Science.gov

Sample records for mining heterogeneous multivariate

  1. Visual Data Mining of Large, Multivariate Space-Time Data

    NASA Astrophysics Data System (ADS)

    Cook, D.

    2001-12-01

    Interest in understanding global climate change is generating monitoring efforts that yield a huge amount of multivariate space-time data. While analytical methods for univariate space-time data may be mature and substantial, methods for multivariate space-time data analysis are still in their infancy. The urgency of understanding climate change on a global scale begs for input from data analysts, and to work effectively they need new tools to explore multivariate aspects of climate. This talk describes interactive and dynamic visual tools for mining information from multivariate space-time data. Methods for small amounts of data will be discussed, followed by approaches to scaling up methods for large quantities of data. We focus on the ``multiple views'' approach for viewing multivariate data, and how these extend to include space-time contextual information. We also will describe dynamic graphics methods such as tours in the space-time context. Data mining is the current terminology for exploratory analyses of data, typically associated with large databases. Exploratory analysis has a goal of finding anomalies, quirks and deviations from a trend, and basically extracting unexpected information from data. It oft-times emphasizes model-free methods, although model-based approaches are also integral components to the analysis process. Visual data mining concentrates on the use of visual tools in the exploratory process. As such it often involves highly interactive and dynamic graphics environments which facilitate quick queries and visual responses. Visual methods are especially important in exploratory analysis because they provide an interface for using the human eye to digest complex information. A good plot can convey far more information than a numerical summary. Visual tools enhance the chances of discovering the unexpected, and detecting the anomalous events.

  2. 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…

  3. 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

  4. 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.

  5. Mining Large Heterogeneous Graphs using Cray s Urika

    SciTech Connect

    Sukumar, Sreenivas R; Bond, Nathaniel A

    2013-01-01

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

  6. 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.…

  7. 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

  8. 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

  9. 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.

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

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  11. Mining Heterogeneous Social Networks for Egocentric Information Abstraction

    NASA Astrophysics Data System (ADS)

    Li, Cheng-Te; Lin, Shou-De

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

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

    PubMed Central

    Liu, Dungang; Liu, Regina; Xie, Minge

    2014-01-01

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

  13. 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

  14. 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

  15. 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

  16. 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.

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

    PubMed Central

    2011-01-01

    Background Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering. Results We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network. Conclusions The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE. PMID:21624156

  18. Correlating microbial diversity patterns with geochemistry in an extreme and heterogeneous environment of mine tailings.

    PubMed

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

    2014-06-01

    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

  19. 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

  20. 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

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

    PubMed Central

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

    2012-01-01

    Summary: InterMine is an open-source data warehouse system that facilitates the building of databases with complex data integration requirements and a need for a fast customizable query facility. Using InterMine, large biological databases can be created from a range of heterogeneous data sources, and the extensible data model allows for easy integration of new data types. The analysis tools include a flexible query builder, genomic region search and a library of ‘widgets’ performing various statistical analyses. The results can be exported in many commonly used formats. InterMine is a fully extensible framework where developers can add new tools and functionality. Additionally, there is a comprehensive set of web services, for which client libraries are provided in five commonly used programming languages. Availability: Freely available from http://www.intermine.org under the LGPL license. Contact: g.micklem@gen.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23023984

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

    PubMed

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

    2009-11-15

    Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold. PMID:19782239

  3. 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

  4. 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.

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

    NASA Astrophysics Data System (ADS)

    Vavrycuk, V.; Kuehn, D.

    2012-04-01

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

  6. DebugIT for patient safety - improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data.

    PubMed

    Lovis, Christian; Colaert, Dirk; Stroetmann, Veli N

    2008-01-01

    The concepts and architecture underlying a large-scale integrating project funded within the 7th EU Framework Programme (FP7) are discussed. The main objective of the project is to build a tool that will have a significant impact for the monitoring and the control of infectious diseases and antimicrobial resistances in Europe; This will be realized by building a technical and semantic infrastructure able to share heterogeneous clinical data sets from different hospitals in different countries, with different languages and legislations; to analyze large amounts of this clinical data with advanced multimedia data mining and finally apply the obtained knowledge for clinical decisions and outcome monitoring. There are numerous challenges in this project at all levels, technical, semantical, legal and ethical that will have to be addressed. PMID:18487803

  7. 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. PMID:26357403

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  10. Multivariate normality

    NASA Technical Reports Server (NTRS)

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

    1976-01-01

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

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

    SciTech Connect

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

    2008-02-15

    Lakes formed because of coal mining are characterized by low pH and high concentrations of Fe(II) and sulfate. The anoxic sediment is often separated into an upper acidic zone (pH 3; zone 1) with large amounts of reactive iron and a deeper slightly acidic zone (pH 5.5; zone III) with smaller amounts of iron. In this study, the impact of pH on the Fe(III)-reducing activities in both of these sediment zones was investigated, and molecular analyses that elucidated the sediment microbial diversity were performed. The results demonstrated that the upper acidic sediment was inhabited by acidophiles or moderate acidophiles which can also reduce Fe(III) under slightly acidic conditions. The majority of Fe(III) reducers inhabiting the slightly acidic sediment had only minor capacities to be active under acidic conditions.

  12. Introduction to multivariate discrimination

    NASA Astrophysics Data System (ADS)

    Kégl, Balázs

    2013-07-01

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

  13. 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…

  14. 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

  15. 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.

  16. Multivariate Intraclass Correlation.

    ERIC Educational Resources Information Center

    Wiley, David E.; Hawkes, Thomas H.

    This paper is an explication of a statistical model which will permit an interpretable intraclass correlation coefficient that is negative, and a generalized extension of that model to cover a multivariate problem. The methodological problem has its practical roots in an attempt to find a statistic which could indicate the degree of similarity or…

  17. 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

  18. Multivariate Data EXplorer (MDX)

    Energy Science and Technology Software Center (ESTSC)

    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

  19. 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.

  20. Multivariate volume rendering

    SciTech Connect

    Crawfis, R.A.

    1996-03-01

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

  1. Primer on multivariate calibration

    SciTech Connect

    Thomas, E.V. )

    1994-08-01

    In analytical chemistry, calibration is the procedure that relates instrumental measurements to an analyte of interest. Typically, instrumental measurements are obtained from specimens in which the amount (or level) of the analyte has been determined by some independent and inherently accurate assay (e.g., wet chemistry). Together, the instrumental measurements and results from the independent assays are used to construct a model that relates the analyte level to the instrumental measurements. The advent of high-speed digital computers has greatly increased data acquisition and analysis capabilities and has provided the analytical chemist with opportunities to use many measurements - perhaps hundreds - for calibrating an instrument (e.g., absorbances at multiple wave-lengths). To take advantage of this technology, however, new methods (i.e., multivariate calibration methods) were needed for analyzing and modeling the experimental data. The purpose of this report is to introduce several evolving multivariate calibration methods and to present some important issues regarding their use. 30 refs., 7 figs.

  2. 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

  3. 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

  4. 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)

  5. 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.

  6. Network structure of multivariate time series.

    PubMed

    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

  7. 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

  8. 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.

  9. Web Mining

    NASA Astrophysics Data System (ADS)

    Fürnkranz, Johannes

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

  10. 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…

  11. 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.…

  12. Visual cues for data mining

    NASA Astrophysics Data System (ADS)

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

    1996-04-01

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

  13. Mine system

    SciTech Connect

    Stoppani, B.R.

    1983-10-04

    A mine system comprises at least one mining machine adapted to haul itself, in a reciprocating manner, along a mineral face, and a control box housing means to control the various electrical elements of the machine(s), the box being located in a mine roadway at one end of the mineral face along which the machine(s) is reciprocating, and the box being electrically connected to a terminal box housed in a body of the machine(s).

  14. 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)

  15. 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…

  16. A "Model" Multivariable Calculus Course.

    ERIC Educational Resources Information Center

    Beckmann, Charlene E.; Schlicker, Steven J.

    1999-01-01

    Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

  17. 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.

  18. Optimizing Functional Network Representation of Multivariate Time Series

    PubMed Central

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

    2012-01-01

    By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. PMID:22953051

  19. 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.

  20. 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.

  1. Information extraction from multivariate images

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  2. Mining apparatus

    SciTech Connect

    Ingle, J.E.; Lane, A.J.; Mcgee, D.A.

    1981-03-10

    An improved mining apparatus for excavating material, such as coal, for example, from an earth formation, such as a coal seam, for example, wherein a miner, having a forward and a rearward cutter, is guided through the coal seam and excavates a borehole therein, the borehole being filled with a working fluid during the operation of the miner, the working fluid facilitating the operation of the miner and providing a vehicle for removing the mined material. Substantially all of the operations of the miner are controlled from the earth's surface thereby eliminating the necessity and accompanying hazards and costs involved in utilizing personnel underground during the mining operations.

  3. 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.

  4. 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.

  5. 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.

  6. ROTATING DISC BIOLOGICAL TREATMENT OF ACID MINE DRAINAGE

    EPA Science Inventory

    Pilot scale (0.5-m diameter) and prototype (2.0-m diameter) rotating biological contactors (RBC) were investigated for oxidation of ferrous Fe(II) iron contained in six heterogeneous mine waters located at three coal mining sites in Pennsylvania and West Virginia. Continuous biol...

  7. 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).

  8. 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).

  9. System identification for multivariable control

    NASA Astrophysics Data System (ADS)

    Vanzee, G. A.

    1981-05-01

    System identification methods and modern control theory are applied to industrial processes. These processes must often be controlled in order to meet certain requirements with respect to the product quality, safety, energy consumption, and environmental load. Modern control system design methods which take the occurring interaction phenomena and stochastic disturbances into account are used. An accurate dynamic mathematical model of the process, by theoretical modelling and/or by system identification is obtained. The computational aspects of two important types of identifications methods, i.e., stochastic realization and prediction error based parameter estimation are studied. The studied computational aspects are the robustness, the accuracy, and the computational costs of the methods. Theoretical analyses and applications to a multivariable pilot scale process, operating under closed loop conditions are investigated.

  10. Multivariable Burchnall-Chaundy theory.

    PubMed

    Previato, Emma

    2008-03-28

    Burchnall & Chaundy (Burchnall & Chaundy 1928 Proc. R. Soc. A 118, 557-583) classified the (rank 1) commutative subalgebras of the algebra of ordinary differential operators. To date, there is no such result for several variables. This paper presents the problem and the current state of the knowledge, together with an interpretation in differential Galois theory. It is known that the spectral variety of a multivariable commutative ring will not be associated to a KP-type hierarchy of deformations, but examples of related integrable equations were produced and are reviewed. Moreover, such an algebro-geometric interpretation is made to fit into A.N. Parshin's newer theory of commuting rings of partial pseudodifferential operators and KP-type hierarchies which uses higher local fields. PMID:17588865

  11. Mining machine

    SciTech Connect

    Parrott, G.A.

    1985-05-07

    A haulage system for a mining machine comprises a mining machine mounted on and/or guided by a conveyor and reciprocable with respect thereto, the conveyor being provided with a rack having plural rows of teeth of identical pitch, with the teeth of one row staggered with respect to an adjacent row(s), and the machine being provided with at least one power driven haulage sprocket comprising plural sets of peripherally arranged teeth of identical pitch, one set being angularly staggered with respect to an adjacent set(s), whereby one set is engageable with each row of teeth of the rack. The invention also includes a mining machine provided with such a power driven haulage sprocket, and a rack as above described and provided with end fittings for securing in articulated manner to an adjacent rack.

  12. 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.

  13. Mining equipment

    SciTech Connect

    Goff, J.R.; Spence, A.M.

    1981-06-23

    In a system for the supply of fluid under pressure to machinery of an underground mine working, lengths of fixed conduit are secured to parts of the conveyor assembly, prior to the assembly of said parts at the underground mine working. When the conveyor assembly has been assembled, the length of fixed conduits are interconnected, either by straight lengths of flexible conduit, or by branched lengths of flexible conduit, where take off for fluid under pressure is required for the machinery, for example a roof support unit.

  14. 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

  15. 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

  16. Underground mining methods handbook

    SciTech Connect

    Hustrulid, W.A.

    1982-01-01

    Sections discuss: mine design considerations; stopes requiring minimum support (includes room-and-pillar mining and sublevel stoping); stopes requiring some additional support other than pillars (includes shrinkage stoping, cut-and-fill stoping, undercut-and-fill mining, timber-supported system, top-slice mining, longwall mining and shortwall mining); caving methods (sublevel and block caving); underground equipment; financial considerations; design; and mine ventilation.

  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. 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.

  19. 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

  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. 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.

  2. 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)

  3. 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.

  4. Mining review

    USGS Publications Warehouse

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

    2004-01-01

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

  5. CLUSTERING CRITERIA AND MULTIVARIATE NORMAL MIXTURES

    EPA Science Inventory

    New clustering criteria for use when a mixture of multivariate normal distributions is an appropriate model are presented. They are derived from maximum likelihood and Bayesian approaches corresponding to different assumptions about the covariance matrices of the mixture componen...

  6. A Course in... Multivariable Control Methods.

    ERIC Educational Resources Information Center

    Deshpande, Pradeep B.

    1988-01-01

    Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

  7. Multivariate data analysis of proteome data.

    PubMed

    Engkilde, Kåre; Jacobsen, Susanne; Søndergaard, Ib

    2007-01-01

    We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image processing software, transfer of data, multivariate data analysis, interpretation of the results, and finally we return to biology. PMID:17093312

  8. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

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

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  9. 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

  10. 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.

  11. 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.

  12. 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

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

    PubMed Central

    Basu, Sanjay; McKee, Martin; Lurie, Mark

    2011-01-01

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

  14. 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

  15. 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.

  16. 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.

  17. A multivariable control scheme for robot manipulators

    NASA Technical Reports Server (NTRS)

    Tarokh, M.; Seraji, H.

    1991-01-01

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

  18. Biological sequence classification with multivariate string kernels.

    PubMed

    Kuksa, Pavel P

    2013-01-01

    String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on the analysis of discrete 1D string data (e.g., DNA or amino acid sequences). In this paper, we address the multiclass biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physicochemical descriptors) and a class of multivariate string kernels that exploit these representations. On three protein sequence classification tasks, the proposed multivariate representations and kernels show significant 15-20 percent improvements compared to existing state-of-the-art sequence classification methods. PMID:24384708

  19. Biological Sequence Analysis with Multivariate String Kernels.

    PubMed

    Kuksa, Pavel P

    2013-03-01

    String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete one-dimensional (1D) string data (e.g., DNA or amino acid sequences). In this work we address the multi-class biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors) and a class of multivariate string kernels that exploit these representations. On a number of protein sequence classification tasks proposed multivariate representations and kernels show significant 15-20\\% improvements compared to existing state-of-the-art sequence classification methods. PMID:23509193

  20. Modeling blood flow heterogeneity.

    PubMed

    King, R B; Raymond, G M; Bassingthwaighte, J B

    1996-01-01

    It has been known for some time that regional blood flows within an organ are not uniform. Useful measures of heterogeneity of regional blood flows are the standard deviation and coefficient of variation or relative dispersion of the probability density function (PDF) of regional flows obtained from the regional concentrations of tracers that are deposited in proportion to blood flow. When a mathematical model is used to analyze dilution curves after tracer solute administration, for many solutes it is important to account for flow heterogeneity and the wide range of transit times through multiple pathways in parallel. Failure to do so leads to bias in the estimates of volumes of distribution and membrane conductances. Since in practice the number of paths used should be relatively small, the analysis is sensitive to the choice of the individual elements used to approximate the distribution of flows or transit times. Presented here is a method for modeling heterogeneous flow through an organ using a scheme that covers both the high flow and long transit time extremes of the flow distribution. With this method, numerical experiments are performed to determine the errors made in estimating parameters when flow heterogeneity is ignored, in both the absence and presence of noise. The magnitude of the errors in the estimates depends upon the system parameters, the amount of flow heterogeneity present, and whether the shape of the input function is known. In some cases, some parameters may be estimated to within 10% when heterogeneity is ignored (homogeneous model), but errors of 15-20% may result, even when the level of heterogeneity is modest. In repeated trials in the presence of 5% noise, the mean of the estimates was always closer to the true value with the heterogeneous model than when heterogeneity was ignored, but the distributions of the estimates from the homogeneous and heterogeneous models overlapped for some parameters when outflow dilution curves were

  1. Optimal and multivariable control of a turbogenerator

    NASA Astrophysics Data System (ADS)

    Lahoud, M. A.; Harley, R. G.; Secker, A.

    The use of modern control methods to design multivariable controllers which improve the performance of a turbogenerator was investigated. The turbogenerator nonlinear mathematical model from which a linearized model is deduced is presented. The inverse Nyquist Array method and the theory of optimal control are both applied to the linearized model to generate two alternative control schemes. The schemes are implemented on the nonlinear simulation model to assess their dynamic performance. Results from modern multivariable control schemes are compared with the classical automatic voltage regulator and speed governor system.

  2. Multivariate multiscale entropy for brain consciousness analysis.

    PubMed

    Ahmed, Mosabber Uddin; Li, Ling; Cao, Jianting; Mandic, Danilo P

    2011-01-01

    The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case. This allows us to gain a greater insight into the complexity of the underlying signal generating system, producing multifaceted and more robust estimates than standard single channel MSE. Simulations on both synthetic data and brain consciousness analysis support the approach. PMID:22254434

  3. Sparse Multivariate Regression With Covariance Estimation

    PubMed Central

    Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji

    2014-01-01

    We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268

  4. Heterogeneous Atmospheric Chemistry

    NASA Astrophysics Data System (ADS)

    Schryer, David R.

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

  5. 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.

  6. Microbial and geochemical assessment of bauxitic un-mined and post-mined chronosequence soils from Mocho Mountains, Jamaica.

    PubMed

    Lewis, Dawn E; Chauhan, Ashvini; White, John R; Overholt, Will; Green, Stefan J; Jasrotia, Puja; Wafula, Denis; Jagoe, Charles

    2012-10-01

    Microorganisms are very sensitive to environmental change and can be used to gauge anthropogenic impacts and even predict restoration success of degraded environments. Here, we report assessment of bauxite mining activities on soil biogeochemistry and microbial community structure using un-mined and three post-mined sites in Jamaica. The post-mined soils represent a chronosequence, undergoing restoration since 1987, 1997, and 2007. Soils were collected during dry and wet seasons and analyzed for pH, organic matter (OM), total carbon (TC), nitrogen (TN), and phosphorus. The microbial community structure was assessed through quantitative PCR and massively parallel bacterial ribosomal RNA (rRNA) gene sequencing. Edaphic factors and microbial community composition were analyzed using multivariate statistical approaches and revealed a significant, negative impact of mining on soil that persisted even after greater than 20 years of restoration. Seasonal fluctuations contributed to variation in measured soil properties and community composition, but they were minor in comparison to long-term effects of mining. In both seasons, post-mined soils were higher in pH but OM, TC, and TN decreased. Bacterial rRNA gene analyses demonstrated a general decrease in diversity in post-mined soils and up to a 3-log decrease in rRNA gene abundance. Community composition analyses demonstrated that bacteria from the Proteobacteria (α, β, γ, δ), Acidobacteria, and Firmicutes were abundant in all soils. The abundance of Firmicutes was elevated in newer post-mined soils relative to the un-mined soil, and this contrasted a decrease, relative to un-mined soils, in proteobacterial and acidobacterial rRNA gene abundances. Our study indicates long-lasting impacts of mining activities to soil biogeochemical and microbial properties with impending loss in soil productivity. PMID:22391797

  7. Multivariate Outliers. Review of the Literature.

    ERIC Educational Resources Information Center

    Jarrell, Michele G.

    Research in the area of multivariate outliers is reviewed, emphasizing the problems associated with definition and identification. Treatment of the problem can be traced to 1777 and the work of D. Bernoulli. Most of the many procedures developed for identifying outliers proceed sequentially starting with the most aberrant observation, or proceed…

  8. 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...

  9. 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,…

  10. Multivariate statistical mapping of spectroscopic imaging data.

    PubMed

    Young, Karl; Govind, Varan; Sharma, Khema; Studholme, Colin; Maudsley, Andrew A; Schuff, Norbert

    2010-01-01

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

  11. Intermittent control of unstable multivariate systems.

    PubMed

    Loram, I; Gawthrop, P; Gollee, H

    2015-08-01

    A sensorimotor architecture inspired from biological, vertebrate control should (i) explain the interface between high dimensional sensory analysis, low dimensional goals and high dimensional motor mechanisms and (ii) provide both stability and flexibility. Our interest concerns whether single-input-single-output intermittent control (SISO_IC) generalized to multivariable intermittent control (MIC) can meet these requirements.We base MIC on the continuous-time observer-predictorstate-feedback architecture. MIC uses event detection. A system matched hold (SMH), using the underlying continuoustime optimal control design, generates multivariate open-loop control signals between samples of the predicted state. Combined, this serial process provides a single-channel of control with optimised sensor fusion and motor synergies. Quadratic programming provides constrained, optimised equilibrium control design to handle unphysical configurations, redundancy and provides minimum, necessary reduction of open loop instability through optimised joint impedance. In this multivariate form, dimensionality is linked to goals rather than neuromuscular or sensory degrees of freedom. The biological and engineering rationale for intermittent rather than continuous multivariate control, is that the generalised hold sustains open loop predictive control while the open loop interval provides time within the feedback loop for online centralised, state dependent optimisation and selection. PMID:26736539

  12. 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

  13. 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

  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. 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.

  16. Towards heterogeneous distributed debugging

    SciTech Connect

    Damodaran-Kamal, S.K.

    1995-04-01

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

  17. Mining lease handbook

    SciTech Connect

    Not Available

    1992-01-01

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

  18. 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.

  19. 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.

  20. 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.

  1. 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

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

    NASA Astrophysics Data System (ADS)

    Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

    2014-05-01

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

  3. 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.

  4. 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.

  5. 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.

  6. 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. PMID:26490901

  7. Mine waste management

    SciTech Connect

    Hutchinson, I.P.G.; Ellison, R.D.

    1992-01-01

    This book reports on mine waste management. Topics covered include: Performance review of modern mine waste management units; Mine waste management requirements; Prediction of acid generation potential; Attenuation of chemical constituents; Climatic considerations; Liner system design; Closure requirements; Heap leaching; Ground water monitoring; and Economic impact evaluation.

  8. Mountaintop mining update

    SciTech Connect

    Buchsbaum, L.

    2006-07-15

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

  9. 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.

  10. Advancing emotion theory with multivariate pattern classification

    PubMed Central

    Kragel, Philip A.; LaBar, Kevin S.

    2016-01-01

    Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system.

  11. 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.

  12. 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.

  13. Design of multivariable controllers for robot manipulators

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1986-01-01

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

  14. 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

  15. The Evolution of Multivariate Maternal Effects

    PubMed Central

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

    2014-01-01

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

  16. Multivariable PID Controller For Robotic Manipulator

    NASA Technical Reports Server (NTRS)

    Seraji, Homayoun; Tarokh, Mahmoud

    1990-01-01

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

  17. Simplified Linear Multivariable Control Of Robots

    NASA Technical Reports Server (NTRS)

    Seraji, Homayoun

    1989-01-01

    Simplified method developed to design control system that makes joints of robot follow reference trajectories. Generic design includes independent multivariable feedforward and feedback controllers. Feedforward controller based on inverse of linearized model of dynamics of robot and implements control law that contains only proportional and first and second derivatives of reference trajectories with respect to time. Feedback controller, which implements control law of proportional, first-derivative, and integral terms, makes tracking errors converge toward zero as time passes.

  18. Multivariate streamflow forecasting using independent component analysis

    NASA Astrophysics Data System (ADS)

    Westra, Seth; Sharma, Ashish; Brown, Casey; Lall, Upmanu

    2008-02-01

    Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.

  19. Damage detection using multivariate recurrence quantification analysis

    NASA Astrophysics Data System (ADS)

    Nichols, J. M.; Trickey, S. T.; Seaver, M.

    2006-02-01

    Recurrence-quantification analysis (RQA) has emerged as a useful tool for detecting subtle non-stationarities and/or changes in time-series data. Here, we extend the RQA analysis methods to multivariate observations and present a method by which the "length scale" parameter ɛ (the only parameter required for RQA) may be selected. We then apply the technique to the difficult engineering problem of damage detection. The structure considered is a finite element model of a rectangular steel plate where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments. Time series, recorded at nine separate locations on the structure, are used to reconstruct the phase space of the system's dynamics and subsequently generate the multivariate recurrence (and cross-recurrence) plots. Multivariate RQA is then used to detect damage-induced changes to the structural dynamics. These results are then compared with shifts in the plate's natural frequencies. Two of the RQA-based features are found to be more sensitive to damage than are the plate's frequencies.

  20. 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. PMID:15646279

  1. Multivariate postprocessing techniques for probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Hemri, S.; Lisniak, D.; Klein, B.

    2015-09-01

    Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location 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) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers 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. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.

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

    PubMed

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

    2005-06-15

    In this paper, mortality in the immediate aftermath of an earthquake is studied on a worldwide scale using multivariate analysis. A statistical method is presented that analyzes reported earthquake fatalities as a function of a heterogeneous set of parameters selected on the basis of their presumed influence on earthquake mortality. The ensemble was compiled from demographic, seismic, and reported fatality data culled from available records of past earthquakes organized in a geographic information system. The authors consider the statistical relation between earthquake mortality and the available data ensemble, analyze the validity of the results in view of the parametric uncertainties, and propose a multivariate mortality analysis prediction method. The analysis reveals that, although the highest mortality rates are expected in poorly developed rural areas, high fatality counts can result from a wide range of mortality ratios that depend on the effective population size. PMID:15937024

  3. 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.

  4. 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

  5. Data Mining Model Comparison

    NASA Astrophysics Data System (ADS)

    Giudici, Paolo

    The aim of this contribution is to illustrate the role of statistical models and, more generally, of statistics, in choosing a Data Mining model. After a preliminary introduction on the distinction between Data Mining and statistics, we will focus on the issue of how to choose a Data Mining methodology. This well illustrates how statistical thinking can bring real added value to a Data Mining analysis, as otherwise it becomes rather difficult to make a reasoned choice. In the third part of the paper we will present, by means of a case study in credit risk management, how Data Mining and statistics can profitably interact.

  6. Mined area detection overview

    NASA Astrophysics Data System (ADS)

    Burch, Ian A.; Deas, Robert M.; Port, Daniel M.

    2002-08-01

    An overview of the progress on the UK MOD Applied Research Program for Land Mine Detection. The Defense Science and Technology Laboratory (Dstl) carries out and manages the whole of the UK MOD's Mined Area Detection Applied Research Program both within its own laboratories and in partnership with industrial and academic research organizations. This paper will address two specific areas of Applied Research: hand held mine detection and vehicle mounted mine detection in support of the Mine Detection Neutralization and Route Marking System which started in April 1997. Both are multi-sensor systems, incorporating between them metal detection, ground penetrating radar, nuclear quadrupole resonance, ultra-wideband radar, and polarized thermal imaging.

  7. 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

  8. 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

  9. 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.

  10. 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

  11. Multivariate Padé Approximations For Solving Nonlinear Diffusion Equations

    NASA Astrophysics Data System (ADS)

    Turut, V.

    2015-11-01

    In this paper, multivariate Padé approximation is applied to power series solutions of nonlinear diffusion equations. As it is seen from tables, multivariate Padé approximation (MPA) gives reliable solutions and numerical results.

  12. Evaluating foam heterogeneity

    NASA Technical Reports Server (NTRS)

    Liou, D. W.; Lee, W. M.

    1972-01-01

    New analytical tool is available to calculate the degree of foam heterogeneity based on the measurement of gas diffusivity values. Diffusion characteristics of plastic foam are described by a system of differential equations based on conventional diffusion theory. This approach saves research and computation time in studying mass or heat diffusion problems.

  13. 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.

  14. Linear models of coregionalization for multivariate lattice data: a general framework for coregionalized multivariate CAR models.

    PubMed

    MacNab, Ying C

    2016-09-20

    We present a general coregionalization framework for developing coregionalized multivariate Gaussian conditional autoregressive (cMCAR) models for Bayesian analysis of multivariate lattice data in general and multivariate disease mapping data in particular. This framework is inclusive of cMCARs that facilitate flexible modelling of spatially structured symmetric or asymmetric cross-variable local interactions, allowing a wide range of separable or non-separable covariance structures, and symmetric or asymmetric cross-covariances, to be modelled. We present a brief overview of established univariate Gaussian conditional autoregressive (CAR) models for univariate lattice data and develop coregionalized multivariate extensions. Classes of cMCARs are presented by formulating precision structures. The resulting conditional properties of the multivariate spatial models are established, which cast new light on cMCARs with richly structured covariances and cross-covariances of different spatial ranges. The related methods are illustrated via an in-depth Bayesian analysis of a Minnesota county-level cancer data set. We also bring a new dimension to the traditional enterprize of Bayesian disease mapping: estimating and mapping covariances and cross-covariances of the underlying disease risks. Maps of covariances and cross-covariances bring to light spatial characterizations of the cMCARs and inform on spatial risk associations between areas and diseases. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27091685

  15. 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…

  16. 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

  17. 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

  18. Multivariate tests for trend in water quality

    NASA Astrophysics Data System (ADS)

    Loftis, Jim C.; Taylor, Charles H.; Chapman, Phillip L.

    1991-07-01

    Several methods of testing for multivariate trend have been discussed in the statistical and water quality literature. We review both parametric and nonparametric approaches and compare their performance using, synthetic data. A new method, based on a robust estimation and testing approach suggested by Sen and Puri, performed very well for serially independent observations. A modified version of the covariance inversion approach presented by Dietz and Killeen also performed well for serially independent observations. For serially correlated observations, the covariance eigenvalue method suggested by Lettenmaier was the best performer.

  19. 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

  20. 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.

  1. COSIMA data analysis using multivariate techniques

    NASA Astrophysics Data System (ADS)

    Silén, J.; Cottin, H.; Hilchenbach, M.; Kissel, J.; Lehto, H.; Siljeström, S.; Varmuza, K.

    2015-02-01

    We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.

  2. New multivariable capabilities of the INCA program

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

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

  3. 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.

  4. Bayesian Transformation Models for Multivariate Survival Data

    PubMed Central

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

    2014-01-01

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

  5. 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

  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. 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.

  8. Query-Based Outlier Detection in Heterogeneous Information Networks

    PubMed Central

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

    2015-01-01

    Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

  9. 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

  10. Collaborative Data Mining

    NASA Astrophysics Data System (ADS)

    Moyle, Steve

    Collaborative Data Mining is a setting where the Data Mining effort is distributed to multiple collaborating agents - human or software. The objective of the collaborative Data Mining effort is to produce solutions to the tackled Data Mining problem which are considered better by some metric, with respect to those solutions that would have been achieved by individual, non-collaborating agents. The solutions require evaluation, comparison, and approaches for combination. Collaboration requires communication, and implies some form of community. The human form of collaboration is a social task. Organizing communities in an effective manner is non-trivial and often requires well defined roles and processes. Data Mining, too, benefits from a standard process. This chapter explores the standard Data Mining process CRISP-DM utilized in a collaborative setting.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. A semiparametric multivariate and multisite weather generator

    NASA Astrophysics Data System (ADS)

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

    2007-11-01

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

  17. 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.

  18. 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

  19. 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

  20. Mining agreements III

    SciTech Connect

    Not Available

    1991-01-01

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

  1. Heterogeneities in granular dynamics.

    PubMed

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

    2008-06-17

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

  2. 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.

  3. Heterogeneity in Melanoma.

    PubMed

    Shannan, Batool; Perego, Michela; Somasundaram, Rajasekharan; Herlyn, Meenhard

    2016-01-01

    Melanoma is among the most aggressive and therapy-resistant human cancers. While great strides in therapy have generated enthusiasm, many challenges remain. Heterogeneity is the most pressing issue for all types of therapy. This chapter summarizes the clinical classification of melanoma, of which the research community now adds additional layers of classifications for better diagnosis and prediction of therapy response. As the search for new biomarkers increases, we expect that biomarker analyses will be essential for all clinical trials to better select patient populations for optimal therapy. While individualized therapy that is based on extensive biomarker analyses is an option, we expect in the future genetic and biologic biomarkers will allow grouping of melanomas in such a way that we can predict therapy outcome. At this time, tumor heterogeneity continues to be the major challenge leading inevitably to relapse. To address heterogeneity therapeutically, we need to develop complex therapies that eliminate the bulk of the tumor and, at the same time, the critical subpopulations. PMID:26601857

  4. 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.

  5. Canada's largest mining scheme

    SciTech Connect

    Not Available

    1984-05-01

    A large coal mining development in Canada's British Columbia, is opening up the wilderness in the northeastern part of that province. North East Coal Development, two open-pit mines operated by Quintette Coal Ltd., and Teck Corporation, both Vancouver-based mining companies, has started to ship to a group of Japanese steel companies 6,500,000 tons annually of metallurgical and additional quantities of thermal coal. To open this wilderness, some 80 miles southwest of Dawson Creek, and to develop the two surface mines, processing plants, and associated facilities involved several massive multimillion-dollar projects. These projects are discussed.

  6. Mining and Integration of Environmental Data

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  7. Is ventilation heterogeneity related to asthma control?

    PubMed

    Svenningsen, Sarah; Nair, Parameswaran; Guo, Fumin; McCormack, David G; Parraga, Grace

    2016-08-01

    In asthma patients, magnetic resonance imaging (MRI) and the lung clearance index (LCI) have revealed persistent ventilation heterogeneity, although its relationship to asthma control is not well understood. Therefore, our goal was to explore the relationship of MRI ventilation defects and the LCI with asthma control and quality of life in patients with severe, poorly controlled asthma.18 patients with severe, poorly controlled asthma (mean±sd 46±12 years, six males/12 females) provided written informed consent to an ethics board approved protocol, and underwent spirometry, LCI and (3)He MRI during a single 2-h visit. Asthma control and quality of life were evaluated using the Asthma Control Questionnaire (ACQ) and Asthma Quality of Life Questionnaire (AQLQ). Ventilation heterogeneity was quantified using the LCI and (3)He MRI ventilation defect percent (VDP).All participants reported poorly controlled disease (mean±sd ACQ score=2.3±0.9) and highly heterogeneous ventilation (mean±sd VDP=12±11% and LCI=10.5±3.0). While VDP and LCI were strongly correlated (r=0.86, p<0.0001), in a multivariate model that included forced expiratory volume in 1 s, VDP and LCI, VDP was the only independent predictor of asthma control (R(2)=0.38, p=0.01). There was also a significantly worse VDP, but not LCI in asthma patients with an ACQ score >2 (p=0.04) and AQLQ score <5 (p=0.04), and a trend towards worse VDP (p=0.053), but not LCI in asthma patients reporting ≥1 exacerbation in the past 6 months.In patients with poorly controlled, severe asthma MRI ventilation, but not LCI was significantly worse in those with worse ACQ and AQLQ. PMID:27174885

  8. 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.

  9. 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.

  10. Compensator improvement for multivariable control systems

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

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

  11. Nested Taylor decomposition in multivariate function decomposition

    NASA Astrophysics Data System (ADS)

    Baykara, N. A.; Gürvit, Ercan

    2014-12-01

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

  12. Multivariate Markov chain modeling for stock markets

    NASA Astrophysics Data System (ADS)

    Maskawa, Jun-ichi

    2003-06-01

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

  13. Multivariable Harmonic Balance for Central Pattern Generators★

    PubMed Central

    Iwasaki, Tetsuya

    2009-01-01

    The central pattern generator (CPG) is a nonlinear oscillator formed by a group of neurons, providing a fundamental control mechanism underlying rhythmic movements in animal locomotion. We consider a class of CPGs modeled by a set of interconnected identical neurons. Based on the idea of multivariable harmonic balance, we show how the oscillation profile is related to the connectivity matrix that specifies the architecture and strengths of the interconnections. Specifically, the frequency, amplitudes, and phases are essentially encoded in terms of a pair of eigenvalue and eigenvector. This basic principle is used to estimate the oscillation profile of a given CPG model. Moreover, a systematic method is proposed for designing a CPG-based nonlinear oscillator that achieves a prescribed oscillation profile. PMID:19956774

  14. Design of feedforward controllers for multivariable plants

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1987-01-01

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

  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. A complete procedure for multivariate index-flood model application

    NASA Astrophysics Data System (ADS)

    Requena, Ana Isabel; Chebana, Fateh; Mediero, Luis

    2016-04-01

    Multivariate frequency analyses are needed to study floods due to dependence existing among representative variables of the flood hydrograph. Particularly, multivariate analyses are essential when flood-routing processes significantly attenuate flood peaks, such as in dams and flood management in flood-prone areas. Besides, regional analyses improve at-site quantile estimates obtained at gauged sites, especially when short flow series exist, and provide estimates at ungauged sites where flow records are unavailable. However, very few studies deal simultaneously with both multivariate and regional aspects. This study seeks to introduce a complete procedure to conduct a multivariate regional hydrological frequency analysis (HFA), providing guidelines. The methodology joins recent developments achieved in multivariate and regional HFA, such as copulas, multivariate quantiles and the multivariate index-flood model. The proposed multivariate methodology, focused on the bivariate case, is applied to a case study located in Spain by using hydrograph volume and flood peak observed series. As a result, a set of volume-peak events under a bivariate quantile curve can be obtained for a given return period at a target site, providing flexibility to practitioners to check and decide what the design event for a given purpose should be. In addition, the multivariate regional approach can also be used for obtaining the multivariate distribution of the hydrological variables when the aim is to assess the structure failure for a given return period.

  17. 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.

  18. Mining Glossary and Games.

    ERIC Educational Resources Information Center

    National Energy Foundation, Salt Lake City, UT.

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

  19. Underground Coal Mining

    NASA Technical Reports Server (NTRS)

    Hill, G. M.

    1980-01-01

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

  20. 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.

  1. PRB mines mature

    SciTech Connect

    Buchsbaum, L.

    2007-08-15

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

  2. Heterogeneity in expected longevities.

    PubMed

    Pijoan-Mas, Josep; Ríos-Rull, José-Víctor

    2014-12-01

    We develop a new methodology to compute differences in the expected longevity of individuals of a given cohort who are in different socioeconomic groups at a certain age. We address the two main problems associated with the standard use of life expectancy: (1) that people's socioeconomic characteristics change, and (2) that mortality has decreased over time. Our methodology uncovers substantial heterogeneity in expected longevities, yet much less heterogeneity than what arises from the naive application of life expectancy formulae. We decompose the longevity differences into differences in health at age 50, differences in the evolution of health with age, and differences in mortality conditional on health. Remarkably, education, wealth, and income are health-protecting but have very little impact on two-year mortality rates conditional on health. Married people and nonsmokers, however, benefit directly in their immediate mortality. Finally, we document an increasing time trend of the socioeconomic gradient of longevity in the period 1992-2008, and we predict an increase in the socioeconomic gradient of mortality rates for the coming years. PMID:25391225

  3. Biclustering with heterogeneous variance.

    PubMed

    Chen, Guanhua; Sullivan, Patrick F; Kosorok, Michael R

    2013-07-23

    In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, and methods that do not consider heterogeneity of variance can lead to inaccurate subgroup prediction. Research has shown that hypervariability is a common feature among cancer subtypes. In this paper, we present a statistical approach that can capture both mean and variance structure in genetic data. We demonstrate the strength of our method in both synthetic data and in two cancer data sets. In particular, our method confirms the hypervariability of methylation level in cancer patients, and it detects clearer subgroup patterns in lung cancer data. PMID:23836637

  4. 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.

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

    ERIC Educational Resources Information Center

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

    2000-01-01

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

  6. Motivating Complex Dependence Structures in Data Mining: A Case Study with Anomaly Detection in Climate

    SciTech Connect

    Kao, Shih-Chieh; Ganguly, Auroop R; Steinhaeuser, Karsten J K

    2009-01-01

    While data mining aims to identify hidden knowledge from massive and high dimensional datasets, the importance of dependence structure among time, space, and between different variables is less emphasized. Analogous to the use of probability density functions in modeling individual variables, it is now possible to characterize the complete dependence space mathematically through the application of copulas. By adopting copulas, the multivariate joint probability distribution can be constructed without constraint to specific types of marginal distributions. Some common assumptions, like normality and independence between variables, can also be relieved. This study provides fundamental introduction and illustration of dependence structure, aimed at the potential applicability of copulas in general data mining. The case study in hydro-climatic anomaly detection shows that the frequency of multivariate anomalies is affected by the dependence level between variables. The appropriate multivariate thresholds can be determined through a copula-based approach.

  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. 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

  9. 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

  10. Model selection using multivariate functional data analysis for fast uncertainty quantification in subsurface reservoir forecasting

    NASA Astrophysics Data System (ADS)

    Grujic, O.; Caers, J.

    2014-12-01

    Modern approaches to uncertainty quantification in the subsurface rely on complex procedures of geological modeling combined with numerical simulation of flow & transport. This approach requires long computational times rendering any full Monte Carlo simulation infeasible, in particular solving the flow & transport problem takes hours of computing time in real field problems. This motivated the development of model selection methods aiming to identify a small subset of models that capture important statistics of a larger ensemble of geological model realization. A recent method, based on model selection in metric space, termed distance-kernel method (DKM) allows selecting representative models though kernel k-medoid clustering. The distance defining the metric space is usually based on some approximate flow model. However, the output of an approximate flow model can be multi-variate (reporting heads/pressures, saturation, rates). In addition, the modeler may have information from several other approximate models (e.g. upscaled models) or summary statistical information about geological heterogeneity that could allow for a more accurate selection. In an effort to perform model selection based on multivariate attributes, we rely on functional data analysis which allows for an exploitation of covariances between time-varying multivariate numerical simulation output. Based on mixed functional principal component analysis, we construct a lower dimensional space in which kernel k-medoid clustering is used for model selection. In this work we demonstrate the functional approach on a complex compositional flow problem where the geological uncertainty consists of channels with uncertain spatial distribution of facies, proportions, orientations and geometries. We illustrate that using multivariate attributes and multiple approximate models provides accuracy improvement over using a single attribute.

  11. 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

  12. 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. PMID:26340888

  13. 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.

  14. Bioharness™ Multivariable Monitoring Device: Part. I: Validity

    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 there is limited information on its validity. The objective of this study was to assess the validity of all 5 Bioharness™ variables using a laboratory based treadmill protocol. 22 healthy males participated. Heart rate (HR), Breathing Frequency (BF) and Accelerometry (ACC) precision were assessed during a discontinuous incremental (0-12 km·h-1) treadmill protocol. Infra-red skin temperature (ST) was assessed during a 45 min-1 sub-maximal cycle ergometer test, completed twice, with environmental temperature controlled at 20 ± 0.1 °C and 30 ± 0.1 °C. Posture (P) was assessed using a tilt table moved through 160°. Adopted precision of measurement devices were; HR: Polar T31 (Polar Electro), BF: Spirometer (Cortex Metalyser), ACC: Oxygen expenditure (Cortex Metalyser), ST: Skin thermistors (Grant Instruments), P:Goniometer (Leighton Flexometer). Strong relationships (r = .89 to .99, p < 0.01) were reported for HR, BF, ACC and P. Limits of agreement identified differences in HR (-3.05 ± 32.20 b·min-1), BF (-3.46 ± 43.70 br·min-1) and P (0.20 ± 2.62°). ST established a moderate relationships (-0.61 ± 1.98 °C; r = 0.76, p < 0.01). Higher velocities on the treadmill decreased the precision of measurement, especially HR and BF. Global results suggest that the BioharressTM is a valid multivariable monitoring device within the laboratory environment. Key pointsDifferent levels of precision exist for each variable in the Bioharness™ (Version 1) multi-variable monitoring deviceAccelerometry and posture variables presented the most precise dataData from the heart rate and breathing frequency variable decrease in precision at velocities ≥ 10 km·h-1Clear understanding of the limitations of new applied monitoring technology is required before it is used by the exercise scientist PMID:24149346

  15. 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

  16. Land Mines Removal

    NASA Technical Reports Server (NTRS)

    1999-01-01

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

  17. 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

  18. Strata mechanics in coal mining

    SciTech Connect

    Jeremic, M.L.

    1985-01-01

    This book considers the following topics: coal measure; coal seam feature; roof and floor strata; virgin strata pressure; deformation and failure of structure; room and pillar mining; longwall mining; slice mining; open slope mining; sub-level caving; and coal pillar structure.

  19. Data Mining in Child Welfare.

    ERIC Educational Resources Information Center

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

    2000-01-01

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

  20. 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.

  1. 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.

  2. 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

  3. Multivariate models of adult Pacific salmon returns.

    PubMed

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

    2013-01-01

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

  4. 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.

  5. 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.

  6. 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. PMID:22034351

  7. 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

  8. [Neutrophilic functional heterogeneity].

    PubMed

    2006-02-01

    Blood neutrophilic functional heterogeneity is under discussion. The neutrophils of one subpopulation, namely killer neutrophils (Nk), potential phagocytes, constitute a marginal pool and a part of the circulating pool, intensively produce active oxygen forms (AOF) and they are adherent to the substrate. The neutrophils of another subpopulation, cager neutrophils (Nc), seem to perform a transport function of delivering foreign particles to the competent organs, to form about half of the circulating pool, to produce APC to a lesser extent, exclusively for self-defense and, probably, in usual conditions, to fail to interact with substrate. Analysis of the experimental findings suggests that the phylogenetic age of Nk is older than that of Nc and Nk has predominantly a tendency to spontaneous apoptosis under physiological conditions. PMID:16610631

  9. 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.

  10. Interconnecting heterogeneous database management systems

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  12. 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.

  13. 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

  14. Detecting and Dealing with Outliers in Univariate and Multivariate Contexts.

    ERIC Educational Resources Information Center

    Wiggins, Bettie Caroline

    Because multivariate statistics are increasing in popularity with social science researchers, the challenge of detecting multivariate outliers warrants attention. Outliers are defined as cases which, in regression analyses, generally lie more than three standard deviations from Yhat and therefore distort statistics. There are, however, some…

  15. 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…

  16. 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…

  17. 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…

  18. 78 FR 39531 - Mine Rescue Teams

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-07-01

    ... July 1, 2013 Part V Department of Labor Mine Safety and Health Administration 30 CFR Part 49 Mine... and Regulations#0;#0; ] DEPARTMENT OF LABOR Mine Safety and Health Administration 30 CFR Part 49 Mine... Miner Act Requirements for Underground Coal Mine Operators and Mine Rescue Teams Type of mine...

  19. Coal mine subsidence

    SciTech Connect

    Rahall, N.J.

    1991-05-01

    This paper examines the efficacy of the Department of the Interior's Office of Surface Mining Reclamation and Enforcement's (OSMRE) efforts to implement the federally assisted coal mine subsidence insurance program. Coal mine subsidence, a gradual settling of the earth's surface above an underground mine, can damage nearby land and property. To help protect property owners from subsidence-related damage, the Congress passed legislation in 1984 authorizing OSMRE to make grants of up to $3 million to each state to help the states establish self-sustaining, state-administered insurance programs. Of the 21 eligible states, six Colorado, Indiana, Kentucky, Ohio, West Virginia, and Wyoming applied for grants. This paper reviews the efforts of these six states to develop self-sustaining insurance programs and assessed OSMRE's oversight of those efforts.

  20. Minerals and mine drainage

    SciTech Connect

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

    1996-11-01

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

  1. 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.

  2. Reactivation mechanisms of heterogeneous, complex fault zones

    NASA Astrophysics Data System (ADS)

    Heesakkers, Vincent

    Fault reactivation occurs on a short-term cycle of tens to thousands of years between infrequent earthquakes, and on long-term cycles of fault inactivity for 106 -- 107 years. During long-term cycles, faults may heal and renew their strength. The objective of the present work is to study the mechanisms of fault reactivation after a long dormant period, when the pre-existing fault is not necessarily "weak". The study is conducted along the Pretorius fault, TauTona mine, South Africa. The deep gold mines in South Africa provide access to earthquake processes at focal depth, which was motivation for the NELSAM (Natural Earthquake Laboratory in South African Mines) project to develop an underground earthquake laboratory at ˜3.5 km depth within TauTona mine (Ch. 1). The present study is conducted within the NELSAM site that includes the 2.7 Ga Pretorius fault, which has been inactive for at least 2.0 Ga and is currently being reactivated due to nearby mining activity. I characterize the fault zone by 3D underground mapping within mining tunnels at 3.6 km depth (Ch. 2). The structural analysis is accompanied by fracture analysis from borehole image logs and micro-structural studies. I find that the Pretorius fault is structurally complex, with a 20-30 m wide zone of anastomosing, dominantly steep fault segments that contain a strong cohesive sintered cataclasite. Despite the size of the Pretorius fault, a few km long with ˜200m horizontal and 30-60 m vertical displacement, its complexity reflects the fault zone immaturity. The exposed rupture zone of the M2.2 of December 12, 2004, was mapped in detail at focal depth (Ch. 3). It reactivated three to four quasi-planar, non-parallel segments of the Pretorius fault, with characteristic generation of fresh fine grained rock powder along the contact of the quartzitic host rock and the cataclasite, indicating localization of slip during the event. To investigate the mechanism responsible for such localization, rock mechanics

  3. Data Stream Mining

    NASA Astrophysics Data System (ADS)

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

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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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).

  9. 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.

  10. "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.

  11. 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…

  12. Biomarker Discovery for Heterogeneous Diseases

    PubMed Central

    Wallstrom, Garrick; Anderson, Karen S.; LaBaer, Joshua

    2013-01-01

    Background Modern genomic and proteomic studies reveal that many diseases are heterogeneous, comprising multiple different subtypes. The common notion that one biomarker can be predictive for all patients may need to be replaced by an understanding that each subtype has its own set of unique biomarkers, affecting how discovery studies are designed and analyzed. Methods We used Monte Carlo simulation to measure and compare the performance of eight selection methods with homogeneous and heterogeneous diseases using both single-stage and two-stage designs. We also applied the selection methods in an actual proteomic biomarker screening study of heterogeneous breast cancer cases. Results Different selection methods were optimal and more than 2-fold larger sample sizes were needed for heterogeneous diseases compared with homogeneous diseases. We also found that for larger studies, two-stage designs can achieve nearly the same statistical power as single-stage designs at significantly reduced cost. Conclusions We found that disease heterogeneity profoundly affected biomarker performance. We report sample size requirements and provide guidance on the design and analysis of biomarker discovery studies for both homogeneous and heterogeneous diseases. Impact We have shown that studies to identify biomarkers for the early detection of heterogeneous disease require different statistical selection methods and larger sample sizes than if the disease were homogeneous. These findings provide a methodological platform for biomarker discovery of heterogeneous diseases. PMID:23462916

  13. Heterogeneity of an earth

    NASA Astrophysics Data System (ADS)

    Litvinova, T.; Petrova, A.

    2009-04-01

    The study of magnetic anomaly field structure of the Barents Sea water area along seismic and extended profiles intersecting known fields is carried out. Geomagnetic and density sections down to 40 km depth are constructed. This allowed the estimation of heterogeneities of the Barents Sea water area deep structure. The analysis of geomagnetic and density sections along extended profiles showed the confinedness of oil-and-gas bearing provinces to deep permeable zones characterized by reduced magnetic and density features. Based on the analysis of permeable zones, regional diagnostic features similar to those obtained earlier in oil-and-gas bearing provinces in other regions, for example, in Timan-Pechora, Volga-Urals and Siberian, as well as in the Northern and Norwegian seas water areas, are revealed. The analysis of magnetic and gravity fields over the region area allowed the delineation of weakened zones as intersection areas of weakly magnetic areals with reduced density. Within the Barents Sea water area, permeable areas with lenticular-laminated structure of the upper and lower Earth's crust containing weakly magnetic areals with reduced rock density within the depth range of 8-12 and 15-20 km are revealed. Such ratio of magnetic and density heterogeneities in the Earth's crust is characteristic for zones with proved oil-and-gas content in the European part of the Atlantic Ocean water area. North Kildin field on 1 AR profile is confined to a trough with thick weakly magnetic stratum discontinuously traced to a depth of 6-10 km. At a depth of approximately 15 km, a lens of weakly magnetic and porous formations is observed. Ludlov field in the North Barents trough is confined to a zone of weakly magnetic rocks with reduced density traced to a depth of 8-9 km. Deeper, at Н=15 km, a lenticular areal of weakly magnetic formations with reduced density is observed. The profile transecting the Stockman field shows that it is located in the central part of a permeable

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

    NASA Astrophysics Data System (ADS)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

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

  15. 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

  16. Text Mining Driven Drug-Drug Interaction Detection.

    PubMed

    Yan, Su; Jiang, Xiaoqian; Chen, Ying

    2013-01-01

    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

  17. 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.

  18. 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.

  19. 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

  20. Underground at Black Diamond Mines

    SciTech Connect

    Higgins, C.T.

    1989-10-01

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

  1. 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.

  2. 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…

  3. 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.

  4. 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.

  5. Multivariate Bayesian Models of Extreme Rainfall

    NASA Astrophysics Data System (ADS)

    Rahill-Marier, B.; Devineni, N.; Lall, U.; Farnham, D.

    2013-12-01

    Accounting for spatial heterogeneity in extreme rainfall has important ramifications in hydrological design and climate models alike. Traditional methods, including areal reduction factors and kriging, are sensitive to catchment shape assumptions and return periods, and do not explicitly model spatial dependence between between data points. More recent spatially dense rainfall simulators depend on newer data sources such as radar and may struggle to reproduce extremes because of physical assumptions in the model and short historical records. Rain gauges offer the longest historical record, key when considering rainfall extremes and changes over time, and particularly relevant in today's environment of designing for climate change. In this paper we propose a probabilistic approach of accounting for spatial dependence using the lengthy but spatially disparate hourly rainfall network in the greater New York City area. We build a hierarchical Bayesian model allowing extremes at one station to co-vary with concurrent rainfall fields occurring at other stations. Subsequently we pool across the extreme rainfall fields of all stations, and demonstrate that the expected catchment-wide events are significantly lower when considering spatial fields instead of maxima-only fields. We additionally demonstrate the importance of using concurrent spatial fields, rather than annual maxima, in producing covariance matrices that describe true storm dynamics. This approach is also unique in that it considers short duration storms - from one hour to twenty-four hours - rather than the daily values typically derived from rainfall gauges. The same methodology can be extended to include the radar fields available in the past decade. The hierarchical multilevel approach lends itself easily to integration of long-record parameters and short-record parameters at a station or regional level. In addition climate covariates can be introduced to support the relationship of spatial covariance with

  6. XML algebras for data mining

    NASA Astrophysics Data System (ADS)

    Zhang, Ming; Yao, JingTao

    2004-04-01

    The XML is a new standard for data representation and exchange on the Internet. There are studies on XML query languages as well as XML algebras in literature. However, attention has not been paid to research on XML algebras for data mining due to partially the fact that there is no widely accepted definition of XML mining tasks. This paper tries to examine the XML mining tasks and provide guidelines to design XML algebras for data mining. Some summarization and comparison have been done to existing XML algebras. We argue that by adding additional operators for mining tasks, XML algebras may work well for data mining with XML documents.

  7. Conveyorized applications to surface mining

    SciTech Connect

    Peterson, A.N.

    1983-06-01

    Worldwide, there are numerous conveyorized surface mines successfully operating today. Each surface mine is unique and requires a custom made system which ideally begins as part of the mine development plan. When properly applied, conveyorized systems can offer major cost savings in both capital costs and operating and maintenance costs. With modern technology, both required availability and flexibility have been satisfied on the many currently operating mines. Conveyorized systems offer a valuable ''tool'' which when combined with the other proven mining technology results in the most efficient surface mine operations which spells lower costs and higher profits.

  8. 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.

  9. Alternating multivariate trigonometric functions and corresponding Fourier transforms

    NASA Astrophysics Data System (ADS)

    Klimyk, A. U.; Patera, J.

    2008-04-01

    We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group An, which is a subgroup of the permutation (symmetric) group Sn. These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel.

  10. (Anti)symmetric multivariate trigonometric functions and corresponding Fourier transforms

    NASA Astrophysics Data System (ADS)

    Klimyk, A.; Patera, J.

    2007-09-01

    Four families of special functions, depending on n variables, are studied. We call them symmetric and antisymmetric multivariate sine and cosine functions. They are given as determinants or antideterminants of matrices, whose matrix elements are sine or cosine functions of one variable each. These functions are eigenfunctions of the Laplace operator, satisfying specific conditions at the boundary of a certain domain F of the n-dimensional Euclidean space. Discrete and continuous orthogonality on F of the functions within each family allows one to introduce symmetrized and antisymmetrized multivariate Fourier-like transforms involving the symmetric and antisymmetric multivariate sine and cosine functions.

  11. Multivariable synthesis with transfer functions. [applications to gas turbine engines

    NASA Technical Reports Server (NTRS)

    Peczkowski, J. L.

    1980-01-01

    A transfer function design theory for multivariable control synthesis is highlighted. The use of unique transfer function matrices and two simple, basic relationships - a synthesis equation and a design equation - are presented and illustrated. This multivariable transfer function approach provides the designer with a capability to specify directly desired dynamic relationships between command variables and controlled or response variables. At the same time, insight and influence over response, simplifications, and internal stability is afforded by the method. A general, comprehensive multivariable synthesis capability is indicated including nonminmum phase and unstable plants. Gas turbine engine examples are used to illustrate the ideas and method.

  12. Deep mine cooling system

    SciTech Connect

    Conan, J.

    1984-11-06

    A deep mine cooling system comprising a compressor supplied with air and rotatively driven by a motor and an expansion turbine supplied with compressed air from said compressor and driving an actuating unit, wherein the compressed air, after leaving the compressor but prior to reaching the expansion turbine, passes through a steam generator whose output provides the energy required to operate an absorption refrigeration machine used to cool utility water for mining, said compressed air on leaving the steam generator going to a first heat exchanger in which it yields calories to a water circuit comprising a second heat exchanger, said second heat exchanger giving off the calories absorbed by the water in the first heat exchanger to the air fed by the second heat exchanger to a drying cell that is regenerated by said air from the second heat exchanger, said drying cell being part of a set of two cells working in alternation, the other cell in the set receiving the compressed air from the first heat exchanger, such that the compressed air is fed to said expansion turbine after leaving said drying unit, and wherein the air exhausted from said expansion turbine is sent to a third heat exchanger after which it is distributed according to the needs of the mine, said third exchanger being traversed by the water collected in the mine, cooled in said exchanger and circulated upon leaving said exchanger to meet the cool water requirements of the mine.

  13. 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].

  14. 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.

  15. 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.

  16. Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

    PubMed Central

    Sabit, Hakilo; Al-Anbuky, Adnan

    2014-01-01

    Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

  17. Multivariate spatial condition mapping using subtractive fuzzy cluster means.

    PubMed

    Sabit, Hakilo; Al-Anbuky, Adnan

    2014-01-01

    Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

  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. Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers*

    PubMed Central

    Rivet, Catherine A.; Hill, Abby S.; Lu, Hang; Kemp, Melissa L.

    2011-01-01

    Adoptive T-cell transfer therapy relies upon in vitro expansion of autologous cytotoxic T cells that are capable of tumor recognition. The success of this cell-based therapy depends on the specificity and responsiveness of the T cell clones before transfer. During ex vivo expansion, CD8+ T cells present signs of replicative senescence and loss of function. The transfer of nonresponsive senescent T cells is a major bottleneck for the success of adoptive T-cell transfer therapy. Quantitative methods for assessing cellular age and responsiveness will facilitate the development of appropriate cell expansion and selection protocols. Although several biomarkers of lymphocyte senescence have been identified, these proteins in isolation are not sufficient to determine the age-dependent responsiveness of T cells. We have developed a multivariate model capable of extracting combinations of markers that are the most informative to predict cellular age. To acquire signaling information with high temporal resolution, we designed a microfluidic chip enabling parallel lysis and fixation of stimulated cell samples on-chip. The acquisition of 25 static biomarkers and 48 dynamic signaling measurements at different days in culture, integrating single-cell and population based information, allowed the multivariate regression model to accurately predict CD8+ T-cell age. From surface marker expression and early phosphorylation events following T-cell receptor stimulation, the model successfully predicts days in culture and number of population doublings with R2 = 0.91 and 0.98, respectively. Furthermore, we found that impairment of early signaling events following T cell receptor stimulation because of long term culture allows prediction of costimulatory molecules CD28 and CD27 expression levels and the number of population divisions in culture from a limited subset of signaling proteins. The multivariate analysis highlights the information content of both averaged biomarker values and

  1. 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

  2. Multivariate Probabilistic Analysis of an Hydrological Model

    NASA Astrophysics Data System (ADS)

    Franceschini, Samuela; Marani, Marco

    2010-05-01

    Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model

  3. 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

  4. 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.

  5. 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

  6. Heavy flavor identification using multivariate analysis at H1

    SciTech Connect

    Pandurovic, Mila; Bozovic-Jelisavcic, Ivanka; Mudrinic, Mihajlo

    2010-01-21

    We discuss b quark identification in deep inelastic scattering of electron on proton at H1 by applying multivariate analysis method. Separation between heavy and light flavors can be further used to extract proton quark content.

  7. Search for the top quark using multivariate analysis techniques

    SciTech Connect

    Bhat, P.C.; D0 Collaboration

    1994-08-01

    The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the e{mu} channel and neural networks to the e+jets channel.

  8. 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.

  9. 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)

  10. 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

  11. 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)

  12. 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.

  13. An uncertain journey around the tails of multivariate hydrological distributions

    NASA Astrophysics Data System (ADS)

    Serinaldi, Francesco

    2013-10-01

    Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

  14. 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.

  15. 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.

  16. 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.

  17. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 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...

  18. 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...

  19. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 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...

  20. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 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...

  1. 30 CFR 77.1200 - Mine map.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 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...

  2. 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...

  3. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Mine map. 75.1200 Section 75.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall...

  4. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Mine map. 75.1200 Section 75.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall...

  5. 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...

  6. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mine map. 75.1200 Section 75.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall...

  7. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Mine map. 75.1200 Section 75.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall...

  8. 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...

  9. 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...

  10. 30 CFR 75.1200 - Mine map.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Mine map. 75.1200 Section 75.1200 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200 Mine map. The operator of a coal mine shall...

  11. 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...

  12. WIRELESS MINE WIDE TELECOMMUNICATIONS TECHNOLOGY

    SciTech Connect

    Zvi H. Meiksin

    2002-04-01

    Two industrial prototype units for through-the-earth wireless communication were constructed and tested. Preparation for a temporary installation in NIOSH's Lake Lynn mine for the through-the-earth and the in-mine system were completed. Progress was made in the programming of the in-mine system to provide data communication. Work has begun to implement a wireless interface between equipment controllers and our in-mine system.

  13. 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.

  14. 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.

  15. 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. PMID:25480104

  16. 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

  17. 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.

  18. 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....

  19. 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.

  20. 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)

  1. 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.

  2. 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.

  3. 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. PMID:3410718

  4. Mineral mining equipment

    SciTech Connect

    Monks, H.

    1980-11-25

    A mineral mining machine hauls itself along a working face by engaging a round link chain. The links of the chain are fed sequentially from link-retaining pockets in a track component arranged around the working face, around a driven sprocket assembly on the machine and returned to the pockets.

  5. 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.

  6. Mining Task Force Report.

    ERIC Educational Resources Information Center

    Saskatchewan Inst. of Applied Science and Technology, Saskatoon.

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

  7. 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].

  8. 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…

  9. Multisource causal data mining

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Gosnell, Michael; Shallenberger, Kevin

    2012-06-01

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

  10. 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

  11. 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

  12. 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

  13. Coal mining methods in Australia

    SciTech Connect

    Not Available

    1980-09-01

    Continuous miner methods dominate underground while dragline stripping is today's and tomorrow's favored method on surface. Poor roof conditions in Australian underground mines and the need to operate more efficient surface mines in view of rising fuel costs are key factors in determining the mining methods for the country's premier cash commodity - coal. These and other new developments in underground and surface mining in Australia were discussed in detail at the Australian Coal Association's 1980 coal conference which was held last April in Surfers Paradise, Queensland. Two papers presented at the conference form the basis of this article; H.L. Pearce, general superintendent of the Steel Division Collieries of the Broken Hill Proprietary Company, described underground mining and K.J. Foots, manager of the Utah Development Company's Blackwater mine, talked about surface mining.

  14. Mine ventilation and air conditioning. 3. edition

    SciTech Connect

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

    1998-12-31

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

  15. 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.

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

    NASA Astrophysics Data System (ADS)

    Haaland, David M.

    1992-03-01

    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 mid- or near-infrared spectra of the blood. Progress toward the noninvasive determination of glucose levels in diabetics is an ultimate goal of this research.

  17. 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

  18. 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.

  19. 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

  20. 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

  1. Quantifying tumour heterogeneity with CT

    PubMed Central

    Miles, Kenneth A.

    2013-01-01

    Abstract Heterogeneity is a key feature of malignancy associated with adverse tumour biology. Quantifying heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying tumour biology that CT texture analysis may reflect includes (but is not limited to) tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about tumour characterization, prognosis and treatment prediction and response. PMID:23545171

  2. Heterogeneous surfaces to repel proteins.

    PubMed

    Shen, Lei; Zhu, Jintao

    2016-02-01

    The nonspecific adsorption of proteins is usually undesirable on solid surfaces as it induces adverse responses, such as platelet adhesion on medical devices, negative signals of biosensors and contamination blockage of filtration membranes. Thus, an important scheme in material science is to design and fabricate protein-repulsive surfaces. Early approaches in this field focused on homogeneous surfaces comprised of single type functionality. Yet, recent researches have demonstrated that surfaces with heterogeneities (chemistry and topography) show promising performance against protein adsorption. In this review, we will summarize the recent achievements and discuss the new perspectives in the research of developing and characterizing heterogeneous surfaces to repel proteins. The protein repulsion mechanisms of different heterogeneous surfaces will also be discussed in details, followed by the perspective and challenge of this emerging field. PMID:26691416

  3. Resource heterogeneity can facilitate cooperation

    PubMed Central

    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. PMID:24088665

  4. 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.

  5. 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.

  6. Robust multivariable strategy and its application to a powered wheelchair.

    PubMed

    Nguyen, Nghia; Nguyen, Hung T; Su, Steven

    2009-01-01

    The paper proposes a systematic robust multivariable control strategy based on combination of systematic triangularization technique and robust control strategies. Two design stages are required. In the first design stage, multivariable control problem is reduced into a series of scalar control problems via triangularization technique. For each specific scalar system, two advanced control strategies are proposed and implemented in the second design stage. The first one is based on Model Predictive Control, which is an iterative, finite horizon optimization procedure. The second control strategy is known as Neuro-Sliding Mode Control, which integrates Sliding Mode Control (SMC) and Neural Network Design to achieve both chattering-free and system robustness. Real-time implementation on a powered wheelchair system confirms that robustness and desired performance of a multivariable system under model uncertainties and unknown external disturbances can indeed be achieved by the combination of triangularization technique and Neuro-Sliding Mode Control. PMID:19963948

  7. 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.

  8. 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.

  9. Are propensity scores really superior to standard multivariable analysis?

    PubMed

    Biondi-Zoccai, Giuseppe; Romagnoli, Enrico; Agostoni, Pierfrancesco; Capodanno, Davide; Castagno, Davide; D'Ascenzo, Fabrizio; Sangiorgi, Giuseppe; Modena, Maria Grazia

    2011-09-01

    Clinicians often face difficult decisions despite the lack of evidence from randomized trials. Thus, clinical evidence is often shaped by non-randomized studies exploiting multivariable approaches to limit the extent of confounding. Since their introduction, propensity scores have been used more and more frequently to estimate relevant clinical effects adjusting for established confounders, especially in small datasets. However, debate persists on their real usefulness in comparison to standard multivariable approaches such as logistic regression and Cox proportional hazard analysis. This holds even truer in light of key quantitative developments such as bootstrap and Bayesian methods. This qualitative review aims to provide a concise and practical guide to choose between propensity scores and standard multivariable analysis, emphasizing strengths and weaknesses of both approaches. PMID:21616172

  10. A note on rank reduction in sparse multivariate regression

    PubMed Central

    Chen, Kun; Chan, Kung-Sik

    2016-01-01

    A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model. PMID:26997938

  11. Scalable Software for Multivariate Integration on Hybrid Platforms

    NASA Astrophysics Data System (ADS)

    de Doncker, E.; Yuasa, F.; Kapenga, J.; Olagbemi, O.

    2015-09-01

    The paper describes the software infrastructure of the PARINT package for multivariate numerical integration, layered over a hybrid parallel environment with distributed memory computations (on MPI). The parallel problem distribution is typically performed on the region level in the adaptive partitioning procedure. Our objective has been to provide the end-user with state of the art problem solving power packaged as portable software. We will give test results of the multivariate ParInt engine, with significant speedups for a set of 3-loop Feynman integrals. An extrapolation with respect to the dimensional regularization parameter (ε) is applied to sequences of multivariate ParInt results Q(ε) to obtain the leading asymptotic expansion coefficients as ε → 0. This paper further introduces a novel method for a parallel computation of the Q(ε) sequence as the components of the integral of a vector function.

  12. 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.

  13. Neuro-sliding mode multivariable control of a powered wheelchair.

    PubMed

    Nguyen, Nghia; Nguyen, Hung T; Su, Steven

    2008-01-01

    This paper proposes a neuro-sliding mode multivariable control approach for the control of a powered wheelchair system. In the first stage, a systematic decoupling technique is applied to the wheelchair system in order to reduce the multivariable control problem into two independent scalar control problems. Then two Neuro-Sliding Mode Controllers (NSMCs) are designed for these independent subsystems to guarantee system robustness under model uncertainties and unknown external disturbances. Both off-line and on-line trainings are involved in the second stage. Real-time experimental results confirm that robust performance for this multivariable wheelchair control system under model uncertainties and unknown external disturbances can indeed be achieved. PMID:19163456

  14. Power system stability improvement with multivariable self-tuning control

    SciTech Connect

    Fan, J.Y.; Ortmeyer, T.H.; Mukundan, R. )

    1990-02-01

    A multivariable self-tuning adaptive control scheme is presented. This scheme is of a decentralized nature and is implemented locally for individual generating units. A discrete multivariable auto-regressive-moving-average model is developed to represent a generating unit. The recursive-least-squares (RLS) estimation algorithm with variable-forgetting factor and the generalized-minimum-variance control technique are utilized to synthesize the local controllers. A dynamic goal-point-generating model is introduced to provide varying goal point for the local controller which leads the subsystem output to its equilibrium gradually. Extensive simulations are performed on the IEEE 10-machine test system. The results show that the proposed multivariable adaptive control scheme is effective in damping the severe oscillations after large disturbances as well as improving the system dynamics under small oscillations and is better than the conventional PSS method. The controller demonstrates robustness and is compatible with the existing conventional controllers in multimachine systems.

  15. 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

  16. 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.

  17. 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.

  18. Principles of data mining.

    PubMed

    Hand, David J

    2007-01-01

    Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, 'global' structures, and the aim is to model the shapes, or features of the shapes, of distributions. The other concerns small-scale, 'local' structures, and the aim is to detect these anomalies and decide if they are real or chance occurrences. In the context of signal detection in the pharmaceutical sector, most interest lies in the second of the above two aspects; however, signal detection occurs relative to an assumed background model, therefore, some discussion of the first aspect is also necessary. This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions. PMID:17604416

  19. Trends in underground mining

    SciTech Connect

    Not Available

    1983-11-01

    This article presents some of the recent equipments developments in underground mining. Recent improvements in the transmission and braking systems of electric load-haul-dumpers are increasing cycle times and lowering maintenance costs. Torque produced by electric motors increases in response to load. This results in very high stall torque and fast cycling machines. Some of the other features becoming available include: improved switch-gear and adjustable solid-state overload relays, the new motor across the line starter device has vacuum contactors, power factor correction capacitors, a DC operating coil to eliminate contact chatter at low voltage, remote radio control of LHDs in mine areas, water-cooled and air cooled engines and hydraulic braking systems. The advantages of hydraulic drifters and roof bolters are presented.

  20. 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

  1. 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.

  2. Mining microarray expression data by literature profiling

    PubMed Central

    Chaussabel, Damien; Sher, Alan

    2002-01-01

    Background The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information. Results To address this need, we have developed a mining technique based on the analysis of literature profiles generated by extracting the frequencies of certain terms from thousands of abstracts stored in the Medline literature database. Terms are then filtered on the basis of both repetitive occurrence and co-occurrence among multiple gene entries. Finally, clustering analysis is performed on the retained frequency values, shaping a coherent picture of the functional relationship among large and heterogeneous lists of genes. Such data treatment also provides information on the nature and pertinence of the associations that were formed. Conclusions The analysis of patterns of term occurrence in abstracts constitutes a means of exploring the biological significance of large and heterogeneous lists of genes. This approach should contribute to optimizing the exploitation of microarray technologies by providing investigators with an interface between complex expression data and large literature resources. PMID:12372143

  3. GROUNDWATER QUALITY MONITORING OF WESTERN COAL STRIP MINING: PRELIMINARY DESIGNS FOR ACTIVE MINE SOURCES OF POLLUTION

    EPA Science Inventory

    Three potential pollution source categories have been identified for Western coal strip mines. These sources include mine stockpiles, mine waters, and miscellaneous active mine sources. TEMPO's stepwise monitoring methodology (Todd et al., 1976) is used to develop groundwater qua...

  4. Longwall mining in Illinois

    SciTech Connect

    Conroy, P.J.; Curth, E.A.

    1981-01-01

    The use of shields has been a major factor contributing to adequate roof control in Illinois. Short spans of exposed roof, quickly applied support, stability of structure, and full shelter for face crews are some of the significant advantages that led to the adoption of shields. The successful application of shields that were designed to sustain high yield loads resulting in a mean load density of 862.2 KN/m/sup 2/ (9 tons/ft/sup 2/) has encouraged the growth of longwall mining in the Illinois coal basin from one face to six in less than 4 years. Double-drum shearers of 1.5 x 10/sup 8/ kg/m (400 hp), using 995-volt power, with chainless haulage and high capacity face conveyors and stage loaders are standard face equipment. New coal mines are being developed for longwall mining with panel blocks as large as limiting factors allow. They are projected to have two active longwall faces and as many continuous miner units as are required for development and extraction.

  5. 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.

  6. Organizational Data Mining

    NASA Astrophysics Data System (ADS)

    Nemati, Hamid R.; Barko, Christopher D.

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

  7. 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.

  8. 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

  9. Extracting meaningful information from metabonomic data using multivariate statistics.

    PubMed

    Bylesjö, Max

    2015-01-01

    Metabonomics aims to identify and quantify all small-molecule metabolites in biologically relevant samples using high-throughput techniques such as NMR and chromatography/mass spectrometry. This generates high-dimensional data sets with properties that require specialized approaches to data analysis. This chapter describes multivariate statistics and analysis tools to extract meaningful information from metabonomic data sets. The focus is on the use and interpretation of latent variable methods such as principal component analysis (PCA), partial least squares/projections to latent structures (PLS), and orthogonal PLS (OPLS). Descriptions of the key steps of the multivariate data analyses are provided with demonstrations from example data. PMID:25677152

  10. 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

  11. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

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

    1973-01-01

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

  12. Multivariable quadratic synthesis of an advanced turbofan engine controller

    NASA Technical Reports Server (NTRS)

    Dehoff, R. L.; Hall, W. E., Jr.

    1978-01-01

    A digital controller for an advanced turbofan engine utilizing multivariate feedback is described. The theoretical background of locally linearized control synthesis is reviewed briefly. The application of linear quadratic regulator techniques to the practical control problem is presented. The design procedure has been applied to the F100 turbofan engine, and details of the structure of this system are explained. Selected results from simulations of the engine and controller are utilized to illustrate the operation of the system. It is shown that the general multivariable design procedure will produce practical and implementable controllers for modern, high-performance turbine engines.

  13. 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.

  14. 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.

  15. Automation of anaesthesia: a review on multivariable control.

    PubMed

    Chang, Jing Jing; Syafiie, S; Kamil, Raja; Lim, Thiam Aun

    2015-04-01

    Anaesthesia is a multivariable problem where a combination of drugs are used to induce desired hypnotic, analgesia and immobility states. The automation of anaesthesia may improve the safety and cost-effectiveness of anaesthesia. However, the realization of a safe and reliable multivariable closed-loop control of anaesthesia is yet to be achieved due to a manifold of challenges. In this paper, several significant challenges in automation of anaesthesia are discussed, namely model uncertainty, controlled variables, closed-loop application and dependability. The increasingly reliable measurement device, robust and adaptive controller, and better fault tolerance strategy are paving the way for automation of anaesthesia. PMID:24961365

  16. Expert Mining for Solving Social Harmony Problems

    NASA Astrophysics Data System (ADS)

    Gu, Jifa; Song, Wuqi; Zhu, Zhengxiang; Liu, Yijun

    Social harmony problems are being existed in social system, which is an open giant complex system. For solving such kind of problems the Meta-synthesis system approach proposed by Qian XS et al will be applied. In this approach the data, information, knowledge, model, experience and wisdom should be integrated and synthesized. Data mining, text mining and web mining are good techniques for using data, information and knowledge. Model mining, psychology mining and expert mining are new techniques for mining the idea, opinions, experiences and wisdom. In this paper we will introduce the expert mining, which is based on mining the experiences, knowledge and wisdom directly from experts, managers and leaders.

  17. 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....

  18. 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....

  19. 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....

  20. Analysis on Underground Coal Mining Subsidence Using Small Baseline InSAR in Yunjialing Mining Areas

    NASA Astrophysics Data System (ADS)

    Yan, Dapeng; Yang, Jin; Zeng, Qiming

    2013-01-01

    66.6 percent of China's energy production come from underground coal mining (fig. 1.) Hundreds of mining cities were affected by mining subsidence. Long-term underground mining activities ,which results in large areas of mined areas, are threatening the local ecological environment and people property.Coal mining development has become a major factor of restricting local economic and threatening the safety of future mine production. The research on mining subsidence takes a important practical significance.

  1. 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…

  2. Floodplain heterogeneity and meander migration

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  3. 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…

  4. Electoral Competition in Heterogeneous Districts

    ERIC Educational Resources Information Center

    Callander, Steven

    2005-01-01

    This paper considers a model of elections in which parties compete simultaneously for multiple districts. I show that if districts are heterogeneous, then a unique two-party equilibrium exists under plurality rule in which further entry is deterred. The equilibrium requires that parties choose noncentrist policy platforms and not converge to the…

  5. 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

  6. 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.

  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. 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

  9. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions

    NASA Astrophysics Data System (ADS)

    Zimroz, Radoslaw; Bartkowiak, Anna

    2013-07-01

    This paper deals with the diagnostics of planetary gearboxes under nonstationary operating conditions. In most diagnostics applications, energy of vibration signals (calculated directly from time series or extracted from spectral representation of signal) is used. Unfortunately energy based features are sensitive to load conditions and it makes diagnostics difficult. In this paper we used energy based 15D data vectors (namely spectral amplitudes of planetary mesh frequency and its harmonics) in order to investigate if it is possible to improve diagnostics efficiency in comparison to previous, one dimensional, approaches proposed for the same problem. Two multivariate methods, Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), were used as techniques for data analysis. We used these techniques in order to investigate dimensionality of the data and to visualize data in 3D and 2D spaces in order to understand data behavior and assess classification ability. As a case study the data from two planetary gearboxes used in complex mining machines (one in bad condition and the other in good condition) were analyzed. For these two machines more than 2000 15D vectors were acquired. It should be noted that due to non-stationarity of loading conditions, previous diagnostics results obtained using other techniques were moderately good (ca. 80% recognition efficiency); however there is still some need to improve diagnostics classification ability. After application of the proposed approaches it was found that the entire data could be reduced to 2 dimensions whereby data instances became visible and a good discriminant function (characterized by a misclassification rate of .0023, i.e. only 5 erroneous classifications for a total of 2183 instances) could be derived. This paper suggests a novel way for condition monitoring of planetary gearboxes based on multivariate statistics. The emphasis is put on the algebraic and geometric interpretations of the PCA

  10. Surface heterogeneity of small asteroids

    NASA Astrophysics Data System (ADS)

    Sasaki, Sho

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

  11. 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.

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 30 CFR 819.21 - Auger mining: Protection of underground mining.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

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

  17. 30 CFR 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...

  18. 30 CFR 49.3 - Alternative mine rescue capability for small and remote mines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

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

  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. 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

  1. 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

  2. Seismic imaging in hardrock environments: The role of heterogeneity?

    NASA Astrophysics Data System (ADS)

    Bongajum, Emmanuel; Milkereit, Bernd; Adam, Erick; Meng, Yijian

    2012-10-01

    We investigate the effect of petrophysical scale parameters and structural dips on wave propagation and imaging in heterogeneous media. Seismic wave propagation effects within the heterogeneous media are studied for different velocity models with scale lengths determined via stochastic analysis of petrophysical logs from the Matagami mine, Quebec, Canada. The elastic modeling study reveals that provided certain conditions of the velocity fluctuations are met, strong local distortions of amplitude and arrival times of propagating waves are observed as the degree of scale length anisotropy in the P-wave velocity increases. The location of these local amplitude anomalies is related to the dips characterizing the fabric of the host rocks. This result is different from the elliptical shape of direct waves often defined by effective anisotropic parameters used for layered media. Although estimates of anisotropic parameters suggest weak anisotropy in the investigated models, these effective anisotropic parameters often used in VTI/TTI do not sufficiently describe the effects of scale length anisotropy in heterogeneous media that show such local amplitude, travel time, and phase distortions in the wavefields. Numerical investigations on the implications for reverse time migration (RTM) routines corroborate that mean P-wave velocity of the host rocks produces reliable imaging results. Based on the RTM results, we postulate the following: weak anisotropy in hardrock environments is a sufficient assumption for processing seismic data; and seismic scattering effects due to velocity heterogeneity with a dip component is not sufficient to cause mislocation errors of target structures as observed in the discrepancy between the location of the strong seismic reflections associated to the Matagami sulfide orebody and its true location. Future work will investigate other factors that may provide plausible explanations for these mislocation problems, with the objective of providing a

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Inclusion of Dominance Effects in the Multivariate GBLUP Model

    PubMed Central

    Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; 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

  9. Selective Exposure and Foreign News: A Multivariate Analysis.

    ERIC Educational Resources Information Center

    Kim, Hyun Kap

    This multivariate study examined attitudinal and demographic variables affecting the degree of foreign news exposure on the basis of the data collected from 102 daily newspaper readers in Carbondale, Illinois. The data were obtained in personal interviews with the respondents. The ultimate goal of the study was to contribute to the investigation…

  10. 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,…

  11. 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...

  12. 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.

  13. 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.

  14. An Interactive Approach to Analyzing Incomplete Multivariate Data.

    ERIC Educational Resources Information Center

    Raymond, Mark R.

    This paper examines some of the problems that arise when conducting multivariate analyses with incomplete data. The literature on the effectiveness of several missing data procedures (MDP) is summarized. The most widely used MDPs are: (1) listwise deletion; (2) pairwise deletion; (3) variable mean; (4) correlational methods. No MDP should be used…

  15. 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.

  16. 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…

  17. Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

    NASA Technical Reports Server (NTRS)

    McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

    1999-01-01

    The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

  18. 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...

  19. Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

    PubMed Central

    Habeck, Christian; Foster, Norman L.; Perneczky, Robert; Kurz, Alexander; Alexopoulos, Panagiotis; Koeppe, Robert A.; Drzezga, Alexander; Stern, Yaakov

    2008-01-01

    We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer’s disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker’s diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures. PMID:18343688

  20. Validation of multivariate model of leaf ionome is fundamentally confounded.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The multivariable signature model reported by Baxter et al. (1) to predict Fe and P homeostasis in Arabidopsis is fundamentally flawed for two reasons: 1) The initial experiments identified a correlation between trace metal (Mn, Co, Zn, Mo, Cd) signature and “Fe-deficiency,” which was used to train ...

  1. Common Personality Patterns Among Alcoholic Males: A Multivariate Study

    ERIC Educational Resources Information Center

    Nerviano, Vincent J.

    1976-01-01

    This investigation assumed that proper measures and multivariate methods would identify homogeneous subgroups of alcoholics and that these would reflect known clinical syndromes. Data for alcoholic veterans (N=366) were gathered and examined by factor and correlational analysis. Contrasts among seven obtained types led to characterization of each…

  2. 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…

  3. 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…

  4. 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…

  5. Strength of Relationship in Multivariate Analysis of Variance.

    ERIC Educational Resources Information Center

    Smith, I. Leon

    Methods for the calculation of eta coefficient, or correlation ratio, squared have recently been presented for examining the strength of relationship in univariate analysis of variance. This paper extends them to the multivariate case in which the effects of independent variables may be examined in relation to two or more dependent variables, and…

  6. Multivariable-Multimethod Convergence in the Domain of Interpersonal Behavior

    ERIC Educational Resources Information Center

    Golding, Stephen L.; Knudson, Roger M.

    1975-01-01

    Subjects participated in a multivariable-multimethod investigation and completed a variety of self-report assessment devices, direct self ratings, and peer ratings. Substantial convergence for three dimensions of interpersonal behavior, Agressive Dominance, Affiliation-Sociability, and Autonomy, was obtained across all modes of measurement.…

  7. 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.

  8. Supporting Inquiry Learning by Promoting Normative Understanding of Multivariable Causality

    ERIC Educational Resources Information Center

    Keselman, Alla

    2003-01-01

    Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to…

  9. 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…

  10. 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...

  11. 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…

  12. 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,…

  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. 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

  15. 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 ...

  16. Estimating the decomposition of predictive information in multivariate systems.

    PubMed

    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. PMID:25871169

  17. 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

  18. 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…

  19. 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.

  20. Total Information in Multivariate Data from Dual Scaling Perspectives

    ERIC Educational Resources Information Center

    Nishisato, Shizuhiko

    2003-01-01

    It is an established matter that the total information in multivariate data is defined as the sum of eigenvalues of the variance-covariance matrix. In this article, we challenge this time-honored tradition and look at another definition of the total information in data from a dual scaling perspective. This proposal is a step toward unifying the…

  1. 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.

  2. 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)

  3. 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.

  4. 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…

  5. 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),…

  6. 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

  7. Heterogeneous Viscoelasticity: A Combined Theory of Dynamic and Elastic Heterogeneity.

    PubMed

    Schirmacher, Walter; Ruocco, Giancarlo; Mazzone, Valerio

    2015-07-01

    We present a heterogeneous version of Maxwell's theory of viscoelasticity based on the assumption of spatially fluctuating local viscoelastic coefficients. The model is solved in coherent-potential approximation. The theory predicts an Arrhenius-type temperature dependence of the viscosity in the vanishing-frequency limit, independent of the distribution of the activation energies. It is shown that this activation energy is generally different from that of a diffusing particle with the same barrier-height distribution, which explains the violation of the Stokes-Einstein relation observed frequently in glasses. At finite but low frequencies, the theory describes low-temperature asymmetric alpha relaxation. As examples, we report the good agreement obtained for selected inorganic, metallic, and organic glasses. At high frequencies, the theory reduces to heterogeneous elasticity theory, which explains the occurrence of the boson peak and related vibrational anomalies. PMID:26182108

  8. Abnormal Brain Areas Common to the Focal Epilepsies: Multivariate Pattern Analysis of fMRI.

    PubMed

    Pedersen, Mangor; Curwood, Evan K; Vaughan, David N; Omidvarnia, Amir H; Jackson, Graeme D

    2016-04-01

    Individuals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of amplitude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy. PMID:26537783

  9. A new multivariate time series data analysis technique: Automated detection of flux transfer events using Cluster data

    NASA Astrophysics Data System (ADS)

    Karimabadi, H.; Sipes, T. B.; Wang, Y.; Lavraud, B.; Roberts, A.

    2009-06-01

    A new data mining technique called MineTool-TS is introduced which captures the time-lapse information in multivariate time series data through extraction of global features and metafeatures. This technique is developed into a JAVA-based data mining software which automates all the steps in the model building to make it more accessible to nonexperts. As its first application in space sciences, MineTool-TS is used to develop a model for automated detection of flux transfer events (FTEs) at Earth's magnetopause in the Cluster spacecraft time series data. The model classifies a given time series into one of three categories of non-FTE, magnetosheath FTE, or magnetospheric FTE. One important feature of MineTool-TS is the ability to explore the importance of each variable or combination of variables as indicators of FTEs. FTEs have traditionally been identified on the basis of their magnetic field signatures, but here we find that some plasma variables can also be effective indicators of FTEs. For example, the perpendicular ion temperature yields a model accuracy of ˜93%, while a model based solely on the normal magnetic field BN yields an accuracy of ˜95%. This opens up the possibility of searching for more unusual FTEs that may, for example, have no clear BN signature and create a more comprehensive and less biased list of FTEs for statistical studies. We also find that models using GSM coordinates yield comparable accuracy to those using boundary normal coordinates. This is useful since there are regions where magnetopause models are not accurate. Another surprising result is the finding that the algorithm can largely detect FTEs, and even distinguish between magnetosheath and magnetospheric FTEs, solely on the basis of models built from single parameters, something that experts may not do so straightforwardly on the basis of short time series intervals. The most accurate models use a combination of plasma and magnetic field variables and achieve a very high

  10. 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.

  11. Multivariate analysis of pathophysiological factors in reflux oesophagitis.

    PubMed Central

    Cadiot, G; Bruhat, A; Rigaud, D; Coste, T; Vuagnat, A; Benyedder, Y; Vallot, T; Le Guludec, D; Mignon, M

    1997-01-01

    BACKGROUND: Reflux oesophagitis is considered a multifactorial disease, but the respective roles of the main factors involved in its pathophysiology have not been clearly established. AIMS: To attempt to assign these roles by means of a multivariate logistic regression analysis of the main parameters associated with reflux oesophagitis. PATIENTS: Eighty seven patients with gastro-oesophageal reflux disease were studied: 41 without oesophagitis and 46 with reflux oesophagitis grade 1 to 3. METHODS: (1) Monovariate comparison of patients' characteristics and of parameters derived from in hospital 24 hour oesophageal pH monitoring, oesophageal manometry, double isotope gastric emptying studies, and basal and pentagastrin stimulated gastric acid and pepsin output determinations, between patients with and without oesophagitis. (2) Multivariate logistic regression analysis including the parameters significant in the monovariate analysis. RESULTS: Among the 16 significant parameters from monovariate analysis, three significant independent parameters were identified by multivariate logistic regression analysis: number of refluxes lasting more than five minutes, reflecting oesophageal acid clearance (p = 0.002); basal lower oesophageal sphincter pressure (p = 0.008); and peak acid output (p = 0.012). These three parameters were not correlated with each other. The multivariate model was highly discriminant (correct classification of 81.3% of the cases (95% confidence intervals 0.723, 0.903). Risk for oesophagitis increased as a function of the tercile threshold values of the three parameters. Odds ratios of the three parameters for oesophagitis risk were similar, regardless of whether they were calculated when the patients were compared as a function of oesophagitis grade or the presence or absence of oesophagitis. CONCLUSIONS: This multivariate approach adds evidence that impaired oesophageal acid clearance and hypotonic lower oesophageal sphincter are the two major

  12. 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.

  13. 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...

  14. 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

  15. 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.

  16. Web data mining

    NASA Astrophysics Data System (ADS)

    Wibonele, Kasanda J.; Zhang, Yanqing

    2002-03-01

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

  17. Noise in longwall mining

    SciTech Connect

    Antel, J.W.

    1983-03-01

    A series of full-shift noise surveys conducted on longwall sections of six randomly selected coal mines has indicated that the potential exists for 58 percent of the total number of longwall operators surveyed (i.e. shearer operators, headgate and tailgate operators) to be overexposed according to limits set forth in the Code of Federal Regulation; Title 30, Subpart F, Part 70.510. A breakdown by occupation shows that 50 percent of the headgate operators surveyed and 67 percent of the shearer operators surveyed potentially failed to comply with the noise standards.

  18. 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.

  19. Continuous mining machine

    SciTech Connect

    Wilcox, A.G. Jr.; Plumley, R.D.

    1987-06-02

    This patent describes a power operated endless track assembly for use in transporting underground mining equipment comprising: a pair of separate transversely spaced parallel power driven endless track units; each of the endless track units including an endless track frame carrying forward and rearward sprocket wheels thereon and an endless track extending around the frame and the forward and rearward sprocket wheels; a carriage structure between the pair of endless track units; and a generally horizontally disposed mounting arm assembly extending transversely outwardly from each side of the carriage structure to the associated endless track unit.

  20. 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

  1. Mineral mining installation

    SciTech Connect

    Plevak, L.; Weirich, W.

    1982-04-20

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

  2. 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.

  3. Environmental noise from surface mining operations

    SciTech Connect

    Staiano, M.A.

    1982-01-01

    A sample of five mines were observed as part of a study to determine the magnitude and sources of environmental noise from surface mining operations and to identify noise abatement methods. These mining operations consisted of: a Minnesota iron ore mine, Midwestern and Appalachian coal mines, a Missouri clay pit operation, and a Texas limestone quarry. These operations are typical of Eastern and Midwestern mining activities. This paper highlights the findings of the noise measurement portion of that study.

  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 PAGESBeta

    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. 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.

  8. Biodiesel production using heterogeneous catalysts.

    PubMed

    Semwal, Surbhi; Arora, Ajay K; Badoni, Rajendra P; Tuli, Deepak K

    2011-02-01

    The production and use of biodiesel has seen a quantum jump in the recent past due to benefits associated with its ability to mitigate greenhouse gas (GHG). There are large number of commercial plants producing biodiesel by transesterification of vegetable oils and fats based on base catalyzed (caustic) homogeneous transesterification of oils. However, homogeneous process needs steps of glycerol separation, washings, very stringent and extremely low limits of Na, K, glycerides and moisture limits in biodiesel. Heterogeneous catalyzed production of biodiesel has emerged as a preferred route as it is environmentally benign needs no water washing and product separation is much easier. The present report is review of the progress made in development of heterogeneous catalysts suitable for biodiesel production. This review shall help in selection of suitable catalysts and the optimum conditions for biodiesel production. PMID:21106371

  9. 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.

  10. Specialization and Bet Hedging in Heterogeneous Populations

    NASA Astrophysics Data System (ADS)

    Rulands, Steffen; Jahn, David; Frey, Erwin

    2014-09-01

    Phenotypic heterogeneity is a strategy commonly used by bacteria to rapidly adapt to changing environmental conditions. Here, we study the interplay between phenotypic heterogeneity and genetic diversity in spatially extended populations. By analyzing the spatiotemporal dynamics, we show that the level of mobility and the type of competition qualitatively influence the persistence of phenotypic heterogeneity. While direct competition generally promotes persistence of phenotypic heterogeneity, specialization dominates in models with indirect competition irrespective of the degree of mobility.

  11. On comparing heterogeneity across biomarkers

    PubMed Central

    Steininger, Robert J.; Rajaram, Satwik; Girard, Luc; Minna, John D.; Wu, Lani F.; Altschuler, Steven J.

    2015-01-01

    Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell’s phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring co-staining biomarkers on the same cells; instead we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the “gold standard” of co-staining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems. PMID:25425168

  12. Translational Implications of Tumor Heterogeneity

    PubMed Central

    Jamal-Hanjani, Mariam; Quezada, Sergio A.; Larkin, James; Swanton, Charles

    2015-01-01

    Advances in next-generation sequencing and bioinformatics have led to an unprecedented view of the cancer genome and its evolution. Genomic studies have demonstrated the complex and heterogeneous clonal landscape of tumors of different origins, and the potential impact of intratumor heterogeneity on treatment response and resistance, cancer progression and the risk of disease relapse. However, the significance of subclonal mutations, in particular mutations in driver genes, and their evolution through time and their dynamics in response to cancer therapies, is yet to be determined. The necessary tools are now available to prospectively determine whether clonal heterogeneity can be used as a biomarker of clinical outcome, and to what extent subclonal somatic alterations might influence clinical outcome. Studies that employ longitudinal tissue sampling, integrating both genomic and clinical data, have the potential to reveal the subclonal composition and track the evolution of tumors in order to address these questions, and to begin to define the breadth of genetic diversity in different tumor types, and its relevance to patient outcome. Such studies may provide further evidence for novel drug resistance mechanisms informing novel combinatorial, adaptive and tumour immune-therapies placed within the context of tumor evolution. PMID:25770293

  13. 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.

  14. 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.

  15. 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.

  16. 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…

  17. 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.

  18. 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.

  19. 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…

  20. 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

  1. 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

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

    SciTech Connect

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

    2007-07-01

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

  3. 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…

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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. PMID:26576498

  10. 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

  11. 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.

  12. Beta-binomial ANOVA for multivariate randomized response data.

    PubMed

    Fox, Jean-Paul

    2008-11-01

    There is much empirical evidence that randomized response methods improve the cooperation of the respondents when asking sensitive questions. The traditional methods for analysing randomized response data are restricted to univariate data and only allow inferences at the group level due to the randomized response sampling design. Here, a novel beta-binomial model is proposed for analysing multivariate individual count data observed via a randomized response sampling design. This new model allows for the estimation of individual response probabilities (response rates) for multivariate randomized response data utilizing an empirical Bayes approach. A common beta prior specifies that individuals in a group are tied together and the beta prior parameters are allowed to be cluster-dependent. A Bayes factor is proposed to test for group differences in response rates. An analysis of a cheating study, where 10 items measure cheating or academic dishonesty, is used to illustrate application of the proposed model. PMID:17612461

  13. A note concerning multivariate total positivity on the simplex

    SciTech Connect

    Rayens, W.S.

    1996-12-31

    Although compositional data arise frequently in many different disciplines, relatively little is known about families of parametric densities that reside on a simplex, which is the natural sample space for such data. An exception to this is a recent paper by Gupta and Richards wherein the Liouville family is defined and discussed. They sought to characterize dependence properties within this new family using, among other ideas, multivariate total positivity. Further investigation reveals that their results are not very useful. In fact, it is shown that there does not exist a Liouville density, as defined by Gupta and Richards, that is also multivariate totally positive of order 2. Fatal flaws associated with an obvious alternative to their definition are discussed.

  14. 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.

  15. 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.

  16. Fundamental elements of vector enhanced multivariance product representation

    NASA Astrophysics Data System (ADS)

    Kalay, Berfin; Demiralp, Metin

    2012-09-01

    A new version of High Dimensional Model Representation (HDMR) is presented in this work. Vector HDMR has been quite recently developed to deal with the decomposition of vector valued multivariate functions. It was an extension from scalars to vectors by possibly using matrix weights. However, that expansion is based on an ascending multivariance starting from a constant term via a set of appropriately imposed conditions which can be related to orthogonality in a conveniently chosen Hilbert space. This work adds more flexibility by introducing certain matrix valued univariate support functions. We assume weight matrices proportional to unit matrices. This work covers only the basic issues related to the fundamental elements of the new approach.

  17. Multivariate analysis of prognostic factors in early stage Hodgkin's disease

    SciTech Connect

    Tubiana, M.; Henry-Amar, M.; van der Werf-Messing, B.; Henry, J.; Abbatucci, J.; Burgers, M.; Hayat, M.; Somers, R.; Laugier, A.; Carde, P.

    1985-01-01

    A multivariate analysis of the prognostic factors was carried out with a Cox model on 1,139 patients with clinical Stage I + II Hodgkin's disease included in three controlled clinical trials. The following indicators had been prospectively registered: aged, sex, systemic symptoms, erythrocyte sedimentation, results of staging laparotomy when performed, as well as the date and type of treatment. A linear logistic analysis showed that most of the indicators are interrelated. This emphasizes the necessity of a multivariate analysis in order to assess the independent influence of each of them. The two main prognostic indicators for relapse-free survival are systemic symptoms and/or ESR and number of involved areas. The only significant factor for survival after relapse is age. Sex has a small but significant influence on relapse-free survival. The relative influence of each indicator varies with the type of treatment and these variations may help in understanding the biologic significance of the indicators.

  18. 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.

  19. [Anomaly Detection of Multivariate Time Series Based on Riemannian Manifolds].

    PubMed

    Xu, Yonghong; Hou, Xiaoying; Li Shuting; Cui, Jie

    2015-06-01

    Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective. PMID:26485975

  20. A multivariate rate equation for variable-interval performance

    PubMed Central

    McDowell, J. J; Kessei, Robert

    1979-01-01

    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. PMID:16812130