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Sample records for mining heterogeneous multivariate

  1. Temporal pattern mining for multivariate clinical decision support.

    PubMed

    Saini, Sheetal; Dua, Sumeet

    2013-01-01

    Multivariate temporal data are collections of contiguous data values that reflect complex temporal changes over a given duration. Technological advances have resulted in significant amounts of such data in high-throughput disciplines, including EEG and iEEG data for effective and efficient healthcare informatics, and decision support. Most data analytics and data-mining algorithms are effective in capturing global trends, but fail to capture localized behavioral changes in large temporal data sets. We present a two-step algorithmic methodology to uncover temporal patterns and exploiting them for an efficient and accurate decision support system. This methodology aids the discovery of previously unknown, nontrivial, and potentially useful temporal patterns for enhanced patient-specific clinical decision support with high degrees of sensitivity and specificity. Classification results on multivariate time series iEEG data for epileptic seizure detection also demonstrate the efficacy and accuracy of the technique to uncover interesting and effective domain class-specific temporal patterns.

  2. A Pattern Mining Approach for Classifying Multivariate Temporal Data

    PubMed Central

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

    2012-01-01

    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. PMID:22267987

  3. Analyzing Multivariate Repeated Measures Designs When Covariance Matrices Are Heterogeneous.

    ERIC Educational Resources Information Center

    Lix, Lisa M.; And Others

    Methods for the analysis of within-subjects effects in multivariate groups by trials repeated measures designs are considered in the presence of heteroscedasticity of the group variance-covariance matrices and multivariate nonnormality. Under a doubly multivariate model approach to hypothesis testing, within-subjects main and interaction effect…

  4. 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…

  5. 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

  6. Adaptive Semantic Tag Mining from Heterogeneous Clinical Research Texts

    PubMed Central

    Hao, Tianyong; Weng, Chunhua

    2015-01-01

    Summary Objectives To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts. Methods We develop a “plug-n-play” framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts. Results Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the baseline ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed. Conclusions This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods. PMID:25327613

  7. 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.

  8. Using data mining to address heterogeneity in the Southampton data.

    PubMed

    Chang, C J; Fann, C S

    2001-01-01

    When analyzing complex traits such as asthma, heterogeneity needs to be assumed. With this in mind, to identify a more homogeneous group of asthmatic patients, we analyzed the Southampton data using the data mining technique known as the regression tree method and the two most inheritable quantitative phenotypes (LnIgE and RAST) as the target variables. Two-point and multipoint nonparametric linkage analyses were carried out using one of the subgroups as affected. In addition, we performed quantitative trait loci nonparametric linkage analysis using each phenotype as the outcome. The results from the affected-sibpairs method and quantitative linkage analysis were compared.

  9. 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.

  10. 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

  11. Multivariate sensitivity analysis of saturated flow through simulated highly heterogeneous groundwater aquifers

    SciTech Connect

    Winter, C.L. . E-mail: lwinter@ucar.edu; Guadagnini, A.; Nychka, D.; Tartakovsky, D.M.

    2006-09-01

    A multivariate Analysis of Variance (ANOVA) is used to measure the relative sensitivity of groundwater flow to two factors that indicate different dimensions of aquifer heterogeneity. An aquifer is modeled as the union of disjoint volumes, or blocks, composed of different materials with different hydraulic conductivities. The factors are correlation between the hydraulic conductivities of the different materials and the contrast between mean conductivities in the different materials. The precise values of aquifer properties are usually uncertain because they are only sparsely sampled, yet are highly heterogeneous. Hence, the spatial distribution of blocks and the distribution of materials in blocks are uncertain and are modeled as stochastic processes. The ANOVA is performed on a large sample of Monte Carlo simulations of a simple model flow system composed of two materials distributed within three disjoint blocks. Our key finding is that simulated flow is much more sensitive to the contrast between mean conductivities of the blocks than it is to the intensity of correlation, although both factors are statistically significant. The methodology of the experiment - ANOVA performed on Monte Carlo simulations of a multi-material flow system - constitutes the basis of additional studies of more complicated interactions between factors that define flow and transport in aquifers with uncertain properties.

  12. 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.…

  13. Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

    PubMed

    Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

    2014-01-01

    The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file. PMID:24345245

  14. Multivariate, non-linear trend analysis of heterogeneous water quality monitoring data

    NASA Astrophysics Data System (ADS)

    Lischeid, Gunnar; Kalettka, Thomas; Steidl, Jörg; Merz, Christoph; Lehr, Christian

    2014-05-01

    Comprehensive water quality monitoring is considered a necessary prerequisite for sound water resources management and a valuable source for science. In practice, however, use of large monitoring data sets is often limited due to heterogeneous data sources, spatially and temporally variable monitoring schemes, non-equidistant sampling, large natural variability, and, last but not least, by the sheer size of the data sets that makes identification of unexpected peculiarities a tedious task. As a consequence, any initiation of gradual long-term system shifts can hardly be detected, especially as long as it is restricted to a small fraction of sampling sites. In addition, trends might be limited to a rather small subset of sampling sites or to certain periods of time and might thus escape attention. Usually, numerous solutes are monitored in parallel, but trend analyses are performed for each solute separately. However, in water quality samples trends are hardly restricted to single solutes, but affect various solutes synchronously in a characteristic way. Thus performing joint multivariate trend analyses would not only save effort and time, but would yield more robust assessments of system shifts. We present a non-linear multivariate data visualization approach that allows a rapid assessment of non-linear, possibly local trends and unexpected behaviour in large water quality monitoring data sets. It consists of a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM). The approach was applied to a data set of 2900 water samples, each comprising 13 solutes, compiled from various monitoring programs in the Federal State of Brandenburg (Germany). In total, 128 stream water, groundwater and small pond sites had been sampled between 1994 and 2012 at different and irregular time intervals. The SOM-SM product is a graph where every sample is represented by a symbol. Location of the symbols in the graph is optimized such that the distance between any two symbols

  15. Multi-variate flood damage assessment: a tree-based data-mining approach

    NASA Astrophysics Data System (ADS)

    Merz, B.; Kreibich, H.; Lall, U.

    2013-01-01

    The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damage-influencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.

  16. 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.

  17. 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

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

    PubMed

    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/.

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

    PubMed

    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

  20. Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases.

    PubMed

    Altiparmak, Fatih; Ferhatosmanoglu, Hakan; Erdal, Selnur; Trost, Donald C

    2006-04-01

    An effective analysis of clinical trials data involves analyzing different types of data such as heterogeneous and high dimensional time series data. The current time series analysis methods generally assume that the series at hand have sufficient length to apply statistical techniques to them. Other ideal case assumptions are that data are collected in equal length intervals, and while comparing time series, the lengths are usually expected to be equal to each other. However, these assumptions are not valid for many real data sets, especially for the clinical trials data sets. An addition, the data sources are different from each other, the data are heterogeneous, and the sensitivity of the experiments varies by the source. Approaches for mining time series data need to be revisited, keeping the wide range of requirements in mind. In this paper, we propose a novel approach for information mining that involves two major steps: applying a data mining algorithm over homogeneous subsets of data, and identifying common or distinct patterns over the information gathered in the first step. Our approach is implemented specifically for heterogeneous and high dimensional time series clinical trials data. Using this framework, we propose a new way of utilizing frequent itemset mining, as well as clustering and declustering techniques with novel distance metrics for measuring similarity between time series data. By clustering the data, we find groups of analytes (substances in blood) that are most strongly correlated. Most of these relationships already known are verified by the clinical panels, and, in addition, we identify novel groups that need further biomedical analysis. A slight modification to our algorithm results an effective declustering of high dimensional time series data, which is then used for "feature selection." Using industry-sponsored clinical trials data sets, we are able to identify a small set of analytes that effectively models the state of normal health.

  1. A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data

    PubMed Central

    Ghassemi, Marzyeh; Pimentel, Marco A.F.; Naumann, Tristan; Brennan, Thomas; Clifton, David A.; Szolovits, Peter

    2016-01-01

    The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC). PMID:27182460

  2. Modelling the effect of chemical heterogeneity on acidification and solute leaching in overburden mine spoils

    NASA Astrophysics Data System (ADS)

    Gerke, Horst H.; Molson, John W.; Frind, Emil O.

    1998-08-01

    The generation of acid mine drainage from overburden spoil piles at open-pit lignite mines is impacting the quality of groundwater and surface water bodies in large parts of the Lusatian mining area in Germany. Values of pH as low as 1 have been observed in the groundwater. After decommissioning, mine pits are generally converted to lakes which may also be acidic owing to the acidic groundwater discharge. The acidic effluent is generated by sulphide oxidation in the unsaturated zone of the spoil pile which generally extends to large depths as a result of dewatering. The long-term evolution of the acidification is still largely unknown. Our research focuses on the effects of physical and chemical heterogeneity caused by mixing of soil materials that may have already been oxidized to different degrees during the deposition of the spoil pile. Processes considered include variably saturated groundwater flow, oxygen diffusion in the soil gas, kinetic pyrite oxidation and acidic effluent generation, advective-dispersive transport of the aqueous components, equilibrium geochemical reactions between the chemical components and the soil minerals, and possible buffering and acid neutralization. Several existing numerical codes were coupled to represent the complete set of processes. Simulations were carried out in one- and two dimensions using representative characteristics of mine spoil piles, with the two-dimensional representation being based on spatially heterogeneous random fields of hydraulic conductivity and sulphide mineral fractions. Results show the long-term evolution of the oxidation front, the mass flux of oxidation products and the effects of system heterogeneity. Under conditions of constant flow, the system is found to return to neutral conditions over a time period on the order of several decades. Further work, including sensitivity analyses with respect to the controlling parameters and model calibration using site-specific field data, will be necessary to

  3. 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.

  4. 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

  5. Preferential flow in heterogeneous, forest-reclaimed lignitic mine soil. III. 1- and 2-dimensional modelling

    NASA Astrophysics Data System (ADS)

    Buczko, U.; Gerke, H. H.; Hangen, E.; Hüttl, R. F.

    2003-04-01

    Water balances of forest sites are often estimated using 1-dimensional numerical models and tensiometer data from different depths. The magnitude of groundwater recharge calculated in such a way in most cases cannot be verified experimentally. In heterogeneous soils, water flows are spatially highly variable. The objective of this contribution is to compare the flow and deep percolation within a reclaimed mine soil which was calculated with a 1D numerical model, with seepage water collected, spatially-resolved, in-situ. Further, it is aimed at improving the methodology for calculating water balances and element budgets on heterogeneous mine soils, using 2D models with spatial variability. At the study site “Bärenbrück” near Cottbus, a lignitic mine soil afforested in 1982 with Pinus nigra, the components of the water balance were simulated with a 1D numerical model (SOIL/COUP) for a period from May 1995 to September 2001, using meteorological data and measured water tensions in soil depths 15, 60, and 100 cm. At the same site, soil water percolates were extracted continually in-situ at a soil depth of 110 cm from June 2000 until September 2001 within the framework of a cell-lysimeter study. 2D simulations were performed with the numerical model HYDRUS-2D, using evapotranspiration data obtained with the 1D-model. In the balance period between 4/96 and 3/99, the simulated deep percolation ranges between 30.4 and 35.2 mm per year, whereas during the dryer years 6/1999 5/2000 and 6/2000 5/2001 it amounts to 6.6 mm and 1.5 mm, respectively. The average deep percolation based on the in-situ suction plate data during the same period was 11 mm for the period 6/1999 5/2000 and 24.3 mm for 6/2000 5/2001, although spatially highly variable. Consequently, for the period 6/2000 5/2001, groundwater recharge based on measured in-situ data is by one order of magnitude higher than those simulated with the 1D model. The 2D numerical simulations are used to explain this

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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. PMID:21920665

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

    ERIC Educational Resources Information Center

    Thompson, Bruce

    Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

  18. Multivariate Regression with Calibration*

    PubMed Central

    Liu, Han; Wang, Lie; Zhao, Tuo

    2014-01-01

    We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861

  19. 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.

  20. 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.

  1. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

    Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916

  2. Multivariate Data EXplorer (MDX)

    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 wherebymore » 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.« less

  3. Multivariate respiratory motion prediction

    NASA Astrophysics Data System (ADS)

    Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.

    2014-10-01

    In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.

  4. Performance of Four Multivariate Tests under Variance-Covariance Heteroscedasticity.

    ERIC Educational Resources Information Center

    Tang, K. Linda; Algina, James

    1993-01-01

    Type I error rates of four multivariate tests (Pilai-Bartlett trace, Johansen's test, James' first-order test, and James' second-order test) were compared for heterogeneous covariance matrices in 360 simulated experiments. The superior performance of Johansen's test and James' second-order test is discussed. (SLD)

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

    USGS Publications Warehouse

    Naftz, David L.; Walton-Day, Katie

    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

  6. Multivariate Hypergeometric Similarity Measure

    PubMed Central

    Kaddi, Chanchala D.; Parry, R. Mitchell; Wang, May D.

    2016-01-01

    We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets. PMID:24407308

  7. 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)

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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.…

  14. 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…

  15. 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.

  16. 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

  17. 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…

  18. Parameter Sensitivity in Multivariate Methods

    ERIC Educational Resources Information Center

    Green, Bert F., Jr.

    1977-01-01

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

  19. 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.

  20. 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.

  1. 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.

  2. Extending mine life

    SciTech Connect

    Not Available

    1984-06-01

    Mine layouts, new machines and techniques, research into problem areas of ground control and so on, are highlighted in this report on extending mine life. The main resources taken into account are coal mining, uranium mining, molybdenum and gold mining.

  3. 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.

  4. 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.

  5. Mining drill

    SciTech Connect

    Sarin, V.K.

    1983-08-16

    In a mine tool of the type having a drive body holding a bit, the drive body includes a pair of forwardly projecting flanges forming air passages in proximity to the cutting edges for the convey of detritus.

  6. 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.

  7. 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.

  8. 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.

  9. 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).

  10. 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.

  11. 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).

  12. Cytometric fingerprinting: quantitative characterization of multivariate distributions.

    PubMed

    Rogers, Wade T; Moser, Allan R; Holyst, Herbert A; Bantly, Andrew; Mohler, Emile R; Scangas, George; Moore, Jonni S

    2008-05-01

    Recent technological advances in flow cytometry instrumentation provide the basis for high-dimensionality and high-throughput biological experimentation in a heterogeneous cellular context. Concomitant advances in scalable computational algorithms are necessary to better utilize the information that is contained in these high-complexity experiments. The development of such tools has the potential to expand the utility of flow cytometric analysis from a predominantly hypothesis-driven mode to one of discovery, or hypothesis-generating research. A new method of analysis of flow cytometric data called Cytometric Fingerprinting (CF) has been developed. CF captures the set of multivariate probability distribution functions corresponding to list-mode data and then "flattens" them into a computationally efficient fingerprint representation that facilitates quantitative comparisons of samples. An experimental and synthetic data were generated to act as reference sets for evaluating CF. Without the introduction of prior knowledge, CF was able to "discover" the location and concentration of spiked cells in ungated analyses over a concentration range covering four orders of magnitude, to a lower limit on the order of 10 spiked events in a background of 100,000 events. We describe a new method for quantitative analysis of list-mode cytometric data. CF includes a novel algorithm for space subdivision that improves estimation of the probability density function by dividing space into nonrectangular polytopes. Additionally it renders a multidimensional distribution in the form of a one-dimensional multiresolution hierarchical fingerprint that creates a computationally efficient representation of high dimensionality distribution functions. CF supports both the generation and testing of hypotheses, eliminates sources of operator bias, and provides an increased level of automation of data analysis.

  13. 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.

  14. 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.

  15. Multivariate Time Series Similarity Searching

    PubMed Central

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  16. Multivariate time series similarity searching.

    PubMed

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  17. 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

  18. Piecewise aggregate representations and lower-bound distance functions for multivariate time series

    NASA Astrophysics Data System (ADS)

    Li, Hailin

    2015-06-01

    Dimensionality reduction is one of the most important methods to improve the efficiency of the techniques that are applied to the field of multivariate time series data mining. Due to multivariate time series with the variable-based and time-based dimensions, the reduction techniques must take both of them into consideration. To achieve this goal, we use a center sequence to represent a multivariate time series so that the new sequence can be seen as a univariate time series. Thus two sophisticated piecewise aggregate representations, including piecewise aggregate approximation and symbolization applied to univariate time series, are used to further represent the extended sequence that is derived from the center one. Furthermore, some distance functions are designed to measure the similarity between two representations. Through being proven by some related mathematical analysis, the proposed functions are lower bound on Euclidean distance and dynamic time warping. In this way, false dismissals can be avoided when they are used to index the time series. In addition, multivariate time series with different lengths can be transformed into the extended sequences with equal length, and their corresponding distance functions can measure the similarity between two unequal-length multivariate time series. The experimental results demonstrate that the proposed methods can reduce the dimensionality, and their corresponding distance functions satisfy the lower-bound condition, which can speed up the calculation of similarity search and indexing in the multivariate time series datasets.

  19. 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…

  20. 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.

  1. 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.

  2. A multivariate CAR model for mismatched lattices.

    PubMed

    Porter, Aaron T; Oleson, Jacob J

    2014-10-01

    In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598

  3. 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.

  4. 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)

  5. Uniqueness of medical data mining.

    PubMed

    Cios, Krzysztof J; Moore, G William

    2002-01-01

    This article addresses the special features of data mining with medical data. Researchers in other fields may not be aware of the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous data of medicine. Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues. The mathematical understanding of estimation and hypothesis formation in medical data may be fundamentally different than those from other data collection activities. Medicine is primarily directed at patient-care activity, and only secondarily as a research resource; almost the only justification for collecting medical data is to benefit the individual patient. Finally, medical data have a special status based upon their applicability to all people; their urgency (including life-or-death); and a moral obligation to be used for beneficial purposes.

  6. Multivariate dispersion as a measure of beta diversity.

    PubMed

    Anderson, Marti J; Ellingsen, Kari E; McArdle, Brian H

    2006-06-01

    Beta diversity can be defined as the variability in species composition among sampling units for a given area. We propose that it can be measured as the average dissimilarity from individual observation units to their group centroid in multivariate space, using an appropriate dissimilarity measure. Differences in beta diversity among different areas or groups of samples can be tested using this approach. The choice of transformation and dissimilarity measure has important consequences for interpreting results. For kelp holdfast assemblages from New Zealand, variation in species composition was greater in smaller holdfasts, while variation in relative abundances was greater in larger holdasts. Variation in community structure of Norwegian continental shelf macrobenthic fauna increased with increases in environmental heterogeneity, regardless of the measure used. We propose a new dissimilarity measure which allows the relative weight placed on changes in composition vs. abundance to be specified explicitly.

  7. 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.

  8. 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.

  9. 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.

  10. Characterization of the desiccation of wheat kernels by multivariate imaging.

    PubMed

    Jaillais, B; Perrin, E; Mangavel, C; Bertrand, D

    2011-06-01

    Variations in the quality of wheat kernels can be an important problem in the cereal industry. In particular, desiccation conditions play an essential role in both the technological characteristics of the kernel and its ability to sprout. In planta desiccation constitutes a key stage in the determinism of the functional properties of seeds. The impact of desiccation on the endosperm texture of seed is presented in this work. A simple imaging system had previously been developed to acquire multivariate images to characterize the heterogeneity of food materials. A special algorithm for the use under principal component analysis (PCA) was developed to process the acquired multivariate images. Wheat grains were collected at physiological maturity, and were subjected to two types of drying conditions that induced different kinetics of water loss. A data set containing 24 images (dimensioned 702 × 524 pixels) corresponding to the different desiccation stages of wheat kernels was acquired at different wavelengths and then analyzed. A comparison of the images of kernel sections highlighted changes in kernel texture as a function of their drying conditions. Slow drying led to a floury texture, whereas fast drying caused a glassy texture. The automated imaging system thus developed is sufficiently rapid and economical to enable the characterization in large collections of grain texture as a function of time and water content.

  11. Single cell heterogeneity

    PubMed Central

    Abdallah, Batoul Y; Horne, Steven D; Stevens, Joshua B; Liu, Guo; Ying, Andrew Y; Vanderhyden, Barbara; Krawetz, Stephen A; Gorelick, Root; Heng, Henry HQ

    2013-01-01

    Multi-level heterogeneity is a fundamental but underappreciated feature of cancer. Most technical and analytical methods either completely ignore heterogeneity or do not fully account for it, as heterogeneity has been considered noise that needs to be eliminated. We have used single-cell and population-based assays to describe an instability-mediated mechanism where genome heterogeneity drastically affects cell growth and cannot be accurately measured using conventional averages. First, we show that most unstable cancer cell populations exhibit high levels of karyotype heterogeneity, where it is difficult, if not impossible, to karyotypically clone cells. Second, by comparing stable and unstable cell populations, we show that instability-mediated karyotype heterogeneity leads to growth heterogeneity, where outliers dominantly contribute to population growth and exhibit shorter cell cycles. Predictability of population growth is more difficult for heterogeneous cell populations than for homogenous cell populations. Since “outliers” play an important role in cancer evolution, where genome instability is the key feature, averaging methods used to characterize cell populations are misleading. Variances quantify heterogeneity; means (averages) smooth heterogeneity, invariably hiding it. Cell populations of pathological conditions with high genome instability, like cancer, behave differently than karyotypically homogeneous cell populations. Single-cell analysis is thus needed when cells are not genomically identical. Despite increased attention given to single-cell variation mediated heterogeneity of cancer cells, continued use of average-based methods is not only inaccurate but deceptive, as the “average” cancer cell clearly does not exist. Genome-level heterogeneity also may explain population heterogeneity, drug resistance, and cancer evolution. PMID:24091732

  12. 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. PMID:27537692

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Patterns of Emphysema Heterogeneity

    PubMed Central

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

    2016-01-01

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

  19. 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

  20. 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.

  1. 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.

  2. Geckos as indicators of mining pollution.

    PubMed

    Fletcher, Dean E; Hopkins, William A; Saldaña, Teresa; Baionno, Jennifer A; Arribas, Carmen; Standora, Michelle M; Fernández-Delgado, Carlos

    2006-09-01

    Catastrophic collapse of a mine tailings dam released several million cubic meters of toxic mud and acidic water into the Guadiamar River valley, southern Spain, in 1998. Remediation efforts removed most of the sludge from the floodplain, but contamination persists. Clean-up activities also produced clouds of aerosolized materials that further contaminated the surrounding landscape. Whole-body concentrations of 21 elements in the Moorish wall gecko, Tarentola mauritanica, a common inhabitant of both rural and urban areas, were compared among seven locations. Locations spanned an expected contamination gradient and included a rural and an urban non-mine-affected location, two mine-affected towns, and three locations on the contaminated floodplain. Multivariate analyses of whole-body concentrations identified pollutants that increased across the expected contamination gradient, a trend particularly evident for As, Pb, and Cd. Additionally, higher contaminant concentrations occurred in prey items eaten by geckos from mine-affected areas. Comparison of element concentrations in tails and whole bodies suggests that tail clips are a viable nondestructive index of contaminant accumulation. Our results indicate that areas polluted by the mine continue to experience contamination of the terrestrial food chain. Where abundant, geckos represent useful taxa to study the bioavailability of some hazardous pollutants.

  3. Multivariate data analysis for outcome studies.

    PubMed

    Spector, P E

    1981-02-01

    The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures are collected. MANOVA offers two specific advantages over more familiar univariate approaches: it presents better control over Type 1 error rates while preserving statistical power, and it allows more thorough analysis of complex data. PMID:7223728

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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...

  10. Using Matlab in a Multivariable Calculus Course.

    ERIC Educational Resources Information Center

    Schlatter, Mark D.

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

  11. MBIS: multivariate Bayesian image segmentation tool.

    PubMed

    Esteban, Oscar; Wollny, Gert; Gorthi, Subrahmanyam; Ledesma-Carbayo, María-J; Thiran, Jean-Philippe; Santos, Andrés; Bach-Cuadra, Meritxell

    2014-07-01

    We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

  12. Experimental evidence for multivariate stabilizing sexual selection.

    PubMed

    Brooks, Robert; Hunt, John; Blows, Mark W; Smith, Michael J; Bussière, Luc F; Jennions, Michael D

    2005-04-01

    Stabilizing selection is a fundamental concept in evolutionary biology. In the presence of a single intermediate optimum phenotype (fitness peak) on the fitness surface, stabilizing selection should cause the population to evolve toward such a peak. This prediction has seldom been tested, particularly for suites of correlated traits. The lack of tests for an evolutionary match between population means and adaptive peaks may be due, at least in part, to problems associated with empirically detecting multivariate stabilizing selection and with testing whether population means are at the peak of multivariate fitness surfaces. Here we show how canonical analysis of the fitness surface, combined with the estimation of confidence regions for stationary points on quadratic response surfaces, may be used to define multivariate stabilizing selection on a suite of traits and to establish whether natural populations reside on the multivariate peak. We manufactured artificial advertisement calls of the male cricket Teleogryllus commodus and played them back to females in laboratory phonotaxis trials to estimate the linear and nonlinear sexual selection that female phonotactic choice imposes on male call structure. Significant nonlinear selection on the major axes of the fitness surface was convex in nature and displayed an intermediate optimum, indicating multivariate stabilizing selection. The mean phenotypes of four independent samples of males, from the same population as the females used in phonotaxis trials, were within the 95% confidence region for the fitness peak. These experiments indicate that stabilizing sexual selection may play an important role in the evolution of male call properties in natural populations of T. commodus.

  13. 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.

  14. Teaching Heterogeneous Classes.

    ERIC Educational Resources Information Center

    Millrood, Radislav

    2002-01-01

    Discusses an approach to teaching heterogeneous English-as-a-Second/Foreign-Language classes. Draws on classroom research data to describe the features of a success-building lesson context. (Author/VWL)

  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. Distance functions in dynamic integration of data mining techniques

    NASA Astrophysics Data System (ADS)

    Puuronen, Seppo J.; Tsymbal, Alexey; Terziyan, Vagan

    2000-04-01

    One of the most important directions in the improvement of data mining and knowledge discovery is the integration of multiple data mining techniques. An integration method needs to be able either to evaluate and select the most appropriate data mining technique or to combine two or more techniques efficiently. A recent integration method for the dynamic integration of multiple data mining techniques is based on the assumption that each of the data mining techniques is the best one inside a certain subarea of the whole domain area. This method uses an instance-based learning approach to collect information about the competence areas of the mining techniques and applies a distance function to determine how close a new instance is to each instance of the training set. The nearest instance or instances are used to predict the performance of the data mining techniques. Because the quality of the integration depends heavily on the suitability of the used distance function, our goal is to analyze the characteristics of different distance functions. In this paper we investigate several distance functions as the very commonly used Euclidean distance function, the Heterogeneous Euclidean- Overlap Metric (HEOM), and the Heterogeneous Value Difference Metric (HVDM), among others. We analyze the effects of the use of different distance functions to the accuracy achieved by dynamic integration when the parameters describing datasets vary. We include also results of our experiments with different datasets which include both nominal and continuous attributes.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  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. 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. Nonlinear aerodynamic modeling using multivariate orthogonal functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.

    1993-01-01

    A technique was developed for global modeling of nonlinear aerodynamic coefficients using multivariate orthogonal functions based on the data. Each orthogonal function retained in the model was decomposed into an expansion of ordinary polynomials in the independent variables, so that the final model could be interpreted as selectively retained terms from a multivariable power series expansion. A predicted squared-error metric was used to determine the orthogonal functions to be retained in the model; analytical derivatives were easily computed. The approach was demonstrated on the Z-body axis aerodynamic force coefficient (Cz) wind tunnel data for an F-18 research vehicle which came from a tabular wind tunnel and covered the entire subsonic flight envelope. For a realistic case, the analytical model predicted experimental values of Cz very well. The modeling technique is shown to be capable of generating a compact, global analytical representation of nonlinear aerodynamics. The polynomial model has good predictive capability, global validity, and analytical differentiability.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. The evolution of multivariate maternal effects.

    PubMed

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

    2014-04-01

    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.

  9. Multivariate linear recurrences and power series division

    PubMed Central

    Hauser, Herwig; Koutschan, Christoph

    2012-01-01

    Bousquet-Mélou and Petkovšek investigated the generating functions of multivariate linear recurrences with constant coefficients. We will give a reinterpretation of their results by means of division theorems for formal power series, which clarifies the structural background and provides short, conceptual proofs. In addition, extending the division to the context of differential operators, the case of recurrences with polynomial coefficients can be treated in an analogous way. PMID:23482936

  10. Use of multivariate dispersion to assess water quality based on species composition data.

    PubMed

    Jiang, Yong; Xu, Guangjian; Xu, Henglong

    2016-02-01

    Multivariate dispersion is a powerful approach to determine the variability in species composition of a fauna or a flora and has been considered as a broad β-diversity in global ecological research. To explore the availability of the dispersions based on species composition data for assessing water quality, a dataset of ciliated protozoa in a basin ecosystem, northern China, was studied. Samples were collected from five sampling stations, within a significant heterogeneity of environmental stress. The homogeneity of multivariate dispersions in species composition of the ciliate assemblages represented a clear spatial pattern in response to the environmental stress. Multivariate analysis demonstrated that the spatial variation in species composition of the ciliate was significantly correlated with the changes of environmental variables, especially the nutrients, in combination with the salinity and pH, or alone. Furthermore, the dispersion measure was found to be significantly related to the nutrient. Based on our data, we suggest that multivariate dispersion measures based on species presence/absence data might be used as a potential bioindicator of water quality in marine ecosystems.

  11. Water quality change detection: multivariate algorithms

    NASA Astrophysics Data System (ADS)

    Klise, Katherine A.; McKenna, Sean A.

    2006-05-01

    In light of growing concern over the safety and security of our nation's drinking water, increased attention has been focused on advanced monitoring of water distribution systems. The key to these advanced monitoring systems lies in the combination of real time data and robust statistical analysis. Currently available data streams from sensors provide near real time information on water quality. Combining these data streams with change detection algorithms, this project aims to develop automated monitoring techniques that will classify real time data and denote anomalous water types. Here, water quality data in 1 hour increments over 3000 hours at 4 locations are used to test multivariate algorithms to detect anomalous water quality events. The algorithms use all available water quality sensors to measure deviation from expected water quality. Simulated anomalous water quality events are added to the measured data to test three approaches to measure this deviation. These approaches include multivariate distance measures to 1) the previous observation, 2) the closest observation in multivariate space, and 3) the closest cluster of previous water quality observations. Clusters are established using kmeans classification. Each approach uses a moving window of previous water quality measurements to classify the current measurement as normal or anomalous. Receiver Operating Characteristic (ROC) curves test the ability of each approach to discriminate between normal and anomalous water quality using a variety of thresholds and simulated anomalous events. These analyses result in a better understanding of the deviation from normal water quality that is necessary to sound an alarm.

  12. Regional dissociated heterochrony in multivariate analysis.

    PubMed

    Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L

    2004-12-01

    Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony.

  13. 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.

  14. Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.

    PubMed

    Zhang, Sheng; Hu, Sien; Sinha, Rajita; Potenza, Marc N; Malison, Robert T; Li, Chiang-Shan R

    2016-01-01

    Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA) to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD) from 100 demographically matched healthy control individuals (HC). We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001), superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test). Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence. PMID:27556009

  15. Managing Power Heterogeneity

    NASA Astrophysics Data System (ADS)

    Pruhs, Kirk

    A particularly important emergent technology is heterogeneous processors (or cores), which many computer architects believe will be the dominant architectural design in the future. The main advantage of a heterogeneous architecture, relative to an architecture of identical processors, is that it allows for the inclusion of processors whose design is specialized for particular types of jobs, and for jobs to be assigned to a processor best suited for that job. Most notably, it is envisioned that these heterogeneous architectures will consist of a small number of high-power high-performance processors for critical jobs, and a larger number of lower-power lower-performance processors for less critical jobs. Naturally, the lower-power processors would be more energy efficient in terms of the computation performed per unit of energy expended, and would generate less heat per unit of computation. For a given area and power budget, heterogeneous designs can give significantly better performance for standard workloads. Moreover, even processors that were designed to be homogeneous, are increasingly likely to be heterogeneous at run time: the dominant underlying cause is the increasing variability in the fabrication process as the feature size is scaled down (although run time faults will also play a role). Since manufacturing yields would be unacceptably low if every processor/core was required to be perfect, and since there would be significant performance loss from derating the entire chip to the functioning of the least functional processor (which is what would be required in order to attain processor homogeneity), some processor heterogeneity seems inevitable in chips with many processors/cores.

  16. Commercial Data Mining Software

    NASA Astrophysics Data System (ADS)

    Zhang, Qingyu; Segall, Richard S.

    This chapter discusses selected commercial software for data mining, supercomputing data mining, text mining, and web mining. The selected software are compared with their features and also applied to available data sets. The software for data mining are SAS Enterprise Miner, Megaputer PolyAnalyst 5.0, PASW (formerly SPSS Clementine), IBM Intelligent Miner, and BioDiscovery GeneSight. The software for supercomputing are Avizo by Visualization Science Group and JMP Genomics from SAS Institute. The software for text mining are SAS Text Miner and Megaputer PolyAnalyst 5.0. The software for web mining are Megaputer PolyAnalyst and SPSS Clementine . Background on related literature and software are presented. Screen shots of each of the selected software are presented, as are conclusions and future directions.

  17. 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. PMID:25024513

  18. 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

  19. 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.

  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. PMID:25939772

  1. 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.

  2. On a Family of Multivariate Modified Humbert Polynomials

    PubMed Central

    Aktaş, Rabia; Erkuş-Duman, Esra

    2013-01-01

    This paper attempts to present a multivariable extension of generalized Humbert polynomials. The results obtained here include various families of multilinear and multilateral generating functions, miscellaneous properties, and also some special cases for these multivariable polynomials. PMID:23935411

  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. 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…

  5. 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

  6. Time varying, multivariate volume data reduction

    SciTech Connect

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

    2010-01-01

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

  7. Multivariate 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.

  8. 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

  9. 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.

  10. Algorithms for computing the multivariable stability margin

    NASA Technical Reports Server (NTRS)

    Tekawy, Jonathan A.; Safonov, Michael G.; Chiang, Richard Y.

    1989-01-01

    Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems.

  11. 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.

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

    PubMed Central

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

    2016-01-01

    Background 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). Objective 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. Methods 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. Results 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. Conclusions 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. PMID:27011260

  13. 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.

  14. 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.

  15. A multivariate Baltic Sea environmental index.

    PubMed

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

    2012-11-01

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

  16. 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

  17. 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.

  18. Multivariate statistical analysis of environmental monitoring data

    SciTech Connect

    Ross, D.L.

    1997-11-01

    EPA requires statistical procedures to determine whether soil or ground water adjacent to or below waste units is contaminated. These statistical procedures are often based on comparisons between two sets of data: one representing background conditions, and one representing site conditions. Since statistical requirements were originally promulgated in the 1980s, EPA has made several improvements and modifications. There are, however, problems which remain. One problem is that the regulations do not require a minimum probability that contaminated sites will be correctly identified. Another problems is that the effect of testing several correlated constituents on the probable outcome of the statistical tests has not been quantified. Results from computer simulations to determine power functions for realistic monitoring situations are presented here. Power functions for two different statistical procedures: the Student`s t-test, and the multivariate Hotelling`s T{sup 2} test, are compared. The comparisons indicate that the multivariate test is often more powerful when the tests are applied with significance levels to control the probability of falsely identifying clean sites as contaminated. This program could also be used to verify that statistical procedures achieve some minimum power standard at a regulated waste unit.

  19. 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.

  20. 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.

  1. 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.

  2. Benchmarking a reduced multivariate polynomial pattern classifier.

    PubMed

    Toh, Kar-Ann; Tran, Quoc-Long; Srinivasan, Dipti

    2004-06-01

    A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. Basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.

  3. An Integrated Multivariable Artificial Pancreas Control System

    PubMed Central

    Turksoy, Kamuran; Quinn, Lauretta T.; Littlejohn, Elizabeth

    2014-01-01

    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. PMID:24876613

  4. An integrated multivariable artificial pancreas control system.

    PubMed

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

    2014-05-01

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

  5. 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

  6. 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…

  7. 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. PMID:25258737

  8. 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.

  9. 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

  10. 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.

  11. Heterogeneous voter models

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki; Gibert, N.; Redner, S.

    2010-07-01

    We introduce the heterogeneous voter model (HVM), in which each agent has its own intrinsic rate to change state, reflective of the heterogeneity of real people, and the partisan voter model (PVM), in which each agent has an innate and fixed preference for one of two possible opinion states. For the HVM, the time until consensus is reached is much longer than in the classic voter model. For the PVM in the mean-field limit, a population evolves to a preference-based state, where each agent tends to be aligned with its internal preference. For finite populations, discrete fluctuations ultimately lead to consensus being reached in a time that scales exponentially with population size.

  12. Atmospheric Heterogeneous Stereochemistry

    NASA Astrophysics Data System (ADS)

    Stokes, G. Y.; Buchbinder, A. M.; Geiger, F. M.

    2009-12-01

    This paper addresses the timescale and mechanism of heterogeneous interactions of laboratory models of organic-coated mineral dust and ozone. We are particularly interested in investigating the role of stereochemistry in heterogeneous oxidation reactions involving chiral biogenic VOCs. Using the surface-specific nonlinear optical spectroscopy, sum frequency generation, we tracked terpene diastereomers during exposure to 10^11 to 10^13 molecules of ozone per cm^3 in 1 atm helium to model ozone-limited and ozone-rich tropospheric conditions. Our kinetic data indicate that the diastereomers which orient their reactive C=C double bonds towards the gas phase exhibit heterogeneous ozonolysis rate constants that are two times faster than diastereomers that orient their C=C double bonds away from the gas phase. Insofar as our laboratory model studies are representative of real world environments, our studies suggest that the propensity of aerosol particles coated with chiral semivolatile organic compounds to react with ozone may depend on stereochemistry. Implications of these results for chiral markers that would allow for source appointment of anthropogenic versus biogenic carbon emissions will be discussed.

  13. 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.

  14. 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.

  15. Bayesian Local Contamination Models for Multivariate Outliers

    PubMed Central

    Page, Garritt L.; Dunson, David B.

    2013-01-01

    In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465

  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. Acute proliferative retrolental fibroplasia: multivariate risk analysis.

    PubMed Central

    Flynn, J T

    1983-01-01

    This study has presented a two-way analysis of a data set consisting of demographic, diagnostic, and therapeutic variables against the risk of occurrence of APRLF and its location in the retina in a population of 639 infants in birthweights ranging from 600 to 1500 gm. Univariate and multivariate risk analysis techniques were employed to analyze the data. As established from previous studies, birthweight was a powerful predictor of the outcome variable. Oxygen therapy as defined and quantified in this study was not. Duration of ventilatory assistance did seem associated. The population was not uniform. Infants below 1000 gm birthweight had such a high incidence of APRLF that no other exogenous risk factors seemed of significance. Above 1000 gm birthweight, certain factors, particularly duration of ventilation, seemed of predictive strength and significance. Images FIGURE 5 A FIGURE 5 B FIGURE 4 A FIGURE 4 B PMID:6689564

  18. Inferring phase equations from multivariate time series.

    PubMed

    Tokuda, Isao T; Jain, Swati; Kiss, István Z; Hudson, John L

    2007-08-10

    An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.

  19. 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.

  20. 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

  1. 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.

  2. Continuous mining machine

    SciTech Connect

    Kiefer, H.E.

    1992-02-11

    This patent describes a continuous mining machine for excavating a longitudinal shaft or tunnel underneath the surface of the earth, the mining machine. It comprises: transport means for moving the machine over a floor of the shaft or tunnel that is being excavated; a working platform having forward and trailing ends.

  3. 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.

  4. 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. PMID:26987535

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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

  10. Multivariate mixtures of Erlangs for density estimation under censoring.

    PubMed

    Verbelen, Roel; Antonio, Katrien; Claeskens, Gerda

    2016-07-01

    Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.

  11. Heterogeneous broadband network

    NASA Astrophysics Data System (ADS)

    Dittmann, Lars

    1995-11-01

    Although the vision for the future Integrated Broadband Communication Network (IBCN) is an all optical network, it is certain that for a long period to come, the network will remain very heterogeneous, with a mixture of different physical media (fiber, coax and twisted pair), transmission systems (PDH, SDH, ADSL) and transport protocols (TCP/IP, AAL/ATM, frame relay). In the current work towards the IBCN, the ATM concept is considered the generic network protocol for both public and private network, with the ability to use different underlying transmission protocols and, through adaptation protocols, provide the appropriate services (old as well as new) to the customer. One of the major difficulties of heterogeneous network is the restriction that is usually given by the lowest common denominator, e.g. in terms of single channel capacity. A possible way to overcome these limitations is by extending the ATM concept with a multilink capability, that allows us to use separate resources as one common. The improved flexibility obtained by this protocol extension further allows a real time optimization of network and call configuration, without any impact on the quality of service seen from the user. This paper describes an example of an ATM based multilink protocol that has been experimentally implemented within the RACE project 'STRATOSPHERIC'. The paper outlines the complexity of introducing an extra network functionality compared with the added value, such as an improved ability to recover an error due to a malfunctioning network component.

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

    PubMed

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

    2015-06-01

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

  13. 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

  14. 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.

  15. Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes

    NASA Astrophysics Data System (ADS)

    Yang, Hui; Tang, Ming; Gross, Thilo

    2015-08-01

    One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

  16. Flexible Linked Axes for multivariate data visualization.

    PubMed

    Claessen, Jarry H T; van Wijk, Jarke J

    2011-12-01

    Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.

  17. 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.

  18. Composite density maps for multivariate trajectories.

    PubMed

    Scheepens, Roeland; Willems, Niels; van de Wetering, Huub; Andrienko, Gennady; Andrienko, Natalia; van Wijk, Jarke J

    2011-12-01

    We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains. PMID:22034373

  19. 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.

  20. Multivariate volume visualization through dynamic projections

    SciTech Connect

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

    2014-11-01

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

  1. 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

  2. Multivariate sensitivity to voice during auditory categorization

    PubMed Central

    Peelle, Jonathan E.; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-01-01

    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. PMID:26245316

  3. 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. PMID:26245316

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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

  10. Multipartite entanglement in heterogeneous systems

    NASA Astrophysics Data System (ADS)

    Goyeneche, Dardo; Bielawski, Jakub; Życzkowski, Karol

    2016-07-01

    Heterogeneous bipartite quantum pure states, composed of two subsystems with a different number of levels, cannot have both reductions maximally mixed. In this work, we demonstrate the existence of a wide range of highly entangled states of heterogeneous multipartite systems consisting of N >2 parties such that every reduction to one and two parties is maximally mixed. Two constructions of generating genuinely multipartite maximally entangled states of heterogeneous systems for an arbitrary number of subsystems are presented. Such states are related to quantum error correction codes over mixed alphabets and mixed orthogonal arrays. Additionally, we show the advantages of considering heterogeneous systems in practical implementations of multipartite steering.

  11. 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.

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

    PubMed

    Jupiter, Daniel C

    2014-01-01

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

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

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

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

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

    ERIC Educational Resources Information Center

    Burdenski, Thomas K., Jr.

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

  15. Multivariate Analysis of Ipsative Data: Problems and Solutions.

    ERIC Educational Resources Information Center

    McLean, James E.; Chissom, Brad S.

    The term "ipsative" refers to measurement based on intra-individual comparisons. The research literature in the social sciences contains many cautions about using ipsative data in multivariate analysis. The purpose of this paper is to identify the problems associated with the multivariate and regression analyses of ipsative data and to provide…

  16. Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

    ERIC Educational Resources Information Center

    Ruscio, John; Kaczetow, Walter

    2008-01-01

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

  17. Relationship between Multiple Regression and Selected Multivariable Methods.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

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

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

    ERIC Educational Resources Information Center

    Barcikowski, Robert S.; Elliott, Ronald S.

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

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

    PubMed Central

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

    2012-01-01

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

  20. ibr: Iterative bias reduction multivariate smoothing

    SciTech Connect

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

    2009-01-01

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

  1. Multivariate statistical analysis of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

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

  2. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

    We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

  3. 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.

  4. "easyMine" - realistic and systematic mine detection simulation tooltion

    NASA Astrophysics Data System (ADS)

    Böttger, U.; Beier, K.; Biering, B.; Müller, C.; Peichl, M.; Spyra, W.

    2004-05-01

    Mine detection is to date mainly performed with metal detectors, although new methods for UXO detection are explored worldwide. The main problem for the mine detection to date is, that there exist some ideas of which sensor combinations could yield a high score, but until now there is no systematic analysis of mine detection methods together with realistic environmental conditions to conclude on a physically and technically optimized sensor combination. This gap will be removed by a project "easyMine" (Realistic and systematic Mine Detection Simulation Tool) which will result in a simulation tool for optimizing land mine detection in a realistic mine field. The project idea for this software tool is presented, that will simulate the closed chain of mine detection, including the mine in its natural environment, the sensor, the evaluation and application of the measurements by an user. The tool will be modularly designed. Each chain link will be an independent, exchangeable sub- module and will describe a stand alone part of the whole mine detection procedure. The advantage of the tool will be the evaluation of very different kinds of sensor combinations in relation of their real potential for mine detection. Three detection methods (metal detector, GPR and imaging IR-radiometry) will be explained to be introduced into the easyMine software tool in a first step. An actual example for land mine detection problem will be presented and approaches for solutions with easyMine will be shown.

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

    NASA Astrophysics Data System (ADS)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

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

  7. Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

    PubMed Central

    Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

    2008-01-01

    Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

  8. Text Mining Driven Drug-Drug Interaction Detection

    PubMed Central

    Yan, Su; Jiang, Xiaoqian; Chen, Ying

    2014-01-01

    Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The difficulty lies in mining the true patterns embedded in text data and developing efficient and effective ways to combine heterogenous types of information. We demonstrate a novel approach of leveraging augmented text-mining features to build a logistic regression model with improved prediction performance (in terms of discrimination and calibration). Our model based on synthesized features significantly outperforms the model trained with only structured features (AUC: 96% vs. 91%, Sensitivity: 90% vs. 82% and Specificity: 88% vs. 81%). Along with the quantitative results, we also show learned “latent topics”, an intermediary result of our text mining module, and discuss their implications. PMID:25131635

  9. Hydraulic properties of mine soils with embedded lignitic fragments

    NASA Astrophysics Data System (ADS)

    Gerke, Horst H.

    2014-05-01

    Lignitic mine soils represent a typical two-scale dual-porosity medium consisting of a technogenic mixture of overburden sediments that include lignitic components as dust and as porous fragments embedded within a mostly coarse-textured matrix. Flow and transport processes in such soils are not sufficiently understood to predict the course of soil reclamation or of mine drainage. The objective of this contribution is to identify the most appropriate conceptual model for describing small-scale heterogeneity effects on flow on the basis of the physical structure of the system. Two-domain hydraulic properties were derived based on multi-step outflow data. The interface between fragments and sandy matrix represents an additional pore region that cannot be derived from information of either the matrix or the fragments. New information is required on the geometry (size and shape) and spatial distribution of fragments to describe the properties of the mine soil as a whole.

  10. Political Jurisdictions in Heterogeneous Communities.

    ERIC Educational Resources Information Center

    Alesina, Alberto; Baqir, Reza; Hoxby, Caroline

    2004-01-01

    We investigate whether political jurisdictions form in response to the trade-off between economies of scale and the costs of a heterogeneous population. We consider heterogeneity in income, race, ethnicity, and religion, and we test the model using American school districts, school attendance areas, municipalities, and special districts. We find…

  11. Query Expansion Using Heterogeneous Thesauri.

    ERIC Educational Resources Information Center

    Mandala, Rila; Tokunaga, Takenobu; Tanaka, Hozumi

    2000-01-01

    Proposes a method to improve the performance of information retrieval systems by expanding queries using heterogeneous thesauri. Experiments show that using heterogeneous thesauri with an appropriate weighting method results in better retrieval performance than using only one type of thesaurus. (Author/LRW)

  12. 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.

  13. Multivariate distributions of soil hydraulic parameters

    NASA Astrophysics Data System (ADS)

    Qu, Wei; Pachepsky, Yakov; Huisman, Johan Alexander; Martinez, Gonzalo; Bogena, Heye; Vereecken, Harry

    2014-05-01

    on pedotransfer relationships not only within a given textural class but also on pedotransfer relationships within other textural classes since the pedotransfer relationships are developed across the database containing data for several textural classes. Therefore, joint multivariate parameter distributions for a specific class may not be sufficiently accurate. Currently PTF may give the best prediction of the parameter itself, but they are not designed to estimate correlations between parameters. Covariance matrices for soil hydraulic parameters present an additional type of pedotransfer information that needs to be acquired and used whenever random sets of those parameters are to be generated.

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

    NASA Astrophysics Data System (ADS)

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

    2011-05-01

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

  15. 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.

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

    PubMed

    Guglietta, Daniela; Migliozzi, Antonello; Ricotta, Carlo

    2015-07-01

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

  17. A Multivariate Approach for Mapping Fire Ignition Risk: The Example of the National Park of Cilento (Southern Italy)

    NASA Astrophysics Data System (ADS)

    Guglietta, Daniela; Migliozzi, Antonello; Ricotta, Carlo

    2015-07-01

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

  18. 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.

  19. Reference Point Heterogeneity.

    PubMed

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

    2016-01-01

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

  20. 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

  1. 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

  2. On Heterogeneous Covert Networks

    NASA Astrophysics Data System (ADS)

    Lindelauf, Roy; Borm, Peter; Hamers, Herbert

    Covert organizations are constantly faced with a tradeoff between secrecy and operational efficiency. Lindelauf, Borm and Hamers [13] developed a theoretical framework to determine optimal homogeneous networks taking the above mentioned considerations explicitly into account. In this paper this framework is put to the test by applying it to the 2002 Jemaah Islamiyah Bali bombing. It is found that most aspects of this covert network can be explained by the theoretical framework. Some interactions however provide a higher risk to the network than others. The theoretical framework on covert networks is extended to accommodate for such heterogeneous interactions. Given a network structure the optimal location of one risky interaction is established. It is shown that the pair of individuals in the organization that should conduct the interaction that presents the highest risk to the organization, is the pair that is the least connected to the remainder of the network. Furthermore, optimal networks given a single risky interaction are approximated and compared. When choosing among a path, star and ring graph it is found that for low order graphs the path graph is best. When increasing the order of graphs under consideration a transition occurs such that the star graph becomes best. It is found that the higher the risk a single interaction presents to the covert network the later this transition from path to star graph occurs.

  3. 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.

  4. 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.

  5. 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.

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

    ERIC Educational Resources Information Center

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

    2008-01-01

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

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

    PubMed

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

    2014-09-01

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

  8. Simplifying complex QSAR's (quantitative structure-activity relationships) in toxicity studies with multivariate statistics

    SciTech Connect

    Niemi, G.J.; McKim, J.M.

    1988-07-01

    During the past several decades many quantitative structure-activity relationships (QSAR's) have been derived from relatively small data sets of chemicals in a homologous series and selected empirical observations. An alternative approach is to analyze large data sets consisting of heterogeneous groups of chemicals and to explore QSAR's among these chemicals for generalized patterns of chemical behavior. The use of exploratory multivariate statistical techniques for simplifying complex QSAR problems is demonstrated through the use of research data on biodegradation and mode of toxic action. In these examples, a large number of explanatory variables were examined to explore which variables might best explain whether a chemical biodegrades or whether a toxic response by an organism can be used to identify a mode of toxic action. In both cases, the procedures reduced the number of potential explanatory variables and generated hypotheses about biodegradation and mode of toxic action for future research without explicitly testing an existing hypothesis.

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

    PubMed

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-09-01

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

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

    PubMed

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

    1990-01-01

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

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

    PubMed

    Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

    2015-03-01

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

  12. 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].

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  16. 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.

  17. Node assignment in heterogeneous computing

    NASA Technical Reports Server (NTRS)

    Som, Sukhamoy

    1993-01-01

    A number of node assignment schemes, both static and dynamic, are explored for the Algorithm to Architecture Mapping Model (ATAMM). The architecture under consideration consists of heterogeneous processors and implements dataflow models of real-time applications. Terminology is developed for heterogeneous computing. New definitions are added to the ATAMM for token and assignment classifications. It is proved that a periodic execution is possible for dataflow graphs. Assignment algorithms are developed and proved. A design procedure is described for satisfying an objective function in an heterogeneous architecture. Several examples are provided for illustration.

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

    NASA Astrophysics Data System (ADS)

    Saichev, Alexander; Maillart, Thomas; Sornette, Didier

    2013-04-01

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

  19. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

  20. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges.

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

    PubMed

    Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

    2014-05-20

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

  2. 75 years after mining ends stream insect diversity is still affected by heavy metals.

    PubMed

    Lefcort, Hugh; Vancura, James; Lider, Edward L

    2010-11-01

    A century of heavy metal mining in the western United States has left a legacy of abandoned mines. While large operations have left a visible reminder, smaller one and two-man operations have been overgrown and largely forgotten. We revisited an area of northern Idaho that has not had active mining since at least 1932 and probably since 1910. At three sites along each of 10 mountain streams we sampled larval stream insects and correlated their community diversity to stream levels of arsenic, cadmium, lead, zinc, pH, temperature, oxygen content, and conductivity. Although the streams appear pristine, multivariate statistics indicated that cadmium and zinc levels were significantly correlated with fewer animals, fewer families, a smaller percentage of plecopterans (stoneflies), and lower Shannon H diversity values. After at least 75 years, abandoned mines appear to be still influencing stream communities. PMID:20680454

  3. 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.

  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. Describing the Elephant: Structure and Function in Multivariate Data.

    ERIC Educational Resources Information Center

    McDonald, Roderick P.

    1986-01-01

    There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

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

    PubMed

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

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models. PMID:25645551

  7. Multivariate Generalizability Models for Tests Developed from Tables of Specifications.

    ERIC Educational Resources Information Center

    Jarjoura, David; Brennan, Robert L.

    1983-01-01

    Multivariate generalizability techniques are used to bridge the gap between psychometric constraints and the tables of specifications needed in test development. Techniques are illustrated with results from the American College Testing Assessment Program. (Author/PN)

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

    PubMed

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

    2015-04-30

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

  9. 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.

  10. Reclamation of abandoned mines in West Virginia

    SciTech Connect

    Dove, J.L.

    1983-01-01

    Reclamation of abandoned mine lands in West Virginia involves disturbed areas from both surface and deep mining activities. Reclamation of deep mine lands deal with mine waste piles and mine openings. Reclamation of surface mine lands involves shaping and grading material to obtain a stable slope and installing water management practices.

  11. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Maps § 77.1200 Mine...) The location of railroad tracks and public highways leading to the mine, and mine buildings of a permanent nature with identifying names shown; (k) Underground mine workings underlying and within...

  12. 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...

  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...

  14. 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...

  15. 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...

  16. 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...

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  18. Pattern recognition used to investigate multivariate data in analytical chemistry

    SciTech Connect

    Jurs, P.C.

    1986-06-06

    Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

  19. Multivariate analysis of environmental data for two hydrographic basins

    SciTech Connect

    Andrade, J.M.; Prada, D.; Muniategui, S.; Gonzalez, E.; Alonso, E. )

    1992-02-01

    A multivariate study (PCA Analysis and Cluster analysis) of two Spanish hydrographic basins (The Mandeo and Mero basins) was made to achieve reliable conclusions about their actual physico-chemical environmental situation. Two police-samples' are defined, their effects explained, and are introduced in Cluster analysis as a way to examine sample quality. The multivariate analysis shows different qualities in the two hydrographic basins.

  20. Heterogeneous Oxidation of Catechol.

    PubMed

    Pillar, Elizabeth A; Zhou, Ruixin; Guzman, Marcelo I

    2015-10-15

    Natural and anthropogenic emissions of aromatic hydrocarbons from biomass burning, agro-industrial settings, and fossil fuel combustion contribute precursors to secondary aerosol formation (SOA). How these compounds are processed under humid tropospheric conditions is the focus of current attention to understand their environmental fate. This work shows how catechol thin films, a model for oxygenated aromatic hydrocarbons present in biomass burning and combustion aerosols, undergo heterogeneous oxidation at the air-solid interface under variable relative humidity (RH = 0-90%). The maximum reactive uptake coefficient of O3(g) by catechol γO3 = (7.49 ± 0.35) × 10(-6) occurs for 90% RH. Upon exposure of ca. 104-μm thick catechol films to O3(g) mixing ratios between 230 ppbv and 25 ppmv, three main reaction pathways are observed. (1) The cleavage of the 1,2 carbon-carbon bond at the air-solid interface resulting in the formation of cis,cis-muconic acid via primary ozonide and hydroperoxide intermediates. Further direct ozonolysis of cis,cis-muconic yields glyoxylic, oxalic, crotonic, and maleic acids. (2) A second pathway is evidenced by the presence of Baeyer-Villiger oxidation products including glutaconic 4-hydroxy-2-butenoic and 5-oxo-2-pentenoic acids during electrospray ionization mass spectrometry (MS) and ion chromatography MS analyses. (3) Finally, indirect oxidation by in situ produced hydroxyl radical (HO(•)) results in the generation of semiquinone radical intermediates toward the synthesis of polyhydoxylated aromatic rings such as tri-, tetra-, and penta-hydroxybenzene. Remarkably, heavier polyhydroxylated biphenyl and terphenyl products present in the extracted oxidized films result from coupling reactions of semiquinones of catechol and its polyhydroxylated rings. The direct ozonolysis of 1,2,3- and 1,2,4-trihydroxybenezene yields 2- and 3-hydroxy-cis,cis-muconic acid, respectively. The production of 2,4- or 3,4-dihdroxyhex-2-enedioic acid is

  1. Sampling effort affects multivariate comparisons of stream assemblages

    USGS Publications Warehouse

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

    2002-01-01

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

  2. 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)

  3. Review of South American mines

    SciTech Connect

    Not Available

    1984-07-01

    A general overview is presented of the mining activity and plans for South America. The countries which are presented are Columbia, Argentina, Brazil, Venezuela, Chile, Peru, and Bolivia. The products of the mines include coal, bauxite, gold, iron, uranium, copper and numerous other minor materials. A discussion of current production, support and processing facilities, and mining strategies is also given.

  4. 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....

  5. 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.

  6. Land mine detection by time reversal acousto-seismic method

    NASA Astrophysics Data System (ADS)

    Sutin, Alexander; Sarvazyan, Armen; Johnson, Paul; Tencate, James

    2001-05-01

    We present a concept and results of a pilot study on land mine detection based on the use of time reversal acoustics (TRA). TRA provides a possibility of highly effective concentrating of seismic wave energy in time and space in complex heterogeneous media. TRA focusing of seismic waves on a land mine increases the detection abilities of conventional linear and nonlinear acousto-seismic methods. Such factors as medium inhomogeneities, presence of reflecting boundaries, which could critically limit conventional acoustic approaches, do not affect TRA based method. The TRA mine detection system comprises several air borne or seismic sources and a noncontact (laser vibrometer) device for remote measurements of the surface vibration. The TRA system focuses a seismic wave at a surface point where the vibration is measured. The focusing point is scanned across the search area. The amplitude and frequency dependence of the signal from the seismic wave focusing point and nonlinear acoustic effects are analyzed to assess probability of the mine presence. Preliminary experiments confirmed high focusing ability of the TRA seismo-acoustic system in complex conditions (a laboratory tank with sand) and demonstrated a significant increase in the surface vibration in the presence of mine imitator. [Work supported by DoD grant.

  7. Homogeneous, Heterogeneous, and Enzymatic Catalysis.

    ERIC Educational Resources Information Center

    Oyama, S. Ted; Somorjai, Gabor A.

    1988-01-01

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

  8. Heterogeneity in motor driven transport

    NASA Astrophysics Data System (ADS)

    Tabei, Ali

    2015-03-01

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

  9. PUNCH: Population Characterization of Heterogeneity.

    PubMed

    Tunc, Birkan; Ghanbari, Yasser; Smith, Alex R; Pandey, Juhi; Browne, Aaron; Schultz, Robert T; Verma, Ragini

    2014-09-01

    Neuropsychiatric disorders are notoriously heterogeneous in their presentation, which precludes straightforward and objective description of the differences between affected and typical populations that therefore makes finding reliable biomarkers a challenge. This difficulty underlines the need for reliable methods to capture sample characteristics of heterogeneity using a single continuous measure, incorporating the multitude of scores used to describe different aspects of functioning. This study addresses this challenge by proposing a general method of identifying and quantifying the heterogeneity of any clinical population using a severity measure called the PUNCH (Population Characterization of Heterogeneity). PUNCH is a decision level fusion technique to incorporate decisions of various phenotypic scores, while providing interpretable weights for scores. We provide applications of our framework to simulated datasets and to a large sample of youth with Autism Spectrum Disorder (ASD). Next we stratify PUNCH scores in our ASD sample and show how severity moderates findings of group differences in diffusion weighted brain imaging data; more severely affected subgroups of ASD show expanded differences compared to age and gender matched healthy controls. Results demonstrate the ability of our measure in quantifying the underlying heterogeneity of the clinical samples, and suggest its utility in providing researchers with reliable severity assessments incorporating population heterogeneity.

  10. Analysis of active renin heterogeneity.

    PubMed

    Katz, S A; Malvin, R L; Lee, J; Kim, S H; Murray, R D; Opsahl, J A; Abraham, P A

    1991-09-01

    Active renin is a heterogeneous enzyme that can be separated into multiple forms with high-resolution isoelectric focusing. The isoelectric heterogeneity may result from differences in glycosylation between the different forms. In order to determine the relationship between active renin heterogeneity and differences in composition or attachment of oligosaccharides, two separate experiments were performed: (i) Tunicamycin, which interferes with normal glycosylation processing, increased the proportion of relatively basic renin forms secreted into the incubation media by rat renal cortical slices. (ii) Endoglycosidase F, which enzymatically removes carbohydrate from some classes of glycoprotein, similarly increased the proportion of relatively basic forms when incubated with active human recombinant renin. In addition, further studies with inhibitors of human renin activity revealed that the heterogeneous renin forms were similarly inhibited by two separate renin inhibitors. These results are consistent with the hypothesis that renin isoelectric heterogeneity is due in part to differences in carbohydrate moiety attachment and that the heterogeneity of renin does not influence access of direct renin inhibitors to the active site of renin.

  11. Analysis of active renin heterogeneity.

    PubMed

    Katz, S A; Malvin, R L; Lee, J; Kim, S H; Murray, R D; Opsahl, J A; Abraham, P A

    1991-09-01

    Active renin is a heterogeneous enzyme that can be separated into multiple forms with high-resolution isoelectric focusing. The isoelectric heterogeneity may result from differences in glycosylation between the different forms. In order to determine the relationship between active renin heterogeneity and differences in composition or attachment of oligosaccharides, two separate experiments were performed: (i) Tunicamycin, which interferes with normal glycosylation processing, increased the proportion of relatively basic renin forms secreted into the incubation media by rat renal cortical slices. (ii) Endoglycosidase F, which enzymatically removes carbohydrate from some classes of glycoprotein, similarly increased the proportion of relatively basic forms when incubated with active human recombinant renin. In addition, further studies with inhibitors of human renin activity revealed that the heterogeneous renin forms were similarly inhibited by two separate renin inhibitors. These results are consistent with the hypothesis that renin isoelectric heterogeneity is due in part to differences in carbohydrate moiety attachment and that the heterogeneity of renin does not influence access of direct renin inhibitors to the active site of renin. PMID:1908097

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

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

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

  13. Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

    PubMed

    Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

    2015-01-01

    We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site.

  14. 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].

  15. Plutonium mining for cleanup.

    PubMed

    Bramlitt, E T

    1988-08-01

    Cleanup is the act of making a contaminated site relatively free of Pu so it may be used without radiological safety restrictions. Contaminated ground is the focus of major cleanups. Cleanup traditionally involves determining Pu content of soil, digging up soil in which radioactivity exceeds guidelines, and relocating excised soil to a waste-disposal site. Alternative technologies have been tested at Johnston Atoll (JA), where there is as much as 100,000 m3 of Pu-contaminated soil. A mining pilot plant operated for the first 6 mo of 1986 and made 98% of soil tested "clean", from more than 40 kBq kg-1 (1000 pCi g-1) to less than about 500 Bq kg-1 (15 pCi g-1) by concentrating Pu in 2% of the soil. The pilot plant is now installed at the U.S. Department of Energy Nevada Test Site for evaluating cleanup of other contaminated soils and refining cleanup effectiveness. A full-scale cleanup plant has been programmed for JA in 1988. In this paper, previous cleanups are reviewed, and the mining endeavor at JA is detailed. "True soil cleanup" is contrasted with the classical "soil relocation cleanup." The mining technology used for Pu cleanup has been in use for more than a century. Mining for cleanup, however, is unique. It is envisioned as being prominent for radiological and other cleanups in the future.

  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. 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.

  18. 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…

  19. Detecting Neuroimaging Biomarkers for Schizophrenia: A Meta-Analysis of Multivariate Pattern Recognition Studies

    PubMed Central

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

    2015-01-01

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

  20. Comparative assessment of groundwater chemistry evolution at three abandoned mines and its practical implications

    SciTech Connect

    Solc, J.

    1996-12-31

    The reclamation effort typically deals with consequences of mining activity instead of being planned well before the mining. Detailed assessment of principal hydro- and geochemical processes participating in pore and groundwater chemistry evolution was carried out at three surface mine localities in North Dakota-the Fritz mine, the Indian Head mine, and the Velva mine. The geochemical model MINTEQUA2 and advanced statistical analysis coupled with traditional interpretive techniques were used to determine site-specific environmental characteristics and to compare the differences between study sites. Multivariate statistical analysis indicates that sulfate, magnesium, calcium, the gypsum saturation index, and sodium contribute the most to overall differences in groundwater chemistry between study sites. Soil paste extract pH and EC measurements performed on over 3700 samples document extremely acidic soils at the Fritz mine. The number of samples with pH <5.5 reaches 80%-90% of total samples from discrete depth near the top of the soil profile at the Fritz mine. Soil samples from Indian Head and Velva do not indicate the acidity below the pH of 5.5 limit. The percentage of samples with EC > 3 mS cm{sup -1} is between 20% and 40% at the Fritz mine and below 20% for samples from Indian Head and Velva. The results of geochemical modeling indicate an increased tendency for gypsum saturation within the vadose zone, particularly within the lands disturbed by mining activity. This trend is directly associated with increased concentrations of sulfate anions as a result of mineral oxidation. Geochemical modeling, statistical analysis, and soil extract pH and EC measurements proved to be reliable, fast, and relatively cost-effective tools for the assessment of soil acidity, the extent of the oxidation zone, and the potential for negative impact on pore and groundwater chemistry.

  1. Dealing with spatial heterogeneity

    NASA Astrophysics Data System (ADS)

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

    2005-03-01

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

  2. Dealing with spatial heterogeneity

    NASA Astrophysics Data System (ADS)

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

    2005-03-01

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

  3. 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.

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

    PubMed

    Huang, Xingxing; Zhu, Yi; Ji, Hongbing

    2013-10-01

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

  5. Multivariate refutation of aetiological hypotheses in non-experimental epidemiology.

    PubMed

    Maclure, M

    1990-12-01

    Extension of Karl Popper's logic of refutation from the realm of contingency tables to multivariate modelling leads to the conclusion that rigorously scientific multivariate analysis in non-experimental epidemiology differs from the traditional quasi-scientific approach. Instead of aiming for high sensitivity in detecting aetiological agents, the goal in refutation is high specificity--to give the best defence of the 'innocence' of every exposure hypothesized as being a cause. Instead of 'forward selection' or 'backward elimination', multivariate refutation uses the method of 'forward elimination'. This entails a likelihood approach (which may be complemented by, but should be demarcated from, Bayesian methods) not only for statistical inference but also, by analogy, for study design and conduct: one starts with the conclusion (the estimate or hypothesis) and works backwards to the observations (the likelihood of the data or the design of the study). Differences in practice can sometimes be large, as illustrated by a study of hypothesized triggers of myocardial infarction. Multivariate refutation should replace the concept of multivariate modelling in non-experimental epidemiology.

  6. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067

  7. Brushing of attribute clouds for the visualization of multivariate data.

    PubMed

    Jänicke, Heike; Böttinger, Michael; Scheuermann, Gerik

    2008-01-01

    The visualization and exploration of multivariate data is still a challenging task. Methods either try to visualize all variables simultaneously at each position using glyph-based approaches or use linked views for the interaction between attribute space and physical domain such as brushing of scatterplots. Most visualizations of the attribute space are either difficult to understand or suffer from visual clutter. We propose a transformation of the high-dimensional data in attribute space to 2D that results in a point cloud, called attribute cloud, such that points with similar multivariate attributes are located close to each other. The transformation is based on ideas from multivariate density estimation and manifold learning. The resulting attribute cloud is an easy to understand visualization of multivariate data in two dimensions. We explain several techniques to incorporate additional information into the attribute cloud, that help the user get a better understanding of multivariate data. Using different examples from fluid dynamics and climate simulation, we show how brushing can be used to explore the attribute cloud and find interesting structures in physical space.

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

    SciTech Connect

    Haaland, D.M.

    1991-01-01

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

  9. A multivariate prediction model for microarray cross-hybridization

    PubMed Central

    Chen, Yian A; Chou, Cheng-Chung; Lu, Xinghua; Slate, Elizabeth H; Peck, Konan; Xu, Wenying; Voit, Eberhard O; Almeida, Jonas S

    2006-01-01

    Background Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. Results We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used. Conclusion A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments. PMID:16509965

  10. Multicomponent seismic noise attenuation with multivariate order statistic filters

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  11. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

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

  12. 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. PMID:26517999

  13. Reaction Selectivity in Heterogeneous Catalysis

    SciTech Connect

    Somorjai, Gabor A.; Kliewer, Christopher J.

    2009-02-02

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

  14. 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

  15. Derivation of Multivariate Syndromic Outcome Metrics for Consistent Testing across Multiple Models of Cervical Spinal Cord Injury in Rats

    PubMed Central

    Ferguson, Adam R.; Irvine, Karen-Amanda; Gensel, John C.; Nielson, Jessica L.; Lin, Amity; Ly, Johnathan; Segal, Mark R.; Ratan, Rajiv R.; Bresnahan, Jacqueline C.; Beattie, Michael S.

    2013-01-01

    Spinal cord injury (SCI) and other neurological disorders involve complex biological and functional changes. Well-characterized preclinical models provide a powerful tool for understanding mechanisms of disease; however managing information produced by experimental models represents a significant challenge for translating findings across research projects and presents a substantial hurdle for translation of novel therapies to humans. In the present work we demonstrate a novel ‘syndromic’ information-processing approach for capitalizing on heterogeneous data from diverse preclinical models of SCI to discover translational outcomes for therapeutic testing. We first built a large, detailed repository of preclinical outcome data from 10 years of basic research on cervical SCI in rats, and then applied multivariate pattern detection techniques to extract features that are conserved across different injury models. We then applied this translational knowledge to derive a data-driven multivariate metric that provides a common ‘ruler’ for comparisons of outcomes across different types of injury (NYU/MASCIS weight drop injuries, Infinite Horizons (IH) injuries, and hemisection injuries). The findings revealed that each individual endpoint provides a different view of the SCI syndrome, and that considering any single outcome measure in isolation provides a misleading, incomplete view of the SCI syndrome. This limitation was overcome by taking a novel multivariate integrative approach for leveraging complex data from preclinical models of neurological disease to identify therapies that target multiple outcomes. We suggest that applying this syndromic approach provides a roadmap for translating therapies for SCI and other complex neurological diseases. PMID:23544088

  16. Derivation of multivariate syndromic outcome metrics for consistent testing across multiple models of cervical spinal cord injury in rats.

    PubMed

    Ferguson, Adam R; Irvine, Karen-Amanda; Gensel, John C; Nielson, Jessica L; Lin, Amity; Ly, Johnathan; Segal, Mark R; Ratan, Rajiv R; Bresnahan, Jacqueline C; Beattie, Michael S

    2013-01-01

    Spinal cord injury (SCI) and other neurological disorders involve complex biological and functional changes. Well-characterized preclinical models provide a powerful tool for understanding mechanisms of disease; however managing information produced by experimental models represents a significant challenge for translating findings across research projects and presents a substantial hurdle for translation of novel therapies to humans. In the present work we demonstrate a novel 'syndromic' information-processing approach for capitalizing on heterogeneous data from diverse preclinical models of SCI to discover translational outcomes for therapeutic testing. We first built a large, detailed repository of preclinical outcome data from 10 years of basic research on cervical SCI in rats, and then applied multivariate pattern detection techniques to extract features that are conserved across different injury models. We then applied this translational knowledge to derive a data-driven multivariate metric that provides a common 'ruler' for comparisons of outcomes across different types of injury (NYU/MASCIS weight drop injuries, Infinite Horizons (IH) injuries, and hemisection injuries). The findings revealed that each individual endpoint provides a different view of the SCI syndrome, and that considering any single outcome measure in isolation provides a misleading, incomplete view of the SCI syndrome. This limitation was overcome by taking a novel multivariate integrative approach for leveraging complex data from preclinical models of neurological disease to identify therapies that target multiple outcomes. We suggest that applying this syndromic approach provides a roadmap for translating therapies for SCI and other complex neurological diseases. PMID:23544088

  17. An update on multivariate return periods in hydrology

    NASA Astrophysics Data System (ADS)

    Gräler, Benedikt; Petroselli, Andrea; Grimaldi, Salvatore; De Baets, Bernard; Verhoest, Niko

    2016-05-01

    Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.

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

    SciTech Connect

    Tunga, M. Alper; Demiralp, Metin

    2011-09-14

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

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

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.

    1981-01-01

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

  20. Resource heterogeneity can facilitate cooperation.

    PubMed

    Kun, Ádám; Dieckmann, Ulf

    2013-01-01

    Although social structure is known to promote cooperation, by locally exposing selfish agents to their own deeds, studies to date assumed that all agents have access to the same level of resources. This is clearly unrealistic. Here we find that cooperation can be maintained when some agents have access to more resources than others. Cooperation can then emerge even in populations in which the temptation to defect is so strong that players would act fully selfishly if their resources were distributed uniformly. Resource heterogeneity can thus be crucial for the emergence and maintenance of cooperation. We also show that resource heterogeneity can hinder cooperation once the temptation to defect is significantly lowered. In all cases, the level of cooperation can be maximized by managing resource heterogeneity.

  1. Static heterogeneities in liquid water

    NASA Astrophysics Data System (ADS)

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

    2004-10-01

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

  2. Simulator for heterogeneous dataflow architectures

    NASA Technical Reports Server (NTRS)

    Malekpour, Mahyar R.

    1993-01-01

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

  3. 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. PMID:19244711

  4. 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.

  5. 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.

  6. 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.

  7. Realtime mine ventilation simulation

    SciTech Connect

    McDaniel, K.H.; Wallace, K.G. Jr.

    1997-04-01

    This paper describes the development of a Windows based, interactive mine ventilation simulation software program at the Waste Isolation Pilot Plant (WIPP). To enhance the operation of the underground ventilation system, Westinghouse Electric Corporation developed the program called WIPPVENT. While WIPPVENT includes most of the functions of the commercially available simulation program VNETPC and uses the same subroutine to calculate airflow distributions, the user interface has been completely rewritten as a Windows application with screen graphics. WIPPVENT is designed to interact with WIPP ventilation monitoring systems through the sitewise Central monitoring System. Data can be continuously collected from the Underground Ventilation Remote Monitoring and Control System (e.g., air quantity and differential pressure) and the Mine Weather Stations (psychrometric data). Furthermore, WIPPVENT incorporates regulator characteristic curves specific to the site. The program utilizes this data to create and continuously update a REAL-TIME ventilation model. This paper discusses the design, key features, and interactive capabilities of WIPPVENT.

  8. Mining with microbes

    SciTech Connect

    Rawlings., D.E.; Silver, S.

    1995-08-01

    Microbes are playing increasingly important roles in commercial mining operations, where they are being used in the {open_quotes}bioleaching{close_quotes} of copper, uranium, and gold ores. Direct leaching is when microbial metabolism changes the redox state of the metal being harvested, rendering it more soluble. Indirect leaching includes redox chemistry of other metal cations that are then coupled in chemical oxidation or reduction of the harvested metal ion and microbial attack upon and solubilization of the mineral matrix in which the metal is physically embedded. In addition, bacterial cells are used to detoxify the waste cyanide solution from gold-mining operations and as {open_quotes}absorbants{close_quotes} of the mineral cations. Bacterial cells may replace activated carbon or alternative biomass. With an increasing understanding of microbial physiology, biochemistry and molecular genetics, rational approaches to improving these microbial activities become possible. 40 refs., 3 figs.

  9. Qualitative and quantitative analysis of complex temperature-programmed desorption data by multivariate curve resolution

    NASA Astrophysics Data System (ADS)

    Rodríguez-Reyes, Juan Carlos F.; Teplyakov, Andrew V.; Brown, Steven D.

    2010-10-01

    The substantial amount of information carried in temperature-programmed desorption (TPD) experiments is often difficult to mine due to the occurrence of competing reaction pathways that produce compounds with similar mass spectrometric features. Multivariate curve resolution (MCR) is introduced as a tool capable of overcoming this problem by mathematically detecting spectral variations and correlations between several m/z traces, which is later translated into the extraction of the cracking pattern and the desorption profile for each desorbate. Different from the elegant (though complex) methods currently available to analyze TPD data, MCR analysis is applicable even when no information regarding the specific surface reaction/desorption process or the nature of the desorbing species is available. However, when available, any information can be used as constraints that guide the outcome, increasing the accuracy of the resolution. This approach is especially valuable when the compounds desorbing are different from what would be expected based on a chemical intuition, when the cracking pattern of the model test compound is difficult or impossible to obtain (because it could be unstable or very rare), and when knowing major components desorbing from the surface could in more traditional methods actually bias the quantification of minor components. The enhanced level of understanding of thermal processes achieved through MCR analysis is demonstrated by analyzing three phenomena: i) the cryogenic desorption of vinyltrimethylsilane from silicon, an introductory system where the known multilayer and monolayer components are resolved; ii) acrolein hydrogenation on a bimetallic Pt-Ni-Pt catalyst, where a rapid identification of hydrogenated products as well as other desorbing species is achieved, and iii) the thermal reaction of Ti[N(CH 3) 2] 4 on Si(100), where the products of surface decomposition are identified and an estimation of the surface composition after the

  10. 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.

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

    PubMed

    Wang, Yiyi; Kockelman, Kara M

    2013-11-01

    This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. PMID:24036167

  12. 30 CFR 49.13 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ..., DEPARTMENT OF LABOR EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines... the operator as to the number of miners willing to serve on a mine rescue team; (8) The...

  13. 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.

  14. Multivariate concentration determination using principal component regression with residual analysis

    PubMed Central

    Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark

    2009-01-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977

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

    PubMed

    Orlandini, Serena; Gotti, Roberto; Furlanetto, Sandra

    2014-01-01

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

  16. Fixed order dynamic compensation for multivariable linear systems

    NASA Technical Reports Server (NTRS)

    Kramer, F. S.; Calise, A. J.

    1986-01-01

    This paper considers the design of fixed order dynamic compensators for multivariable time invariant linear systems, minimizing a linear quadratic performance cost functional. Attention is given to robustness issues in terms of multivariable frequency domain specifications. An output feedback formulation is adopted by suitably augmenting the system description to include the compensator states. Either a controller or observer canonical form is imposed on the compensator description to reduce the number of free parameters to its minimal number. The internal structure of the compensator is prespecified by assigning a set of ascending feedback invariant indices, thus forming a Brunovsky structure for the nominal compensator.

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

    PubMed

    Gowen, Aoife A; Dorrepaal, Ronan M

    2016-01-01

    Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy. PMID:27384549

  18. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

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

    1973-01-01

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

  19. Steady-state decoupling and design of linear multivariable systems

    NASA Technical Reports Server (NTRS)

    Thaler, G. J.

    1974-01-01

    A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.

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

    NASA Technical Reports Server (NTRS)

    Tubbs, J. D.

    1979-01-01

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

  1. 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....

  2. 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....

  3. 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....

  4. 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....

  5. 30 CFR 75.203 - Mining methods.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining...) A sidecut shall be started only from an area that is supported in accordance with the roof...

  6. 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

  7. 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.

  8. 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.

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

    PubMed Central

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

    2015-01-01

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

  10. Deviation of the statistical fluctuation in heterogeneous anomalous diffusion

    NASA Astrophysics Data System (ADS)

    Itto, Yuichi

    2016-11-01

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

  11. 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...

  12. 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...

  13. 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...

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

    Code of Federal Regulations, 2013 CFR

    2013-07-01

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

  15. 30 CFR 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...

  16. 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...

  17. 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...

  18. 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...

  19. 30 CFR 49.4 - Alternative mine rescue capability for special mining conditions.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

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

  20. 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.

  1. Upstream reciprocity in heterogeneous networks.

    PubMed

    Iwagami, Akio; Masuda, Naoki

    2010-08-01

    Many mechanisms for the emergence and maintenance of altruistic behavior in social dilemma situations have been proposed. Indirect reciprocity is one such mechanism, where other-regarding actions of a player are eventually rewarded by other players with whom the original player has not interacted. The upstream reciprocity (also called generalized indirect reciprocity) is a type of indirect reciprocity and represents the concept that those helped by somebody will help other unspecified players. In spite of the evidence for the enhancement of helping behavior by upstream reciprocity in rats and humans, theoretical support for this mechanism is not strong. In the present study, we numerically investigate upstream reciprocity in heterogeneous contact networks, in which the players generally have different number of neighbors. We show that heterogeneous networks considerably enhance cooperation in a game of upstream reciprocity. In heterogeneous networks, the most generous strategy, by which a player helps a neighbor on being helped and in addition initiates helping behavior, first occupies hubs in a network and then disseminates to other players. The scenario to achieve enhanced altruism resembles that seen in the case of the Prisoner's Dilemma game in heterogeneous networks.

  2. Heterogeneous catalytic alcoholysis of benzonitrile

    SciTech Connect

    Kagarlitskii, A.D.; Dmumakaev, K.Kh.; Bekova, N.S.

    1986-04-01

    The authors investigate the possibility of the direct heterogeneous catalytic synthesis of ethylbenzoate from benzonitrile. The catalysts tested were oxides of aluminium, titanium, and vanadium. The main conversion product detected chromatographically was ethylbenzoate; benzaldehyde, benzamide, and benzanilide were also identified. Aluminium oxide was found to be the most effective catalyst.

  3. Heterogeneity in the gingival fibromatoses.

    PubMed

    Takagi, M; Yamamoto, H; Mega, H; Hsieh, K J; Shioda, S; Enomoto, S

    1991-11-15

    Forty-nine cases of isolated familial and idiopathic gingival fibromatoses, consisting of 12 cases from six families and 37 cases of idiopathic gingival fibromatosis, were reviewed. Pedigrees of five families revealed various penetrances and genetic heterogeneity as suggested by the presence of both autosomal dominant and autosomal recessive inheritances. Ultrastructurally, the lesions were composed of fibroblast-like cells and myofibroblast-like cells, with the former being the predominant cell type. The 267 cases of familial and idiopathic gingival fibromatoses were analyzed, and they with or without hypertrichosis, mental retardation, and/or epilepsy. These included 49 cases seen by the authors, 50 cases from the Japanese literature, and 168 cases from non-Japanese literature. Isolated gingival fibromatosis occurred more frequently after age of 12 years (P less than 0.0074). There was no significant difference in age of onset between generalized and localized forms of the idiopathic gingival fibromatosis. Gingival fibromatosis with hypertrichosis and mental retardation and/or epilepsy occurred frequently before 12 years (P less than 0.069). It has been shown that heterogeneity of the gingival fibromatosis is a result of either histologic heterogeneity, genetic heterogeneity, or a combination with other systemic disorders.

  4. Social Capital and Community Heterogeneity

    ERIC Educational Resources Information Center

    Coffe, Hilde

    2009-01-01

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

  5. Teaching about Heterogeneous Response Models

    ERIC Educational Resources Information Center

    Murray, Michael P.

    2014-01-01

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

  6. Flammability of Heterogeneously Combusting Metals

    NASA Technical Reports Server (NTRS)

    Jones, Peter D.

    1998-01-01

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

  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. 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. PMID:15869215

  9. An affordable humanitarian mine detector

    NASA Astrophysics Data System (ADS)

    Daniels, David J.; Curtis, Paul; Amin, Rajan; Dittmer, Jon

    2004-09-01

    This paper describes the further development of the MINETECT affordable humanitarian mine detector produced by ERA Technology with sponsorship from the UK Department for International Development. Using a radically different patented approach from conventional ground penetrating radar (GPR) designs in terms of the man machine interface, MINETECT offers simplicity of use and affordability, both key factors in humanitarian demining operations. Following trials in 2002 and reported at SPIE 2002, further development work including research on classifying mines, based on data from planned trials in the United Kingdom, is presented. MINETECT has the capability of detecting completely non-metallic mines and offers a considerable improvement in hand-held mine detection.

  10. 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.

  11. 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.

  12. Multivariate Multinomial Logit Models for Dyadic Sequential Interaction Data

    ERIC Educational Resources Information Center

    de Rooij, Mark; Kroonenberg, Pieter M.

    2003-01-01

    The analysis of discrete dyadic sequential behavior and, in particular, the problem of forecasting future behavior from current and past behavior in such data is the main theme of the present article. We propose to use multivariate multinomial logit models and the potential of which will be demonstrated with data on Imagery play therapy. In such a…

  13. MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

    EPA Science Inventory

    We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

  14. FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

    EPA Science Inventory

    This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

  15. The Optimization of Multivariate Generalizability Studies with Budget Constraints.

    ERIC Educational Resources Information Center

    Marcoulides, George A.; Goldstein, Zvi

    1992-01-01

    A method is presented for determining the optimal number of conditions to use in multivariate-multifacet generalizability designs when resource constraints are imposed. A decision maker can determine the number of observations needed to obtain the largest possible generalizability coefficient. The procedure easily applies to the univariate case.…

  16. MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

    EPA Science Inventory

    Abstract

    Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

  17. Causal diagrams and multivariate analysis III: confound it!

    PubMed

    Jupiter, Daniel C

    2015-01-01

    This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined.

  18. Multivariate Normal Integrals and Contingency Tables with Ordered Categories.

    ERIC Educational Resources Information Center

    Wang, Yuchung J.

    1997-01-01

    A k-dimensional multivariate normal distribution is made discrete by partitioning the k-dimensional Euclidean space with rectangular grids. The probability integrals over the partitioned cubes forms a k-dimensional contingency table with ordered categories. A loglinear model with main effects plus two-way interactions provides an approximation for…

  19. Use of Technology to Develop Student Intuition in Multivariable Calculus

    ERIC Educational Resources Information Center

    Kaur, Manmohan

    2006-01-01

    In order to get undergraduates interested in mathematics, it is essential to involve them in its "discovery". In this paper, we will explain how technology and the knowledge of lower dimensional calculus can be used to help them develop intuition leading to their discovering the first derivative rule in multivariable calculus. (Contains 7 figures.)

  20. A Multivariate Generalizability Analysis of the Multistate Bar Examination

    ERIC Educational Resources Information Center

    Yin, Ping

    2005-01-01

    The main purpose of this study is to examine the content structure of the Multistate Bar Examination (MBE) using the "table of specifications" model from the perspective of multivariate generalizability theory. Specifically, using MBE data collected over different years (six administrations: three from the February test and three from July test),…

  1. A Multivariate Kruskal-Wallis Test with Post Hoc Procedures.

    ERIC Educational Resources Information Center

    Katz, Barry M.; McSweeney, Maryellen

    1980-01-01

    An explicit statement of a statistic which is a nonparametric analog to one-way MANOVA is presented. The statistic is a multivariate extension of the nonparametric Kruskal-Wallis test (1952). In addition two post hoc procedures are developed and compared. (Author/JKS)

  2. Remote Multivariable Control Design Using a Competition Game

    ERIC Educational Resources Information Center

    Atanasijevic-Kunc, M.; Logar, V.; Karba, R.; Papic, M.; Kos, A.

    2011-01-01

    In this paper, some approaches to teaching multivariable control design are discussed, with special attention being devoted to a step-by-step transition to e-learning. The approach put into practice and presented here is developed through design projects, from which one is chosen as a competition game and is realized using the E-CHO system,…

  3. Multivariate Meta-Analysis Using Individual Participant Data

    ERIC Educational Resources Information Center

    Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  4. Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM

    ERIC Educational Resources Information Center

    Mair, Patrick; Satorra, Albert; Bentler, Peter M.

    2012-01-01

    This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo…

  5. MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

    EPA Science Inventory

    Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

  6. Multivariate Tests for Correlated Data in Completely Randomized Designs.

    ERIC Educational Resources Information Center

    Mielke, Paul W., Jr.; Berry, Kenneth J.

    1999-01-01

    Provides power comparisons for three permutation tests and the Bartlett-Nanda-Pillai trace test (BNP) (M. Bartlett, 1939; D. Nanda, 1950; K. Pillai, 1955) in completely randomized experimental designs with correlated multivariate-dependent variables. The power of the BNP was generally found to be less than that of at least one of the permutation…

  7. Some Properties of Two Measures of Multivariate Association.

    ERIC Educational Resources Information Center

    van den Burg, Willem; Lewis, Charles

    1988-01-01

    Measures of multivariate association, based on Wilks'"lambda" or the Bartlett-Nanda-Pillai trace criterion "V", are compared in terms of properties of univariate R-squared, which they generalize. A unified set of derivations of properties is provided, which is self-contained and not restricted to decompositions in canonical variates. (Author/TJH)

  8. Bayesian Methods for Scalable Multivariate Value-Added Assessment

    ERIC Educational Resources Information Center

    Lockwood, J. R.; McCaffrey, Daniel F.; Mariano, Louis T.; Setodji, Claude

    2007-01-01

    There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian…

  9. Multivariable feedback design - Concepts for a classical/modern synthesis

    NASA Technical Reports Server (NTRS)

    Doyle, J. C.; Stein, G.

    1981-01-01

    This paper presents a practical design perspective on multivariable feedback control problems. It reviews the basic issue - feedback design in the face of uncertainties - and generalizes known single-input, single-output (SISO) statements and constraints of the design problem to multiinput, multioutput (MIMO) cases. Two major MIMO design approaches are then evaluated in the context of these results.

  10. Multivariate-normality goodness-of-fit tests

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

    Computer program applies chi-square Pearson test to multivariate statistics for application in any field in which data of two or more variables (dimensions) are sampled for statistical purposes. Program handles dimensions two through five, with up to thousand data sets.

  11. Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

    ERIC Educational Resources Information Center

    Mullen, Kenneth; Ennis, Daniel M.

    1987-01-01

    Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

  12. Maximum Likelihood Estimation of Multivariate Polyserial and Polychoric Correlation Coefficients.

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Lee, Sik-Yum

    1987-01-01

    Reparameterization is used to find the maximum likelihood estimates of parameters in a multivariate model having some component variable observable only in polychotomous form. Maximum likelihood estimates are found by a Fletcher Powell algorithm. In addition, the partition maximum likelihood method is proposed and illustrated. (Author/GDC)

  13. Univariate Analysis of Multivariate Outcomes in Educational Psychology.

    ERIC Educational Resources Information Center

    Hubble, L. M.

    1984-01-01

    The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

  14. Inclusion of Dominance Effects in the Multivariate GBLUP Model.

    PubMed

    dos Santos, Jhonathan Pedroso Rigal; Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Balestre, Marcio; Von Pinho, Renzo Garcia

    2016-01-01

    New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056

  15. Estimating the decomposition of predictive information in multivariate systems

    NASA Astrophysics Data System (ADS)

    Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

    2015-03-01

    In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

  16. SAMPLING EFFORT AFFECTS MULTIVARIATE COMPARISONS OF STREAM COMMUNITIES

    EPA Science Inventory

    The estimation of ecological trends and patterns is often dependent on the size of individual samples from each site (sample size) or spatial scale in general. Multivariate analysis is widely used for determining patterns of community structure, inferring species-environment rela...

  17. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2016-01-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

  18. Design of multivariable feedback control systems via spectral assignment

    NASA Technical Reports Server (NTRS)

    Mielke, R. R.; Tung, L. J.; Marefat, M.

    1983-01-01

    The applicability of spectral assignment techniques to the design of multivariable feedback control systems was investigated. A fractional representation design procedure for unstable plants is presented and illustrated with an example. A computer aided design software package implementing eigenvalue/eigenvector design procedures is described. A design example which illustrates the use of the program is explained.

  19. Multivariate classification of infrared spectra of cell and tissue samples

    DOEpatents

    Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.

    1997-01-01

    Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.

  20. Introduction to Kernel Methods: Classification of Multivariate Data

    NASA Astrophysics Data System (ADS)

    Fauvel, M.

    2016-05-01

    In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.

  1. Ways To Evaluate the Assumption of Multivariate Normality.

    ERIC Educational Resources Information Center

    Ashcraft, Alyce S.

    This paper reviews graphical and nongraphical methods for estimating multivariate normality. Prior to exploring this methodology, a foundation is established by presenting ways to assess univariate and bivariate normality. A data set of three variables used by J. Stevens (1986) is analyzed using Q-Q plots, stem and leaf plots, histograms,…

  2. Multivariate Models of Mothers' and Fathers' Aggression toward Their Children

    ERIC Educational Resources Information Center

    Smith Slep, Amy M.; O'Leary, Susan G.

    2007-01-01

    Multivariate, biopsychosocial, explanatory models of mothers' and fathers' psychological and physical aggression toward their 3- to 7-year-old children were fitted and cross-validated in 453 representatively sampled families. Models explaining mothers' and fathers' aggression were substantially similar. Surprisingly, many variables identified as…

  3. Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip; Hendrichs, Todd

    2010-01-01

    This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

  4. SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

    EPA Science Inventory

    Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

  5. Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Wang, Hai-Bin

    2010-01-01

    A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To…

  6. A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

    ERIC Educational Resources Information Center

    Hackett, Paul M. W.; And Others

    1993-01-01

    Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

  7. Choosing the Greenest Synthesis: A Multivariate Metric Green Chemistry Exercise

    ERIC Educational Resources Information Center

    Mercer, Sean M.; Andraos, John; Jessop, Philip G.

    2012-01-01

    The ability to correctly identify the greenest of several syntheses is a particularly useful asset for young chemists in the growing green economy. The famous univariate metrics atom economy and environmental factor provide insufficient information to allow for a proper selection of a green process. Multivariate metrics, such as those used in…

  8. Multivariable feedback design: concepts for a classical/modern synthesis

    SciTech Connect

    Doyle, J C; Stein, G

    1980-01-01

    A practical design perspective on multivariable feedback control problems is presented. The basic issue - feedback design in the face of uncertainites - is reviewed and known SISO statements and constraints of the design problem to MIMO cases are generalized. Two major MIMO design approaches are then evaluated in the context of these results.

  9. Virtual Observatories, Data Mining, and Astroinformatics

    NASA Astrophysics Data System (ADS)

    Borne, Kirk

    The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential

  10. Magnetic heterogeneity of biological systems.

    PubMed

    Piruzyan, L A; Kuznetsov, A A; Chikov, V M

    1980-01-01

    In biological systems nonuniformity of magnetic susceptibility, magnetic heterogeneity, is a reflection of their physical-chemical and morphological heterogeneity, A characteristic value of heterogeneity is delta K approximately 10(-6)-10(-7) CGS units, a quantitative measurement of susceptibility of cells and other small objects, may give qualitatively new information about their life processes. Patterns and features of movement of small biological objects and liquids affected by magnetic forces were studied. A method was developed for measuring magnetic susceptibility of single microobjects based on observation of movement of the objects in a strong heterogeneous field with parameters (formula: see text) grad H2/2 approximately 10(9)-10(10) Oe2/cm. This method does not require knowing the distribution of the field along the path of movement of the particles, and does not require preliminary calibration. Movement of human erythrocytes, rat hepatocytes, and starch granules in liquids at a point of entry into a gap with the field was observed experimentally. With sufficiently large fields Ho approximately (1-2) x 10(4) Oe, the value of the magnetic force was enough to change the rate of sedimentation movement of the objects appreciably (up to stopping it). This made it possible to compute the value delta K for cells approximately 10(-7)-10(-8) CGS units and to obtain the value of K for starch granules (-0.80 x 10(-6) cGS units). In connection with the fact that sensitivity to gravity in plants is coupled with a disturbance of the intracellular starch granules under the influence of gravity, certain problems of stimulating the effect of gravity on plants by magnetic forces were studied. Noncontact force effect on magnetically heterogeneous biological objects is a promising instrument for biophysical studies.

  11. Social Trust Prediction Using Heterogeneous Networks

    PubMed Central

    HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

    2014-01-01

    Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method. PMID:24729776

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

    PubMed

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

    2016-04-15

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

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

    PubMed

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

    2016-04-15

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

  14. Surface deformation induced by water influx in the abandoned coal mines in Limburg, The Netherlands observed by satellite radar interferometry

    NASA Astrophysics Data System (ADS)

    Caro Cuenca, Miguel; Hooper, Andrew J.; Hanssen, Ramon F.

    2013-01-01

    The coal reserves of Limburg, The Netherlands, have been exploited until the mid-1970's, leading to significant land subsidence, a large part of which was due to ground water pumping associated with the mining activities. In 1994, when also the hydrologically-connected neighboring German mining activities ceased, all pumps were finally dismantled. This resulted in rising groundwater levels in the mining areas, continuing until today. Here we report the detection and analysis of heterogeneous surface displacements in the area using satellite radar interferometry. The lack of adequate terrestrial geodetic measurements emphasizes the value of such satellite observations, especially in terms of the temporal and spatial characterization of the signal. Since the lack of direct mine water level measurements hampers predictions on future consequences at the surface, we study the relationship between surface deformation and sub-surface water levels in an attempt to provide rough correlation estimates and map the mine water dynamics.

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

    EPA Science Inventory

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

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

    USGS Publications Warehouse

    Roehl, Edwin A.; Conrads, Paul A.

    2010-01-01

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

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

    PubMed

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

    2015-03-01

    Coal has been extracted via surface and sub-surface mining for decades throughout the Appalachian Mountains. New interest in ridge-top mining has raised concerns about possible waterway impacts. We examined effects of forestry, mining, and road construction-based disturbance on physico-chemistry and macroinvertebrate communities in east-central Tennessee headwater streams. Although 11 of 30 sites failed Tennessee's biocriteria scoring system, invertebrate richness was moderately high and we did not find significant differences in any water chemistry or habitat parameters between sites with passing and failing scores. However, conductivity and dissolved solid concentrations appeared elevated in the majority of study streams. Principal components (PCs) analysis indicated that six PCs accounted for ~77 % of among-site habitat variability. One PC associated with dissolved oxygen and specific conductance explained the second highest proportion of among-site variability after catchment area. Specific conductance was not correlated with catchment area but was strongly correlated with mining activity. Composition and success of multivariate models using habitat PCs to predict macroinvertebrate metrics was highly variable. PC scores associated with water chemistry and substrate composition were most frequently included in significant models. These results suggest that impacts of historical and current coal mining remain a source of water quality and macroinvertebrate community impairment in this region, but effects are subtle. Our results suggest that surface mining may have chronic and system-wide effects on habitat conditions and invertebrate communities in Cumberland Plateau streams. PMID:25528595

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed

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

    2015-03-01

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

  20. The dangers of heterogeneous network computing: heterogeneous networks considered harmful

    SciTech Connect

    Demmel, J.; Stanley, K.; Dongarra, J.; Hammarling, S.; Osstrouchov, S.

    1996-12-31

    This report addresses the issue of writing reliable numerical software for networks of heterogeneous computers. Much software has been written for distributed memory parallel computers and in principal such software could readily be ported to networks of machines, such as a collection of workstations connected by Ethernet, but if such a network is not homogeneous there are special challenges that need to be addressed. The symptoms can range from erroneous results returned without warning to deadlock. Some of the problems are straightforward to solve, but for others the solutions are not so obvious and indeed in some cases, such as the method of bisection which we shall discuss in the report, we have not yet decided upon a satisfactory solution that does not incur an unacceptable overhead. Making software robust on heterogeneous systems often requires additional communication. In this report we describe and illustrate the problems and, where possible, suggest solutions so that others may be aware of the potential pitfalls and either avoid them or, if that is not possible, ensure that their software is not used on heterogeneous networks.

  1. Hydraulic mining method

    DOEpatents

    Huffman, Lester H.; Knoke, Gerald S.

    1985-08-20

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

  2. Mining the Home Environment

    PubMed Central

    Cook, Diane J.; Krishnan, Narayanan

    2014-01-01

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

  3. Airflow obstruction and mining

    SciTech Connect

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

    1993-01-01

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

  4. Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

    SciTech Connect

    Lupoi, Jason S.; Smith-Moritz, Andreia; Singh, Seema; McQualter, Richard; Scheller, Henrik V.; Simmons, Blake A.; Henry, Robert J.

    2015-07-10

    Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focal plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.

  5. Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

    DOE PAGES

    Lupoi, Jason S.; Smith-Moritz, Andreia; Singh, Seema; McQualter, Richard; Scheller, Henrik V.; Simmons, Blake A.; Henry, Robert J.

    2015-07-10

    Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focalmore » plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.« less

  6. Mining fuzzy association rules in spatio-temporal databases

    NASA Astrophysics Data System (ADS)

    Shu, Hong; Dong, Lin; Zhu, Xinyan

    2008-12-01

    A huge amount of geospatial and temporal data have been collected through various networks of environment monitoring stations. For instance, daily precipitation and temperature are observed at hundreds of meteorological stations in Northeastern China. However, these massive raw data from the stations are not fully utilized for meeting the requirements of human decision-making. In nature, the discovery of geographical data mining is the computation of multivariate spatio-temporal correlations through the stages of data mining. In this paper, a procedure of mining association rules in regional climate-changing databases is introduced. The methods of Kriging interpolation, fuzzy cmeans clustering, and Apriori-based logical rules extraction are employed subsequently. Formally, we define geographical spatio-temporal transactions and fuzzy association rules. Innovatively, we make fuzzy data conceptualization by means of fuzzy c-means clustering, and transform fuzzy data items with membership grades into Boolean data items with weights by means ofλ-cut sets. When the algorithm Apriori is executed on Boolean transactions with weights, fuzzy association rules are derived. Fuzzy association rules are more nature than crisp association rules for human cognition about the reality.

  7. Automatic Coal-Mining System

    NASA Technical Reports Server (NTRS)

    Collins, E. R., Jr.

    1985-01-01

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

  8. Education Roadmap for Mining Professionals

    SciTech Connect

    none,

    2002-12-01

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

  9. Data Mining for Intrusion Detection

    NASA Astrophysics Data System (ADS)

    Singhal, Anoop; Jajodia, Sushil

    Data Mining Techniques have been successfully applied in many different fields including marketing, manufacturing, fraud detection and network management. Over the past years there is a lot of interest in security technologies such as intrusion detection, cryptography, authentication and firewalls. This chapter discusses the application of Data Mining techniques to computer security. Conclusions are drawn and directions for future research are suggested.

  10. Coal mining: A petex primer

    SciTech Connect

    Not Available

    1985-01-01

    This book is an introduction to the coal industry - from planning a mine to delivering coal to a power plant. The primer covers what coal is and how it is used, modern underground and surface mining practices, coal preparation and transport, and the relation between coal and the environment.

  11. Process Mining Online Assessment Data

    ERIC Educational Resources Information Center

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

    2009-01-01

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

  12. Finding Gold in Data Mining

    ERIC Educational Resources Information Center

    Flaherty, Bill

    2013-01-01

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

  13. Heterogeneous, weakly coupled map lattices

    NASA Astrophysics Data System (ADS)

    Sotelo Herrera, M.a. Dolores; San Martín, Jesús; Porter, Mason A.

    2016-07-01

    Coupled map lattices (CMLs) are often used to study emergent phenomena in nature. It is typically assumed (unrealistically) that each component is described by the same map, and it is important to relax this assumption. In this paper, we characterize periodic orbits and the laminar regime of type-I intermittency in heterogeneous weakly coupled map lattices (HWCMLs). We show that the period of a cycle in an HWCML is preserved for arbitrarily small coupling strengths even when an associated uncoupled oscillator would experience a period-doubling cascade. Our results characterize periodic orbits both near and far from saddle-node bifurcations, and we thereby provide a key step for examining the bifurcation structure of heterogeneous CMLs.

  14. 30 CFR 49.19 - Mine emergency notification plan.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

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

  15. 30 CFR 49.9 - Mine emergency notification plan.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... TRAINING MINE RESCUE TEAMS § 49.9 Mine emergency notification plan. (a) Each underground mine shall have a mine rescue notification plan outlining the procedures to follow in notifying the mine rescue...

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

    SciTech Connect

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

    2007-07-01

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

  17. NASA GSFC Perspective on Heterogeneous Processing

    NASA Technical Reports Server (NTRS)

    Powell, Wesley A.

    2016-01-01

    This presentation provides an overview of NASA GSFC, our onboard processing applications, the applicability heterogeneous processing to these applications, and necessary developments to enable heterogeneous processing to be infused into our missions.

  18. Data mining and education.

    PubMed

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

    2015-01-01

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

  19. Escondida Mine, Chile

    NASA Technical Reports Server (NTRS)

    2002-01-01

    Full resolution visible and near-infrared image (1.4 MB) Full resolution shortwave infrared image (1.6 MB) This Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image covers 30 by 23 km (full images 30 x 37 km) in the Atacama Desert, Chile, and was acquired on April 23, 2000. The Escondida copper, gold, and silver open-pit mine is at an elevation of 3050 m, and began operations in 1990. Current capacity is 127,000 tons/day of ore; in 1999 production totaled 827,000 tons of copper, 150,000 ounces of gold, and 3.53 million ounces of silver. Primary concentrate of the ore is done on-site; the concentrate is then sent to the coast for further processing through a 170 km long, 9-inch pipe. Escondida is related geologically to three porphyry bodies intruded along the Chilean West Fissure Fault System. A high grade supergene cap overlies primary sulfide ore. The top image is a conventional 3-2-1 (near infrared, red, green) RGB composite. The bottom image displays shortwave infrared bands 4-6-8 (1.65um, 2.205um, 2.33um) in RGB, and highlights the different rock types present on the surface, as well as the changes caused by mining. Image courtesy NASA/GSFC/MITI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team

  20. Data mining and education.

    PubMed

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

    2015-01-01

    An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. PMID:26263424

  1. Temperature chaos and quenched heterogeneities

    NASA Astrophysics Data System (ADS)

    Barucca, Paolo; Parisi, Giorgio; Rizzo, Tommaso

    2014-03-01

    We present a treatable generalization of the Sherrington-Kirkpatrick (SK) model which introduces correlations in the elements of the coupling matrix through multiplicative disorder on the single variables and investigate the consequences on the phase diagram. We define a generalized qEA parameter and test the structural stability of the SK results in this correlated case evaluating the de Almeida-Thouless line of the model. As a main result we demonstrate the increase of temperature chaos effects due to heterogeneities.

  2. Risk factors predicting recurrence in patients operated on for intracranial meningioma. A multivariate analysis.

    PubMed

    Ayerbe, J; Lobato, R D; de la Cruz, J; Alday, R; Rivas, J J; Gómez, P A; Cabrera, A

    1999-01-01

    The authors undertook a follow-up study of 286 patients who underwent surgical treatment for intracranial meningioma between 1973 and 1994, in order to analyse clinical, radiological, topographic, histopathological and therapeutic factors significantly influencing tumour recurrence. All patients were followed by using either computed tomography (CT) or magnetic resonance from 3 months to 17 years since first surgery (mean follow-up: 4.1 years). Forty-four (15.4%) recurrences were detected during this time period. Overall recurrence rates were 14%, 37% and 61% at 5, 10 and 15 years, respectively. Factors significantly associated with tumour relapse in bivariate analysis were: tumour location at petroclival and parasagittal (middle third) regions, incomplete surgical resection (assessed by Simpson's classification), atypical and malignant histological types (WHO classification), presence of nucleolar prominence, presence of more than 2 mitosis per 10 high-power fields, and heterogeneous tumour contrast enhancement on the CT scan. The multivariate analysis using the Cox's proportional hazards model identified the following risk factors for recurrence: incomplete surgical resection (Relative risk: 2.2; 95% Confidence interval: 1.33-3.64), non conventional histological type (RR: 2.13; 95%CI: 1-4.53), heterogeneous contrast enhancement on the CT scan (RR: 2.25; 95%CI: 1.1-4.72) and presence of more than 2 mitosis per 10 high-power fields (RR: 2.28; 95%CI: 0.99-5.27). Patients without any of these features showed low recurrence rates (4% and 18% at 5 and 10 years), and thus, they need less clinical and radiological controls through the follow-up than patients with some of these risk factors. PMID:10526073

  3. Introduction to Space Resource Mining

    NASA Technical Reports Server (NTRS)

    Mueller, Robert P.

    2013-01-01

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

  4. Measuring mine roof bolt strains

    DOEpatents

    Steblay, Bernard J.

    1986-01-01

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

  5. In Brief: Coal mining regulations

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2009-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  7. Characterization Of Spatial Heterogeneity and Structure at Landscape Scale

    NASA Astrophysics Data System (ADS)

    Garrigues, S.; Allard, D.; Baret, F.

    The monitoring of land surface dynamic processes at global scale, such as primary production, carbon and water fluxes, requires high temporal frequency remote sensing observations. Because of technological constraints, the sensors are characterized by coarse spatial resolution, i.e. a resolution from few hundred meters (MERIS/ENVISAT, MODIS/TERRA) up to one or few kilometres (VEGETATION/SPOT, SEVIRI/MSG). However, the scenes observed at this range of scales, present spatial heterogeneity which may have a great influence on land surface characteristic estimation from remotely sensed data. Therefore the characterisation of spatial heterogeneity is an important concern to scale non linear land surface processes. The aim of this study is to discuss a geostatistical approach based on two complementary tools to characterize spatial structure of remote sensing data at the landscape scale. The high spatial resolution NDVI (vegetation index) of SPOT/HRV images (20m resolution) is used to characterize the ground spatial structure of different landscapes. These NDVI images are then aggregated in order to describe the evolution of their structure with the spatial resolution. A classical method consists in describing the image spatial heterogeneity by a geostatistic tool: the variogram. The interest of the variogram is that it jointly allows to model the spatial distribution of a scene as well as to quantify the spatial heterogeneity as a function of the spatial resolution. A typology of spatial heterogeneity is derived from the variogram model parameters computed over several types of landscapes. To account for the availability of multiple wavebands, a multivariate description of the spatial heterogeneity could also be proposed. A first limit of the variogram approach is the assumption of spatial stationarity, necessary for modelling the variogram. Spatial stationarity can be checked by: Dividing the image into local windows and adjusting the corresponding variogram model

  8. Characterization of Spatial Heterogeneity and Structure at Landscape Scale

    NASA Astrophysics Data System (ADS)

    Garrigues, S.; Allard, D.; Baret, F.

    2004-05-01

    The monitoring of land surface dynamic processes at global scale, such as primary production, carbon and water fluxes, requires high temporal frequency remote sensing observations. Because of technological constraints, the sensors are characterized by coarse spatial resolution, i.e. a resolution from few hundred meters (MERIS/ENVISAT, MODIS/TERRA) up to one or few kilometres (VEGETATION/SPOT, SEVIRI/MSG). However, the scenes observed at this range of scales, present spatial heterogeneity which may have a great influence on land surface characteristic estimation from remotely sensed data. Therefore the characterisation of spatial heterogeneity is an important concern to scale non linear land surface processes. The aim of this study is to discuss a geostatistical approach based on two complementary tools to characterize spatial structure of remote sensing data at the landscape scale. The high spatial resolution NDVI (vegetation index) of SPOT/HRV images (20m resolution) is used to characterize the ground spatial structure of different landscapes. These NDVI images are then aggregated in order to describe the evolution of their structure with the spatial resolution. A classical method consists in describing the image spatial heterogeneity by a geostatistic tool: the variogram. The interest of the variogram is that it jointly allows to model the spatial distribution of a scene as well as to quantify the spatial heterogeneity as a function of the spatial resolution. A typology of spatial heterogeneity is derived from the variogram model parameters computed over several types of landscapes. To account for the availability of multiple wavebands, a multivariate description of the spatial heterogeneity could also be proposed. A first limit of the variogram approach is the assumption of spatial stationarity, necessary for modelling the variogram. Spatial stationarity can be checked by: - Dividing the image into local windows and adjusting the corresponding variogram model

  9. Analyzing and modeling heterogeneous behavior

    NASA Astrophysics Data System (ADS)

    Lin, Zhiting; Wu, Xiaoqing; He, Dongyue; Zhu, Qiang; Ni, Jixiang

    2016-05-01

    Recently, it was pointed out that the non-Poisson statistics with heavy tail existed in many scenarios of human behaviors. But most of these studies claimed that power-law characterized diverse aspects of human mobility patterns. In this paper, we suggest that human behavior may not be driven by identical mechanisms and can be modeled as a Semi-Markov Modulated Process. To verify our suggestion and model, we analyzed a total of 1,619,934 records of library visitations (including undergraduate and graduate students). It is found that the distribution of visitation intervals is well fitted with three sections of lines instead of the traditional power law distribution in log-log scale. The results confirm that some human behaviors cannot be simply expressed as power law or any other simple functions. At the same time, we divided the data into groups and extracted period bursty events. Through careful analysis in different groups, we drew a conclusion that aggregate behavior might be composed of heterogeneous behaviors, and even the behaviors of the same type tended to be different in different period. The aggregate behavior is supposed to be formed by "heterogeneous groups". We performed a series of experiments. Simulation results showed that we just needed to set up two states Semi-Markov Modulated Process to construct proper representation of heterogeneous behavior.

  10. Heterogeneous nucleation or homogeneous nucleation?

    NASA Astrophysics Data System (ADS)

    Liu, X. Y.

    2000-06-01

    The generic heterogeneous effect of foreign particles on three dimensional nucleation was examined both theoretically and experimentally. It shows that the nucleation observed under normal conditions includes a sequence of progressive heterogeneous processes, characterized by different interfacial correlation function f(m,x)s. At low supersaturations, nucleation will be controlled by the process with a small interfacial correlation function f(m,x), which results from a strong interaction and good structural match between the foreign bodies and the crystallizing phase. At high supersaturations, nucleation on foreign particles having a weak interaction and poor structural match with the crystallizing phase (f(m,x)→1) will govern the kinetics. This frequently leads to the false identification of homogeneous nucleation. Genuine homogeneous nucleation, which is the up-limit of heterogeneous nucleation, may not be easily achievable under gravity. In order to check these results, the prediction is confronted with nucleation experiments of some organic and inorganic crystals. The results are in excellent agreement with the theory.

  11. ADMIRE framework for data mining and integration

    NASA Astrophysics Data System (ADS)

    Hluchy, Ladislav; Tran, Viet; Habala, Ondrej

    2010-05-01

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

  12. Investigating Population Heterogeneity With Factor Mixture Models

    ERIC Educational Resources Information Center

    Lubke, Gitta H.; Muthen, Bengt

    2005-01-01

    Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models…

  13. Multivariate permutation test to compare survival curves for matched data

    PubMed Central

    2013-01-01

    Background In the absence of randomization, the comparison of an experimental treatment with respect to the standard may be done based on a matched design. When there is a limited set of cases receiving the experimental treatment, matching of a proper set of controls in a non fixed proportion is convenient. Methods In order to deal with the highly stratified survival data generated by multiple matching, we extend the multivariate permutation testing approach, since standard nonparametric methods for the comparison of survival curves cannot be applied in this setting. Results We demonstrate the validity of the proposed method with simulations, and we illustrate its application to data from an observational study for the comparison of bone marrow transplantation and chemotherapy in the treatment of paediatric leukaemia. Conclusions The use of the multivariate permutation testing approach is recommended in the highly stratified context of survival matched data, especially when the proportional hazards assumption does not hold. PMID:23399031

  14. Generalized error-dependent prediction uncertainty in multivariate calibration.

    PubMed

    Allegrini, Franco; Wentzell, Peter D; Olivieri, Alejandro C

    2016-01-15

    Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory. New expressions have been derived to calculate sample dependent prediction standard errors under different scenarios. These expressions allow for a quantitative study of the influence of the different sources of instrumental error affecting the system under analysis. Significant differences are observed when the prediction error is estimated in each of the studied scenarios using the most popular first-order multivariate algorithms, under both simulated and experimental conditions.

  15. Empirical performance of the multivariate normal universal portfolio

    NASA Astrophysics Data System (ADS)

    Tan, Choon Peng; Pang, Sook Theng

    2013-09-01

    Universal portfolios generated by the multivariate normal distribution are studied with emphasis on the case where variables are dependent, namely, the covariance matrix is not diagonal. The moving-order multivariate normal universal portfolio requires very long implementation time and large computer memory in its implementation. With the objective of reducing memory and implementation time, the finite-order universal portfolio is introduced. Some stock-price data sets are selected from the local stock exchange and the finite-order universal portfolio is run on the data sets, for small finite order. Empirically, it is shown that the portfolio can outperform the moving-order Dirichlet universal portfolio of Cover and Ordentlich[2] for certain parameters in the selected data sets.

  16. A novel definition of the multivariate coefficient of variation.

    PubMed

    Albert, Adelin; Zhang, Lixin

    2010-10-01

    The coefficient of variation CV (%) is widely used to measure the relative variation of a random variable to its mean or to assess and compare the performance of analytical techniques/equipments. A review is made of the existing multivariate extensions of the univariate CV where, instead of a random variable, a random vector is considered, and a novel definition is proposed. The multivariate CV obtained only requires the calculation of the mean vector, the covariance matrix and simple quadratic forms. No matrix inversion is needed which makes the new approach equally attractive in high dimensional as in very small sample size problems. As an illustration, the method is applied to electrophoresis data from external quality assessment in laboratory medicine, to phenotypic characteristics of pocket gophers and to a microarray data set.

  17. Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

    NASA Astrophysics Data System (ADS)

    Rajagopalan, Vidya; Schwartzman, Armin; Hua, Xue; Leow, Alex; Thompson, Paul; Lepore, Natasha

    2015-01-01

    We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

  18. Estimation of sparse directed acyclic graphs for multivariate counts data.

    PubMed

    Han, Sung Won; Zhong, Hua

    2016-09-01

    The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this article provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. PMID:26849781

  19. Multivariate geostatistical simulation by minimising spatial cross-correlation

    NASA Astrophysics Data System (ADS)

    Sohrabian, Babak; Tercan, Abdullah Erhan

    2014-03-01

    Joint simulation of attributes in multivariate geostatistics can be achieved by transforming spatially correlated variables into independent factors. In this study, a new approach for this transformation, Minimum Spatial Cross-correlation (MSC) method, is suggested. The method is based on minimising the sum of squares of cross-variograms at different distances. In the approach, the problem in higher space (N × N) is reduced to N×N-1/2 problems in the two-dimensional space and the reduced problem is solved iteratively using Gradient Descent Algorithm. The method is applied to the joint simulation of a set of multivariate data in a marble quarry and the results are compared with Minimum/Maximum Autocorrelation Factors (MAF) method.

  20. Causality networks from multivariate time series and application to epilepsy.

    PubMed

    Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

    2015-08-01

    Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

  1. Multivariate data validation for investigating primary HCMV infection in pregnancy.

    PubMed

    Barberini, Luigi; Noto, Antonio; Saba, Luca; Palmas, Francesco; Fanos, Vassilios; Dessì, Angelica; Zavattoni, Maurizio; Fattuoni, Claudia; Mussap, Michele

    2016-12-01

    We reported data concerning the Gas Chromatography-Mass Spectrometry (GC-MS) based metabolomic analysis of amniotic fluid (AF) samples obtained from pregnant women infected with Human Cytomegalovirus (HCMV). These data support the publication "Primary HCMV Infection in Pregnancy from Classic Data towards Metabolomics: an Exploratory analysis" (C. Fattuoni, F. Palmas, A. Noto, L. Barberini, M. Mussap, et al., 2016) [2]. GC-MS and Multivariate analysis allow to recognize the molecular phenotype of HCMV infected fetuses (transmitters) and that of HCMV non-infected fetuses (non-transmitters); moreover, GC-MS and multivariate analysis allow to distinguish and to compare the molecular phenotype of these two groups with a control group consisting of AF samples obtained in HCMV non-infected pregnant women. The obtained data discriminate controls from transmitters as well as from non-transmitters; no statistically significant difference was found between transmitters and non-transmitters. PMID:27656676

  2. Affymetrix GeneChip microarray preprocessing for multivariate analyses.

    PubMed

    McCall, Matthew N; Almudevar, Anthony

    2012-09-01

    Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.

  3. A method for designing robust multivariable feedback systems

    NASA Technical Reports Server (NTRS)

    Milich, David Albert; Athans, Michael; Valavani, Lena; Stein, Gunter

    1988-01-01

    A new methodology is developed for the synthesis of linear, time-invariant (LTI) controllers for multivariable LTI systems. The aim is to achieve stability and performance robustness of the feedback system in the presence of multiple unstructured uncertainty blocks; i.e., to satisfy a frequency-domain inequality in terms of the structured singular value. The design technique is referred to as the Causality Recovery Methodology (CRM). Starting with an initial (nominally) stabilizing compensator, the CRM produces a closed-loop system whose performance-robustness is at least as good as, and hopefully superior to, that of the original design. The robustness improvement is obtained by solving an infinite-dimensional, convex optimization program. A finite-dimensional implementation of the CRM was developed, and it was applied to a multivariate design example.

  4. Enhanced bio-manufacturing through advanced multivariate statistical technologies.

    PubMed

    Martin, E B; Morris, A J

    2002-11-13

    The paper describes the interrogation of data, from a reaction vessel producing an active pharmaceutical ingredient (API), using advanced multivariate statistical techniques. Due to the limited number of batches available, data augmentation was used to increase the number of batches thereby enabling the extraction of more subtle process behaviour from the data. A second methodology investigated was that of multi-group modelling. This allowed between cluster variability to be removed, thus allowing attention to focus on within process variability. The paper describes how the different approaches enabled the realisation of a better understanding of the factors causing the onset of an impurity formation to be obtained as well demonstrating the power of multivariate statistical data analysis techniques to provide an enhanced understanding of the process.

  5. A multivariate rate equation for variable-interval performance.

    PubMed

    McDowell, J J; Kessel, R

    1979-03-01

    A value-like parameter is introduced into a rate equation for describing variable-interval performance. The equation, derived solely from formal considerations, expresses rate of responding as a joint function of rate of reinforcement and "reinforcer power." Preliminary tests of the rate equation show that it handles univariate data as well as Herrnstein's hyperbola. In addition, a form of Herrnstein's hyperbola can be derived from the equation, and it predicts forms of matching in concurrent situations. For the multivariate case, reinforcer values scaled in concurrent situations where matching is assumed to hold are taken as determinations of reinforcer power. The multivariate rate equation is fitted to an appropriate set of data and found to provide a good description of variable-interval performance when both rate and power of reinforcement are varied. Rate and power measures completely describe reinforcement. The effects of their joint variation are not predicted and cannot be described by Herrnstein's equation.

  6. Scalar and Multivariate Approaches for Optimal Network Design in Antarctica

    NASA Astrophysics Data System (ADS)

    Hryniw, Natalia

    Observations are crucial for weather and climate, not only for daily forecasts and logistical purposes, for but maintaining representative records and for tuning atmospheric models. Here scalar theory for optimal network design is expanded in a multivariate framework, to allow for optimal station siting for full field optimization. Ensemble sensitivity theory is expanded to produce the covariance trace approach, which optimizes for the trace of the covariance matrix. Relative entropy is also used for multivariate optimization as an information theory approach for finding optimal locations. Antarctic surface temperature data is used as a testbed for these methods. Both methods produce different results which are tied to the fundamental physical parameters of the Antarctic temperature field.

  7. A multivariate approach linking reported side effects of clinical antidepressant and antipsychotic trials to in vitro binding affinities

    PubMed Central

    Michl, Johanna; Scharinger, Christian; Zauner, Miriam; Kasper, Siegfried; Freissmuth, Michael; Sitte, Harald H.; Ecker, Gerhard F.; Pezawas, Lukas

    2015-01-01

    The vast majority of approved antidepressants and antipsychotics exhibit a complex pharmacology. The mechanistic understanding of how these psychotropic medications are related to adverse drug reactions (ADRs) is crucial for the development of novel drug candidates and patient adherence. This study aims to associate in vitro assessed binding affinity profiles (39 compounds, 24 molecular drug targets) and ADRs (n=22) reported in clinical trials of antidepressants and antipsychotics (n>59.000 patients) by the use of robust multivariate statistics. Orthogonal projection to latent structures (O-PLS) regression models with reasonable predictability were found for several frequent ADRs such as nausea, diarrhea, hypotension, dizziness, headache, insomnia, sedation, sleepiness, increased sweating, and weight gain. Results of the present study support many well-known pharmacological principles such as the association of hypotension and dizziness with α1-receptor or sedation with H1-receptor antagonism. Moreover, the analyses revealed novel or hardly investigated mechanisms for common ADRs including the potential involvement of 5-HT6-antagonism in weight gain, muscarinic receptor antagonism in dizziness, or 5-HT7-antagonism in sedation. To summarize, the presented study underlines the feasibility and value of a multivariate data mining approach in psychopharmacological development of antidepressants and antipsychotics. PMID:25044049

  8. Studying Resist Stochastics with the Multivariate Poisson Propagation Model

    DOE PAGES

    Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; Bhattarai, Suchit; Neureuther, Andrew

    2014-01-01

    Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

  9. Enhancing scientific reasoning by refining students' models of multivariable causality

    NASA Astrophysics Data System (ADS)

    Keselman, Alla

    Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained

  10. Reconstructing embedding spaces of coupled dynamical systems from multivariate data.

    PubMed

    Boccaletti, S; Valladares, D L; Pecora, Louis M; Geffert, Hite P; Carroll, T

    2002-03-01

    A method for reconstructing dimensions of subspaces for weakly coupled dynamical systems is offered. The tool is able to extrapolate the subspace dimensions from the zero coupling limit, where the division of dimensions as per the algorithm is exact. Implementation of the proposed technique to multivariate data demonstrates its effectiveness in disentangling subspace dimensionalities also in the case of emergent synchronized motions, for both numerical and experimental systems.

  11. A direct-gradient multivariate index of biotic condition

    USGS Publications Warehouse

    Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

    2012-01-01

    Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

  12. Analysis of Forest Foliage Using a Multivariate Mixture Model

    NASA Technical Reports Server (NTRS)

    Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

    1997-01-01

    Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

  13. Multivariable speed synchronisation for a parallel hybrid electric vehicle drivetrain

    NASA Astrophysics Data System (ADS)

    Alt, B.; Antritter, F.; Svaricek, F.; Schultalbers, M.

    2013-03-01

    In this article, a new drivetrain configuration of a parallel hybrid electric vehicle is considered and a novel model-based control design strategy is given. In particular, the control design covers the speed synchronisation task during a restart of the internal combustion engine. The proposed multivariable synchronisation strategy is based on feedforward and decoupled feedback controllers. The performance and the robustness properties of the closed-loop system are illustrated by nonlinear simulation results.

  14. A review of multivariate analyses in imaging genetics.

    PubMed

    Liu, Jingyu; Calhoun, Vince D

    2014-01-01

    Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

  15. Collision prediction models using multivariate Poisson-lognormal regression.

    PubMed

    El-Basyouny, Karim; Sayed, Tarek

    2009-07-01

    This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models. PMID:19540972

  16. Population regulation by habitat heterogeneity or individual adjustment?

    PubMed

    Krüger, Oliver; Chakarov, Nayden; Nielsen, Jan T; Looft, Volkher; Grünkorn, Thomas; Struwe-Juhl, Bernd; Møller, Anders P

    2012-03-01

    1. The habitat heterogeneity (HHH) and individual adjustment (IAH) hypotheses are commonly proposed to explain a decrease in reproduction rate with increasing population density. Higher numbers of low-quality territories with low reproductive success as density increases lead to a decrease in reproduction under the HHH, while more competition at high density decreases reproduction across all territories under the IAH. 2. We analyse the influence of density and habitat heterogeneity on reproductive success in eight populations of long-lived territorial birds of prey belonging to four species. Sufficient reliability in distinguishing between population-wide, site-specific and individual quality effects on reproduction was granted through the minimal duration of 20 years of all data sets and the ability to control for individual quality in five of them. 3. Density increased in five populations but reproduction did not decrease in these. Territory occupancy as a surrogate of territory quality correlated positively with reproductive success but only significantly so in large data sets with more than 100 territories. 4. Reproductive success was always best explained by measures of territory quality in multivariate models. Direct or delayed (t-1) population density entered very few of the best models. Mixed models controlling for individual quality showed an increasing reproductive performance in older individuals and in those laying earlier, but measures of territory quality were also always retained in the best models. 5. We find strong support for the habitat heterogeneity hypothesis but weak support for the individual adjustment hypothesis. Both individual and site characteristics are crucial for reproductive performance in long-lived birds. Proportional occupancy of territories enables recognition of high-quality territories as preferential conservation targets. PMID:21950339

  17. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.

    PubMed

    Forester, Brenna R; Jones, Matthew R; Joost, Stéphane; Landguth, Erin L; Lasky, Jesse R

    2016-01-01

    The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

  18. Multivariate moment closure techniques for stochastic kinetic models

    SciTech Connect

    Lakatos, Eszter Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-09-07

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  19. Various forms of indexing HDMR for modelling multivariate classification problems

    SciTech Connect

    Aksu, Çağrı; Tunga, M. Alper

    2014-12-10

    The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

  20. Multivariate Fronthaul Quantization for Downlink C-RAN

    NASA Astrophysics Data System (ADS)

    Lee, Wonju; Simeone, Osvaldo; Kang, Joonhyuk; Shamai, Shlomo

    2016-10-01

    The Cloud-Radio Access Network (C-RAN) cellular architecture relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable the virtualization of the baseband processing functionalities of distributed radio units (RUs). The standard design of digital fronthauling is based on either scalar quantization or on more sophisticated point to-point compression techniques operating on baseband signals. Motivated by network-information theoretic results, techniques for fronthaul quantization and compression that improve over point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been recently proposed for both the uplink and the downlink. For the downlink, a form of joint compression, known in network information theory as multivariate compression, was shown to be advantageous under a non-constructive asymptotic information-theoretic framework. In this paper, instead, the design of a practical symbol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN downlink. As compared to current standards, the proposed multivariate quantization (MQ) only requires changes in the CU processing while no modification is needed at the RUs. The algorithm is extended to enable the joint optimization of downlink precoding and quantization, reduced-complexity MQ via successive block quantization, and variable-length compression. Numerical results, which include performance evaluations over standard cellular models, demonstrate the advantages of MQ and the merits of a joint optimization with precoding.

  1. A System of Multivariable Krawtchouk Polynomials and a Probabilistic Application

    NASA Astrophysics Data System (ADS)

    Grünbaum, F. Alberto; Rahman, Mizan

    2011-12-01

    The one variable Krawtchouk polynomials, a special case of the 2F1 function did appear in the spectral representation of the transition kernel for a Markov chain studied a long time ago by M. Hoare and M. Rahman. A multivariable extension of this Markov chain was considered in a later paper by these authors where a certain two variable extension of the F1 Appel function shows up in the spectral analysis of the corresponding transition kernel. Independently of any probabilistic consideration a certain multivariable version of the Gelfand-Aomoto hypergeometric function was considered in papers by H. Mizukawa and H. Tanaka. These authors and others such as P. Iliev and P. Tertwilliger treat the two-dimensional version of the Hoare-Rahman work from a Lie-theoretic point of view. P. Iliev then treats the general n-dimensional case. All of these authors proved several properties of these functions. Here we show that these functions play a crucial role in the spectral analysis of the transition kernel that comes from pushing the work of Hoare-Rahman to the multivariable case. The methods employed here to prove this as well as several properties of these functions are completely different to those used by the authors mentioned above.

  2. MToS: A Tree of Shapes for Multivariate Images.

    PubMed

    Carlinet, Edwin; Géraud, Thierry

    2015-12-01

    The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

  3. Extracting bb Higgs Decay Signals using Multivariate Techniques

    SciTech Connect

    Smith, W Clarke; /George Washington U. /SLAC

    2012-08-28

    For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.

  4. Application of multivariate statistical techniques in microbial ecology.

    PubMed

    Paliy, O; Shankar, V

    2016-03-01

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

  5. Poisson and Multinomial Mixture Models for Multivariate SIMS Image Segmentation

    SciTech Connect

    Willse, Alan R.; Tyler, Bonnie

    2002-11-08

    Multivariate statistical methods have been advocated for analysis of spectral images, such as those obtained with imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS). TOF-SIMS images using total secondary ion counts or secondary ion counts at individual masses often fail to reveal all salient chemical patterns on the surface. Multivariate methods simultaneously analyze peak intensities at all masses. We propose multivariate methods based on Poisson and multinomial mixture models to segment SIMS images into chemically homogeneous regions. The Poisson mixture model is derived from the assumption that secondary ion counts at any mass in a chemically homogeneous region vary according to the Poisson distribution. The multinomial model is derived as a standardized Poisson mixture model, which is analogous to standardizing the data by dividing by total secondary ion counts. The methods are adapted for contextual image segmentation, allowing for spatial correlation of neighboring pixels. The methods are applied to 52 mass units of a SIMS image with known chemical components. The spectral profile and relative prevalence for each chemical phase are obtained from estimates of model parameters.

  6. Application of multivariate outlier detection to fluid velocity measurements

    NASA Astrophysics Data System (ADS)

    Griffin, John; Schultz, Todd; Holman, Ryan; Ukeiley, Lawrence S.; Cattafesta, Louis N.

    2010-07-01

    A statistical-based approach to detect outliers in fluid-based velocity measurements is proposed. Outliers are effectively detected from experimental unimodal distributions with the application of an existing multivariate outlier detection algorithm for asymmetric distributions (Hubert and Van der Veeken, J Chemom 22:235-246, 2008). This approach is an extension of previous methods that only apply to symmetric distributions. For fluid velocity measurements, rejection of statistical outliers, meaning erroneous as well as low probability data, via multivariate outlier rejection is compared to a traditional method based on univariate statistics. For particle image velocimetry data, both tests are conducted after application of the current de facto standard spatial filter, the universal outlier detection test (Westerweel and Scarano, Exp Fluids 39:1096-1100, 2005). By doing so, the utility of statistical outlier detection in addition to spatial filters is demonstrated, and further, the differences between multivariate and univariate outlier detection are discussed. Since the proposed technique for outlier detection is an independent process, statistical outlier detection is complementary to spatial outlier detection and can be used as an additional validation tool.

  7. Multivariate gene-set testing based on graphical models.

    PubMed

    Städler, Nicolas; Mukherjee, Sach

    2015-01-01

    The identification of predefined groups of genes ("gene-sets") which are differentially expressed between two conditions ("gene-set analysis", or GSA) is a very popular analysis in bioinformatics. GSA incorporates biological knowledge by aggregating over genes that are believed to be functionally related. This can enhance statistical power over analyses that consider only one gene at a time. However, currently available GSA approaches are based on univariate two-sample comparison of single genes. This means that they cannot test for multivariate hypotheses such as differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. This paper proposes a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. Testing hypotheses concerning networks is challenging due the nature of the underlying estimation problem. Our starting point is a recent, general approach for high-dimensional two-sample testing. We refine the approach and show how it can be used to perform multivariate, network-based gene-set testing. We validate the approach in simulated examples and show results using high-throughput data from several studies in cancer biology.

  8. Multivariate diagnostics and anomaly detection for nuclear safeguards

    SciTech Connect

    Burr, T.; Jones, J.; Wangen, L.

    1994-08-01

    For process control and other reasons, new and future nuclear reprocessing plants are expected to be increasingly more automated than older plants. As a consequence of this automation, the quantity of data potentially available for safeguards may be much greater in future reprocessing plants than in current plants. The authors first review recent literature that applies multivariate Shewhart and multivariate cumulative sum (Cusum) tests to detect anomalous data. These tests are used to evaluate residuals obtained from a simulated three-tank problem in which five variables (volume, density, and concentrations of uranium, plutonium, and nitric acid) in each tank are modeled and measured. They then present results from several simulations involving transfers between the tanks and between the tanks and the environment. Residuals from a no-fault problem in which the measurements and model predictions are both correct are used to develop Cusum test parameters which are then used to test for faults for several simulated anomalous situations, such as an unknown leak or diversion of material from one of the tanks. The leak can be detected by comparing measurements, which estimate the true state of the tank system, with the model predictions, which estimate the state of the tank system as it ``should`` be. The no-fault simulation compares false alarm behavior for the various tests, whereas the anomalous problems allow one to compare the power of the various tests to detect faults under possible diversion scenarios. For comparison with the multivariate tests, univariate tests are also applied to the residuals.

  9. Multivariate moment closure techniques for stochastic kinetic models

    NASA Astrophysics Data System (ADS)

    Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-09-01

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  10. Application of multivariate statistical techniques in microbial ecology.

    PubMed

    Paliy, O; Shankar, V

    2016-03-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. PMID:26786791

  11. Data-based transformations in multivariate analysis. Final report

    SciTech Connect

    Dunn, J.E.

    1980-04-15

    Univariate transformations are considered initially, because of the common practice of transforming separately the marginal distribution of each variable of a multivariate observation. Familiar examples include those based on a priori assumptions about the underlying sampling distribution, as well as several general classes of empirical transformations recommended in a recent text by Mosteller and Tukey. Multi-normal criteria are considered as a basis for obtaining and evaluating multivariate transformations, including the likelihood criterion and various transformations to uniform statistics. The extension of power and shifted-power transformations to multivariate analysis is reviewed in detail, including recently published work involving q-sample problems. Finally, applications of projective transformations are proposed in order to remove the effects of extraneous sources of variation, e.g., specimens of different ages, from different nutritional backgrounds, etc. It is shown that the actual values of these ancillary variables will not be required if the analysis is performed in a subspace which is orthogonal to the gradients attributable to these variables. Models are proposed for principal components analysis, canonical correlation, linear classification functions, and discriminant function analysis in the general MANOVA context.

  12. Survival Models on Unobserved Heterogeneity and their Applications in Analyzing Large-scale Survey Data

    PubMed Central

    Liu, Xian

    2014-01-01

    In survival analysis, researchers often encounter multivariate survival time data, in which failure times are correlated even in the presence of model covariates. It is argued that because observations are clustered by unobserved heterogeneity, the application of standard survival models can result in biased parameter estimates and erroneous model-based predictions. In this article, the author describes and compares four methods handling unobserved heterogeneity in survival analysis: the Andersen-Gill approach, the robust sandwich variance estimator, the hazard model with individual frailty, and the retransformation method. An empirical analysis provides strong evidence that in the presence of strong unobserved heterogeneity, the application of a standard survival model can yield equally robust parameter estimates and the likelihood ratio statistic as does a corresponding model adding an additional parameter for random effects. When predicting the survival function, however, a standard model on multivariate survival time data can result in serious prediction bias. The retransformation method is effective to derive an adjustment factor for correctly predicting the survival function. PMID:25525559

  13. Linkage analysis of quantitative trait loci in the presence of heterogeneity.

    PubMed

    Ekstrøm, Claus Thorn; Dalgaard, Peter

    2003-01-01

    Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.

  14. The importance of gel properties for mucoadhesion measurements: a multivariate data analysis approach.

    PubMed

    Hägerström, Helene; Bergström, Christel A S; Edsman, Katarina

    2004-02-01

    In this study we used tensile strength measurements and a recently developed interpretation procedure to evaluate the mucoadhesive properties of a large set of gel preparations with diverse rheological properties. Multivariate data analysis in the form of principal component analysis (PCA) and partial least square projection to latent structures (PLS) was applied to extract useful information from the rather large quantities of data obtained. PCA showed that the selected series of gels was heterogeneous. Some groupings could be detected but none of the gels was identified as an outlier. By using PLS we investigated the relations between the rheological properties of a gel and the parameters defining the cohesiveness, as measured with the texture analyser used for the mucoadhesion measurements. The rheological properties proved to be important for the results of both the mucoadhesion and the cohesiveness measurements. Furthermore, by using PLS two different measurement configurations were evaluated and it was concluded that the combination of a relatively small volume of gel and two pieces of mucosa seems to be more appropriate than a large volume of gel in combination with one piece of mucosa. PMID:15005874

  15. Combining multivariate statistics and analysis of variance to redesign a water quality monitoring network.

    PubMed

    Guigues, Nathalie; Desenfant, Michèle; Hance, Emmanuel

    2013-09-01

    The objective of this paper was to demonstrate how multivariate statistics combined with the analysis of variance could support decision-making during the process of redesigning a water quality monitoring network with highly heterogeneous datasets in terms of time and space. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were selected to optimise the selection of water quality parameters to be monitored as well as the number and location of monitoring stations. Sampling frequency was specifically investigated through the analysis of variance. The data used were obtained between 2007 and 2010 at the Long-term Environmental Research Monitoring and Testing System (OPE) located in the north-eastern part of France in relation with a geological disposal of radioactive waste project. PCA results showed that no substantial reduction among the parameters was possible as strong correlation only exists between electrical conductivity, calcium or bicarbonates. HCA results were geospatially represented for each field campaign and compared to one another in terms of similarities and differences allowing us to group the monitoring stations into 12 categories. This approach enabled us to take into account not only the spatial variability of water quality but also its temporal variability. Finally, the analysis of variances showed that three very different behaviours occurred: parameters with high temporal variability and low spatial variability (e.g. suspended matter), parameters with high spatial variability and average temporal variability (e.g. calcium) and finally parameters with both high temporal and spatial variability (e.g. nitrate).

  16. Radioecological impacts of tin mining.

    PubMed

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

    2015-12-01

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

  17. Mining human antibody repertoires

    PubMed Central

    2010-01-01

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

  18. Mineral mining installation

    SciTech Connect

    Eggenstein, F.; Plester, K.

    1980-10-14

    A mineral mining installation comprises a longwall structure, such as a conveyor or a winning installation, and a roof support assembly constituted by a plurality of roof support units positioned side-by-side. At least some of the roof support units are provided with hydraulic bracing rams for bracing the longwall structure longitudinally. Each bracing ram is pivotally connected between the longwall structure and the floor sill of a respective roof support unit. Each ram is connected to its floor sill by connection means constituted by a bracket slidably mounted on that floor sill for movement towards, and away from, the longwall structure. Means are provided for securing each of the brackets to its floor sill in any one of a plurality of positions.

  19. Data Mining SIAM Presentation

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

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

  20. Ensemble Data Mining Methods

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2004-01-01

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