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.
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…
Adaptive Semantic Tag Mining from Heterogeneous Clinical Research Texts
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
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.
Mining Large Heterogeneous Graphs using Cray s Urika
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.
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.…
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
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
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.
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…
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.
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
Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.
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
Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach
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
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
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.
Integration and publication of heterogeneous text-mined relationships on the Semantic Web
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
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
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
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
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
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
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
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.
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
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
A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.
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
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
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.
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.
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.
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
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…
Multivariate Regression with Calibration*
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
Multivariate Data EXplorer (MDX)
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.
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…
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
Multivariate Analysis in Metabolomics
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
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.
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.
Primer on multivariate calibration
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.
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
Multivariate Hypergeometric Similarity Measure
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
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)
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.
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.
Network structure of multivariate time series.
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
Network structure of multivariate time series
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
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.
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.…
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…
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.
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).
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…
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…
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)
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.
Optimizing Functional Network Representation of Multivariate Time Series
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
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.
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.
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.
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.
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.
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.
Overview of multivariate methods and their application to studies of wildlife habitat
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.
ROTATING DISC BIOLOGICAL TREATMENT OF ACID MINE DRAINAGE
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...
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).
Method of multivariate spectral analysis
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).
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.
Multivariable Burchnall-Chaundy theory.
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
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.
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.
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.
Multivariate Time Series Similarity Searching
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
Multivariate time series similarity searching.
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
Underground mining methods handbook
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.
Multivariate Analysis of Functional Metagenomes
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
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.
A multivariate CAR model for mismatched lattices.
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
Multivariate pluvial flood damage models
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.
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.
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)
Exploration and Mining Roadmap
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.
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.
CLUSTERING CRITERIA AND MULTIVARIATE NORMAL MIXTURES
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...
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)
Multivariate data analysis of proteome data.
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
Multivariate Longitudinal Analysis with Bivariate Correlation Test.
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
Multivariate Longitudinal Analysis with Bivariate Correlation Test
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
Mine seepage problems in drift mine operations
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.
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.
Patterns of Emphysema Heterogeneity
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
Mining and Risk of Tuberculosis in Sub-Saharan Africa
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
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
Modeling blood flow heterogeneity.
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
Classical least squares multivariate spectral analysis
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.
Biological sequence classification with multivariate string kernels.
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
Biological Sequence Analysis with Multivariate String Kernels.
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
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.
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.
Multivariate multiscale entropy for brain consciousness analysis.
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
Sparse Multivariate Regression With Covariance Estimation
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
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.
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.
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.
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
Intermittent control of unstable multivariate systems.
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
DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)
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...
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…
Multivariate statistical mapping of spectroscopic imaging data.
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
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,…
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)
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.
Towards heterogeneous distributed debugging
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.
4. OVERALL VIEW OF MINE SITE, SHOWING MINE CAR TRACKS, ...
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
1. OVERALL VIEW OF MINE SITE FROM KEETLEY MINE ROAD, ...
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
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.
Heterogeneous recurrence monitoring and control of nonlinear stochastic processes
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.
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.
Accumulation of heavy metals by vegetables grown in mine wastes
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.
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
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
Mine drainage and surface mine reclamation. Volume I. Mine water and mine waste
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.
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.
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.
Use of multivariate dispersion to assess water quality based on species composition data.
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
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.
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.
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.
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.
Advancing emotion theory with multivariate pattern classification
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.
Hybrid least squares multivariate spectral analysis methods
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.
Hybrid least squares multivariate spectral analysis methods
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.
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.
Multivariate linear recurrences and power series division
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
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.
The Evolution of Multivariate Maternal Effects
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
Analysis of worldwide earthquake mortality using multivariate demographic and seismic data.
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
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.
Regional dissociated heterochrony in multivariate analysis.
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
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.
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.
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.
Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.
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
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.
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.
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
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
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.
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
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.
Heterogeneous waste processing
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.
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.
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
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…
Multivariate Analysis of Genotype-Phenotype Association.
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
Time varying, multivariate volume data reduction
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
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.
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.
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.
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
Multivariate curve-fitting in GAUSS
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.
Bayesian Transformation Models for Multivariate Survival Data
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
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.
Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease
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
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…
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.
Query-Based Outlier Detection in Heterogeneous Information Networks
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
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification
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
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
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.
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.
Augmented classical least squares multivariate spectral analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
Adaptable Multivariate Calibration Models for Spectral Applications
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.
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.
Implementation of paste backfill mining technology in Chinese coal mines.
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
Implementation of Paste Backfill Mining Technology in Chinese Coal Mines
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
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.
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.
Heterogeneities in granular dynamics.
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
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
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.
Canada's largest mining scheme
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.
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
Is ventilation heterogeneity related to asthma control?
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
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.
Multivariable Harmonic Balance for Central Pattern Generators★
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
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.
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.
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.
Bayesian Local Contamination Models for Multivariate Outliers
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
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.
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.
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.
Exploration of new multivariate spectral calibration algorithms.
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.
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.
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…
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.
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.
Heterogeneity in expected longevities.
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
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.
Biclustering with heterogeneous variance.
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
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…
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.
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.
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
1. VIEW OF PHILLIPS MINE. CAMERA POINTED SOUTHEAST. SULLIVAN MINE ...
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
1. VIEW OF SULLIVAN MINE ON RIGHT WITH PHILLIPS MINE ...
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
2. EMPIRE STATE MINE. VIEW OF COLLAPSED BUILDINGS AT MINE ...
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
Multivariate mixtures of Erlangs for density estimation under censoring.
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
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.
Bioharness™ Multivariable Monitoring Device: Part. I: Validity
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
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
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.
Disordered hyperuniform heterogeneous materials.
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
Composite density maps for multivariate trajectories.
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
Multivariate models of adult Pacific salmon returns.
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
Apparatus and system for multivariate spectral analysis
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.
Flexible Linked Axes for multivariate data visualization.
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
Multivariate volume visualization through dynamic projections
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.
Multivariate Models of Adult Pacific Salmon Returns
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
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.
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…
Strata mechanics in coal mining
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.
Land reclamation beautifies coal mines
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.
[Neutrophilic functional heterogeneity].
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
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.
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.
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
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.
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
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…
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…
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…
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…
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...
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.
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.
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.
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
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.
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).
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.
ibr: Iterative bias reduction multivariate smoothing
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.
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).
Bioharness™ Multivariable Monitoring Device: Part. II: Reliability
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
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.
"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.