were followed. Serum samples were collected pre-vaccination, one-month post-vaccination and post-season from children and household contacts for hemagglutination inhibition (HI) assay. A multivariate logistic model implemented with generalized estimation equations (GEE) was fitted with morbidity or a four-fold increase in HI titer of the household contacts for two consecutive sera as the dependent variable; with geographical location, vaccination status of each household and previous vaccination history as predictor variables. Results Although our results show no significant reduction in the proportion of infection or clinical morbidity among household contacts, a higher risk of infection, indicated by odds ratio > 1, was consistently observed among household children contacts from the un-vaccinated households after adjusting for confounding variables. Interestingly, a statistically significant lower risk of infection was observed among household adult contacts from rural area when compared to those from urban area (OR = 0.89; 95% CI: 0.82-0.97 for Year 2 and OR = 0.85; 95% CI: 0.75-0.96 for Year 3). Conclusions A significant difference in the risk of influenza infection among household adults due to geographical heterogeneity, independent of schoolchildren vaccination status, was revealed in this study. Its impact on vaccine effectiveness requires further study. PMID:24993483

  6. Grants Mining District

    EPA Pesticide Factsheets

    The Grants Mineral Belt was the focus of uranium extraction and production activities from the 1950s until the late 1990s. EPA is working with state, local, and federal partners to assess and address health risks and environmental effects of the mines

  7. Bioremediation of mine water.

    PubMed

    Klein, Robert; Tischler, Judith S; Mühling, Martin; Schlömann, Michael

    2014-01-01

    Caused by the oxidative dissolution of sulfide minerals, mine waters are often acidic and contaminated with high concentrations of sulfates, metals, and metalloids. Because the so-called acid mine drainage (AMD) affects the environment or poses severe problems for later use, treatment of these waters is required. Therefore, various remediation strategies have been developed to remove soluble metals and sulfates through immobilization using physical, chemical, and biological approaches. Conventionally, iron and sulfate-the main pollutants in mine waters-are removed by addition of neutralization reagents and subsequent chemical iron oxidation and sulfate mineral precipitation. Biological treatment strategies take advantage of the ability of microorganisms that occur in mine waters to metabolize iron and sulfate. As a rule, these can be grouped into oxidative and reductive processes, reflecting the redox state of mobilized iron (reduced form) and sulfur (oxidized form) in AMD. Changing the redox states of iron and sulfur results in iron and sulfur compounds with low solubility, thus leading to their precipitation and removal. Various techniques have been developed to enhance the efficacy of these microbial processes, as outlined in this review.

  8. Pneumatic stowing seals mines

    SciTech Connect

    Brezovec, D.

    1983-11-01

    A pneumatic stowing technique has been used in the US to seal entries to abandoned mines. Limestone mixed with dry cement or bentonite is blown into the opening. Sealing can be accomplished in much less time than with traditional concrete block/clay plug methods.

  9. String Mining in Bioinformatics

    NASA Astrophysics Data System (ADS)

    Abouelhoda, Mohamed; Ghanem, Moustafa

    Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra- and inter-molecular similarities. Identifying intra-molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter-molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string- or pattern mining specific to biological strings. For a long time, this point of view, however, has not been explicitly embraced neither in the data mining nor in the sequence analysis text books, which may be attributed to the co-evolution of the two apparently independent fields. In other words, although the word “data-mining” is almost missing in the sequence analysis literature, its basic concepts have been implicitly applied. Interestingly, recent research in biological sequence analysis introduced efficient solutions to many problems in data mining, such as querying and analyzing time series [49,53], extracting information from web pages [20], fighting spam mails [50], detecting plagiarism [22], and spotting duplications in software systems [14].

  10. Mining Task Force Report.

    ERIC Educational Resources Information Center

    Saskatchewan Inst. of Applied Science and Technology, Saskatoon.

    In fall 1988, the Board of Directors of the Saskatchewan Institute of Applied Science and Technology (SIAST) created a task force to study the training needs of the mining industry in the province and evaluate SIAST's responsiveness to those needs. After assessing the technological changes taking place in the industry, surveying manpower needs,…

  11. Lunabotics Mining Competition

    NASA Technical Reports Server (NTRS)

    Mueller, Rob; Murphy, Gloria

    2010-01-01

    This slide presentation describes a competition to design a lunar robot (lunabot) that can be controlled either remotely or autonomously, isolated from the operator, and is designed to mine a lunar aggregate simulant. The competition is part of a systems engineering curriculum. The 2010 competition winners in five areas of the competition were acknowledged, and the 2011 competition was announced.

  12. Contextual Text Mining

    ERIC Educational Resources Information Center

    Mei, Qiaozhu

    2009-01-01

    With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

  13. Mining (except Oil and Gas) Sector (NAICS 212)

    EPA Pesticide Factsheets

    EPA Regulatory and enforcement information for the mining sector, including metal mining & nonmetallic mineral mining and quarrying. Includes information about asbestos, coal mining, mountaintop mining, Clean Water Act section 404, and abandoned mine lands

  14. Data in support of enhancing metabolomics research through data mining.

    PubMed

    Martínez-Arranz, Ibon; Mayo, Rebeca; Pérez-Cormenzana, Miriam; Mincholé, Itziar; Salazar, Lorena; Alonso, Cristina; Mato, José M

    2015-06-01

    Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this 'omic' discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled "Enhancing metabolomics research through data mining", which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.

  15. Theory of heterogeneous viscoelasticity

    NASA Astrophysics Data System (ADS)

    Schirmacher, Walter; Ruocco, Giancarlo; Mazzone, Valerio

    2016-03-01

    We review a new theory of viscoelasticity of a glass-forming viscous liquid near and below the glass transition. In our model, we assume that each point in the material has a specific viscosity, which varies randomly in space according to a fluctuating activation free energy. We include a Maxwellian elastic term, and assume that the corresponding shear modulus fluctuates as well with the same distribution as that of the activation barriers. The model is solved in coherent potential approximation, for which a derivation is given. The theory predicts an Arrhenius-type temperature dependence of the viscosity in the vanishing frequency limit, independent of the distribution of the activation barriers. The theory implies that this activation energy is generally different from that of a diffusing particle with the same barrier height distribution. If the distribution of activation barriers is assumed to have the Gaussian form, the finite-frequency version of the theory describes well the typical low-temperature alpha relaxation peak of glasses. Beta relaxation can be included by adding another Gaussian with centre at much lower energies than that is responsible for the alpha relaxation. At high frequencies, our theory reduces to the description of an elastic medium with spatially fluctuating elastic moduli (heterogeneous elasticity theory), which explains the occurrence of the boson peak-related vibrational anomalies of glasses.

  16. Angiotensin II receptor heterogeneity

    SciTech Connect

    Herblin, W.F.; Chiu, A.T.; McCall, D.E.; Ardecky, R.J.; Carini, D.J.; Duncia, J.V.; Pease, L.J.; Wong, P.C.; Wexler, R.R.; Johnson, A.L. )

    1991-04-01

    The possibility of receptor heterogeneity in the angiotensin II (AII) system has been suggested previously, based on differences in Kd values or sensitivity to thiol reagents. One of the authors earliest indications was the frequent observation of incomplete inhibition of the binding of AII to adrenal cortical membranes. Autoradiographic studies demonstrated that all of the labeling of the rat adrenal was blocked by unlabeled AII or saralasin, but not by DuP 753. The predominant receptor in the rat adrenal cortex (80%) is sensitive to dithiothreitol (DTT) and DuP 753, and is designated AII-1. The residual sites in the adrenal cortex and almost all of the sites in the rat adrenal medulla are insensitive to both DTT and DuP 753, but were blocked by EXP655. These sites have been confirmed by ligand binding studies and are designated AII-2. The rabbit adrenal cortex is unique in yielding a nonuniform distribution of AII-2 sites around the outer layer of glomerulosa cells. In the rabbit kidney, the sites on the glomeruli are AII-1, but the sites on the kidney capsule are AII-2. Angiotensin III appears to have a higher affinity for AII-2 sites since it inhibits the binding to the rabbit kidney capsule but not the glomeruli. Elucidation of the distribution and function of these diverse sites should permit the development of more selective and specific therapeutic strategies.

  17. Heterogeneous recording media

    NASA Astrophysics Data System (ADS)

    Sukhanov, Vitaly I.

    1991-02-01

    The paper summarizes the results of investigations performed to obtain deep 3-D holograms with 102 i0 mkm physical thickness allowing the postexposure amplification and the a posteriori changing of the grating parameters. This aim has been achieved by developing heterogeneous systems on the basis of porous glass with light-sensitive compositions introduced into it. 1. INTRODUCTION. LIGHT-SENSITIVE MEDIA FOR 3-D HOLOGRAMS RECORDING. The 3-D holograms have many useful properties: very high diffraction efficiency angular and spectral selectivity but low level of noise. It shoud be noted that in this case deep 3-D holograms are dealt with whose physical thickness is as high as 102 -i mkm. Such hologram recording is usually done using homogeneous light-sensitive media for example dyed acid-halide and electrooptical crystals photochrome glass photostructurized polimer compositions and so on. The nature of photophisical and photochemical processes responsible for the light sensitivity of these materials exclude the possibility of post-exposure treatment. This does not allow to enhance the recorded holograms and considerably hampers their fixing or makes it practically impossible. The object of our work is to create the media which are quite suitable for two-stage processes of the deep hologram formation with post-exposure processing. Such material must satisfy the following requirements: a)they must have high permeability for the developing substances in order to make the development duration suitable for practical applications b)they must be shrinkproof to prevent deformation of the

  18. Reference Point Heterogeneity

    PubMed Central

    Terzi, Ayse; Koedijk, Kees; Noussair, Charles N.; Pownall, Rachel

    2016-01-01

    It is well-established that, when confronted with a decision to be taken under risk, individuals use reference payoff levels as important inputs. The purpose of this paper is to study which reference points characterize decisions in a setting in which there are several plausible reference levels of payoff. We report an experiment, in which we investigate which of four potential reference points: (1) a population average payoff level, (2) the announced expected payoff of peers in a similar decision situation, (3) a historical average level of earnings that others have received in the same task, and (4) an announced anticipated individual payoff level, best describes decisions in a decontextualized risky decision making task. We find heterogeneity among individuals in the reference points they employ. The population average payoff level is the modal reference point, followed by experimenter's stated expectation of a participant's individual earnings, followed in turn by the average earnings of other participants in previous sessions of the same experiment. A sizeable share of individuals show multiple reference points simultaneously. The reference point that best fits the choices of the individual is not affected by a shock to her income. PMID:27672374

  19. Reference Point Heterogeneity.

    PubMed

    Terzi, Ayse; Koedijk, Kees; Noussair, Charles N; Pownall, Rachel

    2016-01-01

    It is well-established that, when confronted with a decision to be taken under risk, individuals use reference payoff levels as important inputs. The purpose of this paper is to study which reference points characterize decisions in a setting in which there are several plausible reference levels of payoff. We report an experiment, in which we investigate which of four potential reference points: (1) a population average payoff level, (2) the announced expected payoff of peers in a similar decision situation, (3) a historical average level of earnings that others have received in the same task, and (4) an announced anticipated individual payoff level, best describes decisions in a decontextualized risky decision making task. We find heterogeneity among individuals in the reference points they employ. The population average payoff level is the modal reference point, followed by experimenter's stated expectation of a participant's individual earnings, followed in turn by the average earnings of other participants in previous sessions of the same experiment. A sizeable share of individuals show multiple reference points simultaneously. The reference point that best fits the choices of the individual is not affected by a shock to her income.

  20. Technologies for Decreasing Mining Losses

    NASA Astrophysics Data System (ADS)

    Valgma, Ingo; Väizene, Vivika; Kolats, Margit; Saarnak, Martin

    2013-12-01

    In case of stratified deposits like oil shale deposit in Estonia, mining losses depend on mining technologies. Current research focuses on extraction and separation possibilities of mineral resources. Selective mining, selective crushing and separation tests have been performed, showing possibilities of decreasing mining losses. Rock crushing and screening process simulations were used for optimizing rock fractions. In addition mine backfilling, fine separation, and optimized drilling and blasting have been analyzed. All tested methods show potential and depend on mineral usage. Usage in addition depends on the utilization technology. The questions like stability of the material flow and influences of the quality fluctuations to the final yield are raised.

  1. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

    PubMed Central

    Snell, Kym I.E.; Hua, Harry; Debray, Thomas P.A.; Ensor, Joie; Look, Maxime P.; Moons, Karel G.M.; Riley, Richard D.

    2016-01-01

    Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of “good” performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of “good” performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of “good” performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. PMID:26142114

  2. Multisource causal data mining

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Gosnell, Michael; Shallenberger, Kevin

    2012-06-01

    Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data- Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal- View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings.

  3. Spatio-temporal change in the relationship between habitat heterogeneity and species diversity

    NASA Astrophysics Data System (ADS)

    González-Megías, Adela; Gómez, José María; Sánchez-Piñero, Francisco

    2011-05-01

    Beta diversity plays an important role in mediating species diversity and therefore improves our understanding of species-diversity patterns. One principal theoretical framework exists for such patterns, the "habitat-heterogeneity hypothesis (HHH)", which postulates a positive relationship between species diversity and habitat heterogeneity. Although HHH is widely accepted, spatial and temporal variability has been found in the relationship between diversity and heterogeneity. Species turnover has been proposed as the main factor explaining spatial variation in the relationship between species diversity and habitat heterogeneity. In this study, we tested the role of species turnover in explaining spatial and temporal variability on diversity-heterogeneity relationship in a Mediterranean ecosystem, using beetles as the study organisms. A hierarchical design including different habitats and years was used to test our hypothesis. Using different multivariate analyses, we tested for spatial and temporal variability in beta diversity, and in the beetle diversity-heterogeneity relationship using two diversity indices. Our study showed that beetle composition changed spatially and temporally, although temporal change was evident only between sampling periods but not between years. Notably, there was spatial and temporal change in the relationship between habitat descriptors and beetle diversity. Nevertheless, there was no correlation between the changes in beetle composition with the changes in the habitat-heterogeneity relationships. In this Mediterranean system, spatial and temporal changes in the diversity-heterogeneity relationships cannot be predicted by species turnover, and other mechanisms need to be explored to satisfactorily explain this variability.

  4. Relationship between Multiple Regression and Selected Multivariable Methods.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

  5. Evaluating Univariate, Bivariate, and Multivariate Normality Using Graphical Procedures.

    ERIC Educational Resources Information Center

    Burdenski, Thomas K., Jr.

    This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery…

  6. Exploratory Tobit Factor Analysis for Multivariate Censored Data.

    ERIC Educational Resources Information Center

    Kamakura, Wagner A.; Wedel, Michel

    2001-01-01

    Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

  7. Multivariate Seismic Calibration for the Novaya Zemlya Test Site

    DTIC Science & Technology

    1992-09-30

    every multivariate magnitude combination. A classical confidence interval is presented to estimate future yields, based on estimates of the unknown...multivariate calibration parameters. A test of TTBT compliance and a definition of the F-number, based on the confidence interval , are also provided. F

  8. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  9. Exploratory Multivariate Analysis of Variance: Contrasts and Variables.

    ERIC Educational Resources Information Center

    Barcikowski, Robert S.; Elliott, Ronald S.

    The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…

  10. Causal diagrams and multivariate analysis II: precision work.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision.

  11. Multivariate Display for Quipus to Faces. Program Statistics Research.

    ERIC Educational Resources Information Center

    Wainer, Howard

    The past decade has seen a substantial growth in methods and schemes for the display of multivariate data. This paper encompasses a sketch of the history of multivariate displays, from the pre-Columbian Quipu to Chernoff's Face; examines a number of techniques; describes their construction; illustrates their use; and comments on their efficacy.…

  12. Methods for presentation and display of multivariate data

    NASA Technical Reports Server (NTRS)

    Myers, R. H.

    1981-01-01

    Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

  13. Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

    ERIC Educational Resources Information Center

    Ruscio, John; Kaczetow, Walter

    2008-01-01

    Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

  14. Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks

    NASA Astrophysics Data System (ADS)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

    Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.

  15. Linking multimetric and multivariate approaches to assess the ecological condition of streams.

    PubMed

    Collier, Kevin J

    2009-10-01

    Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

  16. ibr: Iterative bias reduction multivariate smoothing

    SciTech Connect

    Hengartner, Nicholas W; Cornillon, Pierre-andre; Matzner - Lober, Eric

    2009-01-01

    Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.

  17. Bioharness™ Multivariable Monitoring Device: Part. II: Reliability

    PubMed Central

    Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

    2012-01-01

    The Bioharness™ monitoring system may provide physiological information on human performance but the reliability of this data is fundamental for confidence in the equipment being used. The objective of this study was to assess the reliability of each of the 5 Bioharness™ variables using a treadmill based protocol. 10 healthy males participated. A between and within subject design to assess the reliability of Heart rate (HR), Breathing Frequency (BF), Accelerometry (ACC) and Infra-red skin temperature (ST) was completed via a repeated, discontinuous, incremental treadmill protocol. Posture (P) was assessed by a tilt table, moved through 160°. Between subject data reported low Coefficient of Variation (CV) and strong correlations(r) for ACC and P (CV< 7.6; r = 0.99, p < 0.01). In contrast, HR and BF (CV~19.4; r~0.70, p < 0.01) and ST (CV 3.7; r = 0.61, p < 0.01), present more variable data. Intra and inter device data presented strong relationships (r > 0.89, p < 0.01) and low CV (<10.1) for HR, ACC, P and ST. BF produced weaker relationships (r < 0.72) and higher CV (<17.4). In comparison to the other variables BF variable consistently presents less reliability. Global results suggest that the Bioharness™ is a reliable multivariable monitoring device during laboratory testing within the limits presented. Key pointsHeart rate and breathing frequency data increased in variance at higher velocities (i.e. ≥ 10 km.h-1)In comparison to the between subject testing, the intra and inter reliability presented good reliability in data suggesting placement or position of device relative to performer could be important for data collectionUnderstanding a devices variability in measurement is important before it can be used within an exercise testing or monitoring setting PMID:24149347

  18. Gravitational-wave detection using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Adams, Thomas S.; Meacher, Duncan; Clark, James; Sutton, Patrick J.; Jones, Gareth; Minot, Ariana

    2013-09-01

    Searches for gravitational-wave bursts (transient signals, typically of unknown waveform) require identification of weak signals in background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations that are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events in an attempt to maximize the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50%, corresponding to a factor of ˜3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localization error, poor data quality, and the simulated signal waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies from those used in the classifier training and that this improvement extends to false alarm probabilities beyond the 3σ significance level. These findings indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.

  19. Cross-Modal Multivariate Pattern Analysis

    PubMed Central

    Meyer, Kaspar; Kaplan, Jonas T.

    2011-01-01

    Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

  20. Multivariate statistical analysis of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    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).

  1. A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration

    PubMed Central

    Goovaerts, P.; Albuquerque, Teresa; Antunes, Margarida

    2015-01-01

    This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization. PMID:27777638

  2. Mining-Induced Coal Permeability Change Under Different Mining Layouts

    NASA Astrophysics Data System (ADS)

    Zhang, Zetian; Zhang, Ru; Xie, Heping; Gao, Mingzhong; Xie, Jing

    2016-09-01

    To comprehensively understand the mining-induced coal permeability change, a series of laboratory unloading experiments are conducted based on a simplifying assumption of the actual mining-induced stress evolution processes of three typical longwall mining layouts in China, i.e., non-pillar mining (NM), top-coal caving mining (TCM) and protective coal-seam mining (PCM). A theoretical expression of the mining-induced permeability change ratio (MPCR) is derived and validated by laboratory experiments and in situ observations. The mining-induced coal permeability variation under the three typical mining layouts is quantitatively analyzed using the MPCR based on the test results. The experimental results show that the mining-induced stress evolution processes of different mining layouts do have an influence on the mechanical behavior and evolution of MPCR of coal. The coal mass in the PCM simulation has the lowest stress concentration but the highest peak MPCR (approximately 4000 %), whereas the opposite trends are observed for the coal mass under NM. The results of the coal mass under TCM fall between those for PCM and NM. The evolution of the MPCR of coal under different layouts can be divided into three sections, i.e., stable increasing section, accelerated increasing section and reducing section, but the evolution processes are slightly different for the different mining layouts. A coal bed gas intensive extraction region is recommended based on the MPCR distribution of coal seams obtained by simplifying assumptions and the laboratory testing results. The presented results are also compared with existing conventional triaxial compression test results to fully comprehend the effect of actual mining-induced stress evolution on coal property tests.

  3. Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

    ERIC Educational Resources Information Center

    Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

    2008-01-01

    Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

  4. Text Mining for Neuroscience

    NASA Astrophysics Data System (ADS)

    Tirupattur, Naveen; Lapish, Christopher C.; Mukhopadhyay, Snehasis

    2011-06-01

    Text mining, sometimes alternately referred to as text analytics, refers to the process of extracting high-quality knowledge from the analysis of textual data. Text mining has wide variety of applications in areas such as biomedical science, news analysis, and homeland security. In this paper, we describe an approach and some relatively small-scale experiments which apply text mining to neuroscience research literature to find novel associations among a diverse set of entities. Neuroscience is a discipline which encompasses an exceptionally wide range of experimental approaches and rapidly growing interest. This combination results in an overwhelmingly large and often diffuse literature which makes a comprehensive synthesis difficult. Understanding the relations or associations among the entities appearing in the literature not only improves the researchers current understanding of recent advances in their field, but also provides an important computational tool to formulate novel hypotheses and thereby assist in scientific discoveries. We describe a methodology to automatically mine the literature and form novel associations through direct analysis of published texts. The method first retrieves a set of documents from databases such as PubMed using a set of relevant domain terms. In the current study these terms yielded a set of documents ranging from 160,909 to 367,214 documents. Each document is then represented in a numerical vector form from which an Association Graph is computed which represents relationships between all pairs of domain terms, based on co-occurrence. Association graphs can then be subjected to various graph theoretic algorithms such as transitive closure and cycle (circuit) detection to derive additional information, and can also be visually presented to a human researcher for understanding. In this paper, we present three relatively small-scale problem-specific case studies to demonstrate that such an approach is very successful in

  5. Distribution, speciation, and risk assessment of selected metals in the gold and iron mine soils of the catchment area of Miyun Reservoir, Beijing, China.

    PubMed

    Huang, Xingxing; Zhu, Yi; Ji, Hongbing

    2013-10-01

    In order to investigate the metal distribution, speciation, correlation and origin, risk assessment, 86 surface soil samples from the catchment area around the Miyun Reservoir, Beijing, including samples from gold and iron mine areas, were monitored for fractions of heavy metal and total contents. Most of the metal concentrations in the gold and iron mine soil samples exceeded the metal background levels in Beijing. The contents of most elements in the gold mine tailings were noticeably higher than those in the iron mine tailings. Geochemical speciation data of the metals showed that the residual fraction dominated most of the heavy metals in both mines. In both mine areas, Mn had the greatest the acid-soluble fraction (F1) per portion. The high secondary-phase fraction portion of Cd in gold mine samples indicated that there was a direct potential hazard to organisms in the tested areas. Multivariate analysis coupled with the contents of selected metals, showed that Hg, Pb, Cr, and Ni in gold mine areas represented anthropogenic sources; Cd, Pb, and Cr in iron mine areas represented industrial sources. There was moderate to high contamination of a few metals in the gold and iron soil samples, the contamination levels were relatively higher in gold mine than in iron mine soils.

  6. Alchemy and mining: metallogenesis and prospecting in early mining books.

    PubMed

    Dym, Warren Alexander

    2008-11-01

    Historians have assumed that alchemy had a close association with mining, but exactly how and why miners were interested in alchemy remains unclear. This paper argues that alchemical theory began to be synthesised with classical and Christian theories of the earth in mining books after 1500, and served an important practical function. The theory of metals that mining officials addressed spoke of mineral vapours (Witterungen) that left visible markings on the earth's surface. The prospector searched for mineral ore in part by studying these indications. Mineral vapours also explained the functioning of the dowsing rod, which prospectors applied to the discovery of ore. Historians of early chemistry and mining have claimed that mining had a modernising influence by stripping alchemy of its theoretical component, but this paper shows something quite to the contrary: mining officials may have been sceptical of the possibility of artificial transmutation, but they were interested in a theory of the earth that could translate into prospecting knowledge.

  7. A multivariate approach for mapping fire ignition risk: the example of the National Park of Cilento (southern Italy).

    PubMed

    Guglietta, Daniela; Migliozzi, Antonello; Ricotta, Carlo

    2015-07-01

    Recent advances in fire management led landscape managers to adopt an integrated fire fighting strategy in which fire suppression is supported by prevention actions and by knowledge of local fire history and ecology. In this framework, an accurate evaluation of fire ignition risk and its environmental drivers constitutes a basic step toward the optimization of fire management measures. In this paper, we propose a multivariate method for identifying and spatially portraying fire ignition risk across a complex and heterogeneous landscape such as the National Park of Cilento, Vallo di Diano, and Alburni (southern Italy). The proposed approach consists first in calculating the fire selectivity of several landscape features that are usually related to fire ignition, such as land cover or topography. Next, the fire selectivity values of single landscape features are combined with multivariate segmentation tools. The resulting fire risk map may constitute a valuable tool for optimizing fire prevention strategies and for efficiently allocating fire fighting resources.

  8. New insight into monitoring degradation products during the TiO2-photocatalysis process by multivariate molecular spectroscopy.

    PubMed

    Stets, Sandra; do Amaral, Bianca; Bach, Larissa; Nagata, Noemi; Peralta-Zamora, Patricio G

    2016-07-23

    This study focuses on the feasibility of a spectroscopic multivariate method for monitoring the concentration of phenol and its main degradation products during heterogeneous photocatalysis. Phenolic compounds were chosen as model to evaluate the degradation process due to their toxicity and persistence in the environment and also their well-known degradation pathway. The predictive capability of the multivariate method developed by partial least squares regression (PLSR) over the spectral range of 200-350 nm was satisfactory, allowing mean predicted errors below 5.0 % in the simultaneous determination of the target compounds using six latent variables and smoothing spectra. Suitable results were reported for the simultaneous determination of hydroquinone, resorcinol, pyrocatechol, and p-benzoquinone in accordance to the chromatographic method. Characteristics such as simplicity, low cost, and fast data acquisition are remarkable in this procedure, which makes it appropriate for this type of analytical control.

  9. Alma Data Mining Toolkit

    NASA Astrophysics Data System (ADS)

    Friedel, Douglas; Looney, Leslie; Teuben, Peter J.; Pound, Marc W.; Rauch, Kevin P.; Mundy, Lee; Harris, Robert J.; Xu, Lisa

    2016-06-01

    ADMIT (ALMA Data Mining Toolkit) is a Python based pipeline toolkit for the creation and analysis of new science products from ALMA data. ADMIT quickly provides users with a detailed overview of their science products, for example: line identifications, line 'cutout' cubes, moment maps, and emission type analysis (e.g., feature detection). Users can download the small ADMIT pipeline product (< 20MB), analyze the results, then fine-tune and re-run the ADMIT pipeline (or any part thereof) on their own machines and interactively inspect the results. ADMIT has both a web browser and command line interface available for this purpose. By analyzing multiple data cubes simultaneously, data mining between many astronomical sources and line transitions are possible. Users are also able to enhance the capabilities of ADMIT by creating customized ADMIT tasks satisfying any special processing needs. We will present some of the salient features of ADMIT and example use cases.

  10. Mine roof support

    SciTech Connect

    Bollmann, A.

    1981-02-24

    A mine roof support has a base and a roof shield pivoted to the base and carrying at its upper end a pivoted cap which is urged upwardly against the mine roof by a hydraulic pit prop reacting between the cap and the base. The lower end of the roof shield is connected to the base by two links each having a pivot cooperating with a pivot on the roof shield, and a pivot cooperating with a pivot on the base. In addition, the base and/or the lower end of the roof shield has an auxiliary for each link and each link has an auxiliary pivot which can be connected with one of the auxiliary pivots of the base or lower end.

  11. Multievidence microarray mining.

    PubMed

    Seifert, Martin; Scherf, Matthias; Epple, Anton; Werner, Thomas

    2005-10-01

    Microarray mining is a challenging task because of the superposition of several processes in the data. We believe that the combination of microarray data-based analyses (statistical significance analysis of gene expression) with array-independent analyses (literature-mining and promoter analysis) enables some of the problems of traditional array analysis to be overcome. As a proof-of-principle, we revisited publicly available microarray data derived from an experiment with platelet-derived growth factor (PDGF)-stimulated fibroblasts. Our strategy revealed results beyond the detection of the major metabolic pathway known to be linked to the PDGF response: we were able to identify the crosstalking regulatory networks underlying the metabolic pathway without using a priori knowledge about the experiment.

  12. Phosphate Mines, Jordan

    NASA Technical Reports Server (NTRS)

    2008-01-01

    Jordan's leading industry and export commodities are phosphate and potash, ranked in the top three in the world. These are used to make fertilizer. The Jordan Phosphate Mines Company is the sole producer, having started operations in 1935. In addition to mining activities, the company produces phosphoric acid (for fertilizers, detergents, pharmaceuticals), diammonium phosphate (for fertilizer), sulphuric acid (many uses), and aluminum fluoride (a catalyst to make aluminum and magnesium).

    The image covers an area of 27.5 x 49.4 km, was acquired on September 17, 2005, and is located near 30.8 degrees north latitude, 36.1 degrees east longitude.

    The U.S. science team is located at NASA's Jet Propulsion Laboratory, Pasadena, Calif. The Terra mission is part of NASA's Science Mission Directorate.

  13. Drum cutter mining machine

    SciTech Connect

    Oberste-beulmann, K.; Schupphaus, H.

    1980-02-19

    A drum cutter mining machine includes a machine frame with a winch having a drive wheel to engage a rack or chain which extends along the path of travel by the mining machine to propel the machine along a mine face. The mining machine is made up of discrete units which include a machine body and machine housings joined to opposite sides of the machine body. The winch is either coupled through a drive train with a feed drive motor or coupled to the drive motor for cutter drums. The machine housings each support a pivot shaft coupled by an arm to a drum cutter. One of these housings includes a removable end cover and a recess adapted to receive a support housing for a spur gear system used to transmit torque from a feed drive motor to a reduction gear system which is, in turn, coupled to the drive wheel of the winch. In one embodiment, a removable end cover on the machine housing provides access to the feed drive motor. The feed drive motor is arranged so that the rotational axis of its drive output shaft extends transversely to the stow side of the machine frame. In another embodiment, the reduction gear system is arranged at one side of the pivot shaft for the cutter drum while the drive motor therefor is arranged at the other side of the pivot shaft and coupled thereto through the spur gear system. In a further embodiment, the reduction gear system is disposed between the feed motor and the pivot shaft.

  14. National Underground Mines Inventory

    DTIC Science & Technology

    1983-10-01

    that the contents necessaZiy reflect the views and policies of the Federal Emergency Management Agency. FINAL REPORT RTI/2506/OO-O1F NATIONAL...UNDERGROUND MINES INVENTORY Prepared by: M. Wright R. Chessin K. Reeves S. York, III Prepared for: Federal Emergency Management Agency Washington , D.C. 20472...Emergency Management Agency October 1983 Washington , DC 20472 I. NUMBEROFPAGES 80 14. MONITORING AGENCY NAME A ADORESS(1lierent bum Controflhi Office

  15. Germany knows mining

    SciTech Connect

    2006-11-15

    Whether it is the nuance of precision or robust rock breaking strength, German suppliers have the expertise. Germany has about 120 companies in the mining equipment industry, employing some 16,000 people. The article describes some recent developments of the following companies: DBT, Liebherr, Atlas Copco, BASF, Boart Longyear, Eickhoff, IBS, Maschinenfabrik Glueckauf, Komatsu, TAKRA, Terex O & R, Thyssen Krupp Foerdertechnik and Wirtgen. 7 photos.

  16. Homogeneous, Heterogeneous, and Enzymatic Catalysis.

    ERIC Educational Resources Information Center

    Oyama, S. Ted; Somorjai, Gabor A.

    1988-01-01

    Discusses three areas of catalysis: homegeneous, heterogeneous, and enzymatic. Explains fundamentals and economic impact of catalysis. Lists and discusses common industrial catalysts. Provides a list of 107 references. (MVL)

  17. NVESD mine lane facility

    NASA Astrophysics Data System (ADS)

    Habersat, James D.; Marshall, Christopher; Maksymonko, George

    2003-09-01

    The NVESD Mine Lane Facility has recently undergone an extensive renovation. It now consists of an indoor, dry lane portion, a greenhouse portion with moisture-controlled lanes, a control room, and two outdoor lanes. The indoor structure contains six mine lanes, each approximately 2.5m (width) × 1.2m (depth) × 33m(length). These lanes contain six different soil types: magnetite/sand, silt, crusher run gravel (bluestone gravel), bank run gravel (tan gravel), red clay, and white sand. An automated trolley system is used for mounting the various mine detection systems and sensors under test. Data acquisition and data logging is fully automated. The greenhouse structure was added to provide moisture controlled lanes for measuring the effect of moisture on sensor effectiveness. A gantry type crane was installed to permit remotely controlled positioning of a sensor package over any portion of the greenhouse lanes at elevations from ground level up to 5m without shadowing the target area. The roof of the greenhouse is motorized, and can be rolled back to allow full solar loading. A control room overlooking the lanes is complete with recording and monitoring devices and contains controls to operate the trolleys. A facility overview is presented and typical results from recent data collection exercises are presented.

  18. Data Mining and Analysis

    NASA Technical Reports Server (NTRS)

    Samms, Kevin O.

    2015-01-01

    The Data Mining project seeks to bring the capability of data visualization to NASA anomaly and problem reporting systems for the purpose of improving data trending, evaluations, and analyses. Currently NASA systems are tailored to meet the specific needs of its organizations. This tailoring has led to a variety of nomenclatures and levels of annotation for procedures, parts, and anomalies making difficult the realization of the common causes for anomalies. Making significant observations and realizing the connection between these causes without a common way to view large data sets is difficult to impossible. In the first phase of the Data Mining project a portal was created to present a common visualization of normalized sensitive data to customers with the appropriate security access. The tool of the visualization itself was also developed and fine-tuned. In the second phase of the project we took on the difficult task of searching and analyzing the target data set for common causes between anomalies. In the final part of the second phase we have learned more about how much of the analysis work will be the job of the Data Mining team, how to perform that work, and how that work may be used by different customers in different ways. In this paper I detail how our perspective has changed after gaining more insight into how the customers wish to interact with the output and how that has changed the product.

  19. Organizational Data Mining

    NASA Astrophysics Data System (ADS)

    Nemati, Hamid R.; Barko, Christopher D.

    Many organizations today possess substantial quantities of business information but have very little real business knowledge. A recent survey of 450 business executives reported that managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. To reverse this trend, businesses of all sizes would be well advised to adopt Organizational Data Mining (ODM). ODM is defined as leveraging Data Mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage. ODM has helped many organizations optimize internal resource allocations while better understanding and responding to the needs of their customers. The fundamental aspects of ODM can be categorized into Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the key distinction between ODM and Data Mining. In this chapter, we introduce ODM, explain its unique characteristics, and report on the current status of ODM research. Next we illustrate how several leading organizations have adopted ODM and are benefiting from it. Then we examine the evolution of ODM to the present day and conclude our chapter by contemplating ODM's challenging yet opportunistic future.

  20. Heterogeneity in motor driven transport

    NASA Astrophysics Data System (ADS)

    Tabei, Ali

    2015-03-01

    I will discuss quantitative analysis of particle tracking data for motor driven vesicles inside an insulin secreting cell. We use this method to study the dynamical and structural heterogeneity inside the cell. I will discuss our effort to explain the origin of observed heterogeneity in intracellular transport. Finally, I will explain how analyzing directional correlations in transport trajectories reveals self-similarity in the diffusion media.

  1. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mine map. 77.1200 Section 77.1200 Mineral... SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Maps § 77.1200 Mine map. The operator shall maintain an accurate and up-to-date map of the mine, on a scale of not...

  2. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... used to maintain the projected direction of mining in entries, rooms, crosscuts and pillar splits....

  3. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... used to maintain the projected direction of mining in entries, rooms, crosscuts and pillar splits....

  4. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... used to maintain the projected direction of mining in entries, rooms, crosscuts and pillar splits....

  5. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... used to maintain the projected direction of mining in entries, rooms, crosscuts and pillar splits....

  6. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... used to maintain the projected direction of mining in entries, rooms, crosscuts and pillar splits....

  7. Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

    PubMed

    Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

    2014-09-01

    Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.

  8. Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

    PubMed

    Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

    2015-03-01

    During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection.

  9. A MULTIVARIATE FINITE MIXTURE LATENT TRAJECTORY MODEL WITH APPLICATION TO DEMENTIA STUDIES

    PubMed Central

    Lai, Dongbing; Xu, Huiping; Koller, Daniel; Foroud, Tatiana; Gao, Sujuan

    2016-01-01

    Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set (UDS) from the National Alzheimer’s Coordinating Center (NACC) to identify cognitive decline patterns among dementia patients. PMID:27642206

  10. A multivariate model for the meta-analysis of study level survival data at multiple times.

    PubMed

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-09-01

    Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and we compare the results to those obtained from standard methodologies. Our method uses exact binomial within-study distributions and enforces the constraints that both the study specific and the overall mortality rates must not decrease over time. We directly model the probabilities of mortality at each time point, which are the quantities of primary clinical interest. We also present I(2) statistics that quantify the impact of the between-study heterogeneity, which is very considerable in our data set.

  11. [Multivariate analysis as a means of access to optimal pharmacochemical specificity and to molecular archetypes].

    PubMed

    Doré, J C; Viel, C; Lacroix, R; Lacroix, J

    1990-01-01

    For complex works as studies relationships structure-activity, in heterogeneous therapeutic families we have selected mathematical methods founded upon systemic approach rather analytic one, appealing to bibliographical data, taking into consideration a plurality of biological targets and envisaging structural extrapolations rather than interpolations. Compared with classical QSAR, multivariate analysis (factorial analysis and multidimentional data reduction) intend from structuration of whole complex items to definite spheres of correlations between structural parameters and biological ones to issue then on a symetric typology of this two groups of parameters. These approaches have not only a descriptive character but lead to operational conclusions through an interactive dialogue with data bank; for example: --to explore acting potentiality of others molecular families or/and particular sub-structures --to find chemical sequences of molecules synthesized for other aims but not again experimented for this property. The case of antiparasitic agents is here developed.

  12. Ground control for highwall mining

    SciTech Connect

    Zipf, R.K.; Mark, C.

    2007-09-15

    Perhaps the greatest risk to both equipment and personnel associated with highwall mining is from ground control. The two most significant ground control hazards are rock falls from highwall and equipment entrapment underground. In the central Appalachians, where the majority of highwall mining occurs in the USA, hillseams (or mountain cracks) are the most prominent structure that affects highwall stability. The article discusses measures to minimise the risk of failure associated with hillstreams. A 'stuck' or trapped highwall miner, and the ensuring retrieval or recovery operation, can be extremely disruptive to the highwall mining process. Most entrapment, are due to roof falls in the hole. The options for recovery are surface retrieval, surface excavation or underground recovery. Proper pillar design is essential to maintain highwall stability and prevent entrapments. NIOSH has developed the Analysis of Retreat Mining Pillar stability-Highwall Mining (ARMPS-HWM) computer program to help mine planners with this process. 10 figs.

  13. Economics of mining law

    USGS Publications Warehouse

    Long, K.R.

    1995-01-01

    Modern mining law, by facilitating socially and environmentally acceptable exploration, development, and production of mineral materials, helps secure the benefits of mineral production while minimizing environmental harm and accounting for increasing land-use competition. Mining investments are sunk costs, irreversibly tied to a particular mineral site, and require many years to recoup. Providing security of tenure is the most critical element of a practical mining law. Governments owning mineral rights have a conflict of interest between their roles as a profit-maximizing landowner and as a guardian of public welfare. As a monopoly supplier, governments have considerable power to manipulate mineral-rights markets. To avoid monopoly rent-seeking by governments, a competitive market for government-owned mineral rights must be created by artifice. What mining firms will pay for mineral rights depends on expected exploration success and extraction costs. Landowners and mining firms will negotlate respective shares of anticipated differential rents, usually allowing for some form of risk sharing. Private landowners do not normally account for external benefits or costs of minerals use. Government ownership of mineral rights allows for direct accounting of social prices for mineral-bearing lands and external costs. An equitable and efficient method is to charge an appropriate reservation price for surface land use, net of the value of land after reclamation, and to recover all or part of differential rents through a flat income or resource-rent tax. The traditional royalty on gross value of production, essentially a regressive income tax, cannot recover as much rent as a flat income tax, causes arbitrary mineral-reserve sterilization, and creates a bias toward development on the extensive margin where marginal environmental costs are higher. Mitigating environmental costs and resolving land-use conflicts require local evaluation and planning. National oversight ensures

  14. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 3 2013-07-01 2013-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not...

  15. 30 CFR 77.1712 - Reopening mines; notification; inspection prior to mining.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... to mining. 77.1712 Section 77.1712 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... prior to mining. Prior to reopening any surface coal mine after it has been abandoned or declared... an authorized representative of the Secretary before any mining operations in such mine...

  16. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... mining conditions. 49.4 Section 49.4 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT... special mining conditions. (a) If an underground mine is operating under special mining conditions, the... review and approval. (c) To be considered “operating under special mining conditions,” the operator...

  17. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not...

  18. 30 CFR 77.1712 - Reopening mines; notification; inspection prior to mining.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... to mining. 77.1712 Section 77.1712 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... prior to mining. Prior to reopening any surface coal mine after it has been abandoned or declared... an authorized representative of the Secretary before any mining operations in such mine...

  19. 30 CFR 77.1712 - Reopening mines; notification; inspection prior to mining.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... to mining. 77.1712 Section 77.1712 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... prior to mining. Prior to reopening any surface coal mine after it has been abandoned or declared... an authorized representative of the Secretary before any mining operations in such mine...

  20. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 3 2011-07-01 2011-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not...

  1. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 3 2012-07-01 2012-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not...

  2. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 3 2014-07-01 2014-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not...

  3. Multivariate neural network operators with sigmoidal activation functions.

    PubMed

    Costarelli, Danilo; Spigler, Renato

    2013-12-01

    In this paper, we study pointwise and uniform convergence, as well as order of approximation, of a family of linear positive multivariate neural network (NN) operators with sigmoidal activation functions. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of NN operators, activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered. Multivariate NNs approximation finds applications, typically, in neurocomputing processes. Our approach to NN operators allows us to extend previous convergence results and, in some cases, to improve the order of approximation. The case of multivariate quasi-interpolation operators constructed with sigmoidal functions is also considered.

  4. New Equipment for Mine Safety

    NASA Technical Reports Server (NTRS)

    1983-01-01

    While planning for the space shuttle, Bendix Corporation with the help of Johnson Space Center expanded the anthropometric data base for aerospace and nonaerospace use in clothing, workplace, etc. The result was the Anthropometric Source Book which was later utilized by the U.S. Bureau of Mines in designing advanced mining systems. The book was particularly valuable in the design of a remote cab used in mining.

  5. Data Mining in Social Media

    NASA Astrophysics Data System (ADS)

    Barbier, Geoffrey; Liu, Huan

    The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

  6. Characterization of the core microbiota of the drainage and surrounding soil of a Brazilian copper mine

    PubMed Central

    Pereira, Letícia Bianca; Vicentini, Renato; Ottoboni, Laura M.M.

    2015-01-01

    Abstract The core microbiota of a neutral mine drainage and the surrounding high heavy metal content soil at a Brazilian copper mine were characterized by 16S rDNA pyrosequencing. The core microbiota of the drainage was dominated by the generalist genus Meiothermus. The soil samples contained a more heterogeneous bacterial community, with the presence of both generalist and specialist bacteria. Both environments supported mainly heterotrophic bacteria, including organisms resistant to heavy metals, although many of the bacterial groups identified remain poorly characterized. The results contribute to the understanding of bacterial communities in soils impacted by neutral mine drainage, for which information is scarce, and demonstrate that heavy metals can play an important role in shaping the microbial communities in mine environments. PMID:26537607

  7. Using NDT for thickness measurement of shotcrete rock support systems in underground mines

    NASA Astrophysics Data System (ADS)

    Guevremont, P.; Hassani, F. P.; Momayez, M.

    2000-05-01

    Shotcrete is quickly becoming the economical material of choice for rock support systems in underground mines throughout Canada. Although shotcrete technology has gained acceptance in the mining industry, there are some problems which have emerged with respect to quality assessment and thickness measurement of the liner. This paper presents recent tests performed with a miniature seismic reflection device developed primarily for thickness measurement of this heterogeneous liner. Work was performed on site in an underground mine in Ontario, Canada. This paper also presents the results from laboratory tests performed on fiber reinforced an non reinforced shotcrete panels which were used to asses the P-wave velocity in both materials. The results of these tests were used in a second on site investigation. The NDT thickness measurements showed good agreement with the results obtained by mine personnel with conventional methods.

  8. Mining's impact on groundwater assessed

    NASA Astrophysics Data System (ADS)

    Detailed studies have indicated that groundwater is contaminated in the immediate vicinity of many mines in the eastern United States. However, no underground mines and very few refuse disposal areas have monitoring systems that can provide adequate warning of impending threats to groundwater quality.This was one of the conclusions of a 3-year study by Geraghty & Miller, Inc., a firm of consulting groundwater geologists and hydrologists based in Syosset, New York. The study focused on mines east of the 100th meridian. These mines will produce an estimated 1.1 billion tons of coal and 200 million tons of waste by 1985.

  9. Data Mining for Financial Applications

    NASA Astrophysics Data System (ADS)

    Kovalerchuk, Boris; Vityaev, Evgenii

    This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

  10. Data mining applications in healthcare.

    PubMed

    Koh, Hian Chye; Tan, Gerald

    2005-01-01

    Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.

  11. A multivariate-based conflict prediction model for a Brazilian freeway.

    PubMed

    Caleffi, Felipe; Anzanello, Michel José; Cybis, Helena Beatriz Bettella

    2017-01-01

    Real-time collision risk prediction models relying on traffic data can be useful in dynamic management systems seeking at improving traffic safety. Models have been proposed to predict crash occurrence and collision risk in order to proactively improve safety. This paper presents a multivariate-based framework for selecting variables for a conflict prediction model on the Brazilian BR-290/RS freeway. The Bhattacharyya Distance (BD) and Principal Component Analysis (PCA) are applied to a dataset comprised of variables that potentially help to explain occurrence of traffic conflicts; the parameters yielded by such multivariate techniques give rise to a variable importance index that guides variables removal for later selection. Next, the selected variables are inserted into a Linear Discriminant Analysis (LDA) model to estimate conflict occurrence. A matched control-case technique is applied using traffic data processed from surveillance cameras at a segment of a Brazilian freeway. Results indicate that the variables that significantly impacted on the model are associated to total flow, difference between standard deviation of lanes' occupancy, and the speed's coefficient of variation. The model allowed to asses a characteristic behavior of major Brazilian's freeways, by identifying the Brazilian typical heterogeneity of traffic pattern among lanes, which leads to aggressive maneuvers. Results also indicate that the developed LDA-PCA model outperforms the LDA-BD model. The LDA-PCA model yields average 76% classification accuracy, and average 87% sensitivity (which measures the rate of conflicts correctly predicted).

  12. Hierarchy of temporal responses of multivariate self-excited epidemic processes

    NASA Astrophysics Data System (ADS)

    Saichev, Alexander; Maillart, Thomas; Sornette, Didier

    2013-04-01

    Many natural and social systems are characterized by bursty dynamics, for which past events trigger future activity. These systems can be modelled by so-called self-excited Hawkes conditional Poisson processes. It is generally assumed that all events have similar triggering abilities. However, some systems exhibit heterogeneity and clusters with possibly different intra- and inter-triggering, which can be accounted for by generalization into the "multivariate" self-excited Hawkes conditional Poisson processes. We develop the general formalism of the multivariate moment generating function for the cumulative number of first-generation and of all generation events triggered by a given mother event (the "shock") as a function of the current time t. This corresponds to studying the response function of the process. A variety of different systems have been analyzed. In particular, for systems in which triggering between events of different types proceeds through a one-dimension directed or symmetric chain of influence in type space, we report a novel hierarchy of intermediate asymptotic power law decays ˜ 1/ t 1-( m+1) θ of the rate of triggered events as a function of the distance m of the events to the initial shock in the type space, where 0 < θ < 1 for the relevant long-memory processes characterizing many natural and social systems. The richness of the generated time dynamics comes from the cascades of intermediate events of possibly different kinds, unfolding via random changes of types genealogy.

  13. A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

    PubMed

    Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

    2014-05-20

    A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach.

  14. Investigating Microbial (Micro)colony Heterogeneity by Vibrational Spectroscopy

    PubMed Central

    Choo-Smith, L.-P.; Maquelin, K.; van Vreeswijk, T.; Bruining, H. A.; Puppels, G. J.; Thi, N. A. Ngo; Kirschner, C.; Naumann, D.; Ami, D.; Villa, A. M.; Orsini, F.; Doglia, S. M.; Lamfarraj, H.; Sockalingum, G. D.; Manfait, M.; Allouch, P.; Endtz, H. P.

    2001-01-01

    Fourier transform infrared and Raman microspectroscopy are currently being developed as new methods for the rapid identification of clinically relevant microorganisms. These methods involve measuring spectra from microcolonies which have been cultured for as little as 6 h, followed by the nonsubjective identification of microorganisms through the use of multivariate statistical analyses. To examine the biological heterogeneity of microorganism growth which is reflected in the spectra, measurements were acquired from various positions within (micro)colonies cultured for 6, 12, and 24 h. The studies reveal that there is little spectral variance in 6-h microcolonies. In contrast, the 12- and 24-h cultures exhibited a significant amount of heterogeneity. Hierarchical cluster analysis of the spectra from the various positions and depths reveals the presence of different layers in the colonies. Further analysis indicates that spectra acquired from the surface of the colonies exhibit higher levels of glycogen than do the deeper layers of the colony. Additionally, the spectra from the deeper layers present with higher RNA levels than the surface layers. Therefore, the 6-h colonies with their limited heterogeneity are more suitable for inclusion in a spectral database to be used for classification purposes. These results also demonstrate that vibrational spectroscopic techniques can be useful tools for studying the nature of colony development and biofilm formation. PMID:11282591

  15. Unlinear wave processes in the vicinity of tectonic heterogeneities by weak seismic waves passing.

    NASA Astrophysics Data System (ADS)

    Bodin v., V.

    2009-04-01

    The tectonic heterogeneities, which occur throughout the mining fields give a serious trouble for mining works because in the vicinity of faults occur rock shocks. According the statistical data of geodynamic events, which had been registered on the mines, about 80% are located near the tectonic heterogeneities. The one reason of mining-tectonic rock shocks is the overlapping of summed stress field of the fault and man-made field over the breaking point of the rock. It is very needed to achieve the control on the stress-deformed state of the rock massif in the area of the dynamical influence of the fault during the process of it outworking. By seismic natural experiments in the Ural and Siberia mines it was obtained, that by passing of the seismic wave of small amplitude through the tectonic heterogeneity, in it vicinity occurs an anomaly oscillation, which lasts more, then the passing wave. The dynamic parameters of that oscillation differ also from that of the passing wave. So for instance it amplitude is larger of the initial signal from 3 to 10 times, the frequency of the maximum of the amplitude is higher on 10-15 values. The weak attenuation character of the oscillations, existence of multiple harmonics and the shape of oscillation process envelope show that it is a self-oscillating process. The space belonging of the anomaly oscillations to the tectonic structures allow us to assume, that they occur linked to the peculiarity of the stress-deformed state of the local places of the rock massif in the vicinity of the tectonic heterogeneities. That is proved by achieved earlier experimental results obtained by the method of pressure relief and acoustic method. More over the distribution of the frequency maximum of the anomaly oscillations along the fault shows the asym-metry of the two sides of the fault deformation. The comparison of the frequency of the anomaly oscillations with the parameters of the stress field on the pickets of seismic natural experiments

  16. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  17. Multivariable disturbance observer-based H2 analytical decoupling control design for multivariable systems

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Wang, Yagang; Liu, Yurong; Zhang, Weidong

    2016-01-01

    In this paper, an H2 analytical decoupling control scheme with multivariable disturbance observer for both stable and unstable multi-input/multi-output (MIMO) systems with multiple time delays is proposed. Compared with conventional control strategies, the main merit is that the proposed control scheme can improve the system performances effectively when the MIMO processes with severe model mismatches and strong external disturbances. Besides, the design method has three additional advantages. First, the derived controller and observer are given in analytical forms, the design procedure is simple. Second, the orders of the designed controller and observer are low, they can be implemented easily in practice. Finally, the performance and robustness can be adjusted easily by tuning the parameters in the designed controller and observer. It is useful for practical application. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

  18. Molybdenum and zinc stable isotope variation in mining waste rock drainage and waste rock at the Antamina mine, Peru.

    PubMed

    Skierszkan, E K; Mayer, K U; Weis, D; Beckie, R D

    2016-04-15

    The stable isotope composition of molybdenum (Mo) and zinc (Zn) in mine wastes at the Antamina Copper-Zn-Mo mine, Peru, was characterized to investigate whether isotopic variation of these elements indicated metal attenuation processes in mine drainage. Waste rock and ore minerals were analyzed to identify the isotopic composition of Mo and Zn sources, namely molybdenites (MoS2) and sphalerites (ZnS). Molybdenum and Zn stable isotope ratios are reported relative to the NIST-SRM-3134 and PCIGR-1 Zn standards, respectively. δ(98)Mo among molybdenites ranged from -0.6 to +0.6‰ (n=9) while sphalerites showed no δ(66)Zn variations (0.11±0.01‰, 2 SD, n=5). Mine drainage samples from field waste rock weathering experiments were also analyzed to examine the extent of isotopic variability in the dissolved phase. Variations spanned 2.2‰ in δ(98)Mo (-0.1 to +2.1‰) and 0.7‰ in δ(66)Zn (-0.4 to +0.3‰) in mine drainage over a wide pH range (pH2.2-8.6). Lighter δ(66)Zn signatures were observed in alkaline pH conditions, which was consistent with Zn adsorption and/or hydrozincite (Zn5(OH)6(CO3)2) formation. However, in acidic mine drainage Zn isotopic compositions reflected the value of sphalerites. In addition, molybdenum isotope compositions in mine drainage were shifted towards heavier values (0.89±1.25‰, 2 SD, n=16), with some overlap, in comparison to molybdenites and waste rock (0.13±0.82‰, 2 SD, n=9). The cause of heavy Mo isotopic signatures in mine drainage was more difficult to resolve due to isotopic heterogeneity among ore minerals and a variety of possible overlapping processes including dissolution, adsorption and secondary mineral precipitation. This study shows that variation in metal isotope ratios are promising indicators of metal attenuation. Future characterization of isotopic fractionation associated to key environmental reactions will improve the power of Mo and Zn isotope ratios to track the fate of these elements in mine drainage.

  19. Uranium Mines and Mills Location Database

    EPA Pesticide Factsheets

    The Uranium Mines and Mills location database identifies and shows the location of active and inactive uranium mines and mills, as well as mines which principally produced other minerals, but were known to have uranium in the ore.

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

    NASA Astrophysics Data System (ADS)

    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.

  1. Multivariate Generalized Beta Distributions with Applications to Utility Assessment.

    ERIC Educational Resources Information Center

    Libby, David L.; Novick, Melvin R.

    1982-01-01

    Two multivariate probability distributions, a generalized beta distribution and a generalized F distribution, are derived. Formulas for the moments of these distributions are given and an example of the bivariate generalized beta is presented. (Author/JKS)

  2. Multivariate Cryptography Based on Clipped Hopfield Neural Network.

    PubMed

    Wang, Jia; Cheng, Lee-Ming; Su, Tong

    2016-11-23

    Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in GF(p) space. The Diffie--Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

  3. A unifying modeling framework for highly multivariate disease mapping.

    PubMed

    Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models.

  4. Dealing with spatial heterogeneity

    NASA Astrophysics Data System (ADS)

    Marsily, Gh.; Delay, F.; Gonçalvès, J.; Renard, Ph.; Teles, V.; Violette, S.

    2005-03-01

    Heterogeneity can be dealt with by defining homogeneous equivalent properties, known as averaging, or by trying to describe the spatial variability of the rock properties from geologic observations and local measurements. The techniques available for these descriptions are mostly continuous Geostatistical models, or discontinuous facies models such as the Boolean, Indicator or Gaussian-Threshold models and the Markov chain model. These facies models are better suited to treating issues of rock strata connectivity, e.g. buried high permeability channels or low permeability barriers, which greatly affect flow and, above all, transport in aquifers. Genetic models provide new ways to incorporate more geology into the facies description, an approach that has been well developed in the oil industry, but not enough in hydrogeology. The conclusion is that future work should be focused on improving the facies models, comparing them, and designing new in situ testing procedures (including geophysics) that would help identify the facies geometry and properties. A world-wide catalog of aquifer facies geometry and properties, which could combine site genesis and description with methods used to assess the system, would be of great value for practical applications. On peut aborder le problème de l'hétérogénéité en s'efforçant de définir une perméabilité équivalente homogène, par prise de moyenne, ou au contraire en décrivant la variation dans l'espace des propriétés des roches à partir des observations géologiques et des mesures locales. Les techniques disponibles pour une telle description sont soit continues, comme l'approche Géostatistique, soit discontinues, comme les modèles de faciès, Booléens, ou bien par Indicatrices ou Gaussiennes Seuillées, ou enfin Markoviens. Ces modèles de faciès sont mieux capables de prendre en compte la connectivité des strates géologiques, telles que les chenaux enfouis à forte perméabilité, ou au contraire les faci

  5. MINE WASTE TECHNOLOGY PROGRAM - UNDERGROUND MINE SOURCE CONTROL DEMONSTRATION PROJECT

    EPA Science Inventory

    This report presents results of the Mine Waste Technology Program Activity III, Project 8, Underground Mine Source Control Demonstration Project implemented and funded by the U. S. Environmental Protection Agency (EPA) and jointly administered by EPA and the U. S. Department of E...

  6. A Bayesian approach to multivariate measurement system assessment

    SciTech Connect

    Hamada, Michael Scott

    2016-07-01

    This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

  7. Constructing multivariate distributions with generalized marginals and t-copulas

    NASA Astrophysics Data System (ADS)

    Dass, Sarat C.; Huang, Wenmei; Muthuvalu, Mohana S.

    2014-10-01

    Generalized distributions are probability distributions that have both discrete and continuous components. In this paper, a method is proposed for constructing flexible multivariate distributions based on arbitrarily pre-specified generalized marginals and t-copulas. We give theoretical results establishing identifiability of the parameters of the multivariate distribution. These distributions are useful for modeling real data that show non-Gaussian characteristics such as disease trajectories (i.e., malaria and dengue) over time and space.

  8. Heterogeneity of vertebrate brain tubulins.

    PubMed Central

    Field, D J; Collins, R A; Lee, J C

    1984-01-01

    We have examined the extent of brain tubulin heterogeneity in six vertebrate species commonly used in tubulin research (rat, calf, pig, chicken, human, and lamb) using isoelectric focusing, two-dimensional electrophoresis, and peptide mapping procedures that provide higher resolution than previously available. The extent of heterogeneity is extremely similar in all of these organisms, as judged by number, range of isoelectric points, and distribution of the isotubulins. A minimum of 6 alpha and 12 beta tubulins was resolved from all sources. Even the pattern of spots on two-dimensional peptide maps is remarkably similar. These similarities suggest that the populations of tubulin in all of these brains should have similar overall physical properties. It is particularly interesting that chicken, which has only four or five beta-tubulin genes, contains approximately 12 beta tubulins. Thus, post-translational modification must generate at least some of the tubulin heterogeneity. Mammalian species, which contain 15-20 tubulin DNA sequences, do not show any more tubulin protein heterogeneity than does chicken. This suggests that expression of only a small number of the mammalian genes may be required to generate the observed tubulin heterogeneity. Images PMID:6588378

  9. Reaction Selectivity in Heterogeneous Catalysis

    SciTech Connect

    Somorjai, Gabor A.; Kliewer, Christopher J.

    2009-02-02

    The understanding of selectivity in heterogeneous catalysis is of paramount importance to our society today. In this review we outline the current state of the art in research on selectivity in heterogeneous catalysis. Current in-situ surface science techniques have revealed several important features of catalytic selectivity. Sum frequency generation vibrational spectroscopy has shown us the importance of understanding the reaction intermediates and mechanism of a heterogeneous reaction, and can readily yield information as to the effect of temperature, pressure, catalyst geometry, surface promoters, and catalyst composition on the reaction mechanism. DFT calculations are quickly approaching the ability to assist in the interpretation of observed surface spectra, thereby making surface spectroscopy an even more powerful tool. HP-STM has revealed three vitally important parameters in heterogeneous selectivity: adsorbate mobility, catalyst mobility, and selective site-blocking. The development of size controlled nanoparticles from 0.8 to 10 nm, of controlled shape, and of controlled bimetallic composition has revealed several important variables for catalytic selectivity. Lastly, DFT calculations may be paving the way to guiding the composition choice for multi-metallic heterogeneous catalysis for the intelligent design of catalysts incorporating the many factors of selectivity we have learned.

  10. Sampling effort affects multivariate comparisons of stream assemblages

    USGS Publications Warehouse

    Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

    2002-01-01

    Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

  11. Mining the Home Environment

    PubMed Central

    Cook, Diane J.; Krishnan, Narayanan

    2014-01-01

    Individuals spend a majority of their time in their home or workplace and for many, these places are our sanctuaries. As society and technology advance there is a growing interest in improving the intelligence of the environments in which we live and work. By filling home environments with sensors and collecting data during daily routines, researchers can gain insights on human daily behavior and the impact of behavior on the residents and their environments. In this article we provide an overview of the data mining opportunities and challenges that smart environments provide for researchers and offer some suggestions for future work in this area. PMID:25506128

  12. Respiratory cancers in mining

    SciTech Connect

    Reger, R.B.; Morgan, W.K. )

    1993-01-01

    The issue of carcinogenicity among mine workers and among workers in selected nonmining industries is examined. In the late 19th century, a high frequency of lung cancers was noted among metal miners in Bohemia, which probably related to their exposure to radon. Subsequently, other substances, including arsenic, asbestos, chromates, nickel, and chloroethers, have been linked causally to lung cancer. The IARC classification of substances as carcinogens is summarized, and the epidemiologic studies of humans employed in occupations with high rates of lung cancer due to carcinogen exposures are reviewed. 146 refs.

  13. Hydraulic mining method

    DOEpatents

    Huffman, Lester H.; Knoke, Gerald S.

    1985-08-20

    A method of hydraulically mining an underground pitched mineral vein comprising drilling a vertical borehole through the earth's lithosphere into the vein and drilling a slant borehole along the footwall of the vein to intersect the vertical borehole. Material is removed from the mineral vein by directing a high pressure water jet thereagainst. The resulting slurry of mineral fragments and water flows along the slant borehole into the lower end of the vertical borehole from where it is pumped upwardly through the vertical borehole to the surface.

  14. Airflow obstruction and mining

    SciTech Connect

    Stenton, S.C.; Hendrick, D.J. )

    1993-01-01

    Bronchitis and emphysema have long been described as diseases of miners, but the precise contribution of occupational exposures to coal and other mine dusts in causing these disorders, as opposed to cofactors such as social class, environmental pollution, and cigarette smoking, has not been fully defined. Epidemiologic studies have attempted, with varying degrees of success, to determine the incidence and severity of chronic obstructive pulmonary diseases in miners as compared to the general population. The results from these studies, and those in other nonmining industries with dust exposures, are examined. 98 refs.

  15. Web data mining

    NASA Astrophysics Data System (ADS)

    Wibonele, Kasanda J.; Zhang, Yanqing

    2002-03-01

    A web data mining system using granular computing and ASP programming is proposed. This is a web based application, which allows web users to submit survey data for many different companies. This survey is a collection of questions that will help these companies develop and improve their business and customer service with their clients by analyzing survey data. This web application allows users to submit data anywhere. All the survey data is collected into a database for further analysis. An administrator of this web application can login to the system and view all the data submitted. This web application resides on a web server, and the database resides on the MS SQL server.

  16. Mineral mining installation

    SciTech Connect

    Plevak, L.; Weirich, W.

    1982-04-20

    A longwall mineral mining installation has a longwall conveyor and a plurality of roof support units positioned side-by-side at the goaf side of the conveyor. The hydraulic appliances of the roof support units, such as their hydraulic props, hydraulic advance rams and hydraulic control valves, are supplied with pressurized hydraulic fluid from hydraulic supply lines which run along the goaf side of the conveyor. A plurality of flat, platelike intermediate members are provided at the goaf side of the conveyor. These intermediate members are formed with internal ducts for feeding the hydraulic fluid from the supply lines to the hydraulic appliances of the roof support units.

  17. Using Fisher information to track stability in multivariate systems

    EPA Science Inventory

    With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) ...

  18. Simulator for heterogeneous dataflow architectures

    NASA Technical Reports Server (NTRS)

    Malekpour, Mahyar R.

    1993-01-01

    A new simulator is developed to simulate the execution of an algorithm graph in accordance with the Algorithm to Architecture Mapping Model (ATAMM) rules. ATAMM is a Petri Net model which describes the periodic execution of large-grained, data-independent dataflow graphs and which provides predictable steady state time-optimized performance. This simulator extends the ATAMM simulation capability from a heterogenous set of resources, or functional units, to a more general heterogenous architecture. Simulation test cases show that the simulator accurately executes the ATAMM rules for both a heterogenous architecture and a homogenous architecture, which is the special case for only one processor type. The simulator forms one tool in an ATAMM Integrated Environment which contains other tools for graph entry, graph modification for performance optimization, and playback of simulations for analysis.

  19. Simulator for heterogeneous dataflow architectures

    NASA Astrophysics Data System (ADS)

    Malekpour, Mahyar R.

    1993-09-01

    A new simulator is developed to simulate the execution of an algorithm graph in accordance with the Algorithm to Architecture Mapping Model (ATAMM) rules. ATAMM is a Petri Net model which describes the periodic execution of large-grained, data-independent dataflow graphs and which provides predictable steady state time-optimized performance. This simulator extends the ATAMM simulation capability from a heterogenous set of resources, or functional units, to a more general heterogenous architecture. Simulation test cases show that the simulator accurately executes the ATAMM rules for both a heterogenous architecture and a homogenous architecture, which is the special case for only one processor type. The simulator forms one tool in an ATAMM Integrated Environment which contains other tools for graph entry, graph modification for performance optimization, and playback of simulations for analysis.

  20. Static heterogeneities in liquid water

    NASA Astrophysics Data System (ADS)

    Stanley, H. Eugene; Buldyrev, Sergey V.; Giovambattista, Nicolas

    2004-10-01

    The thermodynamic behavior of water seems to be closely related to static heterogeneities. These static heterogeneities are related to the local structure of water molecules, and when properly characterized, may offer an economical explanation of thermodynamic data. The key feature of liquid water is not so much that the existence of hydrogen bonds, first pointed out by Linus Pauling, but rather the local geometry of the liquid molecules is not spherical or oblong but tetrahedral. In the consideration of static heterogeneities, this local geometry is critical. Recent experiments suggested more than one phase of amorphous solid water, while simulations suggest that one of these phases is metastable with respect to another, so that in fact there are only two stable phases.

  1. 30 CFR 49.19 - Mine emergency notification plan.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.19 Mine emergency notification... follow in notifying the mine rescue teams when there is an emergency that requires their services. (b)...

  2. Proceedings, 26th international conference on ground control in mining

    SciTech Connect

    Peng, S.S.; Mark, C.; Finfinger, G.

    2007-07-01

    Papers are presented under the following topic headings: multiple-seam mining, surface subsidence, coal pillar, bunker and roadway/entry supports, mine design and highwall mining, longwall, roof bolting, stone and hardrock mining, rock mechanics and mine seal.

  3. Effects of coal mining, forestry, and road construction on southern Appalachian stream invertebrates and habitats.

    PubMed

    Gangloff, Michael M; Perkins, Michael; Blum, Peter W; Walker, Craig

    2015-03-01

    Coal has been extracted via surface and sub-surface mining for decades throughout the Appalachian Mountains. New interest in ridge-top mining has raised concerns about possible waterway impacts. We examined effects of forestry, mining, and road construction-based disturbance on physico-chemistry and macroinvertebrate communities in east-central Tennessee headwater streams. Although 11 of 30 sites failed Tennessee's biocriteria scoring system, invertebrate richness was moderately high and we did not find significant differences in any water chemistry or habitat parameters between sites with passing and failing scores. However, conductivity and dissolved solid concentrations appeared elevated in the majority of study streams. Principal components (PCs) analysis indicated that six PCs accounted for ~77 % of among-site habitat variability. One PC associated with dissolved oxygen and specific conductance explained the second highest proportion of among-site variability after catchment area. Specific conductance was not correlated with catchment area but was strongly correlated with mining activity. Composition and success of multivariate models using habitat PCs to predict macroinvertebrate metrics was highly variable. PC scores associated with water chemistry and substrate composition were most frequently included in significant models. These results suggest that impacts of historical and current coal mining remain a source of water quality and macroinvertebrate community impairment in this region, but effects are subtle. Our results suggest that surface mining may have chronic and system-wide effects on habitat conditions and invertebrate communities in Cumberland Plateau streams.

  4. Effects of Coal Mining, Forestry, and Road Construction on Southern Appalachian Stream Invertebrates and Habitats

    NASA Astrophysics Data System (ADS)

    Gangloff, Michael M.; Perkins, Michael; Blum, Peter W.; Walker, Craig

    2015-03-01

    Coal has been extracted via surface and sub-surface mining for decades throughout the Appalachian Mountains. New interest in ridge-top mining has raised concerns about possible waterway impacts. We examined effects of forestry, mining, and road construction-based disturbance on physico-chemistry and macroinvertebrate communities in east-central Tennessee headwater streams. Although 11 of 30 sites failed Tennessee's biocriteria scoring system, invertebrate richness was moderately high and we did not find significant differences in any water chemistry or habitat parameters between sites with passing and failing scores. However, conductivity and dissolved solid concentrations appeared elevated in the majority of study streams. Principal components (PCs) analysis indicated that six PCs accounted for ~77 % of among-site habitat variability. One PC associated with dissolved oxygen and specific conductance explained the second highest proportion of among-site variability after catchment area. Specific conductance was not correlated with catchment area but was strongly correlated with mining activity. Composition and success of multivariate models using habitat PCs to predict macroinvertebrate metrics was highly variable. PC scores associated with water chemistry and substrate composition were most frequently included in significant models. These results suggest that impacts of historical and current coal mining remain a source of water quality and macroinvertebrate community impairment in this region, but effects are subtle. Our results suggest that surface mining may have chronic and system-wide effects on habitat conditions and invertebrate communities in Cumberland Plateau streams.

  5. Study Mine-Hunting Techniques

    DTIC Science & Technology

    This report summarizes over ten years of work on problems in the field of mine countermeasures. It deals with problems of clustering--of...deals with the design and performance of a radio-controlled catamaran for marking the position of sonar contacts or for placing a destructive charge near the mine.

  6. Education Roadmap for Mining Professionals

    SciTech Connect

    none,

    2002-12-01

    This document represents the roadmap for education in the U.S. mining industry. It was developed based on the results of an Education Roadmap Workshop sponsored by the National Mining Association in conjunction with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Office of Industrial Technologies. The Workshop was held February 23, 2002 in Phoenix, Arizona.

  7. Finding Gold in Data Mining

    ERIC Educational Resources Information Center

    Flaherty, Bill

    2013-01-01

    Data-mining systems provide a variety of opportunities for school district personnel to streamline operations and focus on student achievement. This article describes the value of data mining for school personnel, finance departments, teacher evaluations, and in the classroom. It suggests that much could be learned about district practices if one…

  8. Process Mining Online Assessment Data

    ERIC Educational Resources Information Center

    Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul

    2009-01-01

    Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…

  9. Automatic Coal-Mining System

    NASA Technical Reports Server (NTRS)

    Collins, E. R., Jr.

    1985-01-01

    Coal cutting and removal done with minimal hazard to people. Automatic coal mine cutting, transport and roof-support movement all done by automatic machinery. Exposure of people to hazardous conditions reduced to inspection tours, maintenance, repair, and possibly entry mining.

  10. Lunar surface mine feasibility study

    NASA Astrophysics Data System (ADS)

    Blair, Brad R.

    This paper describes a lunar surface mine, and demonstrates the economic feasibility of mining oxygen from the moon. The mine will be at the Apollo 16 landing site. Mine design issues include pit size and shape, excavation equipment, muck transport, and processing requirements. The final mine design will be driven by production requirements, and constrained by the lunar environment. This mining scenario assumes the presence of an operating lunar base. Lunar base personnel will set-up a and run the mine. The goal of producing lunar oxygen is to reduce dependence on fuel shipped from Earth. Thus, the lunar base is the customer for the finished product. The perspective of this paper is that of a mining contractor who must produce a specific product at a remote location, pay local labor, and sell the product to an onsite captive market. To make a profit, it must be less costly to build and ship specialized equipment to the site, and pay high labor and operating costs, than to export the product directly to the site.

  11. F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

    NASA Technical Reports Server (NTRS)

    Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

    1977-01-01

    The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

  12. Dynamic fracture of heterogeneous materials

    SciTech Connect

    Stout, M.G.; Liu, C.; Addessio, F.L.; Williams, T.O.; Bennett, J.G.; Haberman, K.S.; Asay, B.W.

    1998-12-31

    This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of this project was to investigate the fundamental aspects of the process of dynamic fracture propagation in heterogeneous materials. The work focused on three important, but poorly understood, aspects of dynamic fracture for materials with a heterogeneous microstructure. These were: the appropriateness of using a single-parameter asymptotic analysis to describe dynamic crack-tip deformation fields, the temperature rises at the tip and on the flanks of a running crack, and the constitutive modeling of damage initiation and accumulation.

  13. Measuring mine roof bolt strains

    DOEpatents

    Steblay, Bernard J.

    1986-01-01

    A mine roof bolt and a method of measuring the strain in mine roof bolts of this type are disclosed. According to the method, a flat portion on the head of the mine roof bolt is first machined. Next, a hole is drilled radially through the bolt at a predetermined distance from the bolt head. After installation of the mine roof bolt and loading, the strain of the mine roof bolt is measured by generating an ultrasonic pulse at the flat portion. The time of travel of the ultrasonic pulse reflected from the hole is measured. This time of travel is a function of the distance from the flat portion to the hole and increases as the bolt is loaded. Consequently, the time measurement is correlated to the strain in the bolt. Compensation for various factors affecting the travel time are also provided.

  14. Lunar surface mining equipment study

    NASA Astrophysics Data System (ADS)

    Podnieks, Egons R.; Siekmeier, John A.

    Results of a NASA-sponsored assessment of the various proposed lunar surface mining equipment concepts submitted to NASA are presented. The proposed equipment was reviewed and evaluated with due consideration of equipment design criteria, basic mining principles, and the lunar environment. On the basis of this assessment, two pieces of mining equipment were conceptualized for surface mining operations: the ripper-excavator-loader, also capable of operating as a load-haul-dump vehicle, and the haulage vehicle, capable of transporting feedstock from the pit, liquid oxygen containers from the processing plant, and materials during construction. Reliable and durable lunar mining equipment is found to be best developed by the evolution of proven terrestrial technology adapted to the lunar environment.

  15. In Brief: Coal mining regulations

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2009-12-01

    The U.S. Department of the Interior (DOI) announced on 18 November measures to strengthen the oversight of state surface coal mining programs and to promulgate federal regulations to protect streams affected by surface coal mining operations. DOI's Office of Surface Mining Reclamation and Enforcement (OSM) is publishing an advance notice of a proposed rule about protecting streams from adverse impacts of surface coal mining operations. A rule issued by the Bush administration in December 2008 allows coal mine operators to place excess excavated materials into streams if they can show it is not reasonably possible to avoid doing so. “We are moving as quickly as possible under the law to gather public input for a new rule, based on sound science, that will govern how companies handle fill removed from mountaintop coal seams,” according to Wilma Lewis, assistant secretary for Land and Minerals Management at DOI.

  16. Introduction to Space Resource Mining

    NASA Technical Reports Server (NTRS)

    Mueller, Robert P.

    2013-01-01

    There are vast amounts of resources in the solar system that will be useful to humans in space and possibly on Earth. None of these resources can be exploited without the first necessary step of extra-terrestrial mining. The necessary technologies for tele-robotic and autonomous mining have not matured sufficiently yet. The current state of technology was assessed for terrestrial and extraterrestrial mining and a taxonomy of robotic space mining mechanisms was presented which was based on current existing prototypes. Terrestrial and extra-terrestrial mining methods and technologies are on the cusp of massive changes towards automation and autonomy for economic and safety reasons. It is highly likely that these industries will benefit from mutual cooperation and technology transfer.

  17. Robust stochastic mine production scheduling

    NASA Astrophysics Data System (ADS)

    Kumral, Mustafa

    2010-06-01

    The production scheduling of open pit mines aims to determine the extraction sequence of blocks such that the net present value (NPV) of a mining project is maximized under capacity and access constraints. This sequencing has significant effect on the profitability of the mining venture. However, given that the values of coefficients in the optimization procedure are obtained in a medium of sparse data and unknown future events, implementations based on deterministic models may lead to destructive consequences to the company. In this article, a robust stochastic optimization (RSO) approach is used to deal with mine production scheduling in a manner such that the solution is insensitive to changes in input data. The approach seeks a trade off between optimality and feasibility. The model is demonstrated on a case study. The findings showed that the approach can be used in mine production scheduling problems efficiently.

  18. Data mining and education.

    PubMed

    Koedinger, Kenneth R; D'Mello, Sidney; McLaughlin, Elizabeth A; Pardos, Zachary A; Rosé, Carolyn P

    2015-01-01

    An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board.

  19. Data mining for water resource management part 2 - methods and approaches to solving contemporary problems

    USGS Publications Warehouse

    Roehl, Edwin A.; Conrads, Paul A.

    2010-01-01

    This is the second of two papers that describe how data mining can aid natural-resource managers with the difficult problem of controlling the interactions between hydrologic and man-made systems. Data mining is a new science that assists scientists in converting large databases into knowledge, and is uniquely able to leverage the large amounts of real-time, multivariate data now being collected for hydrologic systems. Part 1 gives a high-level overview of data mining, and describes several applications that have addressed major water resource issues in South Carolina. This Part 2 paper describes how various data mining methods are integrated to produce predictive models for controlling surface- and groundwater hydraulics and quality. The methods include: - signal processing to remove noise and decompose complex signals into simpler components; - time series clustering that optimally groups hundreds of signals into "classes" that behave similarly for data reduction and (or) divide-and-conquer problem solving; - classification which optimally matches new data to behavioral classes; - artificial neural networks which optimally fit multivariate data to create predictive models; - model response surface visualization that greatly aids in understanding data and physical processes; and, - decision support systems that integrate data, models, and graphics into a single package that is easy to use.

  20. Pedogenesis evolution of mine technosols: focus onto organic matter implication

    NASA Astrophysics Data System (ADS)

    Grégoire, Pascaud; Marilyne, Soubrand; Laurent, Lemee; Husseini Amelène, El-Mufleh Al; Marion, Rabiet; Emmanuel, Joussein

    2014-05-01

    Keywords: Mine technosols, pedogenesis, organic matter, environmental impact, pyr-GC-MS Technosols include soils subject to strong anthropogenic pressure and particularly to soil influenced by human transformed materials. In this context, abandoned mine sites contain a large amount of transformed waste materials often enriched with metals and/or metalloids. The natural evolution of technosols (pedogenesis) may induces the change in contaminants behaviour in term of stability of bearing phases, modification of pH oxydo-reduction conditions, organic matter turnover, change in permeability, or influence of vegetation cover. The fate of these elements in the soil can induce major environmental problems (contamination of biosphere and water resource). This will contribute to a limited potential use of these soils, which represent yet a large area around the world. The initial contamination of the parental material suggests that the pedological cover would stabilize the soil; however, the chemical reactivity must be taken in consideration particularly with respect to potential metal leachings. In this case, it is quite important to understand the development of soil in this specific context. Consequently, the global aims of this study are to understand the functioning of mine Technosols focusing onto the organic matter implication in their pedogenesis. Indeed, soil organic matter constitutes an heterogeneous fraction of organic compounds that plays an important role in the fate and the transport of metals and metalloids in soils. Three different soil profiles were collected representative to various mining context (contamination, time, climat), respectively to Pb-Ag, Sn and Au exploitations. Several pedological parameters were determined like CEC, pH, %Corg, %Ntot, C/N ratio, grain size distribution and chemical composition. The evolution of the nature of organic matter in Technosol was studied by elemental analyses and thermochemolysis was realized on the total and

  1. ADMIRE framework for data mining and integration

    NASA Astrophysics Data System (ADS)

    Hluchy, Ladislav; Tran, Viet; Habala, Ondrej

    2010-05-01

    In this paper we presents the data mining and integration of environmental applications in EU IST project ADMIRE. It briefly presents the project ADMIRE and data mining of spatio-temporal data in general. The application, originally targeting flood simulation and prediction is now being extended into the broader context of environmental studies. We describe several interesting scenarios, in which data mining and integration of distributed environmental data can improve our knowledge of the relations between various hydro-meteorological variables. The project ADMIRE aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. Its main target is to provide advanced data mining and integration techniques for distributed environment. In this paper, we will focus on one of its pilot applications with target domain is environmental risk management. Several scenarios have been proposed including short-term weather forecasting using radar images, complex hydrological scenarios with waterworks, measured data from water stations and meteorological data from models. Historical data for mining are supplied mainly by Slovak Hydrometeorological Institute and Slovak Water Enterprise. The main characteristics of data sets describing phenomena from environment applications are spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. In alignment with ADMIRE project vision, the processing elements (a data integration workflow that can be executed at a single resource.) are specified in Data Mining and Integration Language DISPEL that is being developed within the project. The goal of DISPEL

  2. Social Trust Prediction Using Heterogeneous Networks.

    PubMed

    Huang, Jin; Nie, Feiping; Huang, Heng; Tu, Yi-Cheng; Lei, Yu

    2013-11-01

    Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.

  3. Assessing Heterogeneity of Osteolytic Lesions in Multiple Myeloma by 1H HR-MAS NMR Metabolomics

    PubMed Central

    Tavel, Laurette; Fontana, Francesca; Garcia Manteiga, Josè Manuel; Mari, Silvia; Mariani, Elisabetta; Caneva, Enrico; Sitia, Roberto; Camnasio, Francesco; Marcatti, Magda; Cenci, Simone; Musco, Giovanna

    2016-01-01

    Multiple myeloma (MM) is a malignancy of plasma cells characterized by multifocal osteolytic bone lesions. Macroscopic and genetic heterogeneity has been documented within MM lesions. Understanding the bases of such heterogeneity may unveil relevant features of MM pathobiology. To this aim, we deployed unbiased 1H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) metabolomics to analyze multiple biopsy specimens of osteolytic lesions from one case of pathological fracture caused by MM. Multivariate analyses on normalized metabolite peak integrals allowed clusterization of samples in accordance with a posteriori histological findings. We investigated the relationship between morphological and NMR features by merging morphological data and metabolite profiling into a single correlation matrix. Data-merging addressed tissue heterogeneity, and greatly facilitated the mapping of lesions and nearby healthy tissues. Our proof-of-principle study reveals integrated metabolomics and histomorphology as a promising approach for the targeted study of osteolytic lesions. PMID:27809247

  4. Molecular ingredients of heterogeneous catalysis

    SciTech Connect

    Somorjai, G.A.

    1982-06-01

    The purpose of this paper is to present a review and status report to those in theoretical chemistry of the rapidly developing surface science of heterogeneous catalysis. The art of catalysis is developing into science. This profound change provides one with opportunities not only to understand the molecular ingredients of important catalytic systems but also to develop new and improved catalyst. The participation of theorists to find answers to important questions is sorely needed for the sound development of the field. It is the authors hope that some of the outstanding problems of heterogeneous catalysis that are identified in this paper will be investigated. For this purpose the paper is divided into several sections. The brief Introduction to the methodology and recent results of the surface science of heterogeneous catalysis is followed by a review of the concepts of heterogeneous catalysis. Then, the experimental results that identified the three molecular ingredients of catalysis, structure, carbonaceous deposit and the oxidation state of surface atoms are described. Each section is closed with a summary and a list of problems that require theoretical and experimental scrutiny. Finally attempts to build new catalyst systems and the theoretical and experimental problems that appeared in the course of this research are described.

  5. Teaching about Heterogeneous Response Models

    ERIC Educational Resources Information Center

    Murray, Michael P.

    2014-01-01

    Individuals vary in their responses to incentives and opportunities. For example, additional education will affect one person differently than another. In recent years, econometricians have given increased attention to such heterogeneous responses and to the consequences of such responses for interpreting regression estimates, especially…

  6. Floodplain heterogeneity and meander migration

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The impact of horizontal heterogeneity of floodplain soils on rates and patterns of meander migration is analyzed with a Ikeda et al. (1981)-type model for hydrodynamics and bed morphodynamics, coupled with a physically-based bank erosion model according to the approach developed by Motta et al. (20...

  7. Heterogeneous porous media in hydrology

    NASA Astrophysics Data System (ADS)

    Ababou, Rachid

    In natural geologic formations, flow and transport-related processes are perturbed by multidimensional and anisotropic material heterogeneities of diverse sizes, shapes, and origins (bedding, layering, inclusions, fractures, grains, for example). Heterogeneity tends to disperse and mix transported quantities and may initiate new transfer mechanisms not seen in ideally homogeneous porous media. Effective properties such as conductivity and dispersivity may not be simple averages of locally measured quantities.The special session, “Effective Constitutive Laws for Heterogeneous Porous Media,” convened at AGU's 1992 Fall Meeting in San Francisco, addressed these issue. Over forty-five contributions, both oral and poster, covering a broad range of physical phenomena were presented. The common theme was the macroscale characterization and modeling of flow and flow-related processes in geologic media that are heterogeneous at various scales (from grain size or fracture aperture, up to regional scales). The processes analyzed in the session included coupled hydro-mechanical processes; Darcy-type flow in the saturated, unsaturated, or two-phase regimes; tracer transport, dilution, and dispersion. These processes were studied for either continuous (porous) or discontinuous (fractured) media.

  8. Social Capital and Community Heterogeneity

    ERIC Educational Resources Information Center

    Coffe, Hilde

    2009-01-01

    Recent findings indicate that more pronounced community heterogeneity is associated with lower levels of social capital. These studies, however, concentrate on specific aspects in which people differ (such as income inequality or ethnic diversity). In the present paper, we introduce the number of parties in the local party system as a more…

  9. Surface science of heterogeneous reactions.

    PubMed

    White, J M

    1982-10-29

    Some of the present and future directions for surface science as a growing and naturally interdisciplinary subject are reviewed. Particular attention is given to surface reaction chemistry as it is related to heterogenous catalysis, a subject area where there are abundant opportunities for detailed measurements of structure and dynamics at the molecular level.

  10. Advancing apparatus for coal-mining machine in underground mine

    SciTech Connect

    Schupphaus, H.

    1984-05-29

    A coal-mining machine is advanced along a face conveyor by providing a rack extending along the conveyor and a plurality of advancing units. Each advancing unit includes a hydraulic motor to rotate a drive wheel while meshing with the teeth of the gear rack. The advancing units arranged side-by-side along the mining machine have curved end faces to abut against one another. Runners are provided on the advancing units at the opposite ends of the mining machine which extend partially around the rack for guiding and maintaining the drive wheel engaged with the teeth of the rack.

  11. Flammability of Heterogeneously Combusting Metals

    NASA Technical Reports Server (NTRS)

    Jones, Peter D.

    1998-01-01

    Most engineering materials, including some metals, most notably aluminum, burn in homogeneous combustion. 'Homogeneous' refers to both the fuel and the oxidizer being in the same phase, which is usually gaseous. The fuel and oxidizer are well mixed in the combustion reaction zone, and heat is released according to some relation like q(sub c) = delta H(sub c)c[((rho/rho(sub 0))]exp a)(exp -E(sub c)/RT), Eq. (1) where the pressure exponent a is usually close to unity. As long as there is enough heat released, combustion is sustained. It is useful to conceive of a threshold pressure beyond which there is sufficient heat to keep the temperature high enough to sustain combustion, and beneath which the heat is so low that temperature drains away and the combustion is extinguished. Some materials burn in heterogeneous combustion, in which the fuel and oxidizer are in different phases. These include iron and nickel based alloys, which burn in the liquid phase with gaseous oxygen. Heterogeneous combustion takes place on the surface of the material (fuel). Products of combustion may appear as a solid slag (oxide) which progressively covers the fuel. Propagation of the combustion melts and exposes fresh fuel. Heterogeneous combustion heat release also follows the general form of Eq.(1), except that the pressure exponent a tends to be much less than 1. Therefore, the increase in heat release with increasing pressure is not as dramatic as it is in homogeneous combustion. Although the concept of a threshold pressure still holds in heterogeneous combustion, the threshold is more difficult to identify experimentally, and pressure itself becomes less important relative to the heat transfer paths extant in any specific application. However, the constants C, a, and E(sub c) may still be identified by suitable data reduction from heterogeneous combustion experiments, and may be applied in a heat transfer model to judge the flammability of a material in any particular actual

  12. Flow mapping and multivariate visualization of large spatial interaction data.

    PubMed

    Guo, Diansheng

    2009-01-01

    Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.

  13. Distance measure with improved lower bound for multivariate time series

    NASA Astrophysics Data System (ADS)

    Li, Hailin

    2017-02-01

    Lower bound function is one of the important techniques used to fast search and index time series data. Multivariate time series has two aspects of high dimensionality including the time-based dimension and the variable-based dimension. Due to the influence of variable-based dimension, a novel method is proposed to deal with the lower bound distance computation for multivariate time series. The proposed method like the traditional ones also reduces the dimensionality of time series in its first step and thus does not directly apply the lower bound function on the multivariate time series. The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation. In addition, an extended lower bound function is designed to obtain good tightness and fast measure the distance between any two center sequences. The experimental results demonstrate that the proposed lower bound function has better tightness and improves the performance of similarity search in multivariate time series datasets.

  14. Multicomponent seismic noise attenuation with multivariate order statistic filters

    NASA Astrophysics Data System (ADS)

    Wang, Chao; Wang, Yun; Wang, Xiaokai; Xun, Chao

    2016-10-01

    The vector relationship between multicomponent seismic data is highly important for multicomponent processing and interpretation, but this vector relationship could be damaged when each component is processed individually. To overcome the drawback of standard component-by-component filtering, multivariate order statistic filters are introduced and extended to attenuate the noise of multicomponent seismic data by treating such dataset as a vector wavefield rather than a set of scalar fields. According to the characteristics of seismic signals, we implement this type of multivariate filtering along local events. First, the optimal local events are recognized according to the similarity between the vector signals which are windowed from neighbouring seismic traces with a sliding time window along each trial trajectory. An efficient strategy is used to reduce the computational cost of similarity measurement for vector signals. Next, one vector sample each from the neighbouring traces are extracted along the optimal local event as the input data for a multivariate filter. Different multivariate filters are optimal for different noise. The multichannel modified trimmed mean (MTM) filter, as one of the multivariate order statistic filters, is applied to synthetic and field multicomponent seismic data to test its performance for attenuating white Gaussian noise. The results indicate that the multichannel MTM filter can attenuate noise while preserving the relative amplitude information of multicomponent seismic data more effectively than a single-channel filter.

  15. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.

  16. Multivariate calibration applied to the quantitative analysis of infrared spectra

    SciTech Connect

    Haaland, D.M.

    1991-01-01

    Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

  17. Deviation of the statistical fluctuation in heterogeneous anomalous diffusion

    NASA Astrophysics Data System (ADS)

    Itto, Yuichi

    2016-11-01

    The exponent of anomalous diffusion of virus in cytoplasm of a living cell is experimentally known to fluctuate depending on localized areas of the cytoplasm, indicating heterogeneity of diffusion. In a recent paper (Itto, 2012), a maximum-entropy-principle approach has been developed in order to propose an Ansatz for the statistical distribution of such exponent fluctuations. Based on this approach, here the deviation of the statistical distribution of the fluctuations from the proposed one is studied from the viewpoint of Einstein's theory of fluctuations (of the thermodynamic quantities). This may present a step toward understanding the statistical property of the deviation. It is shown in a certain class of small deviations that the deviation obeys the multivariate Gaussian distribution.

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

    PubMed Central

    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

  19. Surface heterogeneity of small asteroids

    NASA Astrophysics Data System (ADS)

    Sasaki, Sho

    A rubble pile model of asteroid origin would predict averaged rather homogeneous surface of an asteroid. Previous spacecraft observations (mostly S-type asteroids) did not show large color/albedo variation on the surface. Vesta would be exceptional since HST observation suggested that its surface should be heterogeneous due to the impact excavation of the interior. As for a young asteroid (832) Karin (age being 5Ma), Sasaki et al. (2004) detected variation of infrared spectra which could be explained by the difference of the space weathering degree. They discussed the possibility of the survival of the old surface. However, the variation was not confirmed by later observation (Chapman et al., 2007; Vernazza et al., 2007). Recent observation of a small (550m) asteroid Itokawa by Hayabusa spacecraft revealed that Itokawa is heterogeneous in color and albedo although the overall rocky structure is considered as a rubble pile (Saito et al., 2006). The color difference can be explained by the difference of weathering degree (Ishiguro et al., 2008). The heterogeneity could be explained by mass movement caused by rapid rotation from YORP effect (Scheeres et al., 2007) or seismic shaking (Sasaki, 2006). Probably small silicate asteroids without significant regolith could have heterogeneous in color and albedo. On large asteroids (˜ a few 10km), regolith reaccumulation should have covered the underlying heterogeneity. References: Chapman, C. R. et al (2007) Icarus, 191, 323-329 Ishiguro, M. et al. (2008) MAPS, in press. Saito, J. et al. (2006) Science, 312, 1341-1344 Sasaki, S. (2006) in Spacecraft Reconnaissance of Asteroid and Comet Interiors Sasaki, T. et al (2004) Astrophys. J. 615, L161-L164 Scheeres, D. J. (2007) Icarus 188, 425-429 Vernazza, P. et al. (2007) Icarus 191, 330-336.

  20. Radioecological impacts of tin mining.

    PubMed

    Aliyu, Abubakar Sadiq; Mousseau, Timothy Alexander; Ramli, Ahmad Termizi; Bununu, Yakubu Aliyu

    2015-12-01

    The tin mining activities in the suburbs of Jos, Plateau State, Nigeria, have resulted in technical enhancement of the natural background radiation as well as higher activity concentrations of primordial radionuclides in the topsoil of mining sites and their environs. Several studies have considered the radiological human health risks of the mining activity; however, to our knowledge no documented study has investigated the radiological impacts on biota. Hence, an attempt is made to assess potential hazards using published data from the literature and the ERICA Tool. This paper considers the effects of mining and milling on terrestrial organisms like shrubs, large mammals, small burrowing mammals, birds (duck), arthropods (earth worm), grasses, and herbs. The dose rates and risk quotients to these organisms are computed using conservative values for activity concentrations of natural radionuclides reported in Bitsichi and Bukuru mining areas. The results suggest that grasses, herbs, lichens, bryophytes and shrubs receive total dose rates that are of potential concern. The effects of dose rates to specific indicator species of interest are highlighted and discussed. We conclude that further investigation and proper regulations should be set in place in order to reduce the risk posed by the tin mining activity on biota. This paper also presents a brief overview of the impact of mineral mining on biota based on documented literature for other countries.

  1. [Introduction to medical data mining].

    PubMed

    Zhu, Lingyun; Wu, Baoming; Cao, Changxiu

    2003-09-01

    Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also improve the management level of hospital information and promote the development of telemedicine and community medicine. Because the medical information is characteristic of redundancy, multi-attribution, incompletion and closely related with time, medical data mining differs from other one. In this paper we have discussed the key techniques of medical data mining involving pretreatment of medical data, fusion of different pattern and resource, fast and robust mining algorithms and reliability of mining results. The methods and applications of medical data mining based on computation intelligence such as artificial neural network, fuzzy system, evolutionary algorithms, rough set, and support vector machine have been introduced. The features and problems in data mining are summarized in the last section.

  2. Mining landfills for recyclables

    SciTech Connect

    Spencer, R.

    1991-02-01

    The New York State Energy Research and Development Authority (NYSERDA) and the Department of Environmental Conservation (DEC) began a landfill reclamation experiment in Edinburgh, NY, a rural community in the Adirondack Park. According to NYSERDA's Fact Sheet about the project, landfill reclamation is a process of excavating a landfill using conventional surface mining technology to recover metals, glass, plastics and combustibles, soils, and the land resource itself. The recovered site can then be either upgraded into a state-of-the-art landfill, if appropriate, closed or redeveloped for some other suitable purpose. As an energy-related public benefit corporation, NYSERDA is particularly interested in the potential energy value of combustible material reclaimed from landfills. With an energy content of over 11 million BTUs per ton, this translates to the energy equivalent of 275 million barrels of oil.

  3. Mining human antibody repertoires

    PubMed Central

    2010-01-01

    Human monoclonal antibodies (mAbs) have become drugs of choice for the management of an increasing number of human diseases. Human antibody repertoires provide a rich source for human mAbs. Here we review the characteristics of natural and non-natural human antibody repertoires and their mining with non-combinatorial and combinatorial strategies. In particular, we discuss the selection of human mAbs from naïve, immune, transgenic and synthetic human antibody repertoires using methods based on hybridoma technology, clonal expansion of peripheral B cells, single-cell PCR, phage display, yeast display and mammalian cell display. Our reliance on different strategies is shifting as we gain experience and refine methods to the efficient generation of human mAbs with superior pharmacokinetic and pharmacodynamic properties. PMID:20505349

  4. Data Mining SIAM Presentation

    NASA Technical Reports Server (NTRS)

    Srivastava, Ashok; McIntosh, Dawn; Castle, Pat; Pontikakis, Manos; Diev, Vesselin; Zane-Ulman, Brett; Turkov, Eugene; Akella, Ram; Xu, Zuobing; Kumaresan, Sakthi Preethi

    2006-01-01

    This viewgraph document describes the data mining system developed at NASA Ames. Many NASA programs have large numbers (and types) of problem reports.These free text reports are written by a number of different people, thus the emphasis and wording vary considerably With so much data to sift through, analysts (subject experts) need help identifying any possible safety issues or concerns and help them confirm that they haven't missed important problems. Unsupervised clustering is the initial step to accomplish this; We think we can go much farther, specifically, identify possible recurring anomalies. Recurring anomalies may be indicators of larger systemic problems. The requirement to identify these anomalies has led to the development of Recurring Anomaly Discovery System (ReADS).

  5. Ensemble Data Mining Methods

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  6. Mineral mining installations

    SciTech Connect

    Werner, G.; Wisniewski, P.

    1983-12-15

    A mineral mining installation serves to win mineral by explosive blasting. The installation employs a shuttle conveyor arranged alongside a mineral face. Roof supports stand side-by-side at the side of the conveyor remote from the conveyor. The roof supports are connected to the conveyor through shifting rams and have roof-engageable caps or the like supported on hydraulic props. The pans of the conveyor have upstanding walls at the rear side nearest the roof supports which carry rails at their upper ends. The roof caps have wall components pivoted thereto and hydraulic piston and cylinder units serve to swing the wall components up and down. When explosive blasting takes place the wall components are swung down to engage on the walls of the conveyor pans to form a screen between the winning region and the access region of the working.

  7. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... elevation of any body of water dammed or held back in any portion of the mine: Provided, however, Such bodies of water may be shown on overlays or tracings attached to the mine maps; (g) All prospect drill holes that penetrate the coalbed or coalbeds being mined on the mine property; (h) All auger and...

  8. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... elevation of any body of water dammed or held back in any portion of the mine: Provided, however, Such bodies of water may be shown on overlays or tracings attached to the mine maps; (g) All prospect drill holes that penetrate the coalbed or coalbeds being mined on the mine property; (h) All auger and...

  9. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Mine map. 75.1200 Section 75.1200 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall have... to minimize the danger of destruction by fire or other hazard, an accurate and up-to-date map of...

  10. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Mine map. 75.1200 Section 75.1200 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall have... to minimize the danger of destruction by fire or other hazard, an accurate and up-to-date map of...

  11. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Mine map. 75.1200 Section 75.1200 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall have... to minimize the danger of destruction by fire or other hazard, an accurate and up-to-date map of...

  12. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mine map. 75.1200 Section 75.1200 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall have... to minimize the danger of destruction by fire or other hazard, an accurate and up-to-date map of...

  13. 36 CFR 6.7 - Mining wastes.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... DISPOSAL SITES IN UNITS OF THE NATIONAL PARK SYSTEM § 6.7 Mining wastes. (a) Solid waste from mining... garbage, refuse or sludge associated with mining and mineral operations. (b) A person conducting mining or... operate a solid waste disposal site within the boundaries of a unit only after complying with § 6.5...

  14. 36 CFR 6.7 - Mining wastes.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... DISPOSAL SITES IN UNITS OF THE NATIONAL PARK SYSTEM § 6.7 Mining wastes. (a) Solid waste from mining... garbage, refuse or sludge associated with mining and mineral operations. (b) A person conducting mining or... operate a solid waste disposal site within the boundaries of a unit only after complying with § 6.5...

  15. 36 CFR 6.7 - Mining wastes.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... DISPOSAL SITES IN UNITS OF THE NATIONAL PARK SYSTEM § 6.7 Mining wastes. (a) Solid waste from mining... garbage, refuse or sludge associated with mining and mineral operations. (b) A person conducting mining or... operate a solid waste disposal site within the boundaries of a unit only after complying with § 6.5...

  16. 36 CFR 6.7 - Mining wastes.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... DISPOSAL SITES IN UNITS OF THE NATIONAL PARK SYSTEM § 6.7 Mining wastes. (a) Solid waste from mining... garbage, refuse or sludge associated with mining and mineral operations. (b) A person conducting mining or... operate a solid waste disposal site within the boundaries of a unit only after complying with § 6.5...

  17. 36 CFR 6.7 - Mining wastes.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... DISPOSAL SITES IN UNITS OF THE NATIONAL PARK SYSTEM § 6.7 Mining wastes. (a) Solid waste from mining... garbage, refuse or sludge associated with mining and mineral operations. (b) A person conducting mining or... operate a solid waste disposal site within the boundaries of a unit only after complying with § 6.5...

  18. A Collaborative Educational Association Rule Mining Tool

    ERIC Educational Resources Information Center

    Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; de Castro, Carlos

    2011-01-01

    This paper describes a collaborative educational data mining tool based on association rule mining for the ongoing improvement of e-learning courses and allowing teachers with similar course profiles to share and score the discovered information. The mining tool is oriented to be used by non-expert instructors in data mining so its internal…

  19. Collaborative Data Mining Tool for Education

    ERIC Educational Resources Information Center

    Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos

    2009-01-01

    This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…

  20. 30 CFR 282.24 - Mining Plan.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 2 2010-07-01 2010-07-01 false Mining Plan. 282.24 Section 282.24 Mineral... § 282.24 Mining Plan. All OCS mineral development and production activities shall be conducted in accordance with a Mining Plan submitted by the lessee and approved by the Director. A Mining Plan...

  1. Resource Recovery of Flooded Underground Mine Workings

    EPA Science Inventory

    Butte, Montana has been the site of hard rock mining activities for over a century. Over 400 hundred underground mines were developed and over 10,000 miles of underground mine workings were created. During active mining, groundwater was removed from the workings by large-scale pu...

  2. Resource Recovery from Flooded Underground Mines

    EPA Science Inventory

    Butte, Montana has been the site of hard rock mining activities for over a century. Over 400 hundred underground mines were developed and over 10,000 miles of underground mine workings were created. During active mining, groundwater was removed from the workings by large-scale pu...

  3. MINE WASTE TECHNOLOGY PROGRAM: A SUCCESS STORY

    EPA Science Inventory

    Mining Waste generated by active and inactive mining operations is a growing problem for the mining industry, local governments, and Native American communities because of its impact on human health and the environment. In the US, the reported volume of mine waste is immense: 2 b...

  4. Image Information Mining Utilizing Hierarchical Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  5. POST-MINING DEVELOPMENT USING RESOURCES FROM FLOODED UNDERGROUND MINE WORKINGS

    EPA Science Inventory

    Post-mining issues of land and surface utilization now serve to accentuate how important it is to incorporate sustainable development aspects into hard rock mining. In an effort to revitalize lands degraded by historic mining, 10 acres of mine tailings near the Belmont Mine have...

  6. 30 CFR 49.3 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Alternative mine rescue capability for small and remote mines. 49.3 Section 49.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS § 49.3 Alternative mine rescue capability...

  7. 30 CFR 49.3 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Alternative mine rescue capability for small and remote mines. 49.3 Section 49.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS § 49.3 Alternative mine rescue capability...

  8. 30 CFR 49.13 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Alternative mine rescue capability for small and remote mines. 49.13 Section 49.13 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal...

  9. 30 CFR 49.13 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Alternative mine rescue capability for small and remote mines. 49.13 Section 49.13 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal...

  10. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Alternative mine rescue capability for special mining conditions. 49.4 Section 49.4 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS § 49.4 Alternative mine rescue capability...

  11. 76 FR 63238 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-12

    ... Part 75 RIN 1219-AB65 Proximity Detection Systems for Continuous Mining Machines in Underground Coal... Detection Systems for Continuous Mining Machines in Underground Coal Mines, published on August 31, 2011... Mining Machines in Underground Coal Mines. Due to requests from the public and to provide...

  12. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Metal and... conditions are present: (1) The mine has multiple adits or entries; (2) The mined substance is noncombustible...; (5) The mine shall not have a history of flammable-gas emission or accumulation, and the...

  13. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Metal and... conditions are present: (1) The mine has multiple adits or entries; (2) The mined substance is noncombustible...; (5) The mine shall not have a history of flammable-gas emission or accumulation, and the...

  14. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Metal and... conditions are present: (1) The mine has multiple adits or entries; (2) The mined substance is noncombustible...; (5) The mine shall not have a history of flammable-gas emission or accumulation, and the...

  15. Forest ecosystem development in post-mining landscapes: a case study of the Lusatian lignite district

    NASA Astrophysics Data System (ADS)

    Hüttl, Reinhard F.; Weber, Edwin

    2001-08-01

    The restoration of surface mining landscapes requires the (re)creation of ecosystems. In Lusatia (eastern Germany), large-scale open-cast lignite mining operations generated spoil dumps widely consisting of acidified, phytotoxic substrates. Amelioration and rehabilitation measures have been developed and applied to these substrates since the 1950s. However, it is still not clear whether these approaches are sustainable. This paper reports on collaborative research work into the ecological potential of forest ecosystem development on typical minesites in the Lusatian lignite district. At first sight, pine stands on minesites along a chronosequence comprising about 35 years did not show differences when compared with stands on non-mined sites of the general region. Furthermore, with some modification, conceptual models for flora and fauna succession in forest stands on non-mined sites seem to be applicable, at least for the early stages of forest ecosystem development. For example, soil organism abundance and activity at minesites had already reached levels typical of non-mined sites after about 20-30 years. In contrast, mine soils are very different from non-mined soils of the test region. Chemically, mine soil development is dominated by processes originating from pyrite oxidation. Geogenic, i.e. lignitic, soil organic carbon was shown to substitute for some functions of pedogenic soil organic matter. Rooting was hampered but not completely impeded in strongly acidified soil compartments. Roots and mycorrhizae are apparently able to make use of the characteristic heterogeneity of young mine soils. Considering these recent results and the knowledge accumulated during more than 30 years of research on minesite rehabilitation internationally, it can be stated that minesite restoration might be used as an ideal case study for forest ecosystem development starting at "point zero" on " terra nova".

  16. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.

    PubMed

    Wang, Yiyi; Kockelman, Kara M

    2013-11-01

    This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates.

  17. Domestic uranium mining and milling

    SciTech Connect

    Not Available

    1983-01-01

    A field hearing was held in Riverton, Wyoming on the erosion of the state's uranium industry as production and capital investment have declined and inventories have continued to rise because of a shift to foreign suppliers. The result has been serious unemployment in Wyoming and a decline in uranium mines from 5400 in 1980 to the present 1200. The seven witnesses spoke for the mining industry and state and federal government. Among the issues raised were mining regulations and the cancellation of nuclear rejects which have impacted the health of the industry. Additional statements and a report supplied for the record follow their testimony. (DCK)

  18. Mining and Reclamation Technology Symposium

    SciTech Connect

    None Available

    1999-06-24

    The Mining and Reclamation Technology Symposium was commissioned by the Mountaintop Removal Mining/Valley Fill Environmental Impact Statement (EIS) Interagency Steering Committee as an educational forum for the members of the regulatory community who will participate in the development of the EIS. The Steering Committee sought a balanced audience to ensure the input to the regulatory community reflected the range of perspectives on this complicated and emotional issue. The focus of this symposium is on mining and reclamation technology alternatives, which is one of eleven topics scheduled for review to support development of the EIS. Others include hydrologic, environmental, ecological, and socio-economic issues.

  19. Automated Coal-Mining System

    NASA Technical Reports Server (NTRS)

    Gangal, M. D.; Isenberg, L.; Lewis, E. V.

    1985-01-01

    Proposed system offers safety and large return on investment. System, operating by year 2000, employs machines and processes based on proven principles. According to concept, line of parallel machines, connected in groups of four to service modules, attacks face of coal seam. High-pressure water jets and central auger on each machine break face. Jaws scoop up coal chunks, and auger grinds them and forces fragments into slurry-transport system. Slurry pumped through pipeline to point of use. Concept for highly automated coal-mining system increases productivity, makes mining safer, and protects health of mine workers.

  20. Mining Industry Energy Bandwidth Study

    SciTech Connect

    none,

    2007-07-01

    The Industrial Technologies Program (ITP) relies on analytical studies to identify large energy reduction opportunities in energy-intensive industries and uses these results to guide its R&D portfolio. The energy bandwidth illustrates the total energy-saving opportunity that exists in the industry if the current processes are improved by implementing more energy-efficient practices and by using advanced technologies. This bandwidth analysis report was conducted to assist the ITP Mining R&D program in identifying energy-saving opportunities in coal, metals, and mineral mining. These opportunities were analyzed in key mining processes of blasting, dewatering, drilling, digging, ventilation, materials handling, crushing, grinding, and separations.

  1. Radiological characterization of a uranium mine with no mining activity

    PubMed

    Lozano; Vera Tome F; Gomez Escobar V; Blanco Rodriguez P

    2000-07-01

    We report a radiological study of a uranium mine located in Extremadura, in the south-west of Spain, in which mining work had ceased. One interest in the work is that the results can be used as a reference for the future evaluation of the effects produced by the restoration program. The radiological parameters selected to estimate the impact of the inactive mine were: 222Rn in air and water, 222Rn exhalation, effective 226Ra in soils and sediments, and natural uranium and 226Ra in water. Chemical analyses of water samples and measurements of meteorological variables were also made. Average values of these radiological parameters are presented. We characterize the zone radiologically and estimate the influence of the mine on the basis of some of these parameters, while others are used to reflect the status of the installation, information which could be very useful in the near future when restoration is complete.

  2. Mines and human casualties: a robotics approach toward mine clearing

    NASA Astrophysics Data System (ADS)

    Ghaffari, Masoud; Manthena, Dinesh; Ghaffari, Alireza; Hall, Ernest L.

    2004-10-01

    An estimated 100 million landmines which have been planted in more than 60 countries kill or maim thousands of civilians every year. Millions of people live in the vast dangerous areas and are not able to access to basic human services because of landmines" threats. This problem has affected many third world countries and poor nations which are not able to afford high cost solutions. This paper tries to present some experiences with the land mine victims and solutions for the mine clearing. It studies current situation of this crisis as well as state of the art robotics technology for the mine clearing. It also introduces a survey robot which is suitable for the mine clearing applications. The results show that in addition to technical aspects, this problem has many socio-economic issues. The significance of this study is to persuade robotics researchers toward this topic and to peruse the technical and humanitarian facets of this issue.

  3. Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level

    SciTech Connect

    Tunga, M. Alper; Demiralp, Metin

    2011-09-14

    Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.

  4. A multivariate heuristic model for fuzzy time-series forecasting.

    PubMed

    Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei

    2007-08-01

    Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.

  5. Networks: On the relation of bi- and multivariate measures

    PubMed Central

    Mader, Wolfgang; Mader, Malenka; Timmer, Jens; Thiel, Marco; Schelter, Björn

    2015-01-01

    A reliable inference of networks from observations of the nodes’ dynamics is a major challenge in physics. Interdependence measures such as a the correlation coefficient or more advanced methods based on, e.g., analytic phases of signals are employed. For several of these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate measures relate to the respective multivariate ones; this knowledge will in turn be used to demonstrate the implications of thresholded bivariate measures for network inference. Particularly, we show, that random networks are falsely identified as small-world networks if observations thereof are treated by bivariate methods. We will employ the correlation coefficient as an example for such an interdependence measure. The results can be readily transferred to all interdependence measures partializing for information of thirds in their multivariate counterparts. PMID:26042994

  6. A note on rank reduction in sparse multivariate regression.

    PubMed

    Chen, Kun; Chan, Kung-Sik

    A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model.

  7. The subject-by-formulation interaction in multivariate bioequivalence.

    PubMed

    Cao, Li; Mathew, Thomas

    2007-01-01

    This paper addresses hypothesis testing problems concerning the subject-by-formulation interaction matrix for the assessment of multivariate bioequivalence. Two problems are addressed: (a) the problem of testing if the subject-by-formulation interaction matrix itself is zero, and (b) the problem of testing if suitable scalar valued functions of the subject-by-formulation interaction matrix is below a threshold. Approximate tests are developed in both cases and the accuracy of the approximation is numerically investigated. The results are illustrated with an example. Even though the literature on univariate bioequivalence testing addresses average bioequivalence, variance bioequivalence and subject-by-formulation interaction, the literature on multivariate bioequivalence deals only with the problem of average bioequivalence. This work appears to be the first attempt to address tests for the subject-by-formulation interaction matrix for testing multivariate bioequivalence.

  8. Properties of multivariable root loci. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.

    1981-01-01

    Various properties of multivariable root loci are analyzed from a frequency domain point of view by using the technique of Newton polygons, and some generalizations of the SISO root locus rules to the multivariable case are pointed out. The behavior of the angles of arrival and departure is related to the Smith-MacMillan form of G(s) and explicit equations for these angles are obtained. After specializing to first order and a restricted class of higher order poles and zeros, some simple equations for these angles that are direct generalizations of the SISO equations are found. The unusual behavior of root loci on the real axis at branch points is studied. The SISO root locus rules for break-in and break-out points are shown to generalize directly to the multivariable case. Some methods for computing both types of points are presented.

  9. Evaluation of reclaimed abandoned bentonite mine lands

    SciTech Connect

    Edinger, K.D.; Schuman, G.E.; Vance, G.F.

    1999-07-01

    In 1985, the Abandoned Mined Land Division of the Wyoming Department of Environmental Quality began reclamation of 4,148 ha of abandoned bentonite mined lands. Calcium amendments and sawmill wood wastes were applied to the regraded spoils to enhance water infiltration, displacement of Na on the clay spoil, and leaching of the displaced Na and other soluble salts. Revegetation of these lands was generally successful, but after several years small areas (0.1--0.2 ha) began to show signs of vegetation die-back and to prescribe corrective treatment options. A randomized block design was imposed on study areas near Upton, Colony, and Greybull, Wyoming to characterize spoil chemical properties of good, moderate, and dead vegetation zones, which were subjectively delineated by visual vegetation cover and density differences. Spoil analyses indicated exchangeable-sodium (Na) concentrations were high and the dead vegetation zones exhibited exchangeable-sodium-percentages (ESP) above 50%, while surrounding good vegetation zones exhibited ESP values <10%. This coupled with low soluble-Na concentrations (<2 cmol/kg) suggests insufficient calcium (Ca) amendments were initially applied to ameliorate the sodic conditions of the spoil. The sampling design used to determine Ca amendment rates, which consisted of a composite of 5 spoil cores taken from each 0.8 ha area, was apparently insufficient to account for the highly heterogeneous spoil material that occurred throughout these abandoned bentonite reclamation sites. To revegetate these small degraded sites, additional Ca amendment would be necessary and reseeding would be required. However, the authors recommend further monitoring of the affected sites to determine if unfavorable conditions continue to degrade the reclaimed landscape before any attempt is made to rehabilitate the affected sites. If the degraded sites are stable, further Remediation efforts are not warranted because small areas of little or no vegetation are

  10. Statistical analysis of multivariate atmospheric variables. [cloud cover

    NASA Technical Reports Server (NTRS)

    Tubbs, J. D.

    1979-01-01

    Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

  11. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

    Allen, T.F.H.; Skagen, S.

    1973-01-01

    Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

  12. Multivariate optimization of capillary electrophoresis methods: a critical review.

    PubMed

    Orlandini, Serena; Gotti, Roberto; Furlanetto, Sandra

    2014-01-01

    In this article a review on the recent applications of multivariate techniques for optimization of electromigration methods, is presented. Papers published in the period from August 2007 to February 2013, have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.

  13. Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities.

    PubMed

    Gowen, Aoife A; Dorrepaal, Ronan M

    2016-07-02

    Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy.

  14. Minimal inversion, command matching and disturbance decoupling in multivariable systems

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1989-01-01

    The present treatment of the related problems of minimal inversion and perfect output control in linear multivariable systems uses a simple analytical expression for the inverse of a square multivariate system's transfer-function matrix to construct a minimal-order inverse of the system. Because the poles of the minimal-order inverse are the transmission zeros of the system, necessary and sufficient conditions for the inverse system's stability are simply stated in terms of the zero polynomial of the original system. A necessary and sufficient condition for the existence of the required controllers is that the plant zero polynomial be neither identical to zero nor unstable.

  15. Robust Multivariable Controller Design via Implicit Model-Following Methods.

    DTIC Science & Technology

    1983-12-01

    HD-Ri38 309 ROBUST MULTIVARIABLE CONTROLLER DESIGN VIA IMPLICIT 1/4 MODEL-FOLLOWING METHODS(U) AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL...aaS. a%. 1 .111 I Q~ 18 0 ROBUST MULTIVARIABLE CONTROLLER DESIGN -~ :VIA IMPLICIT MODEL-FOLLOWING METHODS ’.% THESIS , AFIT/GE/EE/83D-48 William G... CONTROLLER DESIGN VIA IMPLICIT MODEL-FOLLOWING METHODS THESIS AFIT/GE/EE/83D-48 William G. Miller Capt USAF ,. Approved for pubi release; distribution

  16. Steady-state decoupling and design of linear multivariable systems

    NASA Technical Reports Server (NTRS)

    Thaler, G. J.

    1974-01-01

    A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.

  17. 30 CFR 49.20 - Requirements for all coal mines.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Requirements for all coal mines. 49.20 Section... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.20 Requirements for all coal mines. (a) The operator of each underground coal mine shall make available two certified mine...

  18. 30 CFR 49.20 - Requirements for all coal mines.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Requirements for all coal mines. 49.20 Section... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.20 Requirements for all coal mines. (a) The operator of each underground coal mine shall make available two certified mine...

  19. 30 CFR 49.20 - Requirements for all coal mines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Requirements for all coal mines. 49.20 Section... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.20 Requirements for all coal mines. (a) The operator of each underground coal mine shall make available two certified mine...

  20. 30 CFR 49.20 - Requirements for all coal mines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Requirements for all coal mines. 49.20 Section... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.20 Requirements for all coal mines. (a) The operator of each underground coal mine shall make available two certified mine...

  1. 30 CFR 49.20 - Requirements for all coal mines.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Requirements for all coal mines. 49.20 Section... TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.20 Requirements for all coal mines. (a) The operator of each underground coal mine shall make available two certified mine...

  2. A Mine Explosion Source Phenomenology Experiment

    DTIC Science & Technology

    2000-09-01

    Alternatively, if controlled mine blast experiments could be performed, in close collaboration with the mining industry , significant knowledge could be...gleaned to reduce the chance of false alarms from such mining activities and, hopefully, provide the mining industry with useful information to conduct...including both hard and soft rock environments. One of the goals of this effort is to engage the mining industry in a variety of collaborative

  3. [Association between reported annual gold mining extraction and incidence of malaria in Mato Grosso-Brazil, 1985-1996].

    PubMed

    Duarte, Elisabeth Carmen; Fontes, Cor Jesus Fernandes

    2002-01-01

    A secondary data analysis was performed using an ecological design to study the association between malaria incidence rates, the reported annual production of gold mining extraction and monetary investments for the control of malaria from 1985 to 1996 in Mato Grosso, Brazil. A positive and statistically significant (p<0.001) association between the amount of gold extracted and MIR was obtained in multivariate regression analysis, even after allowing for financial investments in malaria control activities. This finding contributes to an understanding of the decrease observed in malaria incidence in Mato Grosso during the last decade, in view of the significant decrease in gold mining within the region during this period.

  4. Data mining in spontaneous reports.

    PubMed

    Bate, Andrew; Edwards, I R

    2006-03-01

    The increasing size of spontaneous report data sets and the increasing capability for screening such data due to increases in computational power has led to a recent increase in interest and use of data mining on such data. While data mining plays an important role in the analysis of spontaneous reports, there is general debate on how and when data mining should be best performed. While the cornerstone principles for data mining of spontaneous reports have been in place since the 1960s, several significant changes have occurred to make their use widespread. Superficially the Bayesian methods seem unnecessarily complex, particularly given the nature of the data, but in practice implementation in Bayesian framework gives clear benefits. There are difficulties evaluating the performance of the methods, but they work and save resources in managing large data sets. The use of neural networks allows more sophisticated pattern recognition to be performed.

  5. The Leading Edge: Data Mining

    NASA Video Gallery

    When an airplane flies, hundreds of data streams fly from it every second—pilot reports, incident reports, control positions, instrument positions, warning modes. NASA is mining terabytes of avia...

  6. Mining Upgrades to Reduce Pollution

    EPA Pesticide Factsheets

    Settlement with Southern Coal Corporation and 26 affiliates requires the companies to comprehensively upgrade their coal mining and processing operations to prevent polluted wastewater from threatening rivers and streams and communities across Appalachia.

  7. Biodiesel production using heterogeneous catalysts.

    PubMed

    Semwal, Surbhi; Arora, Ajay K; Badoni, Rajendra P; Tuli, Deepak K

    2011-02-01

    The production and use of biodiesel has seen a quantum jump in the recent past due to benefits associated with its ability to mitigate greenhouse gas (GHG). There are large number of commercial plants producing biodiesel by transesterification of vegetable oils and fats based on base catalyzed (caustic) homogeneous transesterification of oils. However, homogeneous process needs steps of glycerol separation, washings, very stringent and extremely low limits of Na, K, glycerides and moisture limits in biodiesel. Heterogeneous catalyzed production of biodiesel has emerged as a preferred route as it is environmentally benign needs no water washing and product separation is much easier. The present report is review of the progress made in development of heterogeneous catalysts suitable for biodiesel production. This review shall help in selection of suitable catalysts and the optimum conditions for biodiesel production.

  8. Mining law and regulations of Mexico

    SciTech Connect

    Miranda, F.C.

    1992-01-01

    The mining law and regulations of Mexico have been of considerable interest to mining lawyers in the united States. Recent wide-ranging changes in Mexican mining regulations have come at a time when the mining industry hopes to broaden its scope to contend with worldwide competition. Article 27 of the Federal Constitution of Mexico governs the mining of metallic, nonmetallic, and coal materials. New regulation implementing this law became effective on December 10, 1990. These regulations, generally regarded as providing far greater flexibility in the acquisition and maintenance of mineral rights, also provide substantial additional flexibility in the ability of non-Mexican companies to own concessions. The Laws section of this book includes: General Provision, ministry of National Patrimony, mining concession, beneficiating plant concessions, execution and proof of exploitation work oppositions, national mineral reserves, special concessions on National Mineral Reserves, Public/Registry of mining, mining promotion and of the assistance to small miners, Industrial Mining Reserves and violations and penalties. The regulations section includes: general dispositions, mineral reserves, mining assignments and concessions, right of mining concession holders, obligations of the holders of mining concessions, mining companies, mining public registry, mining experts, inspections, sanctions and remedies.

  9. On-Board Mining in the Sensor Web

    NASA Astrophysics Data System (ADS)

    Tanner, S.; Conover, H.; Graves, S.; Ramachandran, R.; Rushing, J.

    2004-12-01

    provide capabilities for autonomous data mining, classification and feature extraction using both streaming and buffered data sources. A ground-based testbed provides a heterogeneous, embedded hardware and software environment representing both space-based and ground-based sensor platforms, including wireless sensor mesh architectures. The AODP project explores the EVE concepts in the world of sensor-networks, including ad-hoc networks of small sensor platforms.

  10. NASA GSFC Perspective on Heterogeneous Processing

    NASA Technical Reports Server (NTRS)

    Powell, Wesley A.

    2016-01-01

    This presentation provides an overview of NASA GSFC, our onboard processing applications, the applicability heterogeneous processing to these applications, and necessary developments to enable heterogeneous processing to be infused into our missions.

  11. Microbiological and Geochemical Characterization of Fluvially Deposited Sulfidic Mine Tailings

    PubMed Central

    Wielinga, Bruce; Lucy, Juliette K.; Moore, Johnnie N.; Seastone, October F.; Gannon, James E.

    1999-01-01

    The fluvial deposition of mine tailings generated from historic mining operations near Butte, Montana, has resulted in substantial surface and shallow groundwater contamination along Silver Bow Creek. Biogeochemical processes in the sediment and underlying hyporheic zone were studied in an attempt to characterize interactions consequential to heavy-metal contamination of shallow groundwater. Sediment cores were extracted and fractionated based on sediment stratification. Subsamples of each fraction were assayed for culturable heterotrophic microbiota, specific microbial guilds involved in metal redox transformations, and both aqueous- and solid-phase geochemistry. Populations of cultivable Fe(III)-reducing bacteria were most prominent in the anoxic, circumneutral pH regions associated with a ferricrete layer or in an oxic zone high in organic carbon and soluble iron. Sulfur- and iron-oxidizing bacteria were distributed in discrete zones throughout the tailings and were often recovered from sections at and below the anoxic groundwater interface. Sulfate-reducing bacteria were also widely distributed in the cores and often occurred in zones overlapping iron and sulfur oxidizers. Sulfate-reducing bacteria were consistently recovered from oxic zones that contained high concentrations of metals in the oxidizable fraction. Altogether, these results suggest a highly varied and complex microbial ecology within a very heterogeneous geochemical environment. Such physical and biological heterogeneity has often been overlooked when remediation strategies for metal contaminated environments are formulated. PMID:10103249

  12. Temperature chaos and quenched heterogeneities

    NASA Astrophysics Data System (ADS)

    Barucca, Paolo; Parisi, Giorgio; Rizzo, Tommaso

    2014-03-01

    We present a treatable generalization of the Sherrington-Kirkpatrick (SK) model which introduces correlations in the elements of the coupling matrix through multiplicative disorder on the single variables and investigate the consequences on the phase diagram. We define a generalized qEA parameter and test the structural stability of the SK results in this correlated case evaluating the de Almeida-Thouless line of the model. As a main result we demonstrate the increase of temperature chaos effects due to heterogeneities.

  13. Sources and fates of heavy metals in a mining-impacted stream: Temporal variability and the role of iron oxides

    PubMed Central

    Schaider, Laurel A.; Senn, David B.; Estes, Emily R.; Brabander, Daniel J.; Shine, James P.

    2014-01-01

    Heavy metal contamination of surface waters at mining sites often involves complex interactions of multiple sources and varying biogeochemical conditions. We compared surface and subsurface metal loading from mine waste pile runoff and mine drainage discharge and characterized the influence of iron oxides on metal fate along a 0.9-km stretch of Tar Creek (Oklahoma, USA), which drains an abandoned Zn/Pb mining area. The importance of each source varied by metal: mine waste pile runoff contributed 70% of Cd, while mine drainage contributed 90% of Pb, and both sources contributed similarly to Zn loading. Subsurface inputs accounted for 40% of flow and 40-70% of metal loading along this stretch. Streambed iron oxide aggregate material contained highly elevated Zn (up to 27,000 μg g−1), Pb (up to 550 μg g−1) and Cd (up to 200 μg g−1) and was characterized as a heterogeneous mixture of iron oxides, fine-grain mine waste, and organic material. Sequential extractions confirmed preferential sequestration of Pb by iron oxides, as well as substantial concentrations of Zn and Cd in iron oxide fractions, with additional accumulation of Zn, Pb, and Cd during downstream transport. Comparisons with historical data show that while metal concentrations in mine drainage have decreased by more than an order of magnitude in recent decades, the chemical composition of mine waste pile runoff has remained relatively constant, indicating less attenuation and increased relative importance of pile runoff. These results highlight the importance of monitoring temporal changes at contaminated sites associated with evolving speciation and simultaneously addressing surface and subsurface contamination from both mine waste piles and mine drainage. PMID:24867708

  14. Offshore sand and gravel mining

    SciTech Connect

    Pandan, J.W.

    1983-05-01

    This paper reviews the status of mining offshore for sand and gravel on a world-wide basis. It discusses the technology for exploration and evaluation of sea floor mineral targets, as well as mining, transportation, and processing. Large operations in Japan and Europe are described, based upon personal observations of the author. The U.S. situation is outlined and opinions offered as to the outlook for the future.

  15. Wavelet methods in data mining

    NASA Astrophysics Data System (ADS)

    Manchanda, P.

    2012-07-01

    Data mining (knowledge discovery in data base) is comparatively new interdisciplinary field developed by joint efforts of mathematicians, statisticians, computer scientists and engineers. There are twelve important ingredients of this field along with their applications in real world problems. In this chapter, we have reviewed application of wavelet methods to data mining, particularly denoising, dimension reduction, similarity search, feature extraction and prediction. Meteorological data of Saudi Arabia and Stock market data of India are considered for illustration.

  16. On comparing heterogeneity across biomarkers.

    PubMed

    Steininger, Robert J; Rajaram, Satwik; Girard, Luc; Minna, John D; Wu, Lani F; Altschuler, Steven J

    2015-06-01

    Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

  17. Soft Dielectrics: Heterogeneity and Instabilities

    NASA Astrophysics Data System (ADS)

    Rudykh, Stephan; Debotton, Gal; Bhattacharya, Kaushik

    2012-02-01

    Dielectric Elastomers are capable of large deformations in response to electrical stimuli. Heterogeneous soft dielectrics with proper microstructures demonstrate much stronger electromechanical coupling than their homogeneous constituents. In turn, the heterogeneity is an origin for instability developments leading to drastic change in the composite microstructure. In this talk, the electromechanical instabilities are considered. Stability of anisotropic soft dielectrics is analyzed. Ways to achieve giant deformations and manipulating extreme material properties are discussed. 1. S. Rudykh and G. deBotton, ``Instabilities of Hyperelastic Fiber Composites: Micromechanical Versus Numerical Analyses.'' Journal of Elasticity, 2011. http://dx.doi.org/2010.1007/s10659-011-9313-x 2. S. Rudykh, K. Bhattacharya and G. deBotton, ``Snap-through actuation of thick-wall electroactive balloons.'' International Journal of Non-Linear Mechanics, 2011. http://dx.doi.org/10.1016/j.ijnonlinmec.2011.05.006 3. S. Rudykh and G. deBotton, ``Stability of Anisotropic Electroactive Polymers with Application to Layered Media.'' Zeitschrift f"ur angewandte Mathematik und Physik, 2011. http://dx.doi.org/10.1007/s00033-011-0136-1 4. S. Rudykh, A. Lewinstein, G. Uner and G. deBotton, ``Giant Enhancement of the Electromechanical Coupling in Soft Heterogeneous Dielectrics.'' 2011 http://arxiv.org/abs/1105.4217v1

  18. Measuring heterogeneous remanence in paleomagnetism

    NASA Astrophysics Data System (ADS)

    Borradaile, Graham J.; Geneviciene, Ieva

    2007-06-01

    Remanence directions of from the same block-sample may be inconsistent or unrepresentative due to orientation and location heterogeneity of their remanence-bearing minerals (RBM). Magnetization-heterogeneity is usually undetectable at the specimen-level but we replicated its effects by measuring 8 small specimens with stable magnetizations (8 or 5.2 cm3). These were assembled into a single large multi-specimen inside 125 cm3 containers that were measured in a Molspin ``BigSpin'' magnetometer. Large-specimen remanence directions deflect towards the direction of any strongly magnetized sub-specimen. Differences between the large-specimen remanence and that for the group of individually measured sub-specimens worsened when one sub-specimen was mis-oriented. These discrepancies were cancelled or reduced using larger numbers of specimen orientations in the magnetometer. Conventional schemes with 4 or 6 different measurement-orientations may fail to suppress heterogeneity-effects whereas our 12-orientation protocol may succeed. For most specimens, acceptable remanence-homogeneity is present where similar remanence-directions are recorded from 4, 6, and 12 different spin-orientations.

  19. Analyzing and modeling heterogeneous behavior

    NASA Astrophysics Data System (ADS)

    Lin, Zhiting; Wu, Xiaoqing; He, Dongyue; Zhu, Qiang; Ni, Jixiang

    2016-05-01

    Recently, it was pointed out that the non-Poisson statistics with heavy tail existed in many scenarios of human behaviors. But most of these studies claimed that power-law characterized diverse aspects of human mobility patterns. In this paper, we suggest that human behavior may not be driven by identical mechanisms and can be modeled as a Semi-Markov Modulated Process. To verify our suggestion and model, we analyzed a total of 1,619,934 records of library visitations (including undergraduate and graduate students). It is found that the distribution of visitation intervals is well fitted with three sections of lines instead of the traditional power law distribution in log-log scale. The results confirm that some human behaviors cannot be simply expressed as power law or any other simple functions. At the same time, we divided the data into groups and extracted period bursty events. Through careful analysis in different groups, we drew a conclusion that aggregate behavior might be composed of heterogeneous behaviors, and even the behaviors of the same type tended to be different in different period. The aggregate behavior is supposed to be formed by "heterogeneous groups". We performed a series of experiments. Simulation results showed that we just needed to set up two states Semi-Markov Modulated Process to construct proper representation of heterogeneous behavior.

  20. Mining the hydrosphere

    NASA Astrophysics Data System (ADS)

    Petersen, Ulrich

    1994-05-01

    Rapid technological progress over the past two decades has significantly lowered the cost of water desalination and has spurred an impressive growth of this industry. About half of the desalination capacity uses seawater, the other half uses continental brackish water. Most of the desalted water is consumed for domestic and municipal purposes. However, some of it, especially that derived from brackish water, is also competitive for irrigation of high-value crops, and for some industrial purposes, particularly in water-deficient regions. In addition to fresh water, at present only halite, magnesium, and bromine are commercially obtained from seawater. These commodities plus sodium carbonate (trona), sodium sulfate, I, Li, B, and potash are also produced from natural brines. Prior attempts to obtain potash, U, Au, and other mineral commodities from seawater failed because the market value of the recovered products was too low to cover the capital and operating costs of processing plants exclusively dedicated to recover them separately. The economics are more favorable if these and/or other elements or compounds are obtained as byproducts of seawater desalination, especially when combined with cogeneration of electricity. Under these circumstances the major capital and operating costs for pumping seawater and for disposing of the reject brine are absorbed mostly by the proceeds from freshwater production. The byproducts need only to pay for the additional recovery processes. One advantage of this strategy is to reduce the environmental impact of reject brine disposal. Another is to reduce the environmental, safety, and health impacts of land-based mining. Furthermore, obtaining nonmetallic mineral commodities from seawater at a number of localities scattered over the Earth can significantly reduce their transportation costs, which is a major proportion of their cost to nations lacking these resources. This is particularly pertinent for common salt (halite), potash