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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. Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

    PubMed Central

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

    2012-01-01

    Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

  5. The heterogeneity of bone disease in cirrhosis: a multivariate analysis.

    PubMed

    Crawford, Bronwyn A L; Kam, C; Donaghy, A J; McCaughan, G W

    2003-12-01

    This study aimed to assess the clinical, biochemical and hormonal factors contributing to low bone density in a large ambulatory group of patients with cirrhosis of diverse aetiology. Bone density of the lumbar spine, neck of femur, total hip, total body, as well as total body fat, was measured by dual X-ray (DEXA) absorptiometry in 81 men and 32 women (average age 50.3 years). Morning blood and urine samples were taken for hormonal and biochemical analysis. Viral hepatitis was the most common cause of cirrhosis (54%) and the severity of cirrhosis ranged from Child-Pugh A5-C14. Osteoporosis was most common in the lumbar spine but was present at any site in 31% of women and 22% of men, with osteopenia present in another 40% of both genders. Urinary deoxypyridinoline, a marker of bone resorption, was elevated in 56% of patients and was associated with increasing severity of cirrhosis and a higher prevalence of osteoporosis, particularly of the lumbar spine. Hip-bone density was primarily affected by low 25-hydroxyvitamin D levels and was associated with secondary hyperparathyroidism in one third of these patients. Additional important predictors for low bone density at all sites were age in women and testosterone in men. These findings indicate that, although the pathophysiology of osteoporosis in chronic liver disease is heterogeneous, high bone turnover may be the underlying pathophysiological mechanism in a significant subgroup of cirrhotic patients and may reflect metabolic effects of hypogonadism or secondary hyperparathyroidism on bone.

  6. Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

    PubMed Central

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

    2015-01-01

    Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993

  7. 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.

  8. 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

  9. Methods and tools for mining multivariate temporal data in clinical and biomedical applications.

    PubMed

    Bellazzi, Riccardo; Sacchi, Lucia; Concaro, Stefano

    2009-01-01

    Temporal data mining is becoming an important tool for health care providers and decision makers. The capability of handling and analyzing complex multivariate data may allow to extract useful information coming from the day-by-day activity of health care organizations as well as from patients monitoring. In this paper we review the main approaches presented in the literature to mine biomedical time sequences and we present a novel approach able to deal with "point-like" and "interval-like" events. The methods is described and the results obtained on two clinical data sets are shown.

  10. Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

    PubMed

    Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

    2017-08-17

    Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

  11. Inference of Cell Mechanics in Heterogeneous Epithelial Tissue Based on Multivariate Clone Shape Quantification

    PubMed Central

    Tsuboi, Alice; Umetsu, Daiki; Kuranaga, Erina; Fujimoto, Koichi

    2017-01-01

    Cell populations in multicellular organisms show genetic and non-genetic heterogeneity, even in undifferentiated tissues of multipotent cells during development and tumorigenesis. The heterogeneity causes difference of mechanical properties, such as, cell bond tension or adhesion, at the cell–cell interface, which determine the shape of clonal population boundaries via cell sorting or mixing. The boundary shape could alter the degree of cell–cell contacts and thus influence the physiological consequences of sorting or mixing at the boundary (e.g., tumor suppression or progression), suggesting that the cell mechanics could help clarify the physiology of heterogeneous tissues. While precise inference of mechanical tension loaded at each cell–cell contacts has been extensively developed, there has been little progress on how to distinguish the population-boundary geometry and identify the cause of geometry in heterogeneous tissues. We developed a pipeline by combining multivariate analysis of clone shape with tissue mechanical simulations. We examined clones with four different genotypes within Drosophila wing imaginal discs: wild-type, tartan (trn) overexpression, hibris (hbs) overexpression, and Eph RNAi. Although the clones were previously known to exhibit smoothed or convoluted morphologies, their mechanical properties were unknown. By applying a multivariate analysis to multiple criteria used to quantify the clone shapes based on individual cell shapes, we found the optimal criteria to distinguish not only among the four genotypes, but also non-genetic heterogeneity from genetic one. The efficient segregation of clone shape enabled us to quantitatively compare experimental data with tissue mechanical simulations. As a result, we identified the mechanical basis contributed to clone shape of distinct genotypes. The present pipeline will promote the understanding of the functions of mechanical interactions in heterogeneous tissue in a non-invasive manner. PMID

  12. 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.

  13. 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

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

    PubMed

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

    2016-09-26

    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.

  15. Data-mining for multi-variate flood damage modelling with limited data

    NASA Astrophysics Data System (ADS)

    Wagenaar, Dennis; Bouwer, Laurens

    2017-04-01

    Flood damage assessment is usually done with damage curves only dependent on the water depth. Recent studies have shown that data-mining techniques applied to a multi-dimensional dataset can produce significantly better flood damage estimates. However, creating and applying a multi-variate flood damage model requires an extensive dataset, which is rarely available and this can limit the application of these new techniques. In this paper we enrich a dataset of residential building and content damages from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variate flood damage assessment. Results from 2D flood simulations are used to add information on flow velocity, flood duration and the return period to the dataset, and cadastre data is used to add information on building characteristics. Next, several statistical approaches are used to create multi-variate flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched dataset in combination with the data-mining techniques delivers a significant improvement over a simple model only based on the water depth. We find that with our dataset, the trees based methods perform better than the Bayesian Network, which is in contrast to other studies.

  16. 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.

  17. 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

  18. 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.…

  19. Mining large heterogeneous data sets in drug discovery.

    PubMed

    Wild, David J

    2009-10-01

    Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.

  20. 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.

  1. 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.

  2. Blasting methods for heterogeneous rocks in hillside open-pit mines with high and steep slopes

    NASA Astrophysics Data System (ADS)

    Chen, Y. J.; Chang, Z. G.; Chao, X. H.; Zhao, J. F.

    2017-06-01

    In the arid desert areas in Xinjiang, most limestone quarries are hillside open-pit mines (OPMs) where the limestone is hard, heterogeneous, and fractured, and can be easily broken into large blocks by blasting. This study tried to find effective technical methods for blasting heterogeneous rocks in such quarries based on an investigation into existing problems encountered in actual mining at Hongshun Limestone Quarry in Xinjiang. This study provided blasting schemes for hillside OPMs with different heights and slopes. These schemes involve the use of vertical deep holes, oblique shallow holes, and downslope hole-by-hole sublevel or simultaneous detonation techniques. In each bench, the detonations of holes in a detonation unit occur at intervals of 25-50 milliseconds. The research findings can offer technical guidance on how to blast heterogeneous rocks in hillside limestone quarries.

  3. Toward integrating text and images for multimedia retrieval in heterogeneous data mining

    NASA Astrophysics Data System (ADS)

    Dua, Sumeet; Mannava, Vinay

    2005-10-01

    The problem of heterogeneous data mining deals with the computational challenges of searching multimedia data in a unified computational framework that can answer similarity queries of data mining by accurate and efficient means. The advances in data collection methodologies have generated large data-warehouses, in assortment of application domains, including but not limited to, Internet applications for multimedia retrieval and exchange. Heterogeneous data indexing has proven to be a valuable tool for complex data mining in large data domains inherently semi-structured in nature. We propose a solution to integrate the feature vectors of image and text by cooperatively representing them in a multidimensional spatial data structure, which has previously exhibited superior search performance in image database domains. We have evaluated results of content-based similarity queries on the indexing schema independently in images and textual domains. We have then studied and represented the effect of the choice of similarity metric on the similarity queries. We then propose an indexing schema that integrates the feature vectors of text and images to answer integrated queries on the unified heterogeneous data space. An added advantage of the proposed methodology is embodied by the fact that a textual feature vector can query a heterogeneous database to retrieve both text as well as images as query results. This solves the problem of individually querying each data-domain separately and sequentially scanning the integrated database for similarity results. The proposed methodology is time and space efficient, and is capable of answering complex heterogeneous data mining queries in multimedia domains.

  4. 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…

  5. 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

  6. 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.

  7. 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

  8. Homeland situation awareness through mining and fusing heterogeneous information from intelligence databases and field sensors

    NASA Astrophysics Data System (ADS)

    Digioia, Giusj; Panzieri, Stefano

    2012-06-01

    One of the most felt issues in the defence domain is that of having huge quantities of data stored in databases and acquired from field sensors, without being able to infer information from them. Usually databases are continuously updated with observations, and are related to heterogeneous data. Deep and continuous analysis on data could mine useful correlations, explain relations existing among data and cue searches for further evidences. The solution to the problem addressed before seems to deal both with the domain of Data Mining and with the domain of high level Data Fusion, that is Situation Assessment, Threat Assessment and Process Refinement, also synthesised as Situation Awareness. The focus of this paper is the definition of an architecture for a system adopting data mining techniques to adaptively discover clusters of information and relation among them, to classify observations acquired and to use the model of knowledge and the classification derived in order to assess situations, threats and refine the search for evidences. Sources of information taken into account are those related to the intelligence domain, as IMINT, HUMINT, ELINT, COMINT and other non-conventional sources. The algorithms applied refer to not supervised and supervised classification for rule exploitation, and adaptively built Hidden Markov Model for situation and threat assessment.

  9. 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.

  10. Integration and publication of heterogeneous text-mined relationships on the Semantic Web.

    PubMed

    Coulet, Adrien; Garten, Yael; Dumontier, Michel; Altman, Russ B; Musen, Mark A; Shah, Nigam H

    2011-05-17

    Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering. We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network. The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE.

  11. Correlating Microbial Diversity Patterns with Geochemistry in an Extreme and Heterogeneous Environment of Mine Tailings

    PubMed Central

    Liu, Jun; Hua, Zheng-Shuang; Chen, Lin-Xing; Kuang, Jia-Liang; Li, Sheng-Jin; Shu, Wen-Sheng

    2014-01-01

    Recent molecular surveys have advanced our understanding of the forces shaping the large-scale ecological distribution of microbes in Earth's extreme habitats, such as hot springs and acid mine drainage. However, few investigations have attempted dense spatial analyses of specific sites to resolve the local diversity of these extraordinary organisms and how communities are shaped by the harsh environmental conditions found there. We have applied a 16S rRNA gene-targeted 454 pyrosequencing approach to explore the phylogenetic differentiation among 90 microbial communities from a massive copper tailing impoundment generating acidic drainage and coupled these variations in community composition with geochemical parameters to reveal ecological interactions in this extreme environment. Our data showed that the overall microbial diversity estimates and relative abundances of most of the dominant lineages were significantly correlated with pH, with the simplest assemblages occurring under extremely acidic conditions and more diverse assemblages associated with neutral pHs. The consistent shifts in community composition along the pH gradient indicated that different taxa were involved in the different acidification stages of the mine tailings. Moreover, the effect of pH in shaping phylogenetic structure within specific lineages was also clearly evident, although the phylogenetic differentiations within the Alphaproteobacteria, Deltaproteobacteria, and Firmicutes were attributed to variations in ferric and ferrous iron concentrations. Application of the microbial assemblage prediction model further supported pH as the major factor driving community structure and demonstrated that several of the major lineages are readily predictable. Together, these results suggest that pH is primarily responsible for structuring whole communities in the extreme and heterogeneous mine tailings, although the diverse microbial taxa may respond differently to various environmental conditions

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

    PubMed

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

    2018-01-01

    The influence of gold mining activities on the water quality in the Mari catchment in Bétaré-Oya (East Cameroon) was assessed in this study. Sampling was performed within the period of one hydrological year (2015 to 2016), with 22 sampling sites consisting of groundwater (06) and surface water (16). In addition to measuring the physicochemical parameters, such as pH, electrical conductivity, alkalinity, turbidity, suspended solids and CN(-), eleven major elements (Na(+), K(+), Ca(2+), Mg(2+), NH4(+), Cl(-), NO3(-), HCO3(-), SO4(2-), PO4(3-) and F(-)) and eight heavy metals (Pb, Zn, Cd, Fe, Cu, As, Mn and Cr) were also analyzed using conventional hydrochemical methods, Multivariate Statistical Analysis and the Heavy metal Pollution Index (HPI). The results showed that the water from Mari catchment and Lom River was acidic to basic (5.4050mg NO3(-)/L. This water was found as two main types: calcium magnesium bicarbonate (CaMg-HCO3), which was the most represented, and sodium bicarbonate potassium (NaK-HCO3). As for trace elements in surface water, the contents of Pb, Cd, Mn, Cr and Fe were higher than recommended by the WHO guidelines, and therefore, the surface water was unsuitable for human consumption. Three phenomena were responsible for controlling the quality of the water in the study area: hydrolysis of silicate minerals of plutono-metamorphic rocks, which constitute the geological basement of this area; vegetation and soil leaching; and mining activities. The high concentrations of TSS and trace elements found in this basin were mainly due to gold mining activities (exploration and exploitation) as well as digging of rivers beds, excavation and gold

  13. Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.

    PubMed

    Nabavi, Sheida

    2016-08-15

    With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

  14. InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data.

    PubMed

    Smith, Richard N; Aleksic, Jelena; Butano, Daniela; Carr, Adrian; Contrino, Sergio; Hu, Fengyuan; Lyne, Mike; Lyne, Rachel; Kalderimis, Alex; Rutherford, Kim; Stepan, Radek; Sullivan, Julie; Wakeling, Matthew; Watkins, Xavier; Micklem, Gos

    2012-12-01

    InterMine is an open-source data warehouse system that facilitates the building of databases with complex data integration requirements and a need for a fast customizable query facility. Using InterMine, large biological databases can be created from a range of heterogeneous data sources, and the extensible data model allows for easy integration of new data types. The analysis tools include a flexible query builder, genomic region search and a library of 'widgets' performing various statistical analyses. The results can be exported in many commonly used formats. InterMine is a fully extensible framework where developers can add new tools and functionality. Additionally, there is a comprehensive set of web services, for which client libraries are provided in five commonly used programming languages. Freely available from http://www.intermine.org under the LGPL license. g.micklem@gen.cam.ac.uk Supplementary data are available at Bioinformatics online.

  15. 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.

  16. 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.

  17. Mining the Mind Research Network: A Novel Framework for Exploring Large Scale, Heterogeneous Translational Neuroscience Research Data Sources

    PubMed Central

    Bockholt, Henry J.; Scully, Mark; Courtney, William; Rachakonda, Srinivas; Scott, Adam; Caprihan, Arvind; Fries, Jill; Kalyanam, Ravi; Segall, Judith M.; de la Garza, Raul; Lane, Susan; Calhoun, Vince D.

    2009-01-01

    A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining. PMID:20461147

  18. Preferential flow in heterogeneous forest-reclaimed lignitic mine soil I. Cell-lysimeter and multiple-tracer study

    NASA Astrophysics Data System (ADS)

    Hangen, E.; Gerke, H. H.; Schaaf, W.; Hüttl, R. F.

    2003-04-01

    Flow and transport processes in forest-reclaimed lignitic mine soils are required to quantify water and element budgets, which are important for long-term predictions of restored ecosystem stability and development of mining area water quality. Soil water pressure head and solute concentration measurements using tensiometers and suction cups showed strong spatial heterogeneity possibly indicating preferential flow effects. Properties and spatial structures of the mostly sandy mine soils and transport processes, however, have not sufficiently been known for detailed assessments. The objective of this study was to quantitatively analyse flow paths and measure amount and spatial distribtion of leaching. Water and element fluxes were studied at a reclaimed mine spoil site, which was afforested in 1982 with Pinus nigra. At a 3.3 m2 plot, the total percolating water was collected in 110 cm soil depth by 45 squared suction cells of 27 cm edge length each. A multi-tracer solution containing deuterium, bromide, and terbuthylazine was applied evenly at the plot surface and imposed to natural infiltration. Leaching was measured for a period of about 2 years. One third of the cells never delivered any drainage water while few cells had large drainage rates which in one case even exceeded local infiltration rates. About 71 % of the drainage was through 9 % of the area. The spatial distribution of the leached bromide tracer did not always correspond with that of drainage. Relative concentrations of bromide and deuterium were similar. Terbuthylazine was observed only sporadically during the first drainage period and at relatively small concentrations just above the analytical detection limit. Leaching patterns of the sorptive herbicide indicate only relatively small nonequilibrium-type preferential flow. Sediment structures, water repellent regions, and tree root distributions seem to be important for funneling and flow path formation.

  19. 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

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

    DTIC Science & Technology

    2015-05-13

    Transposable Elements and Relevant to the Expression-based Clustering , BICoB-2015. 05-MAR-15, . : , Lifang He, Xiangnan Kong, Philip S. Yu, Zhifeng...Learning from Homogeneous Data, SIAM Data Mining Conference, 2013. 02-MAY-13, . : , Yuchen Zhao , Philip S. Yu. On Graph Stream Clustering with...Expansion of Human C2H2 Zinc Finger Genes Are Associated with Transposable Elements And Relevant to The Expression-based Clustering ”. BICoB-2015 12. X

  1. Multivariate or Multivariable Regression?

    PubMed Central

    Goodman, Melody

    2013-01-01

    The terms multivariate and multivariable are often used interchangeably in the public health literature. However, these terms actually represent 2 very distinct types of analyses. We define the 2 types of analysis and assess the prevalence of use of the statistical term multivariate in a 1-year span of articles published in the American Journal of Public Health. Our goal is to make a clear distinction and to identify the nuances that make these types of analyses so distinct from one another. PMID:23153131

  2. Mining of Multivariate Temporal Biological Data: A Framework for the Rational Design of Data-Driven Models

    SciTech Connect

    Kamimura R; Bicciato, S; Shimizu, H; Alford, J; Stephanopoulos, G

    2001-05-10

    A framework is presented that emphasizes the need to understand the strengths and weaknesses of the data prior to modeling. In short, given a list of constraints, the idea is to let the data sort itself along those guidelines. Once the data has been organized into some coherent faction, the user has a better understanding of what the strengths and weaknesses of the data are as the analysis proceeds. The goal is to understand the character of the data so that the user is not overwhelmed but is able to systematically organize and decompose information so as to facilitate the analysis and build an effective model. The data analyzed is that from an industrial fermentation but the framework presented is generic enough that it can be used in any application involving multivariate time series data, such as time varying microarray measurements.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

    PubMed

    Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

    2011-11-15

    There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

  8. 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

  9. 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.

  10. Different Scales of os Isotopic Heterogeneity in Ophiolite Chromitites from Sagua de TÁNAMO and MAYARÍ Mining Districts (eastern Cuba)

    NASA Astrophysics Data System (ADS)

    Gervilla, F.; Marchesi, C.; González-Jiménez, J. M.; Proenza, J. A.; Garrido, C. J.; Griffin, W. L.; O'Really, S.; Pearson, N. J.

    2009-04-01

    We performed in situ laser ablation MC-ICP-MS measurements of Os isotopes in platinum-group minerals (PGM) included in unaltered chromite from ophiolite chromitites of the Sagua de Tánamo mining district (eastern Cuba). The results reveal important heterogeneities at the km, hand sample and thin section scales. Initial 187Os/188Os (calculated at 90Ma, the estimated age of ophiolite formation) spans from 0.1185 to 0.1295 in the whole district. These values correspond to γOs = -8.1-0.4, calculated by comparison with the Os isotopic evolution of the primitive upper mantle (PUM; Meisel et al., 2001, GCA 65), and all but one PGMs have γOs lower than PUM. PGMs in a single hand sample from the Caridad Mine exhibit 187Os/188Os ratios from 0.1185 to 0.1274, which overlap almost the entire range of values measured in the Sagua de Tánamo district. In one thin section from the same mine 187Os/188Os varies between 0.1200 and 0.1263 in two PGMs that are only few millimetres from each other. The few analyzed PGM grains from the Mayarí district have 187Os/188Os = 0.1271-0.1272 (γOs = -1.4) that are generally higher than in Sagua de Tánamo and much more homogeneous. The sub-PUM (i.e. negative) initial γOs values can be explained by Re depletion during a long history of partial melting starting at 1.61 Ga, as indicated by calculated Os model ages. However, the heterogeneous isotopic signature of PGMs in a single hand sample and thin section suggests a more complex magmatic scenario for the formation of PGMs and the host chromite. At such small scales, the formation of PGMs with variable Os isotopic signatures requires a heterogeneous genetic environment where melts with different Os isotopic compositions coexist in space and/or time. This scenario can be achieved during chromite crystallization by mixing in mantle conduits of primitive and differentiated melts. Each new batch of primitive melt (with its own Os isotopic signature inherited from a highly heterogeneous

  11. Anthropogenic sources and environmentally relevant concentrations of heavy metals in surface water of a mining district in Ghana: a multivariate statistical approach.

    PubMed

    Armah, Frederick A; Obiri, Samuel; Yawson, David O; Onumah, Edward E; Yengoh, Genesis T; Afrifa, Ernest K A; Odoi, Justice O

    2010-11-01

    The levels of heavy metals in surface water and their potential origin (natural and anthropogenic) were respectively determined and analysed for the Obuasi mining area in Ghana. Using Hawth's tool an extension in ArcGIS 9.2 software, a total of 48 water sample points in Obuasi and its environs were randomly selected for study. The magnitude of As, Cu, Mn, Fe, Pb, Hg, Zn and Cd in surface water from the sampling sites were measured by flame Atomic Absorption Spectrophotometry (AAS). Water quality parameters including conductivity, pH, total dissolved solids and turbidity were also evaluated. Principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to identify possible sources of these heavy metals. Pearson correlation coefficients among total metal concentrations and selected water properties showed a number of strong associations. The results indicate that apart from tap water, surface water in Obuasi has elevated heavy metal concentrations, especially Hg, Pb, As, Cu and Cd, which are above the Ghana Environmental Protection Agency (GEPA) and World Health Organisation (WHO) permissible levels; clearly demonstrating anthropogenic impact. The mean heavy metal concentrations in surface water divided by the corresponding background values of surface water in Obuasi decrease in the order of Cd > Cu > As > Pb > Hg > Zn > Mn > Fe. The results also showed that Cu, Mn, Cd and Fe are largely responsible for the variations in the data, explaining 72% of total variance; while Pb, As and Hg explain only 18.7% of total variance. Three main sources of these heavy metals were identified. As originates from nature (oxidation of sulphide minerals particularly arsenopyrite-FeAsS). Pb derives from water carrying drainage from towns and mine machinery maintenance yards. Cd, Zn, Fe and Mn mainly emanate from industry sources. Hg mainly originates from artisanal small-scale mining. It cannot be said that the difference in concentration

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

    SciTech Connect

    Blothe, M.; Akob, D.M.; Kostka, J.E.; Goschel, K.; Drake, H.L.; Kusel, K.

    2008-02-15

    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 1) 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. The 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 Fe(III) reducers inhabiting the slightly acidic sediment had only minor capacities to be active under acidic conditions.

  13. Integrated analysis of seaweed components during seasonal fluctuation by data mining across heterogeneous chemical measurements with network visualization.

    PubMed

    Ito, Kengo; Sakata, Kenji; Date, Yasuhiro; Kikuchi, Jun

    2014-01-21

    Biological information is intricately intertwined with several factors. Therefore, comprehensive analytical methods such as integrated data analysis, combining several data measurements, are required. In this study, we describe a method of data preprocessing that can perform comprehensively integrated analysis based on a variety of multimeasurement of organic and inorganic chemical data from Sargassum fusiforme and explore the concealed biological information by statistical analyses with integrated data. Chemical components including polar and semipolar metabolites, minerals, major elemental and isotopic ratio, and thermal decompositional data were measured as environmentally responsive biological data in the seasonal variation. The obtained spectral data of complex chemical components were preprocessed to isolate pure peaks by removing noise and separating overlapping signals using the multivariate curve resolution alternating least-squares method before integrated analyses. By the input of these preprocessed multimeasurement chemical data, principal component analysis and self-organizing maps of integrated data showed changes in the chemical compositions during the mature stage and identified trends in seasonal variation. Correlation network analysis revealed multiple relationships between organic and inorganic components. Moreover, in terms of the relationship between metal group and metabolites, the results of structural equation modeling suggest that the structure of alginic acid changes during the growth of S. fusiforme, which affects its metal binding ability. This integrated analytical approach using a variety of chemical data can be developed for practical applications to obtain new biochemical knowledge including genetic and environmental information.

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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).

  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. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  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. Text Mining.

    ERIC Educational Resources Information Center

    Trybula, Walter J.

    1999-01-01

    Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

  10. 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.…

  11. Surface mining

    Treesearch

    Robert Leopold; Bruce Rowland; Reed Stalder

    1979-01-01

    The surface mining process consists of four phases: (1) exploration; (2) development; (3) production; and (4) reclamation. A variety of surface mining methods has been developed, including strip mining, auger, area strip, open pit, dredging, and hydraulic. Sound planning and design techniques are essential to implement alternatives to meet the myriad of laws,...

  12. 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.…

  13. 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.

  14. 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.

  15. Latent mixture models for multivariate and longitudinal outcomes.

    PubMed

    Pickles, Andrew; Croudace, Tim

    2010-06-01

    Repeated measures and multivariate outcomes are an increasingly common feature of trials. Their joint analysis by means of random effects and latent variable models is appealing but patterns of heterogeneity in outcome profile may not conform to standard multivariate normal assumptions. In addition, there is much interest in both allowing for and identifying sub-groups of patients who vary in treatment responsiveness. We review methods based on discrete random effects distributions and mixture models for application in this field.

  16. 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.

  17. 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)

  18. 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)

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Target discovery from data mining approaches.

    PubMed

    Yang, Yongliang; Adelstein, S James; Kassis, Amin I

    2009-02-01

    Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

  5. Target discovery from data mining approaches.

    PubMed

    Yang, Yongliang; Adelstein, S James; Kassis, Amin I

    2012-02-01

    Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced. Published by Elsevier Ltd.

  6. 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.

  7. Eagle Mine

    EPA Pesticide Factsheets

    This Web page contains Eagle Mine Superfund site information, site description, site risk, cleanup progress, community involvement, reuse, land use controls, five-year reviews, site documents, contacts and links.

  8. 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.

  9. Solar Data Mining at Georgia State University

    NASA Astrophysics Data System (ADS)

    Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

    2016-12-01

    In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

  10. 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.

  11. 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.

  12. Mining with backfill

    SciTech Connect

    Granholm, S.

    1983-01-01

    This book reviews the fill mining practice in Sweden and other countries. Research results and technological innovations are presented on mining methods, mining operations, mining machinery and geomechanics. Other topics discussed are fill properties, technology, geomechanics, and new development.

  13. Coastal mining

    NASA Astrophysics Data System (ADS)

    Bell, Peter M.

    The Exclusive Economic Zone (EEZ) declared by President Reagan in March 1983 has met with a mixed response from those who would benefit from a guaranteed, 200-nautical-mile (370-km) protected underwater mining zone off the coasts of the United States and its possessions. On the one hand, the U.S. Department of the Interior is looking ahead and has been very successful in safeguarding important natural resources that will be needed in the coming decades. On the other hand, the mining industry is faced with a depressed metals and mining market.A report of the Exclusive Economic Zone Symposium held in November 1983 by the U.S. Geological Survey, the Mineral Management Service, and the Bureau of Mines described the mixed response as: “ … The Department of Interior … raring to go into promotion of deep-seal mining but industrial consortia being very pessimistic about the program, at least for the next 30 or so years.” (Chemical & Engineering News, February 5, 1983).

  14. 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.

  15. Network structure of multivariate time series

    NASA Astrophysics Data System (ADS)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. Spatio-Temporal Data Mining and Knowledge Discovery: Issues Overview

    DTIC Science & Technology

    2002-06-01

    Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large...databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data...and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the

  2. 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

  3. 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.

  4. 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

  5. Multivariate stochastic simulation with subjective multivariate normal distributions

    Treesearch

    P. J. Ince; J. Buongiorno

    1991-01-01

    In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

  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

    PubMed Central

    Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco del; 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. PMID:22953051

  8. 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.

  9. Data mining

    SciTech Connect

    Lee, K.; Kargupta, H.; Stafford, B.G.; Buescher, K.L.; Ravindran, B.

    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 develop and implement data mining technology suited to the analysis of large collections of unstructured data. This has taken the form of a software tool, PADMA (Parallel Data Mining Agents), which incorporates parallel data accessing, parallel scalable hierarchical clustering algorithms, and a web-based user interface for submitting Structured Query Language (SQL) queries and interactive data visualization. The authors have demonstrated the viability and scalability of PADMA by applying it to an unstructured text database of 25,000 documents running on an IBM SP2 at Argonne National Laboratory. The utility of PADMA for discovering patterns in data has also been demonstrated by applying it to laboratory test data for Hepatitis C patients and autopsy reports in collaboration with the University of New Mexico School of Medicine.

  10. Identification of multivariate linear systems

    SciTech Connect

    Griffith, J.M.

    1981-01-01

    This paper considers the problem of modeling multivariate linear systems where noisy output measurements are the only available data. The techniques presented are valid for a class of canonical forms. Results from several simulations demonstrate the capability for structure and parameter estimation.

  11. 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…

  12. Text mining.

    PubMed

    Clegg, Andrew B; Shepherd, Adrian J

    2008-01-01

    One of the fastest-growing fields in bioinformatics is text mining: the application of natural language processing techniques to problems of knowledge management and discovery, using large collections of biological or biomedical text such as MEDLINE. The techniques used in text mining range from the very simple (e.g., the inference of relationships between genes from frequent proximity in documents) to the complex and computationally intensive (e.g., the analysis of sentence structures with parsers in order to extract facts about protein-protein interactions from statements in the text). This chapter presents a general introduction to some of the key principles and challenges of natural language processing, and introduces some of the tools available to end-users and developers. A case study describes the construction and testing of a simple tool designed to tackle a task that is crucial to almost any application of text mining in bioinformatics--identifying gene/protein names in text and mapping them onto records in an external database.

  13. 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)

  14. 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)

  15. 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…

  16. 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.

  17. 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.

  18. Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection

    DTIC Science & Technology

    Streaming heterogeneous information is ubiquitous in the era of Big Data, which provides versatile perspectives for more comprehensive understanding...which effectively parse and distill such data. However, the complicated nature of streaming heterogeneous data prevents the conventional multivariate...better reveal the latentrelations among heterogeneous information and adapt to slow variations in streaming data. We applied our method on both synthetic

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Mine seepage problems in drift mine operations

    SciTech Connect

    DeRossett, C.; Johnson, D.E.; Bradshaw, D.B.

    1996-12-31

    Extensive mining in the Eastern Kentucky Coal Region has occurred in coal deposits located above valley floors. Underground mines present unique stability problems resulting from the creation of mine pools in abandoned works. {open_quotes}Blowouts{close_quotes} occur when hydrostatic pressures result in the cataclysmic failure of an outcrop-barrier. Additionally, seepage from flooded works results in saturation of colluvium, which may ultimately mobilize as landslides. Several case studies of both landslides and blowouts illustrate that considerations should be taken into account to control or prevent these problems. Underground mine maps and seepage conditions at the individual sites were examined to determine the mine layouts, outcrop-barrier widths, and structure of the mine floors. Discharge monitoring points were established in and near the landslides. These studies depict how mine layout, operation, and geology influence drainage conditions. The authors suggest that mine designs should incorporate drainage control to insure long-term stability and limit liability. The goal of the post-mining drainage plan is control of the mine drainage, which will reduce the size of mine pools and lower the hydrostatic pressure. Recommendations are made as to several methods that may be useful in controlling mine drainage.

  7. Topics in Multivariate Approximation Theory.

    DTIC Science & Technology

    1982-05-01

    of the Bramble -Hilbert lemma (see Bramble & H𔃻hert (13ŕ). Kergin’s scheme raises some questions. In .ontrast £.t its univar- iate antecedent, it...J. R. Rice (19791# An adaptive algorithm for multivariate approximation giving optimal convergence rates, J.Approx. Theory 25, 337-359. J. H. Bramble ...J.Numer.Anal. 7, 112-124. J. H. Bramble & S. R. Hilbert (19711, BoUnds for a class of linear functionals with applications to Hermite interpolation

  8. 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.

  9. 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.

  10. Mining machine

    SciTech Connect

    Becker, H.R.

    1984-12-04

    A mining machine is disclosed comprising a mobile base and a cutting head assembly at a forward end of the mobile base having a cutter drum rotatable about an output shaft disposed along the longitudinal axis of the cutter drum. A drive system for the cutting head assembly comprises at least one motor for driving at least one toothed motor pinion and a generally cylindrical combination gear having generally circular end surfaces. A bevel or face gear is formed in at least one of the end surfaces, having teeth adapted to mate with and be driven by the toothed motor pinion. The combination gear has a worm gear formed in the outside cylindrical surface, which is disposed in driving engagement with the teeth of an output gear integrally and coaxially connected to the output shaft of the cutter drum.

  11. [Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis].

    PubMed

    Kambeitz, J; Koutsouleris, N

    2014-06-01

    Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. Literature review of current studies. Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.

  12. 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.

  13. 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.

  14. 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.

  15. 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).

  16. Multivariate analyses in microbial ecology

    PubMed Central

    Ramette, Alban

    2007-01-01

    Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology. PMID:17892477

  17. 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.

  18. 1. OVERALL VIEW OF MINE SITE FROM KEETLEY MINE ROAD, ...

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

    1. OVERALL VIEW OF MINE SITE FROM KEETLEY MINE ROAD, SHOWING TAILING DUMP. VIEW TO WEST. - Park Utah Mining Company: Keetley Mine Complex, 1 mile East of U.S. 40 at Keetley, Heber City, Wasatch County, UT

  19. 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

  20. Mining and Risk of Tuberculosis in Sub-Saharan Africa

    PubMed Central

    Basu, Sanjay; McKee, Martin; Lurie, Mark

    2011-01-01

    Objectives. We estimated the relationship between mining and tuberculosis (TB) among countries in sub-Saharan Africa. Methods. We used multivariate regression to estimate the contribution of mining activity to TB incidence, prevalence, and mortality, as well as rates of TB among people living with HIV, with control for economic, health system, and population confounders. Results. Mining production was associated with higher population TB incidence rates (adjusted b = 0.093; 95% confidence interval [CI] = 0.067, 0.120; with an increase of mining production of 1 SD corresponding to about 33% higher TB incidence or 760 000 more incident cases), after adjustment for economic and population controls. Similar results were observed for TB prevalence and mortality, as well as with alternative measures of mining activity. Independent of HIV, there were significant associations between mining production and TB incidence in countries with high HIV prevalence (≥ 4% antenatal HIV prevalence; HIV-adjusted B = 0.066; 95% CI = 0.050, 0.082) and between log gold mining production and TB incidence in all studied countries (HIV-adjusted B = 0.053; 95% CI = 0.032, 0.073). Conclusions. Mining is a significant determinant of countrywide variation in TB among sub-Saharan African nations. Comprehensive TB control strategies should explicitly address the role of mining activity and environments in the epidemic. PMID:20516372

  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. 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.

  3. Multivariate Analysis of Functional Metagenomes

    PubMed Central

    Dinsdale, Elizabeth A.; Edwards, Robert A.; Bailey, Barbara A.; Tuba, Imre; Akhter, Sajia; McNair, Katelyn; Schmieder, Robert; Apkarian, Naneh; Creek, Michelle; Guan, Eric; Hernandez, Mayra; Isaacs, Katherine; Peterson, Chris; Regh, Todd; Ponomarenko, Vadim

    2013-01-01

    Metagenomics is a primary tool for the description of microbial and viral communities. The sheer magnitude of the data generated in each metagenome makes identifying key differences in the function and taxonomy between communities difficult to elucidate. Here we discuss the application of seven different data mining and statistical analyses by comparing and contrasting the metabolic functions of 212 microbial metagenomes within and between 10 environments. Not all approaches are appropriate for all questions, and researchers should decide which approach addresses their questions. This work demonstrated the use of each approach: for example, random forests provided a robust and enlightening description of both the clustering of metagenomes and the metabolic processes that were important in separating microbial communities from different environments. All analyses identified that the presence of phage genes within the microbial community was a predictor of whether the microbial community was host-associated or free-living. Several analyses identified the subtle differences that occur with environments, such as those seen in different regions of the marine environment. PMID:23579547

  4. 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.

  5. Conceptual Design of Future Undersea Unmanned Vehicle (UUV) System for Mine Disposal

    DTIC Science & Technology

    2012-01-01

    small charged deliverable vehicles. New underwater optical communication systems are introduced to improve onboard mine reconnaissance and decision... optical communication system between heterogeneous underwater and surface vehicle units in operations. At the same time, efficient and reliable

  6. 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

  7. Overview of multivariate methods and their application to studies of wildlife habitat

    SciTech Connect

    Shugart, Jr., H. H.

    1980-01-01

    Multivariate statistical techniques as methods of choice in analyzing habitat relations among animals have distinct advantages over competitive methodologies. These considerations, joined with a reduction in the cost of computer time, the increased availability of multivariate statistical packages, and an increased willingness on the part of ecologists to use mathematics and statistics as tools, have created an exponentially increasing interest in multivariate statistical methods over the past decade. It is important to note that the earliest multivariate statistical analyses in ecology did more than introduce a set of appropriate and needed methodologies to ecology. The studies emphasized different spatial and organizational scales from those typically emphasized in habitat studies. The new studies, that used multivariate methods, emphasized individual organisms' responses in a heterogeneous environment. This philosophical (and to some degree, methodological) emphasis on heterogeneity has led to a potential to predict the consequences of disturbances and management on wildlife habitat. One recent development in this regard has been the coupling of forest succession simulators with multivariate analysis of habitat to predict habitat availability under different timber management procedures.

  8. Multivariate classification of urine metabolome profiles for breast cancer diagnosis.

    PubMed

    Kim, Younghoon; Koo, Imhoi; Jung, Byung Hwa; Chung, Bong Chul; Lee, Doheon

    2010-04-16

    Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers. We proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer. In this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.

  9. Robust tests for multivariate factorial designs under heteroscedasticity.

    PubMed

    Vallejo, Guillermo; Ato, Manuel

    2012-06-01

    The question of how to analyze several multivariate normal mean vectors when normality and covariance homogeneity assumptions are violated is considered in this article. For the two-way MANOVA layout, we address this problem adapting results presented by Brunner, Dette, and Munk (BDM; 1997) and Vallejo and Ato (modified Brown-Forsythe [MBF]; 2006) in the context of univariate factorial and split-plot designs and a multivariate version of the linear model (MLM) to accommodate heterogeneous data. Furthermore, we compare these procedures with the Welch-James (WJ) approximate degrees of freedom multivariate statistics based on ordinary least squares via Monte Carlo simulation. Our numerical studies show that of the methods evaluated, only the modified versions of the BDM and MBF procedures were robust to violations of underlying assumptions. The MLM approach was only occasionally liberal, and then by only a small amount, whereas the WJ procedure was often liberal if the interactive effects were involved in the design, particularly when the number of dependent variables increased and total sample size was small. On the other hand, it was also found that the MLM procedure was uniformly more powerful than its most direct competitors. The overall success rate was 22.4% for the BDM, 36.3% for the MBF, and 45.0% for the MLM.

  10. Verification of predicted robustness and accuracy of multivariate analysis☆

    PubMed Central

    Markiewicz, P.J.; Matthews, J.C.; Declerck, J.; Herholz, K.

    2012-01-01

    The assessment of accuracy and robustness of multivariate analysis of FDG-PET brain images as presented in [Markiewicz, P.J., Matthews, J.C., Declerck, J., Herholz, K., 2009. Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease. Neuroimage 46, 472–485.] using a homogeneous sample (from one centre) of small size is here verified using a heterogeneous sample (from multiple centres) of much larger size. Originally the analysis, which included principal component analysis (PCA) and Fisher discriminant analysis (FDA), was established using a sample of 42 subjects (19 Normal Controls (NCs) and 23 Alzheimer's disease (AD) patients) and here the analysis is verified using an independent sample of 166 subjects (86 NCs and 80 ADs) obtained from the ADNI database. It is shown that bootstrap resampling combined with the metric of the largest principal angle between PCA subspaces as well as the deliberate clinical misdiagnosis simulation can predict robustness of the multivariate analysis when used with new datasets. Cross-validation (CV) and the .632 bootstrap overestimated the predictive accuracy encouraging less robust solutions. Also, it is shown that the type of PET scanner and image reconstruction method has an impact on such analysis and affects the accuracy of the verification sample. PMID:21338696

  11. 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.

  12. Mine drainage and surface mine reclamation. Volume II. Mine reclamation, abandoned mine lands and policy issues

    SciTech Connect

    Not Available

    1988-01-01

    Mine waste and mine reclamation are topics of major interest to the mining industry, the government and the general public. This publication and its companion volume are the proceedings of a conference held in Pittsburgh, April 19-21, 1988. There were nine sessions (50 papers) that dealt with the geochemistry, hydrology and problems of mine waste and mine water, especially acid mine drainage. These comprise Volume 1. The nine sessions (43 papers) that dealt with reclamation and restoration of disturbed lands, as well as related policy issues, are included in volume 2. Volume 2 also contains the ten papers that pertained to control of subsidence and mine fires at abandoned mines. Poster session presentations are, in general, represented by abstracts; these have been placed in the back of both volumes.

  13. Mine drainage and surface mine reclamation. Volume I. Mine water and mine waste

    SciTech Connect

    Not Available

    1988-01-01

    Mine waste and mine reclamation are topics of major interest to the mining industry, the government and the general public. This publication and its companion volume are the proceedings of a conference held in Pittsburgh, April 19-21, 1988. There were nine sessions (50 papers) that dealt with the geochemistry, hydrology and problems of mine waste and mine water, especially acid mine drainage. These comprise Volume 1. The nine sessions (43 papers) that dealt with reclamation and restoration of disturbed lands, as well as related policy issues, are included in volume 2. Volume 2 also contains the ten papers that pertained to control of subsidence and mine fires at abandoned mines. Poster session presentations are, in general, represented by abstracts; these have been placed in the back of both volumes.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package.

    PubMed

    Chen, Rong; Herskovits, Edward H

    2012-04-01

    The GAMMA suite is an open-source, cross-platform data-mining software package designed to analyze neuroimaging data. Analyzing brain image volumes is a very challenging problem, due to undersampling and the potential for multivariate nonlinear interactions among variables. The GAMMA suite provides a set of tools to facilitate the analysis of neuroimaging data.

  19. Mountaintop mining consequences

    Treesearch

    M.A. Palmer; E.S. Bernhardt; W.H. Schlesinger; K.N. Eshleman; E. Foufoula-Georgiou; M.S. Hendryx; A.D. Lemly; G.E. Likens; O.L. Loucks; M.E. Power; P.S. White; P.R. Wilcock

    2010-01-01

    There has been a global, 30-year increase in surface mining (1), which is now the dominant driver of land-use change in the central Appalachian ecoregion of the United States (2). One major form of such mining, mountaintop mining with valley fills (MTM/VF) (3), is widespread throughout eastern Kentucky, West Virginia (WV), and southwestern Virginia. Upper elevation...

  20. Orapa Diamond Mine, Botswana

    NASA Image and Video Library

    2015-11-16

    This image from NASA Terra spacecraft shows the Orapa diamond mine, the world largest diamond mine by area. The mine is located in Botswana. It is the oldest of four mines operated by the same company, having begun operations in 1971. Orapa is an open pit style of mine, located on two kimberlite pipes. Currently, the Orapa mine annually produces approximately 11 million carats (2200 kg) of diamonds. The Letlhakane diamond mine is also an open pit construction. In 2003, the Letlhakane mine produced 1.06 million carats of diamonds. The Damtshaa diamond mine is the newest of four mines, located on top of four distinct kimberlite pipes of varying ore grade. The mine is forecast to produce about 5 million carats of diamond over the projected 31 year life of the mine. The image was acquired October 5, 2014, covers an area of 28 by 45 km, and is located at 21.3 degrees south, 25.4 degrees east. http://photojournal.jpl.nasa.gov/catalog/PIA20104

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Data mining in radiology

    PubMed Central

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

    2014-01-01

    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. PMID:25024513

  9. 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.

  10. 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…

  11. 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.

  12. Cross-country transferability of multi-variable damage models

    NASA Astrophysics Data System (ADS)

    Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

    2017-04-01

    Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

  13. 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

  14. 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

  15. Basics of Multivariate Analysis in Neuroimaging Data

    PubMed Central

    Habeck, Christian Georg

    2010-01-01

    Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic

  16. 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.

  17. 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

  18. Multivariate meta-analysis: Potential and promise

    PubMed Central

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

    2011-01-01

    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. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

  19. Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer

    SciTech Connect

    Hoffman, F.M.

    1999-06-28

    The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to affect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a 9-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message passing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone SouperComputer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented.

  20. Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer

    SciTech Connect

    Hargrove, W.W.; Hoffman, F.M.

    1999-06-28

    The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to aflect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a g-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message pawing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone Souper-Computer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. Development of a controlled vocabulary for semantic interoperability of mineral exploration geodata for mining projects

    NASA Astrophysics Data System (ADS)

    Ma, Xiaogang; Wu, Chonglong; Carranza, Emmanuel John M.; Schetselaar, Ernst M.; van der Meer, Freek D.; Liu, Gang; Wang, Xinqing; Zhang, Xialin

    2010-12-01

    Semantic interoperability of mineral exploration geodata is a long-term concern in mining projects. Inconsistent conceptual schemas and heterogeneous professional terms among various geodata sources in a mining project often hinder their efficient use and/or reuse. Our study of a controlled vocabulary focuses on interoperability of mineral exploration geodata of different mining projects of a mining group in China. In order to achieve this purpose, a proper representation of concepts and their inter-relationships in the knowledge domain of mineral exploration for mining projects is proposed. In addition, we propose that for wider interoperability of mining project geodata the controlled vocabulary underpinning them should be interoperable with concepts in related applications in the mineral exploration domain. In developing our controlled vocabulary, we adopted/adapted national standards of geosciences taxonomies and terminologies. The organization structure of terms, coding method, metadata schema for database applications and an extensible structure of our controlled vocabulary are discussed. The controlled vocabulary we developed was then used to reconcile heterogeneous geodata and to set up integrated databases for various mining projects of the mining group. Our study shows that a properly organized controlled vocabulary not only allows for efficient reconciliation of heterogeneous geodata sources in similar or related projects, but also makes related geodata to be interoperable with extramural applications in the same knowledge domain.

  6. 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.

  7. 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.

  8. National Underground Mines Inventory

    DTIC Science & Technology

    1983-10-01

    temperature is 134 0F. These high temperatures could prevent the use as shelter of over 50 percent of the habitable mine area unless cooled air is used...estimates of habitable area assume that some parts of wet mines are usable space. If a mine is reported to have 50 percent or more of its area dry, percent...habitable is computed as the proauct of percent intact and percent dry. If a mine is reported to have less than 50 percent of its area dry, percent

  9. 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.

  10. 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.

  11. Stability of Heterogeneous Ecosystem

    NASA Astrophysics Data System (ADS)

    Liu, Yang-Yu; Yan, Gang; Barabasi, Alber-Laszlo

    2014-03-01

    Stability of ecosystem measures the tendency of a community to return to equilibrium after environmental perturbation, which is severely constrained by the underlying network structure. Despite significant advances in uncovering the relationship between stability and network structure, little attention has been paid to the impact of the degree heterogeneity that exists in real ecosystems. Here we show that for networks with mixed interactions of competition and mutualism the degree heterogeneity always destabilizes the ecosystem. Surprisingly, for predator-prey interactions (e.g., food webs) high heterogeneity is destabilizing yet moderate heterogeneity is stabilizing. These findings deepen our understanding of the stability of real ecosystems and may also have implications in studying the stability of more general complex dynamical systems.

  12. 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.

  13. 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.

  14. Quantum Communication and Quantum Multivariate Polynomial Interpolation

    NASA Astrophysics Data System (ADS)

    Diep, Do Ngoc; Giang, Do Hoang

    2017-09-01

    The paper is devoted to the problem of multivariate polynomial interpolation and its application to quantum secret sharing. We show that using quantum Fourier transform one can produce the protocol for quantum secret sharing distribution.

  15. 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

  16. 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

  17. 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.

  18. Multivariate regional frequency analysis: Two new methods to increase the accuracy of measures

    NASA Astrophysics Data System (ADS)

    Abdi, Amin; Hassanzadeh, Yousef; Talatahari, Siamak; Fakheri-Fard, Ahmad; Mirabbasi, Rasoul; Ouarda, Taha B. M. J.

    2017-09-01

    The accurate detection of discordant sites in a heterogeneous region and the estimation of the regional parameters of a statistical distribution are two important issues in multivariate regional frequency analysis. In this study, two new methods are proposed for increasing the accuracy of the multivariate L-moment approach. The first one, the optimization-based method (OBM) is utilized to estimate the best distribution parameters. The second one is the rank-based method (RBM), which is used in the robust discordancy measure for identifying discordant sites. In order to assess the performance of the proposed approaches on the heterogeneity measure, real and simulated regions of drought characteristics are considered. The results confirm the usefulness of the new methods in comparison with some well-established techniques.

  19. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    PubMed Central

    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. PMID:27537692

  20. 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. © 2015 SETAC.

  1. 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.

  2. 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

  3. Mine wastes and human health

    USGS Publications Warehouse

    Plumlee, Geoffrey S.; Morman, Suzette A.

    2011-01-01

    Historical mining and mineral processing have been linked definitively to health problems resulting from occupational and environmental exposures to mine wastes. Modern mining and processing methods, when properly designed and implemented, prevent or greatly reduce potential environmental health impacts. However, particularly in developing countries, there are examples of health problems linked to recent mining. In other cases, recent mining has been blamed for health problems but no clear links have been found. The types and abundances of potential toxicants in mine wastes are predictably influenced by the geologic characteristics of the deposit being mined. Hence, Earth scientists can help understand, anticipate, and mitigate potential health issues associated with mining and mineral processing.

  4. 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

  5. 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.

  6. 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.

  7. Mining Glossary and Games.

    ERIC Educational Resources Information Center

    National Energy Foundation, Salt Lake City, UT.

    This booklet was produced in an effort to increase the awareness and appreciation of young people for the Earth's resources. The Mining Education Glossary is intended to provide easy reference to mining terms which are used in the minerals recovery industry and as a useful resource for teaching basic learning skills. Accompanying the glossary are…

  8. 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.

  9. 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.

  10. Mine reclamation in Arkansas

    Treesearch

    Floyd Durham; James G. Barnum

    1980-01-01

    Open cut mine land reclamation laws have only been effective since 1971 in Arkansas. Since that time all land affected by mining had to be reclaimed. To guarantee reclamation, the first law required a $500 per acre surety bond be posted with the Arkansas Department of Pollution Control and Ecology. The Arkansas Open Cut Land Reclamation Act of 1977 changed the bonding...

  11. 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.

  12. New mines of Kuzbass

    SciTech Connect

    Yalevsky, V.D.; Shimotyuk, V.D.

    1996-12-31

    The Kuznetsky coal basin (Kuzbass), with respect to coal quality, natural conditions of the coal seams occurrence in the majority of the coal-bearing area, and reserves, may be referred to one of the best in the world coal mining industry. Being located in the Southern part of Western Siberia the coal basin quite closely falls within the borderline of Kemerovo Region, which has population of 3.2 million people, This region is characteristic of highly developed industrial infrastructure providing 18% of industrial national income of Russia. Coal mining, ferrous metallurgy, and chemical industry are among basic industries in there. Kuzbass coal mining covers 36% of demand in entire Russia, and 66% of the demand for coking coals. Mining conditions are vary throughout the region. Geological reserves are evaluated about 700 billion ton. 25 billion ton reserves among them are thoroughly explored and developed for commercial mining, including 12 billion ton of coking coals.

  13. 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.

  14. Drakelands Mine, England

    NASA Image and Video Library

    2015-08-21

    The Drakelands Mine (previously known as the Hemerdon Mine) is a historic tungsten and tin mine located northeast of Plymouth, England. Tin and tungsten deposits were discovered in 1867, and the mine operated until 1944. Last year work started to re-open the mine, as it hosts the fourth-largest tungsten and tin deposits in the world. Tungsten has innumerable uses due to its incredible density and high melting temperature. Yet more than 80% of world supply is controlled by China, who has imposed restriction on export of the metal. The image covers an area of 17 by 18.9 km, was acquired June 5, 2013, and is located at 50.4 degrees north, 4 degrees west. http://photojournal.jpl.nasa.gov/catalog/PIA19757

  15. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-23

    College team members watch a live display of their mining robots during test runs in the mining arena at NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. will use their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  16. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    Team members from West Virginia University prepare their mining robot for a test run in a giant sandbox before their scheduled mining run in the arena during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  17. 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.

  18. 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

  19. 1. VIEW OF PHILLIPS MINE. CAMERA POINTED SOUTHEAST. SULLIVAN MINE ...

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

    1. VIEW OF PHILLIPS MINE. CAMERA POINTED SOUTHEAST. SULLIVAN MINE IS LOCATED ROUGHLY 75 YARDS BEYOND AND ROUGHLY IN LINE WITH THE SNOW ON THE RIGHT SIDE OF THE IMAGE. - Florida Mountain Mining Sites, Phillips Mine, East side of Florida Mountain, Silver City, Owyhee County, ID

  20. 1. VIEW OF SULLIVAN MINE ON RIGHT WITH PHILLIPS MINE ...

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

    1. VIEW OF SULLIVAN MINE ON RIGHT WITH PHILLIPS MINE LOCATED APPROXIMATELY 200 YARDS THROUGH TREES IN THE DIRECTION OF THE MOUND ON THE LEFT SIDE OF ROAD. CAMERA POINTING NORTH-NORTHEAST. - Florida Mountain Mining Sites, Sullivan Mine, East side of Florida Mountain, Silver City, Owyhee County, ID

  1. Heterogeneity in breast cancer.

    PubMed

    Polyak, Kornelia

    2011-10-01

    Breast cancer is a heterogeneous disease. There is a high degree of diversity between and within tumors as well as among cancer-bearing individuals, and all of these factors together determine the risk of disease progression and therapeutic resistance. Advances in technologies such as whole-genome sequencing and functional viability screens now allow us to analyze tumors at unprecedented depths. However, translating this increasing knowledge into clinical practice remains a challenge in part due to tumor evolution driven by the diversity of cancer cell populations and their microenvironment. The articles in this Review series discuss recent advances in our understanding of breast tumor heterogeneity, therapies tailored based on this knowledge, and future ways of assessing and treating heterogeneous tumors.

  2. Monocyte and macrophage heterogeneity.

    PubMed

    Gordon, Siamon; Taylor, Philip R

    2005-12-01

    Heterogeneity of the macrophage lineage has long been recognized and, in part, is a result of the specialization of tissue macrophages in particular microenvironments. Circulating monocytes give rise to mature macrophages and are also heterogeneous themselves, although the physiological relevance of this is not completely understood. However, as we discuss here, recent studies have shown that monocyte heterogeneity is conserved in humans and mice, allowing dissection of its functional relevance: the different monocyte subsets seem to reflect developmental stages with distinct physiological roles, such as recruitment to inflammatory lesions or entry to normal tissues. These advances in our understanding have implications for the development of therapeutic strategies that are targeted to modify particular subpopulations of monocytes.

  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. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. Small-for-gestational age prevalence risk factors in central Appalachian states with mountain-top mining.

    PubMed

    Ferdosi, Hamid; Lamm, Steve H; Afari-Dwamena, Nana Ama; Dissen, Elisabeth; Chen, Rusan; Li, Ji; Feinleib, Manning

    2017-09-27

    To identify risk factors for small-for-gestational age (SGA) for counties in central Appalachian states (Kentucky (KY), Tennessee (TN), Virginia (VA), and West Virginia (WV)) with varied coal mining activities. Live birth certificate files (1990-2002) were used for obtaining SGA prevalence rates for mothers based on the coal mining activities of their counties of residence, mountain-top mining (MTM) activities, underground mining activities but no mountain-top mining activity (non-MTM), or having no mining activities (non-mining). Co-variable information, including maternal tobacco use, was also obtained from the live birth certificate. Adjusted odds ratios were obtained using multivariable logistic regression comparing SGA prevalence rates for counties with coal mining activities to those without coal mining activities and comparing SGA prevalence rates for counties with coal mining activities for those with and without mountain-top mining activities. Comparisons were also made among those who had reported tobacco use and those who had not. Both tobacco use prevalence and SGA prevalence were significantly greater for mining counties than for non-mining counties and for MTM counties than for non-MTM counties. Adjustment for tobacco use alone explained 50% of the increased SGA risk for mining counties and 75% of the risk for MTM counties, including demographic pre-natal care co-variables that explained 75% of the increased SGA risk for mining counties and 100% of the risk for MTM. The increased risk of SGA was limited to the third trimester births among tobacco users and independent of the mining activities of their counties of residence. This study demonstrates that the increased prevalence of SGA among residents of counties with mining activity was primarily explained by the differences in maternal tobacco use prevalence, an effect that itself was gestational-age dependent. Self-reported tobacco use marked the population at the increased risk for SGA in central

  14. 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.

  15. 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.

  16. 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…

  17. 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…

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Why does heterogeneity matter?

    Treesearch

    K.B. Pierce

    2007-01-01

    This is a review of the book "Ecosystem function in heterogeneous landscapes" published in 2005. The authors are G. Lovett, C. Jones, M.G. Turner, and K.C. Weathers. It was published by Springer, New York. The book is a synthesis of the 10th Gary conference held at the Institute of Ecosystem Studies in Millbrook, New York, in 2003.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. Query-Based Outlier Detection in Heterogeneous Information Networks

    PubMed Central

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

    2015-01-01

    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. PMID:27064397

  10. 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.

  11. Multivariate Time Series Decomposition into Oscillation Components.

    PubMed

    Matsuda, Takeru; Komaki, Fumiyasu

    2017-08-01

    Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

  12. 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.

  13. Multivariate And Phylogenetic Analyses Of Galaxies

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, Didier; Chattopadhyay, Tanuka; D'Onofrio, Mauro; Marziani, Paula; Mondal, Saptarshi

    2017-06-01

    Investigating the formation and evolution of galaxies is becoming a complicated process with the increased availability of huge databases as a result of instrumental improvements. In this poster we present preliminary results on two statistical studies using multivariate partitioning and cladistic analyses to find homogeneous groups and their evolutionary relationships.

  14. 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...

  15. 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...

  16. 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,…

  17. Some Recent Advances in Multivariate Polynomial Interpolation

    NASA Astrophysics Data System (ADS)

    Carnicer, J. M.; Gasca, M.

    2007-09-01

    Multivariate polynomial interpolation has received much attention in the last part of the 20th century. In this talk we comment on some recent advances in the last decade, with special emphasis in distributions of points which give rise to unisolvent (or poised) problems in the space of polynomials of a given total degree and simple interpolation formulae.

  18. Multivariate polynomial interpolation under projectivities part I

    NASA Astrophysics Data System (ADS)

    Mühlbach, G.; Gasca, M.

    1991-10-01

    In this note interpolation by real polynomials of several real variables is treated. Existence and unicity of the interpolant for knot systems being the perspective images of certain regular knot systems is discussed. Moreover, for such systems a Newton interpolation formula is derived allowing a recursive computation of the interpolant via multivariate divided differences. A numerical example is given.

  19. Multivariate polynomial interpolation under projectivities III

    NASA Astrophysics Data System (ADS)

    Mühlbach, G.; Gasca, M.

    1994-03-01

    This is the third part of a note on multivariate interpolation. Some remainder formulas for interpolation on knot sets that are perspective images of standard lower data sets are given. They apply to all knot systems considered in parts I and II.

  20. On the history of multivariate polynomial interpolation

    NASA Astrophysics Data System (ADS)

    Gasca, Mariano; Sauer, Thomas

    2000-10-01

    Multivariate polynomial interpolation is a basic and fundamental subject in Approximation Theory and Numerical Analysis, which has received and continues receiving not deep but constant attention. In this short survey, we review its development in the first 75 years of this century, including a pioneering paper by Kronecker in the 19th century.

  1. 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

  2. Multivariate analysis: greater insights into complex systems

    USDA-ARS?s Scientific Manuscript database

    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 ...

  3. 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.

  4. 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.

  5. 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.

  6. Minerals and mine drainage

    SciTech Connect

    Thomson, B.M.; Turney, W.R. |

    1995-06-01

    This paper briefly lists the various literature reviews dealing with (a) Environmental regulations and impacts, and (b) Characterization, prevention, treatment and reclamation, with respect to minerals and mine drainage. 47 refs.

  7. Minerals and mine drainage

    SciTech Connect

    Thomson, B.M.; Turney, W.R.

    1996-11-01

    This paper provides a review of literature published in 1995 on the subject of wastewater related to minerals and mine drainage. Topics covered include: environmental regulations and impacts; and characterization, prevention, treatment and reclamation. 65 refs.

  8. 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.

  9. Urban Combat Data Mining

    DTIC Science & Technology

    2004-12-01

    We describe an approach and its implementation involving simulation and data mining for improved understanding of the potential relationships among battle parameters and battle outcomes in an urban setting.

  10. Diavik Mine, Canada

    NASA Image and Video Library

    2017-04-13

    This image from NASA Terra spacecraft shows the Diavik Mine in northern Canada.The largest diamond found in North America came from the Diavik Mine. The Foxfire diamond weighs an impressive 187 carats, and was discovered in August 2015; it has been displayed in several museums throughout North America. The Diavik mine is located on an island in Lac de Gras, within the Lac de Gras kimberlite field, among other diamond mines. The image was acquired September 23, 2016, covers an area of 13.8 by 19.4 km, and is located at 64.5 degrees north, 110.2 degrees west. https://photojournal.jpl.nasa.gov/catalog/PIA21536

  11. Lithium Mining, Nevada

    NASA Image and Video Library

    2014-08-05

    This image from NASA Terra spacecraft shows the once-abandoned mining town of Silver Peak, Nevada, which began to thrive again when Foote Mineral Company began extracting lithium from brine below the floor of Clayton Valley in 1966.

  12. Goldstrike Mine, Nevada

    NASA Image and Video Library

    2017-05-15

    This image from NASA Terra spacecraft shows Goldstrike in northeast Nevada, the largest gold mine in North America. The mine complex, (including the Betze-Post-Screamer open-pit, and Meikle and Rodeo underground mines) is owned and operated by the world's largest gold mining company, Barrick Gold. Gold occurs as microscopically fine grains, with an average grade of 0.1 ounces per ton of ore. Estimates of reserves are as high as 35 million ounces of gold. The image was acquired September 25, 2010, covers an area of 15 by 15 km, and is located at 41 degrees north, 116.4 degrees west. https://photojournal.jpl.nasa.gov/catalog/PIA21665

  13. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    A robotic miner digs in the mining arena during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  14. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-23

    College team members prepare to enter the robotic mining arena for a test run during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. will use their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  15. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    The robotic miner from Mississippi State University digs in the mining arena during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  16. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-23

    Team Raptor members from the University of North Dakota College of Engineering and Mines check their robot, named "Marsbot," in the RoboPit at NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. will use their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  17. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    Twin mining robots from the University of Iowa dig in a supersized sandbox filled with BP-1, or simulated Martian soil, during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  18. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    Team members from the New York University Tandon School of Engineering transport their robot to the mining arena during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  19. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    Derrick Matthews, left, with Kennedy Space Center's Communication and Public Engagement Directorate, and Kurt Leucht, event emcee, provide commentary at the mining arena during NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. are using their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  20. 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

  1. Mine Waste at The Kherzet Youcef Mine : Environmental Characterization

    NASA Astrophysics Data System (ADS)

    Issaad, Mouloud; Boutaleb, Abdelhak; Kolli, Omar

    2017-04-01

    Mining activity in Algeria has existed since antiquity. But it was very important since the 20th century. This activity has virtually ceased since the beginning of the 1990s, leaving many mine sites abandoned (so-called orphan mines). The abandonment of mining today poses many environmental problems (soil pollution, contamination of surface water, mining collapses...). The mining wastes often occupy large volumes that can be hazardous to the environment and human health, often neglected in the past: Faulting geotechnical implementation, acid mine drainage (AMD), alkalinity, presence of pollutants and toxic substances (heavy metals, cyanide...). The study started already six years ago and it covers all mines located in NE Algeria, almost are stopped for more than thirty years. So the most important is to have an overview of all the study area. After the inventory job of the abandoned mines, the rock drainage prediction will help us to classify sites according to their acid generating potential.

  2. 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.

  3. Land Mines (Landminen)

    DTIC Science & Technology

    1978-02-02

    Probably the most sensitive area of an armored vehicle at that time was the track. Therefore, all countries endeavored to design prepared charges...of mines, for instance through improved explosives and the shaped charge principle, the basic principle of the funcion and design of antitank mines...dispersed over a large area . In most cases, the fragments are lethal up to adistance of 10 m and beyond, and cause serious injuries even at a distance of

  4. 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).

  5. 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.

  6. Identification of Multivariate Time Series and Multivariate Input-Output Models

    NASA Astrophysics Data System (ADS)

    Cooper, David M.; Wood, Eric F.

    1982-08-01

    The problem of linear model structure identification for multivariate time series or multiple input-output models is presented and solved. The identification is obtained using canonical correlations to determine model order. The equivalence between state-space model structure and multivariate autoregressive moving average with exogenous inputs (ARMAX) models is presented. The class of models open to analysis includes rainfall-runoff models, multivariate streamflow models, and time invariant state-space models used in Kaiman filtering. Examples include a rainfall-runoff model using three precipitation inputs, a four-site monthly streamflow model, and a four-season streamflow model.

  7. 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.

  8. Heterogeneity of monoclonal antibodies.

    PubMed

    Liu, Hongcheng; Gaza-Bulseco, Georgeen; Faldu, Dinesh; Chumsae, Chris; Sun, Joanne

    2008-07-01

    Heterogeneity of monoclonal antibodies is common due to the various modifications introduced over the lifespan of the molecules from the point of synthesis to the point of complete clearance from the subjects. The vast number of modifications presents great challenge to the thorough characterization of the molecules. This article reviews the current knowledge of enzymatic and nonenzymatic modifications of monoclonal antibodies including the common ones such as incomplete disulfide bond formation, glycosylation, N-terminal pyroglutamine cyclization, C-terminal lysine processing, deamidation, isomerization, and oxidation, and less common ones such as modification of the N-terminal amino acids by maleuric acid and amidation of the C-terminal amino acid. In addition, noncovalent associations with other molecules, conformational diversity and aggregation of monoclonal antibodies are also discussed. Through a complete understanding of the heterogeneity of monoclonal antibodies, strategies can be employed to better identify the potential modifications and thoroughly characterize the molecules.

  9. 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.

  10. 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

  11. CAERs's mine mapping program and Kentucky's mine mapping initiative

    SciTech Connect

    Hiett, J.

    2007-07-01

    Since 1884 the Kentucky Department of Mines and Minerals (KDMM now OMSL) has had a mine mapping function as it relates to mine safety. The CAER's Mine Mapping Program has provided this service to that agency since 1972. The program has been in continuous operation under the current staff and management over that period. Functions include operating the Mine Map Repository/Mine Map Information Center of the OMSL; and receiving and processing all annual coal mine license maps, old maps, and related data. The Kentucky Mine Mapping Initiative's goal is to ensure that every underground and surface mine map in Kentucky is located, digitized and online. The Kentucky mine mapping website plays a vital role in the safety of Kentuckians. The purpose of the web service is to make available electronic maps of mined out areas and approximately 32,000 engineering drawings of operating or closed mines that are located in the state. Future phases of the project will include the archival scanning of all submitted mine maps; the recovery from outside sources of maps that were destroyed in a 1948 fire; and the development of further technology to process maps and related data. 7 photos.

  12. Bioaccessibility of arsenic in mine waste-contaminated soils: a case study from an abandoned arsenic mine in SW England (UK).

    PubMed

    Palumbo-Roe, Barbara; Klinck, Ben

    2007-07-15

    This study characterises the total As concentrations and As bioaccessibility in 109 soils from Devon Great Consols Mine, an abandoned Cu-As mine in Devon, SW England, UK and discusses the soil and mineralogical factors that influence the bioaccessibility of this element. These data provide the basis for developing more accurate exposure estimates for use in human health risk assessments. The median value of the percent bioaccesible As of 15% for these As rich soils contaminated by mining activities indicated that relatively little of the total As is present in a bioaccessible form. Spatial variability of As bioaccesibility in the soils was also recognised throughout the mine site as a function of mineralogy. Multivariate statistical analysis identified a sulphide component responsible for the reduced As bioaccessibility of one cluster of soils. In the larger cluster of acidic mine soils covered by woodland As is mainly hosted in Fe oxyhydroxides whose partial dissolution is responsible for the bioaccessible As fraction. It was highlighted that the degree of Fe oxyhydroxide crystallinity might represent an important factor influencing arsenic bioaccessibility. Mine soils from Devon Great Consols Mine showed overall higher As bioaccessibility (15%) than other mineralised soils not affected by mining activities and background soils within the Tamar Catchment whose percent bioaccessible As median values were 9%.

  13. 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.

  14. Transfer entropy between multivariate time series

    NASA Astrophysics Data System (ADS)

    Mao, Xuegeng; Shang, Pengjian

    2017-06-01

    It is a crucial topic to identify the direction and strength of the interdependence between time series in multivariate systems. In this paper, we propose the method of transfer entropy based on the theory of time-delay reconstruction of a phase space, which is a model-free approach to detect causalities in multivariate time series. This method overcomes the limitation that original transfer entropy only can capture which system drives the transition probabilities of another in scalar time series. Using artificial time series, we show that the driving character is obviously reflected with the increase of the coupling strength between two signals and confirm the effectiveness of the method with noise added. Furthermore, we utilize it to real-world data, namely financial time series, in order to characterize the information flow among different stocks.

  15. 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.

  16. Multivariate analysis of endometrial tissue fluorescence spectra

    NASA Astrophysics Data System (ADS)

    Vaitkuviene, Aurelija; Auksorius, E.; Fuchs, D.; Gavriushin, V.

    2002-10-01

    Background and Objective: The detailed multivariate analysis of endometrial tissue fluorescence spectra was done. Spectra underlying features and classification algorithm were analyzed. An effort has been made to determine the importance of neopterin component in endometrial premalignization. Study Design/Materials and Methods: Biomedical tissue fluorescence was measured by excitation with the Nd YAG laser third harmonic. Multivariate analysis techniques were used to analyze fluorescence spectra. Biomedical optics group at Vilnius University analyzed the neopterin substance supplied by the Institute of Medical Chemistry and Biochemistry of Innsbruck University. Results: Seven statistically significant spectral compounds were found. The classification algorithm classifying samples to histopathological categories was developed and resulted in sensitivity of 80% and specificity 93% for malignant vs. hyperplastic and normal. Conclusions: Fluorescence spectra could be classified with high accuracy. Spectral variation underlying features can be extracted. Neopterin component might play an important role in endometrial hyperplasia development.

  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. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Underground mine communications: a survey

    SciTech Connect

    Yarkan, S.; Guzelgoz, S.; Arslan, H.; Murphy, R.R.

    2009-07-01

    After a recent series of unfortunate underground mining disasters, the vital importance of communications for underground mining is underlined one more time. Establishing reliable communication is a very difficult task for underground mining due to the extreme environmental conditions. Until now, no single communication system exists which can solve all of the problems and difficulties encountered in underground mine communications. However, combining research with previous experiences might help existing systems improve, if not completely solve all of the problems. In this survey, underground mine communication is investigated. Major issues which underground mine communication systems must take into account are discussed. Communication types, methods, and their significance are presented.

  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. 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

  6. MvDAT: Multivariate Dependence Analysis Toolbox

    NASA Astrophysics Data System (ADS)

    Sadegh, M.; Ragno, E.; AghaKouchak, A.

    2016-12-01

    Hydrologic and climatic variables are interdependent, and it is often necessary to analyze association among variables using multivariate methods. Univariate marginal distributions may not be sufficient to describe hydrologic variables (or events) that bear intrinsic multivariate characteristics. The concept of copula is widely used to model the dependence structure of two (or more) random variables. Multivariate methods and copulas have been used in drought monitoring, frequency analysis, and extreme value analysis, among others. Here, we present a newly developed MultiVariate Dependence Analysis Toolbox (MvDAT) for assessing the dependence structure of target variables using 26 copulas. Copulas included in MvDAT differ in complexity with one to three tunable parameters. The Graphical User Interface (GUI) of this program enables users to conveniently browse the input data, select the desired copula family (one, multiple, or all), and finally choose the optimization approach (local/global) for dependence analysis. The program will automatically plot posterior parameter distributions of selected copula(s), if global optimization is selected, as well as fitted versus empirical probability isolines. Moreover, a summary report is automatically generated that rank the performance of selected copulas based on Maximum Likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Summary report also details on the best and 95% uncertainty ranges of parameters of each copula, and its best performance in terms of root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) criteria. This package is developed in MATLAB and enables the community to perform dependence analysis using a more rigorous and comprehensive approach.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. Assessing causality in multivariate accident models.

    PubMed

    Elvik, Rune

    2011-01-01

    This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality. Copyright © 2010 Elsevier Ltd. All rights reserved.

  12. PYCHEM: a multivariate analysis package for python.

    PubMed

    Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

    2006-10-15

    We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

  13. 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.

  14. Multivariable geostatistics in S: the gstat package

    NASA Astrophysics Data System (ADS)

    Pebesma, Edzer J.

    2004-08-01

    This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simultaneous fitting, and visualisation of a large number of direct and cross (residual) variograms very easy. Gstat was started 10 years ago and was released under the GPL in 1996; gstat.org was started in 1998. Gstat was not initially written for teaching purposes, but for research purposes, emphasising flexibility, scalability and portability. It can deal with a large number of practical issues in geostatistics, including change of support (block kriging), simple/ordinary/universal (co)kriging, fast local neighbourhood selection, flexible trend modelling, variables with different sampling configurations, and efficient simulation of large spatially correlated random fields, indicator kriging and simulation, and (directional) variogram and cross variogram modelling. The formula/models interface of the S language is used to define multivariable geostatistical models. This paper introduces the gstat S package, and discusses a number of design and implementation issues. It also draws attention to a number of papers on integration of spatial statistics software, GIS and the S environment that were presented on the spatial statistics workshop and sessions during the conference Distributed Statistical Computing 2003.

  15. Simultaneous confidence regions for multivariate bioequivalence.

    PubMed

    Pallmann, Philip; Jaki, Thomas

    2017-08-30

    Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available. Copyright © 2017 John Wiley & Sons, Ltd.

  16. 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.

  17. 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…

  18. 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].

  19. 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.

  20. 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.

  1. 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.

  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. 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

  4. 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)

  5. Mine drainage and surface-mine reclamation. Volume 2. Mine reclamation, abandoned mine lands, and policy issues. Information Circular/1988

    SciTech Connect

    Not Available

    1988-01-01

    Mine waste and mine reclamation are topics of major interest to the mining industry, the government and the general public. The publication and its companion volume are the proceedings of a conference held in Pittsburgh, April 19-21, 1988. There were nine sessions (50 papers) that dealt with the geochemistry, hydrology and problems of mine waste and mine water, especially acid mine drainage. The nine sessions (43 papers) that dealt with reclamation and restoration of disturbed lands, as well as related policy issues, are included in volume 2. Volume 2 also contains the ten papers that pertained to control of subsidence and mine fires at abandoned mines. Poster session presentations are, in general, represented by abstracts.

  6. 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)

  7. Minerals and mine drainage

    SciTech Connect

    Liang, H.C.; Thomson, B.M.

    2009-09-15

    A review of literature published in 2008 and early 2009 on research related to the production of acid mine drainage and/or in the dissolution of minerals as a result of mining, with special emphasis on the effects of these phenomena on the water quality in the surrounding environment, is presented. This review is divided into six sections: 1) Site Characterization and Assessment, 2) Protection, Prevention, and Restoration, 3) Toxicity Assessment, 4) Environmental Fate and Transport, 5) Biological Characterization, and 6) Treatment Technologies. Because there is much overlap in research areas associated with minerals and mine drainage, many papers presented in this review can be classified into more than one category, and the six sections should not be regarded as being mutually-exclusive, nor should they be thought of as being all-inclusive.

  8. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-24

    Energy levels are high in the RoboPit as teams prepare for NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. arel using their mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  9. 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.

  10. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-23

    Team members from Purdue University prepare their uniquely-designed robot miner in the RoboPit at NASA's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. will use their uniquely-designed mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  11. 2017 Robotic Mining Competition

    NASA Image and Video Library

    2017-05-23

    NASA Kennedy Space Center Director Bob Cabana welcomes participants to the agency's 8th Annual Robotic Mining Competition at the Kennedy Space Center Visitor Complex in Florida. More than 40 student teams from colleges and universities around the U.S. will use their mining robots to dig in a supersized sandbox filled with BP-1, or simulated Martian soil, and participate in other competition requirements. The Robotic Mining Competition is a NASA Human Exploration and Operations Mission Directorate project designed to encourage students in science, technology, engineering and math, or STEM fields. The project provides a competitive environment to foster innovative ideas and solutions that could be used on NASA's Journey to Mars.

  12. Intratumor Heterogeneity in Breast Cancer.

    PubMed

    Beca, Francisco; Polyak, Kornelia

    2016-01-01

    Intratumor heterogeneity is the main obstacle to effective cancer treatment and personalized medicine. Both genetic and epigenetic sources of intratumor heterogeneity are well recognized and several technologies have been developed for their characterization. With the technological advances in recent years, investigators are now elucidating intratumor heterogeneity at the single cell level and in situ. However, translating the accumulated knowledge about intratumor heterogeneity to clinical practice has been slow. We are certain that better understanding of the composition and evolution of tumors during disease progression and treatment will improve cancer diagnosis and the design of therapies. Here we review some of the most important considerations related to intratumor heterogeneity. We discuss both genetic and epigenetic sources of intratumor heterogeneity and review experimental approaches that are commonly used to quantify it. We also discuss the impact of intratumor heterogeneity on cancer diagnosis and treatment and share our perspectives on the future of this field.

  13. 30 CFR 75.373 - Reopening mines.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Reopening mines. 75.373 Section 75.373 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.373 Reopening mines. After a mine is abandoned...

  14. 30 CFR 75.373 - Reopening mines.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Reopening mines. 75.373 Section 75.373 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.373 Reopening mines. After a mine is abandoned...

  15. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Mine map. 77.1200 Section 77.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Maps § 77.1200 Mine...

  16. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Mine map. 77.1200 Section 77.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Maps § 77.1200 Mine...

  17. 30 CFR 75.373 - Reopening mines.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Reopening mines. 75.373 Section 75.373 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.373 Reopening mines. After a mine is abandoned...

  18. 30 CFR 75.373 - Reopening mines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Reopening mines. 75.373 Section 75.373 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.373 Reopening mines. After a mine is abandoned...

  19. 30 CFR 75.373 - Reopening mines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Reopening mines. 75.373 Section 75.373 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.373 Reopening mines. After a mine is abandoned...

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

    PubMed Central

    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

  1. 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

  2. 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).

  3. 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.

  4. Chiquicamata Mine, Chile

    NASA Image and Video Library

    2016-08-24

    Chuquicamata, in Chile's Atacama Desert, is the largest open pit copper mine in the world, by excavated volume. The copper deposits were first exploited in pre-Hispanic times. Open pit mining began in the early 20th century when a method was developed to work low grade oxidized copper ores. The image was acquired September 2, 2007, covers an area of 19.5 by 29.3 km, and is located at 22.1 degrees south, 68.9 degrees west. http://photojournal.jpl.nasa.gov/catalog/PIA20973

  5. 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.

  6. Heterogeneity in tuberculosis.

    PubMed

    Cadena, Anthony M; Fortune, Sarah M; Flynn, JoAnne L

    2017-07-24

    Infection with Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), results in a range of clinical presentations in humans. Most infections manifest as a clinically asymptomatic, contained state that is termed latent TB infection (LTBI); a smaller subset of infected individuals present with symptomatic, active TB. Within these two seemingly binary states, there is a spectrum of host outcomes that have varying symptoms, microbiologies, immune responses and pathologies. Recently, it has become apparent that there is diversity of infection even within a single individual. A good understanding of the heterogeneity that is intrinsic to TB - at both the population level and the individual level - is crucial to inform the development of intervention strategies that account for and target the unique, complex and independent nature of the local host-pathogen interactions that occur in this infection. In this Review, we draw on model systems and human data to discuss multiple facets of TB biology and their relationship to the overall heterogeneity observed in the human disease.

  7. Heterogeneity of reactive astrocytes

    PubMed Central

    Anderson, Mark A.; Ao, Yan; Sofroniew, Michael V.

    2014-01-01

    Astrocytes respond to injury and disease in the central nervous system (CNS) with a process referred to as reactive astrogliosis. Recent progress demonstrates that reactive astrogliosis is not a simple all-or-none phenomenon, but is a finely gradated continuum of changes that range from reversible alterations in gene expression and cell hypertrophy, to scar formation with permanent tissue rearrangement. There is now compelling evidence that reactive astrocytes exhibit a substantial potential for heterogeneity at multiple levels, including gene expression, cell morphology, topography (distance from lesions), CNS regions, local (among neighboring cells), cell signaling and cell function. Structural and functional changes are regulated in reactive astrocytes by many different potential signaling events that occur in a context dependent manner. It is noteworthy that different stimuli of astrocyte reactivity can lead to similar degrees of GFAP upregulation while causing substantially different changes in transcriptome profiles and cell function. Thus, it is not possible to equate simple and uniform measures such as cell hypertrophy and upregulation of GFAP expression with a single, uniform concept of astrocyte reactivity. Instead, it is necessary to recognize the considerable potential for heterogeneity and determine the functional implications of astrocyte reactivity in a context specific manner as regulated by specific signaling events. PMID:24361547

  8. Heterogeneous photonic integrated circuits

    NASA Astrophysics Data System (ADS)

    Fang, Alexander W.; Fish, Gregory; Hall, Eric

    2012-01-01

    Photonic Integrated Circuits (PICs) have been dichotomized into circuits with high passive content (silica and silicon PLCs) and high active content (InP tunable lasers and transceivers) due to the trade-off in material characteristics used within these two classes. This has led to restrictions in the adoption of PICs to systems in which only one of the two classes of circuits are required to be made on a singular chip. Much work has been done to create convergence in these two classes by either engineering the materials to achieve the functionality of both device types on a single platform, or in epitaxial growth techniques to transfer one material to the next, but have yet to demonstrate performance equal to that of components fabricated in their native substrates. Advances in waferbonding techniques have led to a new class of heterogeneously integrated photonic circuits that allow for the concurrent use of active and passive materials within a photonic circuit, realizing components on a transferred substrate that have equivalent performance as their native substrate. In this talk, we review and compare advances made in heterogeneous integration along with demonstrations of components and circuits enabled by this technology.

  9. 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....

  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. Stress distribution around mine workings

    NASA Astrophysics Data System (ADS)

    Gaidachuk, V. V.; Koshel', V. I.; Lugovoi, P. Z.

    2011-02-01

    The finite-element method is used to determine the stress state around arched mine workings in a mineral bed. Both presence and dip of the mineral bed have a strong effect on the stress state around the mine working. Recommendations for design of mine-working support are formulated

  12. 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)

  13. 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.

  14. 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....

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 76 FR 54163 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-31

    ... Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health Administration, Labor. ACTION... proposing to require underground coal mine operators to equip continuous mining machines (except full-face continuous mining machines) with proximity detection systems. Miners working near continuous mining...

  1. 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...

  2. 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.

  3. 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

  4. Mixtures of multivariate power exponential distributions.

    PubMed

    Dang, Utkarsh J; Browne, Ryan P; McNicholas, Paul D

    2015-12-01

    An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness have received much attention in the model-based clustering literature recently, we investigate the use of a distribution that can deal with both varying tail-weight and peakedness of data. A family of parsimonious models is proposed using an eigen-decomposition of the scale matrix. A generalized expectation-maximization algorithm is presented that combines convex optimization via a minorization-maximization approach and optimization based on accelerated line search algorithms on the Stiefel manifold. Lastly, the utility of this family of models is illustrated using both toy and benchmark data.

  5. 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.

  6. 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.

  7. Bayesian Transformation Models for Multivariate Survival Data

    PubMed Central

    DE CASTRO, MÁRIO; CHEN, MING-HUI; IBRAHIM, JOSEPH G.; KLEIN, JOHN P.

    2014-01-01

    In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported. PMID:24904194

  8. Multivariate cubic spline smoothing in multiple prediction.

    PubMed

    Khamis, Harry; Kepler, Michael

    2002-02-01

    Given longitudinal data for several variables, including a given outcome variable, it is desired to predict the outcome for a specific individual, or more generally experimental unit, in such a way that the predicted value is both accurate and resistant (i.e. has good cross-validation). There are certain data-analytic difficulties associated with long-term multivariate longitudinal data that must be overcome in the prediction process. This paper provides a program written in the Statistical Analysis System (SAS) programming language, based generally on the Roche-Wainer-Thissen stature prediction model, that enables the researcher to overcome these difficulties.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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…

  15. Mining Your Own Data

    NASA Astrophysics Data System (ADS)

    Clark, Maurice

    2014-05-01

    Conducting asteroid photometry frequently requires imaging one area of the sky for many hours. Apart from the asteroid being studied, there may be many other objects of interest buried in the data. The value of mining your own asteroid data is discussed, using examples from observations made by the author, primarily at the Preston Gott Observatory at Texas Tech University.

  16. 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.

  17. 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…

  18. 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].

  19. Naica Mine, Chihuahua, Mexico

    NASA Image and Video Library

    2007-10-02

    The Naica mine in Chihuahua, Mexico, with its enormous gypsum crystals, may well be called the "Queen of the Giant Crystals localities." Though the Naica mine is no show mine, but still a working lead-zinc mine hosted in layered limestones, the first of several crystal caves was discovered in 1910. This "Cave of the Swords" contained extraordinary large sword-like selenite (gypsum) crystals up to 2 m long. In 2000 another crystal cave system was discovered at 300 m depth, even more spectacular than the original cave. Inside were free growing gypsum crystals up to 12 m long and 2 m in diameter. The ASTER image uses SWIR bands 4, 6, and 8 in RGB. Limestone is displayed in yellow-green colors, vegetation is red. The image was acquired February 16, 2004, covers an area of 26 x 23.5 km, and is located near 27.8 degrees north latitude, 105.5 degrees west longitude. The photo of crystals was taken from: http://www.thatcrystalsite.com/. http://photojournal.jpl.nasa.gov/catalog/PIA10615

  20. 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.

  1. 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.

  2. 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,…

  3. LPV decoupling for control of multivariable systems

    NASA Astrophysics Data System (ADS)

    Mohammadpour, Javad; Grigoriadis, Karolos; Franchek, Matthew; Wang, Yue-Yun; Haskara, Ibrahim

    2011-08-01

    This article investigates methods for decoupling multivariable linear parameter varying (LPV) systems and proposes a new interaction measure for decoupled proportional-integral (PI) feedback control design in LPV systems. The proposed approach seeks to benefit the multivariable control of multi-input multi-output (MIMO) systems with variable operating conditions, variable parameters or nonlinear behaviour. This method can improve the tracking performance and reduce the operating conditions variability of such systems with significant coupling in the system dynamics. We design MIMO decoupling feedback LPV controllers to address loop interaction effects. The proposed method uses a parameter-dependent static inversion or SVD decomposition of the system to minimise the effects of the off-diagonal terms in the MIMO system transfer function matrix. A new parameter-dependent interaction measure is introduced based on the SVD decomposition and static inversion which is subsequently utilised for tuning multi-loop PI controller gains. Numerical examples are presented to illustrate the validity of the proposed LPV decoupling methods, as well as the use of the proposed interaction measures for a decoupled multi-loop PI control design.

  4. 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

  5. Estimating uncertainty in multivariate responses to selection.

    PubMed

    Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W

    2014-04-01

    Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses.

  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. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Tau identification using multivariate techniques in ATLAS

    NASA Astrophysics Data System (ADS)

    O'Neil, D. C.; ATLAS Collaboration

    2012-06-01

    Tau leptons play an important role in the physics program of the LHC. They are being used in electroweak measurements, in detector related studies and in searches for new phenomena like the Higgs boson or Supersymmetry. In the detector, tau leptons are reconstructed as collimated jets with low track multiplicity. Due to the background from QCD multijet processes, efficient tau identification techniques with large fake rejection are essential. Since single variable criteria are not enough to efficiently separate them from jets and electrons, modern multivariate techniques are used. In ATLAS, several advanced algorithms are applied to identify taus, including a projective likelihood estimator and boosted decision trees. All multivariate methods applied to the ATLAS simulated data perform better than the baseline cut analysis. Their performance is shown using high energy data collected at the ATLAS experiment. The improvement ranges from a factor of 2 to 5 in rejection for the same efficiency, depending on the selected efficiency operating point and the number of prongs in the tau decay. The strengths and weaknesses of each technique are also discussed.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Population heterogeneity and causal inference.

    PubMed

    Xie, Yu

    2013-04-16

    Population heterogeneity is ubiquitous in social science. The very objective of social science research is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. Researchers have long been concerned with two potential sources of bias. The first is bias in unobserved pretreatment factors affecting the outcome even in the absence of treatment. The second is bias due to heterogeneity in treatment effects. In this article, I show how "composition bias" due to population heterogeneity evolves over time when treatment propensity is systematically associated with heterogeneous treatment effects. A form of selection bias, composition bias, arises dynamically at the aggregate level even when the classic assumption of ignorability holds true at the microlevel.

  1. Population heterogeneity and causal inference

    PubMed Central

    Xie, Yu

    2013-01-01

    Population heterogeneity is ubiquitous in social science. The very objective of social science research is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. Researchers have long been concerned with two potential sources of bias. The first is bias in unobserved pretreatment factors affecting the outcome even in the absence of treatment. The second is bias due to heterogeneity in treatment effects. In this article, I show how “composition bias” due to population heterogeneity evolves over time when treatment propensity is systematically associated with heterogeneous treatment effects. A form of selection bias, composition bias, arises dynamically at the aggregate level even when the classic assumption of ignorability holds true at the microlevel. PMID:23530202

  2. 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

  3. 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.

  4. Ecosystem Health Assessment of Mining Cities Based on Landscape Pattern

    NASA Astrophysics Data System (ADS)

    Yu, W.; Liu, Y.; Lin, M.; Fang, F.; Xiao, R.

    2017-09-01

    Ecosystem health assessment (EHA) is one of the most important aspects in ecosystem management. Nowadays, ecological environment of mining cities is facing various problems. In this study, through ecosystem health theory and remote sensing images in 2005, 2009 and 2013, landscape pattern analysis and Vigor-Organization-Resilience (VOR) model were applied to set up an evaluation index system of ecosystem health of mining city to assess the healthy level of ecosystem in Panji District Huainan city. Results showed a temporal stable but high spatial heterogeneity landscape pattern during 2005-2013. According to the regional ecosystem health index, it experienced a rapid decline after a slight increase, and finally it maintained at an ordinary level. Among these areas, a significant distinction was presented in different towns. It indicates that the ecosystem health of Tianjijiedao town, the regional administrative centre, descended rapidly during the study period, and turned into the worst level in the study area. While the Hetuan Town, located in the northwestern suburb area of Panji District, stayed on a relatively better level than other towns. The impacts of coal mining collapse area, land reclamation on the landscape pattern and ecosystem health status of mining cities were also discussed. As a result of underground coal mining, land subsidence has become an inevitable problem in the study area. In addition, the coal mining subsidence area has brought about the destruction of the farmland, construction land and water bodies, which causing the change of the regional landscape pattern and making the evaluation of ecosystem health in mining area more difficult. Therefore, this study provided an ecosystem health approach for relevant departments to make scientific decisions.

  5. Ventilation heterogeneity in obesity.

    PubMed

    Pellegrino, Riccardo; Gobbi, Alessandro; Antonelli, Andrea; Torchio, Roberto; Gulotta, Carlo; Pellegrino, Giulia Michela; Dellacà, Raffaele; Hyatt, Robert E; Brusasco, Vito

    2014-05-01

    Obesity is associated with important decrements in lung volumes. Despite this, ventilation remains normally or near normally distributed at least for moderate decrements in functional residual capacity (FRC). We tested the hypothesis that this is because maximum flow increases presumably as a result of an increased lung elastic recoil. Forced expiratory flows corrected for thoracic gas compression volume, lung volumes, and forced oscillation technique at 5-11-19 Hz were measured in 133 healthy subjects with a body mass index (BMI) ranging from 18 to 50 kg/m(2). Short-term temporal variability of ventilation heterogeneity was estimated from the interquartile range of the frequency distribution of the difference in inspiratory resistance between 5 and 19 Hz (R5-19_IQR). FRC % predicted negatively correlated with BMI (r = -0.72, P < 0.001) and with an increase in slope of either maximal (r = -0.34, P < 0.01) or partial flow-volume curves (r = -0.30, P < 0.01). Together with a slight decrease in residual volume, this suggests an increased lung elastic recoil. Regression analysis of R5-19_IQR against FRC % predicted and expiratory reserve volume (ERV) yielded significantly higher correlation coefficients by nonlinear than linear fitting models (r(2) = 0.40 vs. 0.30 for FRC % predicted and r(2) = 0.28 vs. 0.19 for ERV). In conclusion, temporal variability of ventilation heterogeneities increases in obesity only when FRC falls approximately below 65% of predicted or ERV below 0.6 liters. Above these thresholds distribution is quite well preserved presumably as a result of an increase in lung recoil.

  6. Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease.

    PubMed

    Stevanović, Jelena; Pechlivanoglou, Petros; Kampinga, Marthe A; Krabbe, Paul F M; Postma, Maarten J

    2016-01-01

    There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA). To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation. A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender. A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European"tariff"), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated. The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs.

  7. Mine ventilation and air conditioning. 3. edition

    SciTech Connect

    Hartman, H.L.; Mutmansky, J.M.; Ramani, R.V.; Wang, Y.J.

    1998-12-31

    This revised edition presents an engineering design approach to ventilation and air conditioning as part of the comprehensive environmental control of the mine atmosphere. It provides an in-depth look, for practitioners who design and operate mines, into the health and safety aspects of environmental conditions in the underground workplace. The contents include: Environmental control of the mine atmosphere; Properties and behavior of air; Mine air-quality control; Mine gases; Dusts and other mine aerosols; Mine ventilation; Airflow through mine openings and ducts; Mine ventilation circuits and networks; Natural ventilation; Fan application to mines; Auxiliary ventilation and controlled recirculation; Economics of airflow; Control of mine fires and explosions; Mine air conditioning; Heat sources and effect in mines; Mine air conditioning systems; Appendices; References; Answers to selected problems; and Index.

  8. 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…

  9. 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…

  10. 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…

  11. 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.

  12. An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents

    ERIC Educational Resources Information Center

    Hwang, Heungsun; Montreal, Hec; Dillon, William R.; Takane, Yoshio

    2006-01-01

    An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

  13. 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.

  14. 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.

  15. Multivariate Longitudinal Modeling of Cognitive Aging

    PubMed Central

    Robitaille, Annie; Muniz, Graciela; Piccinin, Andrea M.; Johansson, Boo; Hofer, Scott M.

    2013-01-01

    We illustrate the use of the parallel latent growth curve model using data from OCTO-Twin. We found a significant intercept-intercept and slope-slope association between processing speed and visuospatial ability. Within-person correlations among the occasion-specific residuals were significant, suggesting that the occasion-specific fluctuations around individual’s trajectories, after controlling for intraindividual change, are related between both outcomes. Random and fixed effects for visuospatial ability are reduced when we include structural parameters (directional growth curve model) providing information about changes in visuospatial abilities after controlling for processing speed. We recommend this model to researchers interested in the analysis of multivariate longitudinal change, as it permits decomposition and directly interpretable estimates of association among initial levels, rates of change, and occasion-specific variation. PMID:23589712

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Nested Taylor decomposition in multivariate function decomposition

    NASA Astrophysics Data System (ADS)

    Baykara, N. A.; Gürvit, Ercan

    2014-12-01

    Fluctuationlessness approximation applied to the remainder term of a Taylor decomposition expressed in integral form is already used in many articles. Some forms of multi-point Taylor expansion also are considered in some articles. This work is somehow a combination these where the Taylor decomposition of a function is taken where the remainder is expressed in integral form. Then the integrand is decomposed to Taylor again, not necessarily around the same point as the first decomposition and a second remainder is obtained. After taking into consideration the necessary change of variables and converting the integration limits to the universal [0;1] interval a multiple integration system formed by a multivariate function is formed. Then it is intended to apply the Fluctuationlessness approximation to each of these integrals one by one and get better results as compared with the single node Taylor decomposition on which the Fluctuationlessness is applied.

  5. Multivariate predictors of failed prehospital endotracheal intubation.

    PubMed

    Wang, Henry E; Kupas, Douglas F; Paris, Paul M; Bates, Robyn R; Costantino, Joseph P; Yealy, Donald M

    2003-07-01

    Conventionally trained out-of-hospital rescuers (such as paramedics) often fail to accomplish endotracheal intubation (ETI) in patients requiring invasive airway management. Previous studies have identified univariate variables associated with failed out-of-hospital ETI but have not examined the interaction between the numerous factors impacting ETI success. This study sought to use multivariate logistic regression to identify a set of factors associated with failed adult out-of-hospital ETI. The authors obtained clinical and demographic data from the Prehospital Airway Collaborative Evaluation, a prospective, multicentered observational study involving advanced life support (ALS) emergency medical services (EMS) systems in the Commonwealth of Pennsylvania. Providers used standard forms to report details of attempted ETI, including system and patient demographics, methods used, difficulties encountered, and initial outcomes. The authors excluded data from sedation-facilitated and neuromuscular blockade-assisted intubations. The main outcome measure was ETI failure, defined as failure to successfully place an endotracheal tube on the last out-of-hospital laryngoscopy attempt. Logistic regression was performed to develop a multivariate model identifying factors associated with failed ETI. Data were used from 45 ALS systems on 663 adult ETIs attempted during the period June 1, 2001, to November 30, 2001. There were 89 cases of failed ETI (failure rate 13.4%). Of 61 factors potentially related to ETI failure, multivariate logistic regression revealed the following significant covariates associated with ETI failure (odds ratio; 95% confidence interval; likelihood ratio p-value): presence of clenched jaw/trismus (9.718; 95% CI = 4.594 to 20.558; p < 0.0001); inability to pass the endotracheal tube through the vocal cords (7.653; 95% CI = 3.561 to 16.447; p < 0.0001); inability to visualize the vocal cords (7.638; 95% CI = 3.966 to 14.707; p < 0.0001); intact gag reflex

  6. Modelling lifetime data with multivariate Tweedie distribution

    NASA Astrophysics Data System (ADS)

    Nor, Siti Rohani Mohd; Yusof, Fadhilah; Bahar, Arifah

    2017-05-01

    This study aims to measure the dependence between individual lifetimes by applying multivariate Tweedie distribution to the lifetime data. Dependence between lifetimes incorporated in the mortality model is a new form of idea that gives significant impact on the risk of the annuity portfolio which is actually against the idea of standard actuarial methods that assumes independent between lifetimes. Hence, this paper applies Tweedie family distribution to the portfolio of lifetimes to induce the dependence between lives. Tweedie distribution is chosen since it contains symmetric and non-symmetric, as well as light-tailed and heavy-tailed distributions. Parameter estimation is modified in order to fit the Tweedie distribution to the data. This procedure is developed by using method of moments. In addition, the comparison stage is made to check for the adequacy between the observed mortality and expected mortality. Finally, the importance of including systematic mortality risk in the model is justified by the Pearson's chi-squared test.

  7. 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.

  8. 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%).

  9. 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

  10. 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.

  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

    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. 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). 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. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

  13. 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

  14. 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.

  15. On Design Mining: Coevolution and Surrogate Models.

    PubMed

    Preen, Richard J; Bull, Larry

    2017-01-01

    Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.

  16. 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

  17. Natural forest expansion on reclaimed coal mines in Northern Spain: the role of native shrubs as suitable microsites.

    PubMed

    Alday, Josu G; Zaldívar, Pilar; Torroba-Balmori, Paloma; Fernández-Santos, Belén; Martínez-Ruiz, Carolina

    2016-07-01

    The characterization of suitable microsites for tree seedling establishment and growth is one of the most important tasks to achieve the restoration of native forest using natural processes in disturbed sites. For that, we assessed the natural Quercus petraea forest expansion in a 20-year-old reclaimed open-cast mine under sub-Mediterranean climate in northern Spain, monitoring seedling survival, growth, and recruitment during 5 years in three contrasting environments (undisturbed forest, mine edge, and mine center). Seedling density and proportion of dead branches decreased greatly from undisturbed forest towards the center of the mine. There was a positive effect of shrubs on Q. petraea seedling establishment in both mine environments, which increase as the environment undergoes more stress (from the mine edge to the center of the mine), and it was produced by different shrub structural features in each mine environment. Seedling survival reduction through time in three environments did not lead to a density reduction because there was a yearly recruitment of new seedlings. Seedling survival, annual growth, and height through time were greater in mine sites than in the undisturbed forest. The successful colonization patterns and positive neighbor effect of shrubs on natural seedlings establishment found in this study during the first years support the use of shrubs as ecosystem engineers to increase heterogeneity in micro-environmental conditions on reclaimed mine sites, which improves late-successional Quercus species establishment.

  18. Multivariate biophysical markers predictive of mesenchymal stromal cell multipotency

    PubMed Central

    Lee, Wong Cheng; Shi, Hui; Poon, Zhiyong; Nyan, Lin Myint; Kaushik, Tanwi; Shivashankar, G. V.; Chan, Jerry K. Y.; Lim, Chwee Teck; Han, Jongyoon; Van Vliet, Krystyn J.

    2014-01-01

    The capacity to produce therapeutically relevant quantities of multipotent mesenchymal stromal cells (MSCs) via in vitro culture is a common prerequisite for stem cell-based therapies. Although culture expanded MSCs are widely studied and considered for therapeutic applications, it has remained challenging to identify a unique set of characteristics that enables robust identification and isolation of the multipotent stem cells. New means to describe and separate this rare cell type and its downstream progenitor cells within heterogeneous cell populations will contribute significantly to basic biological understanding and can potentially improve efficacy of stem and progenitor cell-based therapies. Here, we use multivariate biophysical analysis of culture-expanded, bone marrow-derived MSCs, correlating these quantitative measures with biomolecular markers and in vitro and in vivo functionality. We find that, although no single biophysical property robustly predicts stem cell multipotency, there exists a unique and minimal set of three biophysical markers that together are predictive of multipotent subpopulations, in vitro and in vivo. Subpopulations of culture-expanded stromal cells from both adult and fetal bone marrow that exhibit sufficiently small cell diameter, low cell stiffness, and high nuclear membrane fluctuations are highly clonogenic and also exhibit gene, protein, and functional signatures of multipotency. Further, we show that high-throughput inertial microfluidics enables efficient sorting of committed osteoprogenitor cells, as distinct from these mesenchymal stem cells, in adult bone marrow. Together, these results demonstrate novel methods and markers of stemness that facilitate physical isolation, study, and therapeutic use of culture-expanded, stromal cell subpopulations. PMID:25298531

  19. 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.

  20. Deconstructing multivariate decoding for the study of brain function.

    PubMed

    Hebart, Martin N; Baker, Chris I

    2017-08-04

    Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

  1. Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

    PubMed Central

    Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

    2014-01-01

    Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Mining technology and policy issues 1983

    SciTech Connect

    Not Available

    1983-01-01

    This book presents conference papers on advances in mineral processing, coal mining, communications for mining executives, environmental laws and regulations, exploration philosophy, exploration technology, government controls and the environment, management, mine finance, minerals availability, mine safety, occupational health, open pit mining, the precious metals outlook, public lands, system improvements in processing ores, and underground mining. Topics considered include coal pipelines and saline water, an incentive program for coal mines, sandwich belt high-angle conveyors, the development of a mining company, regulations for radionuclides, contracts for western coal production for Pacific Rim exports, and the control of radon daughters in underground mines.

  9. Using an Instrumented Mine to Validate Models Predicting Mine Burial

    DTIC Science & Technology

    2016-06-07

    analogue off Panama City Beach. The above figure represents the three bands of 24 light - emitting diodes (LEDs) around the circumference of the mine...Blacked out regions indicate light paths blocked by sediment. Several crabs and some small fish were noted colonizing the mine as habitat. Sand was...excavated from around the number 11 through 15 light sensors arranged at 15 degree intervals around the mine, resulting in an unimpeded path between

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. Mines and Demolitions

    DTIC Science & Technology

    1974-04-22

    Rough .andling Tests . ~Forty-Foot Drop Test S. " Extremu- Temperatura Functioning Temperature-Humidity " r Extreme-Temperature Storage "* •d...operability. The performance characteristics, a ": Ithrough d below, are determined with unconditioned samples. These samples also serve as control ...firing can eafely be controlled . At least five samples are used for each possible fuza/mine combinacion. The order of functioning of the main

  16. 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.

  17. Data Mining Report

    DTIC Science & Technology

    2009-03-01

    Advanced Research Projects Activity (IARPA) that included research of techniques that could be applied to data mining. Two of those programs ( Tangram ... Tangram program was originally intended to evaluate the effIcacy and intelligence value of a terrorist threat surveillance and warning system concept that...entities. During FY 2008, the Tangram program conducted elementary experiments on the feasibility of building and maintaining a continuously operating

  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. 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.

  20. 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.

  1. 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....

  2. 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....

  3. 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....

  4. 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....

  5. 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....

  6. 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...

  7. Predicting malignant neck lymphadenopathy using color duplex sonography based on multivariate analysis.

    PubMed

    Chammas, Maria C; Macedo, Túlio A A; Lo, Victor W; Gomes, Andrea C; Juliano, Adriana; Cerri, Giovanni G

    2016-11-12

    To select the best predictors of cervical lymph node malignancy based on gray-scale and power Doppler sonography using multivariate analysis. We evaluated sonographically a total of 97 lymph nodes in the neck that were subjected to fine-needle aspiration biopsy. The gray-scale and power Doppler sonography parameters that we analyzed using multivariate logistic regression included size, shape, echogenicity, echotexture, margins, hilum, presence of microcalcifications or necrosis, vascularization, and resistance index (RI). The three variables with a diagnostic accuracy exceeding 80% were an altered vascularization, heterogeneous echotexture, and abnormal hilum. Malignant nodes exhibited higher RI and larger sizes than benign nodes, and the best cutoff values to distinguish malignant from benign lymph nodes were an RI of 0.77 and a short axis ≥ 0.9 cm. Altered vascularization, a short axis ≥ 0.9 cm, and abnormal hilum were the best predictors of malignancy. The best sonographic predictors of lymph node malignancy are, in descending order, an altered vascularization, a short axis ≥ 0.9 cm, an abnormal hilum, and a heterogeneous echotexture. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:587-594, 2016. © 2016 Wiley Periodicals, Inc.

  8. Exploring factors associated with pressure ulcers: a data mining approach.

    PubMed

    Raju, Dheeraj; Su, Xiaogang; Patrician, Patricia A; Loan, Lori A; McCarthy, Mary S

    2015-01-01

    Pressure ulcers are associated with a nearly three-fold increase in in-hospital mortality. It is essential to investigate how other factors besides the Braden scale could enhance the prediction of pressure ulcers. Data mining modeling techniques can be beneficial to conduct this type of analysis. Data mining techniques have been applied extensively in health care, but are not widely used in nursing research. To remedy this methodological gap, this paper will review, explain, and compare several data mining models to examine patient level factors associated with pressure ulcers based on a four year study from military hospitals in the United States. The variables included in the analysis are easily accessible demographic information and medical measurements. Logistic regression, decision trees, random forests, and multivariate adaptive regression splines were compared based on their performance and interpretability. The random forests model had the highest accuracy (C-statistic) with the following variables, in order of importance, ranked highest in predicting pressure ulcers: days in the hospital, serum albumin, age, blood urea nitrogen, and total Braden score. Data mining, particularly, random forests are useful in predictive modeling. It is important for hospitals and health care systems to use their own data over time for pressure ulcer risk prediction, to develop risk models based upon more than the total Braden score, and specific to their patient population. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. 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.

  10. Mine waters: Acidic to circumneutral

    USGS Publications Warehouse

    Nordstrom, D. Kirk

    2011-01-01

    Acid mine waters, often containing toxic concentrations of Fe, Al, Cu, Zn, Cd, Pb, Ni, Co, and Cr, can be produced from the mining of coal and metallic deposits. Values of pH for acid mine waters can range from –3.5 to 5, but even circumneutral (pH ≈ 7) mine waters can have high concentrations of As, Sb, Mo, U, and F. When mine waters are discharged into streams, lakes, and the oceans, serious degradation of water quality and injury to aquatic life can ensue, especially when tailings impoundments break suddenly. The main acid-producing process is the exposure of pyrite to air and water, which promotes oxidative dissolution, a reaction catalyzed by microbes. Current and future mining should plan for the prevention and remediation of these contaminant discharges by the application of hydrogeochemical principles and available technologies, which might include remining and recycling of waste materials.

  11. 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.

  12. Lunar site characterization and mining

    NASA Technical Reports Server (NTRS)

    Glass, Charles E.

    1992-01-01

    Lunar mining requirements do not appear to be excessively demanding in terms of volume of material processed. It seems clear, however, that the labor-intensive practices that characterize terrestrial mining will not suffice at the low-gravity, hard-vacuum, and inaccessible sites on the Moon. New research efforts are needed in three important areas: (1) to develop high-speed, high-resolution through-rock vision systems that will permit more detailed and efficient mine site investigation and characterization; (2) to investigate the impact of lunar conditions on our ability to convert conventional mining and exploration equipment to lunar prototypes; and (3) to develop telerobotic or fully robotic mining systems for operations on the Moon and other bodies in the inner solar system. Other aspects of lunar site characterization and mining are discussed.

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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

  3. 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.

  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. 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

  6. 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.

  7. 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.

  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. Mine soil classification and mapping

    SciTech Connect

    Darmody, R.

    1998-12-31

    This presentation covers the history of surface coal mining and reclamation methods and equipment for the pre-Federal law, interim-Federal law, and post-Federal law periods. It discusses the difficulties with traditional mine soil mapping methods on five soils series in Illinois. These methods fail to recognize the effects of compaction and methods to ameliorate compaction. The current status of mine soil mapping methods on eight soil series in Illinois are presented. Areas where additional work is needed and future potential difficulties are identified for mine soil mapping efforts.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES

    PubMed Central

    Zhu, Hongtu; Li, Runze; Kong, Linglong

    2012-01-01

    Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:23645942

  16. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES

    PubMed Central

    Zhu, Hongtu; Li, Runze; Kong, Linglong

    2012-01-01

    Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:12926711

  17. Cluster analysis using multivariate mixed effects models.

    PubMed

    Villarroel, Luis; Marshall, Guillermo; Barón, Anna E

    2009-09-10

    A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. Recently, methods have been proposed to deal with cases where individuals are classified into clusters through a linear model of mixed univariate effects deriving from a longitudinally measured variable. The method proposed in the current work deals with the case of clustering and then classification based on two or more variables measured longitudinally, through the fitting of non-linear multivariate mixed effect models, and with consideration given to parameter estimation for balanced and unbalanced data using an EM algorithm. The application of the method is illustrated with an example in which the clusters are identified and the classification into clusters is compared with the true membership of individuals in one of two groups, which is known at the end of the follow-up period.

  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. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES.

    PubMed

    Zhu, Hongtu; Li, Runze; Kong, Linglong

    2012-10-01

    Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.

  20. Multivariate models of adult Pacific salmon returns.

    PubMed

    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.