Data Mining Techniques for Customer Relationship Management

NASA Astrophysics Data System (ADS)

Guo, Feng; Qin, Huilin

2017-10-01

Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

New approach for reduction of diesel consumption by comparing different mining haulage configurations.

PubMed

Rodovalho, Edmo da Cunha; Lima, Hernani Mota; de Tomi, Giorgio

2016-05-01

The mining operations of loading and haulage have an energy source that is highly dependent on fossil fuels. In mining companies that select trucks for haulage, this input is the main component of mining costs. How can the impact of the operational aspects on the diesel consumption of haulage operations in surface mines be assessed? There are many studies relating the consumption of fuel trucks to several variables, but a methodology that prioritizes higher-impact variables under each specific condition is not available. Generic models may not apply to all operational settings presented in the mining industry. This study aims to create a method of analysis, identification, and prioritization of variables related to fuel consumption of haul trucks in open pit mines. For this purpose, statistical analysis techniques and mathematical modelling tools using multiple linear regressions will be applied. The model is shown to be suitable because the results generate a good description of the fuel consumption behaviour. In the practical application of the method, the reduction of diesel consumption reached 10%. The implementation requires no large-scale investments or very long deadlines and can be applied to mining haulage operations in other settings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

PubMed

Magenes, G; Bellazzi, R; Malovini, A; Signorini, M G

2016-08-01

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

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

PubMed

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

2009-11-15

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

Applying Data Mining Principles to Library Data Collection.

ERIC Educational Resources Information Center

Guenther, Kim

2000-01-01

Explains how libraries can use data mining techniques for more effective data collection. Highlights include three phases: data selection and acquisition; data preparation and processing, including a discussion of the use of XML (extensible markup language); and data interpretation and integration, including database management systems. (LRW)

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

PubMed

Fallah, Mina; Niakan Kalhori, Sharareh R

2017-10-01

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

Use of an automatic resistivity system for detecting abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

PubMed

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

Text Mining in Organizational Research

PubMed Central

Kobayashi, Vladimer B.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies. PMID:29881248

Text Mining in Organizational Research.

PubMed

Kobayashi, Vladimer B; Mol, Stefan T; Berkers, Hannah A; Kismihók, Gábor; Den Hartog, Deanne N

2018-07-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Traffic Flow Management: Data Mining Update

NASA Technical Reports Server (NTRS)

Grabbe, Shon R.

2012-01-01

This presentation provides an update on recent data mining efforts that have been designed to (1) identify like/similar days in the national airspace system, (2) cluster/aggregate national-level rerouting data and (3) apply machine learning techniques to predict when Ground Delay Programs are required at a weather-impacted airport

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

Technique for predicting ground-water discharge to surface coal mines and resulting changes in head

USGS Publications Warehouse

Weiss, L.S.; Galloway, D.L.; Ishii, Audrey L.

1986-01-01

Changes in seepage flux and head (groundwater level) from groundwater drainage into a surface coal mine can be predicted by a technique that considers drainage from the unsaturated zone. The user applies site-specific data to precalculated head and seepage-flux profiles. Groundwater flow through hypothetical aquifer cross sections was simulated using the U.S. Geological Survey finite-difference model, VS2D, which considers variably saturated two-dimensional flow. Conceptual models considered were (1) drainage to a first cut, and (2) drainage to multiple cuts, which includes drainage effects of an area surface mine. Dimensionless head and seepage flux profiles from 246 simulations are presented. Step-by-step instructions and examples are presented. Users are required to know aquifer characteristics and to estimate size and timing of the mine operation at a proposed site. Calculated groundwater drainage to the mine is from one excavated face only. First cut considers confined and unconfined aquifers of a wide range of permeabilities; multiple cuts considers unconfined aquifers of higher permeabilities only. The technique, developed for Illinois coal-mining regions that use area surface mining and evaluated with an actual field example, will be useful in assessing potential hydrologic impacts of mining. Application is limited to hydrogeologic settings and mine operations similar to those considered. Fracture flow, recharge, and leakage are nor considered. (USGS)

Binary Coded Web Access Pattern Tree in Education Domain

ERIC Educational Resources Information Center

Gomathi, C.; Moorthi, M.; Duraiswamy, K.

2008-01-01

Web Access Pattern (WAP), which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice. Sequential Pattern mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of…

The application of satellite data in monitoring strip mines

NASA Technical Reports Server (NTRS)

Sharber, L. A.; Shahrokhi, F.

1977-01-01

Strip mines in the New River Drainage Basin of Tennessee were studied through use of Landsat-1 imagery and aircraft photography. A multilevel analysis, involving conventional photo interpretation techniques, densitometric methods, multispectral analysis and statistical testing was applied to the data. The Landsat imagery proved adequate for monitoring large-scale change resulting from active mining and land-reclamation projects. However, the spatial resolution of the satellite imagery rendered it inadequate for assessment of many smaller strip mines, in the region which may be as small as a few hectares.

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

Blasting Rocks and Blasting Cars Applied Engineering

ScienceCinema

LBNL

2017-12-09

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed for auto assembly lines.

Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

VRLane: a desktop virtual safety management program for underground coal mine

NASA Astrophysics Data System (ADS)

Li, Mei; Chen, Jingzhu; Xiong, Wei; Zhang, Pengpeng; Wu, Daozheng

2008-10-01

VR technologies, which generate immersive, interactive, and three-dimensional (3D) environments, are seldom applied to coal mine safety work management. In this paper, a new method that combined the VR technologies with underground mine safety management system was explored. A desktop virtual safety management program for underground coal mine, called VRLane, was developed. The paper mainly concerned about the current research advance in VR, system design, key techniques and system application. Two important techniques were introduced in the paper. Firstly, an algorithm was designed and implemented, with which the 3D laneway models and equipment models can be built on the basis of the latest mine 2D drawings automatically, whereas common VR programs established 3D environment by using 3DS Max or the other 3D modeling software packages with which laneway models were built manually and laboriously. Secondly, VRLane realized system integration with underground industrial automation. VRLane not only described a realistic 3D laneway environment, but also described the status of the coal mining, with functions of displaying the run states and related parameters of equipment, per-alarming the abnormal mining events, and animating mine cars, mine workers, or long-wall shearers. The system, with advantages of cheap, dynamic, easy to maintenance, provided a useful tool for safety production management in coal mine.

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

NASA Technical Reports Server (NTRS)

Stolzer, Alan J.; Halford, Carl

2007-01-01

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Big data mining: In-database Oracle data mining over hadoop

NASA Astrophysics Data System (ADS)

Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

2017-07-01

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

Use of Data Mining to Reveal Body Mass Index (BMI): Patterns among Pennsylvania Schoolchildren, Pre-K to Grade 12

ERIC Educational Resources Information Center

YoussefAgha, Ahmed H.; Lohrmann, David K.; Jayawardene, Wasantha P.

2013-01-01

Background: Health eTools for Schools was developed to assist school nurses with routine entries, including height and weight, on student health records, thus providing a readily accessible data base. Data-mining techniques were applied to this database to determine if clinically signi?cant results could be generated. Methods: Body mass index…

Sprouting of thinned hybrid poplars on bituminous strip-mine spoils in Pennsylvania

Treesearch

Walter H. Davidson; Grant Davis

1972-01-01

Various thinning techniques were applied to 5-year old hybrid poplar stands on bituminous strip-mine spoils. Basal and stump sprays of 2, 4, 5-T in diesel oil were effective for killing the trees. There was no evidence that chemical treatments affected adjacent trees. Where trees were cut and stumps were not chemically treated, all clones sprouted prolifically....

Blasting Rocks and Blasting Cars Applied Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

LBNL

2008-07-02

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed formore » auto assembly lines.« less

HYPGEO - A collaboration between geophysics and remote sensing for mineral exploration

NASA Astrophysics Data System (ADS)

Meyer, Uwe; Frei, Michaela; Petersen, Hauke; Papenfuß, Anne; Ibs-von Seht, Malte; Stolz, Ronny; Queitsch, Matthias; Buchholz, Peter; Siemon, Bernhard

2017-04-01

The German Federal Institute for Geosciences and Natural Resources (BGR) aims to promote and design application oriented, generic techniques for the detection and 3D-characterisation of mineral deposits. Most newly developed mineral mining structures are still exploiting near surface sources. Since exploration and exploitation of mineral resources are increasingly under public review concerning environmental issues and social acceptance, non-invasive methods using satellites, fixed-wing aircraft, helicopters or unmanned aerial vehicles are preferred techniques within this investigation. Therefore, a data combination of helicopter-borne gamma ray spectrometry, hyperspectral imagery and full tensor gradient magnetometry is being evaluated. Test areas are open pit mining structures in Aznalcollar and Tharsis within the Pyrite Belt of southern Spain. First test flights using gamma-ray spectrometry and gradient magnetometry using SQUID-based sensors have been performed. Hyperspectral imagery has been applied on ground. Rock and core samples from the mines have been taken or investigated for further analysis. The basic idea is to combine surface triggered signals from gamma-ray spectrometry and hyperspectral imagery to enhance the detection of shallow mineralisation structures. In order to investigate whether these structures are connected with near-surface ore veins, gradient magnetometry was applied to model subsurface formations. To verify that good correlations between the applied methods are given, open pit mining structures were chosen, where the mineral content and the local to regional geology is well known.

Estimating natural background groundwater chemistry, Questa molybdenum mine, New Mexico

USGS Publications Warehouse

Verplanck, Phillip L.; Nordstrom, D. Kirk; Plumlee, Geoffrey S.; Walker, Bruce M.; Morgan, Lisa A.; Quane, Steven L.

2010-01-01

This 2 1/2 day field trip will present an overview of a U.S. Geological Survey (USGS) project whose objective was to estimate pre-mining groundwater chemistry at the Questa molybdenum mine, New Mexico. Because of intense debate among stakeholders regarding pre-mining groundwater chemistry standards, the New Mexico Environment Department and Chevron Mining Inc. (formerly Molycorp) agreed that the USGS should determine pre-mining groundwater quality at the site. In 2001, the USGS began a 5-year, multidisciplinary investigation to estimate pre-mining groundwater chemistry utilizing a detailed assessment of a proximal natural analog site and applied an interdisciplinary approach to infer pre-mining conditions. The trip will include a surface tour of the Questa mine and key locations in the erosion scar areas and along the Red River. The trip will provide participants with a detailed understanding of geochemical processes that influence pre-mining environmental baselines in mineralized areas and estimation techniques for determining pre-mining baseline conditions.

Unmanned Mine of the 21st Centuries

NASA Astrophysics Data System (ADS)

Semykina, Irina; Grigoryev, Aleksandr; Gargayev, Andrey; Zavyalov, Valeriy

2017-11-01

The article is analytical. It considers the construction principles of the automation system structure which realize the concept of «unmanned mine». All of these principles intend to deal with problems caused by a continuous complication of mining-and-geological conditions at coalmine such as the labor safety and health protection, the weak integration of different mining automation subsystems and the deficiency of optimal balance between a quantity of resource and energy consumed by mining machines and their throughput. The authors describe the main problems and neck stage of mining machines autonomation and automation subsystem. The article makes a general survey of the applied «unmanned technology» in the field of mining such as the remotely operated autonomous complexes, the underground positioning systems of mining machines using infrared radiation in mine workings etc. The concept of «unmanned mine» is considered with an example of the robotic road heading machine. In the final, the authors analyze the techniques and methods that could solve the task of underground mining without human labor.

The effects of different representations on static structure analysis of computer malware signatures.

PubMed

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka.

The Effects of Different Representations on Static Structure Analysis of Computer Malware Signatures

PubMed Central

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka. PMID:23983644

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

DrugQuest - a text mining workflow for drug association discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Study of application of ERTS-A imagery to fracture-related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator); Russell, O. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The Kings Station Mine in Gibson County, Indiana has experienced considerable roof fall problems. Detailed fracture mapping of the mine area was done with ERTS-1 and aircraft imagery, and a prediction map of roof problem areas was produced in advance of a visit. The visit to the mine and discussions with the operator indicated that of four zones mapped as potential problem areas, three coincided with areas of excessive roof fall. This positive correlation of 75% lends confidence to the validity of the technique being applied in the investigation. The mine officials expressed an interest in the project and are anxious to see the final product maps which are forthcoming.

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

PubMed Central

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

2015-01-01

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

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.

PubMed

Dagliati, Arianna; Tibollo, Valentina; Cogni, Giulia; Chiovato, Luca; Bellazzi, Riccardo; Sacchi, Lucia

2018-03-01

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.

Discovery of Information Diffusion Process in Social Networks

NASA Astrophysics Data System (ADS)

Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

PubMed Central

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

2016-01-01

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. PMID:28025348

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

DOE PAGES

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; ...

2016-12-26

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singhal, Ayush; Leaman, Robert; Catlett, Natalie

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

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

Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital.

PubMed

Yoo, Sooyoung; Cho, Minsu; Kim, Eunhye; Kim, Seok; Sim, Yerim; Yoo, Donghyun; Hwang, Hee; Song, Minseok

2016-04-01

Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations. Copyright © 2016. Published by Elsevier Ireland Ltd.

Mining problems caused by tectonic stress in Illinois basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, W.J.

1991-08-01

The Illinois basin coalfield is subject to a contemporary tectonic stress field in which the principal compressive stress axis ({sigma}1) is horizontal and strikes N60{degree}E to east-west. This stress is responsible for widespread development of kind zones and directional roof failures in mine headings driven perpendicular to {sigma}1. Also, small thrust faults perpendicular to {sigma}1 and joints parallel to {sigma}1 weaken the mine roof and occasionally admit water and gas to workings, depending upon geologic setting. The direction of magnitude of stress have been identified by a variety of techniques that can be applied both prior to mining and duringmore » development. Mining experience shows that the best method of minimizing stress-related problems is to drive mine headings at about 45 to {sigma}1.« less

A demonstration of ERTS-1 analog and digital techniques applied to strip mining in Maryland and West Virginia

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schubert, J.

1974-01-01

The largest contour strip mining operations in western Maryland and West Virginia are located within the Georges Creek and the Upper Potomac Basins. These two coal basins lie within the Georges Creek (Wellersburg) syncline. The disturbed strip mine areas were delineated with the surrounding geological and vegetation features using ERTS-1 data in both analog (imagery) and digital form. The two digital systems used were: (1) the ERTS-Analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values, and (2) the LARS Automatic Data Processing System. The digital techniques being developed will later be incorporated into a data base for land use planning. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and indicate where future reclamation and revegetation may be necessary.

Hydrogeochemical studies of historical mining areas in the Humboldt River basin and adjacent areas, northern Nevada

USGS Publications Warehouse

Nash, J. Thomas

2005-01-01

The study area comprises the Humboldt River Basin and adjacent areas, with emphasis on mining areas relatively close to the Humboldt River. The basin comprises about 16,840 mi2 or 10,800,000 acres. The mineral resources of the Humboldt Basin have been investigated by many scientists over the past 100 years, but only recently has our knowledge of regional geology and mine geology been applied to the understanding and evaluation of mining effects on water and environmental quality. The investigations reported here apply some of the techniques and perspectives developed in the Abandoned Mine Lands Initiative (AMLI) of the U.S. Geological Survey (USGS), a program of integrated geological-hydrological-biological-chemical studies underway in the Upper Animas River watershed in Colorado and the Boulder River watershed in, Montana. The goal of my studies of sites and districts is to determine the character of mining-related contamination that is actively or potentially a threat to water quality and to estimate the potential for natural attenuation of that contamination. These geology-based studies and recommendations differ in matters of emphasis and data collection from the biology-based assessments that are the cornerstone of environmental regulations.

Information Landscaping: Information Mapping, Charting, Querying and Reporting Techniques for Total Quality Knowledge Management.

ERIC Educational Resources Information Center

Tsai, Bor-sheng

2003-01-01

Total quality management and knowledge management are merged and used as a conceptual model to direct and develop information landscaping techniques through the coordination of information mapping, charting, querying, and reporting. Goals included: merge citation analysis and data mining, and apply data visualization and information architecture…

Significant applications of ERTS-1 data to resource management activities at the state level in Ohio. [strip mining and land use mapping

NASA Technical Reports Server (NTRS)

Sweet, D. C.; Pincura, P. G.; Meier, C. J.; Garrett, G. B.; Herd, L.; Wukelic, G. E.; Stephan, J. G.; Smail, H. E.

1974-01-01

Described are techniques utilized and the progress made in applying ERTS-1 data to (1) detecting, inventorying, and monitoring surface mining activities, particularly in relation to recently passed strip mine legislation in Ohio; (2) updating current land use maps at various scales for multiagency usage, and (3) solving other real-time problems existing throughout the various Ohio governmental agencies. General conclusions regarding current user views as to the opportunities and limitations of operationally using ERTS-1 data at the state level are also noted.

Mine planning and emission control strategies using geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, F.; Kim, Y.C.

1983-03-01

This paper reviews the past four years' research efforts performed jointly by the University of Arizona and the Homer City Owners in which geostatistics were applied to solve various problems associated with coal characterization, mine planning, and development of emission control strategies. Because geostatistics is the only technique which can quantify the degree of confidence associated with a given estimate (or prediction), it played an important role throughout the research efforts. Through geostatistics, it was learned that there is an urgent need for closely spaced sample information, if short-term coal quality predictions are to be made for mine planning purposes.

Visual mining geo-related data using pixel bar charts

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Keim, Daniel A.; Dayal, Umeshwar; Wright, Peter; Schneidewind, Joern

2005-03-01

A common approach to analyze geo-related data is using bar charts or x-y plots. They are intuitive and easy to use. But important information often gets lost. In this paper, we introduce a new interactive visualization technique called Geo Pixel Bar Charts, which combines the advantages of Pixel Bar Charts and interactive maps. This technique allows analysts to visualize large amounts of spatial data without aggregation and shows the geographical regions corresponding to the spatial data attribute at the same time. In this paper, we apply Geo Pixel Bar Charts to visually mining sales transactions and Internet usage from different locations. Our experimental results show the effectiveness of this technique for providing data distribution and exceptions from the map.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

PubMed

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

2016-01-01

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

Process Mining-Based Method of Designing and Optimizing the Layouts of Emergency Departments in Hospitals.

PubMed

Rismanchian, Farhood; Lee, Young Hoon

2017-07-01

This article proposes an approach to help designers analyze complex care processes and identify the optimal layout of an emergency department (ED) considering several objectives simultaneously. These objectives include minimizing the distances traveled by patients, maximizing design preferences, and minimizing the relocation costs. Rising demand for healthcare services leads to increasing demand for new hospital buildings as well as renovating existing ones. Operations management techniques have been successfully applied in both manufacturing and service industries to design more efficient layouts. However, high complexity of healthcare processes makes it challenging to apply these techniques in healthcare environments. Process mining techniques were applied to address the problem of complexity and to enhance healthcare process analysis. Process-related information, such as information about the clinical pathways, was extracted from the information system of an ED. A goal programming approach was then employed to find a single layout that would simultaneously satisfy several objectives. The layout identified using the proposed method improved the distances traveled by noncritical and critical patients by 42.2% and 47.6%, respectively, and minimized the relocation costs. This study has shown that an efficient placement of the clinical units yields remarkable improvements in the distances traveled by patients.

Geomorphic reclmation of a coal refuse pile

NASA Astrophysics Data System (ADS)

Hopkinson, L. C.; Quaranta, J.

2017-12-01

Geomorphic reclamation is a technique that may offer opportunities to improve mine reclamation in Central Appalachia. The design approach is based on constructing a steady-state, mature landform condition and takes into account the long-term climatic conditions, soil types, terrain grade, and vegetation. Geomorphic reclamation has been applied successfully in semi-arid regions but has not yet been applied in Central Appalachia. This work describes a demonstration study where geomorphic landforming techniques are being applied to a coarse coal refuse pile in southern West Virginia, USA. The reclamation design includes four geomorphic watersheds that radially drain runoff from the pile. Each watershed has one central draining channel and incorporates compound slope profiles similarly to naturally eroded slopes. Planar slopes were also included to maintain the impacted area. The intent is to alter the hydrology to decrease water quality treatment costs. The excavation cut and fill volumes are comparable to those of more conventional refuse pile reclamation designs. If proven successful then this technique can be part of a cost-effective solution to improve water quality at active and future refuse facilities, abandoned mine lands, bond forfeiture sites, landfills, and major earthmoving activities within the region.

Design risk assessment for burst-prone mines: Application in a Canadian mine

NASA Astrophysics Data System (ADS)

Cheung, David J.

A proactive stance towards improving the effectiveness and consistency of risk assessments has been adopted recently by mining companies and industry. The next 10-20 years forecasts that ore deposits accessible using shallow mining techniques will diminish. The industry continues to strive for success in "deeper" mining projects in order to keep up with the continuing demand for raw materials. Although the returns are quite profitable, many projects have been sidelined due to high uncertainty and technical risk in the mining of the mineral deposit. Several hardrock mines have faced rockbursting and seismicity problems. Within those reported, mines in countries like South Africa, Australia and Canada have documented cases of severe rockburst conditions attributed to the mining depth. Severe rockburst conditions known as "burst-prone" can be effectively managed with design. Adopting a more robust design can ameliorate the exposure of workers and equipment to adverse conditions and minimize the economic consequences, which can hinder the bottom line of an operation. This thesis presents a methodology created for assessing the design risk in burst-prone mines. The methodology includes an evaluation of relative risk ratings for scenarios with options of risk reduction through several design principles. With rockbursts being a hazard of seismic events, the methodology is based on research in the area of mining seismicity factoring in rockmass failure mechanisms, which results from a combination of mining induced stress, geological structures, rockmass properties and mining influences. The methodology was applied to case studies at Craig Mine of Xstrata Nickel in Sudbury, Ontario, which is known to contain seismically active fault zones. A customized risk assessment was created and applied to rockburst case studies, evaluating the seismic vulnerability and consequence for each case. Application of the methodology to Craig Mine demonstrates that changes in the design can reduce both exposure risk (personnel and equipment), and economical risk (revenue and costs). Fatal and catastrophic consequences can be averted through robust planning and design. Two customized approaches were developed to conduct risk assessment of case studies at Craig Mine. Firstly, the Brownfield Approach utilizes the seismic database to determine the seismic hazard from a rating system that evaluates frequency-magnitude, event size, and event-blast relation. Secondly, the Greenfield Approach utilizes the seismic database, focusing on larger magnitude events, rocktype, and geological structure. The customized Greenfield Approach can also be applied in the evaluation of design risk in deep mines with the same setting and condition as Craig Mine. Other mines with different settings and conditions can apply the principles in the methodology to evaluate design alternatives and risk reduction strategies for burst-prone mines.

Mining large heterogeneous data sets in drug discovery.

PubMed

Wild, David J

2009-10-01

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

Radon dynamics and reduction in an underground mine in Brazil. Implications for workers' exposure.

PubMed

Evangelista, H; Pereira, E B; Fernandes, H M; Sampaio, M

2002-01-01

This work was aimed at studying the behaviour of 222Rn in an experimental underground copper mine in Brazil with a single entrance. The 222Rn concentrations, meaured by using a dynamic radon measuring technique. varied between 30.5 Bq.m(-3), during ventilated conditions applied to the mine galleries, and 19.4 x 10(3) Bq.(-3) for non-ventilated conditions and when operational mining activities were conducted inside. High radon concentration surges were observed after blasting and drilling activities. In the cases of inadequate ventilation, it was estimated that workers could be subjected to exposures as high as 10 microSv.h(-1), only due to 222Rn and its short-lived progeny. The results show the importance of real-time measurements to evaluate radon dynamics during mining operations.

A sentence sliding window approach to extract protein annotations from biomedical articles

PubMed Central

Krallinger, Martin; Padron, Maria; Valencia, Alfonso

2005-01-01

Background Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. Results The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations). Conclusion We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. PMID:15960831

Temporal data mining for the quality assessment of hemodialysis services.

PubMed

Bellazzi, Riccardo; Larizza, Cristiana; Magni, Paolo; Bellazzi, Roberto

2005-05-01

This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Modeling Spatial Dependencies and Semantic Concepts in Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju

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

Determine utility of ERTS-1 to detect and monitor area strip mining and reclamation. [southeastern Ohio

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Pettyjohn, W. A.

1975-01-01

The author has identified the following significant results. Computer techniques were applied to process ERTS tapes acquired over coal mining operations in southeastern Ohio on 21 August 1972 and 3 September 1973. ERTS products obtained included geometrically correct map overlays showing stripped earth, partially reclaimed earth, water, and natural vegetation. Computer-generated tables listing the area covered by each land-water category in square kilometers and acres were produced. By comparing these mapping products, the study demonstrates the capability of ERTS to monitor changes in the extent of stripping, success of reclamation, and the secondary effects of mining on the environment.

Feasibility of applying data mining techniques for predicting technical difficulties during laparoscopic cholecystectomy based on routine patient work-up in a small community hospital.

PubMed

Stanisic, Veselin; Andjelkovic, Igor; Vlaovic, Darko; Babic, Igor; Kocev, Nikola; Nikolic, Bosko; Milicevic, Miroslav

2013-10-01

Predicting technical difficulties in laparoscopic cholecystectomy (LC) in a small regional hospital increases efficacy, cost-benefit and safety of the procedure. The aim of the study was to assess whether it is possible to accurately predict a difficult LC (DLC) in a small regional hospital based only on the routine available clinical work-up parameters (patient history, ultrasound examination and blood chemistry) and their combinations. A prospective, cohort, of 369 consecutive patients operated by the same surgeon was analyzed. Conversion rate was 10 (2.7%). DLC was registered in 55 (14.90%). Various data mining techniques were applied and assessed. Seven significant predictors of DLC were identified: i) shrunken (fibrotic) gallbladder (GB); ii) ultrasound (US) GB wall thickness >4 mm; iii) >5 attacks of pain lasting >5 hours; iv) WBC >10x109 g/L; v) pericholecystic fluid; vi) urine amylase >380 IU/L, and vii) BMI >30kg/m2. Bayesian network was selected as the best classifier with accuracy of 94.57, specificity 0.98, sensitivity 0.77, AUC 0.96 and F-measure 0.81. It is possible to predict a DLC with high accuracy using data mining techniques, based on routine preoperative clinical parameters and their combinations. Use of sophisticated diagnostic equipment is not necessary.

[Basic Regularities and Characteristics of Compound Reinforcing--reducing Manipulation of Acu- puncture Revealed by Data Mining].

PubMed

Yang, Qing-qing; Jia, Chun-sheng; Wang, Jian-ling; Li, Jun-lei; Feng, Xin-xin; Tan, Zhan-na; Li, Bo-ying; Zhu, Xue-liang; Shi, Jing; Sun, Yan-hui; Li, Xiao-feng; Xu, Jing; Zhang, Xuan-ping; Zhang, Xin; Du, Yu-zhu; Bao, Na; Wang, Qiong

2016-04-01

To explore the regularities and features of compound reinforcing-reducing manipulation of acupuncture filiform needles in the treatment of clinical conditions or diseases by using data mining technique, so as to guide clinical practice. At first, the data base about the reinforcing-reducing manipulation (CRRM) of filiform needles for different clinical problems was established by collection, sorting, screening, recording, collation, data extraction of the related original papers published in journals and conferences and related academic dissertations from Jan. 1 of 1950 to Jan. 31 of 2015 by using key words of "acupuncture" "moxibustion" "needling" "filiform needle", and according to the included and excluded standards. A total of 130 835 papers met the included standards were collected. Outcomes of data mining in the present study showed that (1) the ORRM is most frequently applied in the internal medicine, followed by surgery, gynecology, ophthalmology and otorhinolaryngology, dermatology, and pediatrics, successively, mostly for lumbago and leg pain; (2) the heat-producing needling manipulation is the most frequently applied technique, followed by cool-producing needling, dragon-tiger warring, yang occluding in yin, yin occluding in yang techniques; (3) the highest effective rate of CRRM is for problems of the pediatrics, followed by those of the internal medicine, surgery, ophthalmology and otorhinolaryngology, dermatology, and gynecology; (4) the most fre- quently used acupoints are Zusanli (ST 36), then Sanyinjiao (SP 6), stimulated by heat-producing needling, and Zusanli (ST 36), then Quchi (LI 11), stimulated by cool-producing needling, and Huantiao (GB 30), stimulated by dragon-tiger warring needling. The compound reinforcing-reducing manipulation of acupuncture is most frequently applied to problems in the inter- nal medicine, predominately for lumbago and leg pain, and the best effectiveness is for pediatric conditions. The heat-producing needling and cool-producing needling are most frequently applied at Zusanli (ST 36) and the dragon-tiger warring manipulation is most frequently applied at Huantiao (GB 30).

[Application of immunologic methods to the analysis of bio-leaching bacteria].

PubMed

Coto, O; Fernández, A I; León, T; Rodríguez, D

1994-09-01

Pure cultures of Thiobacillus ferrooxidans and mixed cultures of Thiobacillus ferrooxidans and Leptospirillum ferrooxidans isolated from the Matahambre mine (Cuba) were used to fit immunodiffusion and immunoelectron microscopy to the study of iron oxidizing bacteria. The possibilities, advantages and limits of those techniques have been studied from both the identification and the serological characterization points of view. Finally, the efficiency of these methods was tested by applying them to the identification of microorganisms from acidic waters from the mine.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Triggering effect of mining at different horizons in the rock mass with excavations. Mathematical modeling

NASA Astrophysics Data System (ADS)

Eremin, M. O.; Makarov, P. V.

2017-12-01

On the basis of a quite simple structural model of rock mass, containing coal seams on two horizons, coal mining is numerically modeled. A finite difference numerical technique is applied. At first, mining starts at the upper horizon and then moves to the lower horizon. It is shown that a mining process at the lower horizon has a significant triggering influence on the growth of damage zones in the roof and floor at the upper horizon. The features of spatiotemporal migration of deformation activity are studied numerically. Foci of large-scale fracture are located at the boundary of the seismic silence zone and the zone where the deformation activity migrates. This boundary has an additional characteristic: the maximum gradient of rock pressure is observed in this zone.

Application of text mining in the biomedical domain.

PubMed

Fleuren, Wilco W M; Alkema, Wynand

2015-03-01

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Copyright © 2015 Elsevier Inc. All rights reserved.

Using remote sensing imagery to monitoring sea surface pollution cause by abandoned gold-copper mine

NASA Astrophysics Data System (ADS)

Kao, H. M.; Ren, H.; Lee, Y. T.

2010-08-01

The Chinkuashih Benshen mine was the largest gold-copper mine in Taiwan before the owner had abandoned the mine in 1987. However, even the mine had been closed, the mineral still interacts with rain and underground water and flowed into the sea. The polluted sea surface had appeared yellow, green and even white color, and the pollutants had carried by the coast current. In this study, we used the optical satellite images to monitoring the sea surface. Several image processing algorithms are employed especial the subpixel technique and linear mixture model to estimate the concentration of pollutants. The change detection approach is also applied to track them. We also conduct the chemical analysis of the polluted water to provide the ground truth validation. By the correlation analysis between the satellite observation and the ground truth chemical analysis, an effective approach to monitoring water pollution could be established.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of advance for the entire mission (convoy, movement to engagement, etc.) is determined by the level of difficulty presented in clearance or avoidance activities required in response to the potential 'targets' marked throughout a detection activity. Therefore the application of fielded hand held systems to convoy operations in clearly impractical. CECOM-NVESD efforts are presently seeking to overcome these operational limitations by substantially increasing speed of detection while reducing the false alarm rate through the application of multisensor techniques. The CECOM-NVESD application of multisensor techniques through integration/fusion methods will be defined in this paper.

Educational Data Mining Application for Estimating Students Performance in Weka Environment

NASA Astrophysics Data System (ADS)

Gowri, G. Shiyamala; Thulasiram, Ramasamy; Amit Baburao, Mahindra

2017-11-01

Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.

Detection of antipersonnel (AP) mines using mechatronics approach

NASA Astrophysics Data System (ADS)

Shahri, Ali M.; Naghdy, Fazel

1998-09-01

At present there are approximately 110 million land-mines scattered around the world in 64 countries. The clearance of these mines takes place manually. Unfortunately, on average for every 5000 mines cleared one mine clearer is killed. A Mine Detector Arm (MDA) using mechatronics approach is under development in this work. The robot arm imitates manual hand- prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil to control the impact caused by contacting a stiff object. An explicit impact control scheme is applied as the main control scheme, while two different intelligent control methods are designed to deal with uncertainties and varying environmental parameters. Firstly, a neuro-fuzzy adaptive gain controller (NFAGC) is designed to adapt the force gain control according to the estimated environment stiffness. Then, an adaptive neuro-fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro-fuzzy impact control (NFIC), when an impact is detected. The developed control schemes are validated through computer simulation and experimental work.

Using data mining to segment healthcare markets from patients' preference perspectives.

PubMed

Liu, Sandra S; Chen, Jie

2009-01-01

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

Geophysical Technologies to Image Old Mine Works

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanaan Hanna; Jim Pfeiffer

2007-01-15

ZapataEngineering, Blackhawk Division performed geophysical void detection demonstrations for the US Department of Labor Mine Safety and Health Administration (MSHA). The objective was to advance current state-of-practices of geophysical technologies for detecting underground mine voids. The presence of old mine works above, adjacent, or below an active mine presents major health and safety hazards to miners who have inadvertently cut into locations with such features. In addition, the presence of abandoned mines or voids beneath roadways and highway structures may greatly impact the performance of the transportation infrastructure in terms of cost and public safety. Roads constructed over abandoned minesmore » are subject to potential differential settlement, subsidence, sinkholes, and/or catastrophic collapse. Thus, there is a need to utilize geophysical imaging technologies to accurately locate old mine works. Several surface and borehole geophysical imaging methods and mapping techniques were employed at a known abandoned coal mine in eastern Illinois to investigate which method best map the location and extent of old works. These methods included: 1) high-resolution seismic (HRS) using compressional P-wave (HRPW) and S-wave (HRSW) reflection collected with 3-D techniques; 2) crosshole seismic tomography (XHT); 3) guided waves; 4) reverse vertical seismic profiling (RVSP); and 5) borehole sonar mapping. In addition, several exploration borings were drilled to confirm the presence of the imaged mine voids. The results indicated that the RVSP is the most viable method to accurately detect the subsurface voids with horizontal accuracy of two to five feet. This method was then applied at several other locations in Colorado with various topographic, geologic, and cultural settings for the same purpose. This paper presents the significant results obtained from the geophysical investigations in Illinois.« less

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Underground Mining Method Selection Using WPM and PROMETHEE

NASA Astrophysics Data System (ADS)

Balusa, Bhanu Chander; Singam, Jayanthu

2018-04-01

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight's calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method.

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

NASA Astrophysics Data System (ADS)

Salvini, Riccardo; Mastrorocco, Giovanni; Esposito, Giuseppe; Di Bartolo, Silvia; Coggan, John; Vanneschi, Claudio

2018-01-01

The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

Customizing FP-growth algorithm to parallel mining with Charm++ library

NASA Astrophysics Data System (ADS)

Puścian, Marek

2017-08-01

This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Applying Fuzzy Data Mining to Telecom Churn Management

NASA Astrophysics Data System (ADS)

Liao, Kuo-Hsiung; Chueh, Hao-En

Customers tend to change telecommunications service providers in pursuit of more favorable telecommunication rates. Therefore, how to avoid customer churn is an extremely critical topic for the intensely competitive telecommunications industry. To assist telecommunications service providers in effectively reducing the rate of customer churn, this study used fuzzy data mining to determine effective marketing strategies by analyzing the responses of customers to various marketing activities. These techniques can help telecommunications service providers determine the most appropriate marketing opportunities and methods for different customer groups, to reduce effectively the rate of customer turnover.

Realising the knowledge spiral in healthcare: the role of data mining and knowledge management.

PubMed

Wickramasinghe, Nilmini; Bali, Rajeev K; Gibbons, M Chris; Schaffer, Jonathan

2008-01-01

Knowledge Management (KM) is an emerging business approach aimed at solving current problems such as competitiveness and the need to innovate which are faced by businesses today. The premise for the need for KM is based on a paradigm shift in the business environment where knowledge is central to organizational performance . Organizations trying to embrace KM have many tools, techniques and strategies at their disposal. A vital technique in KM is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. The healthcare industry is a very information rich industry. The collecting of data and information permeate most, if not all areas of this industry; however, the healthcare industry has yet to fully embrace KM, let alone the new evolving techniques of data mining. In this paper, we demonstrate the ubiquitous benefits of data mining and KM to healthcare by highlighting their potential to enable and facilitate superior clinical practice and administrative management to ensue. Specifically, we show how data mining can realize the knowledge spiral by effecting the four key transformations identified by Nonaka of turning: (1) existing explicit knowledge to new explicit knowledge, (2) existing explicit knowledge to new tacit knowledge, (3) existing tacit knowledge to new explicit knowledge and (4) existing tacit knowledge to new tacit knowledge. This is done through the establishment of theoretical models that respectively identify the function of the knowledge spiral and the powers of data mining, both exploratory and predictive, in the knowledge discovery process. Our models are then applied to a healthcare data set to demonstrate the potential of this approach as well as the implications of such an approach to the clinical and administrative aspects of healthcare. Further, we demonstrate how these techniques can facilitate hospitals to address the six healthcare quality dimensions identified by the Committee for Quality Healthcare.

Impact of climate change on acid mine drainage generation and contaminant transport in water ecosystems of semi-arid and arid mining areas

NASA Astrophysics Data System (ADS)

Anawar, Hossain Md.

Disposal of untreated and treated mining wastes and tailings exerts a significant threat and hazard for environmental contamination including groundwater, surface water, wetlands, land, food chain and animals. In order to facilitate remediation techniques, it is important to understand the oxidation of sulfidic minerals, and the hydrolysis of the oxidation products that result in production of acid mine drainage (AMD), toxic metals, low pH, SO42- and Fe. This review has summarized the impacts of climate change on geochemical reactions, AMD generation, and water quality in semi-arid/arid mining environments. Besides this, the study included the effects of hydrological, seasonal and climate change on composition of AMD, contaminant transport in watersheds and restoration of mining sites. Different models have different types of limitations and benefits that control their adaptability and suitability of application in various mining environments. This review has made a comparative discussion of a few most potential and widely used reactive transport models that can be applied to simulate the effect of climate change on sulfide oxidation and AMD production from mining waste, and contaminant transport in surface and groundwater systems.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

COMMENT ON "PERCHLORATE IDENTIFICATION IN FERTILIZERS" AND THE SUBSEQUENT ADDITION/CORRECTION [LETTER TO EDITOR

EPA Science Inventory

Perchlorate contamination has been reported in several fertilizer materials and not just in mined Chile saltpeter, where it is a welo-known natural impurity. To survey fertilizers for perchlorate, two analytical techniques have been applied to 45 products that span agricultural, ...

Medical data mining: knowledge discovery in a clinical data warehouse.

PubMed Central

Prather, J. C.; Lobach, D. F.; Goodwin, L. K.; Hales, J. W.; Hage, M. L.; Hammond, W. E.

1997-01-01

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis. PMID:9357597

Measurement methods of building structures deflections

NASA Astrophysics Data System (ADS)

Wróblewska, Magdalena

2018-04-01

Underground mining exploitation is leading to the occurrence of deformations manifested by, in particular, sloping terrain. The structures situated on the deforming subsoil are subject to uneven subsidence which is leading in consequence to their deflection. Before a building rectification process takes place by, e.g. uneven raising, the structure's deflection direction and value is determined so that the structure is restored to its vertical position as a result of the undertaken remedial measures. Deflection can be determined by applying classical as well as modern measurement techniques. The article presents examples of measurement methods used considering the measured elements of building structures' constructions and field measurements. Moreover, for a given example of a mining area, the existing deflections of buildings were compared with mining terrain sloping.

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Application and Evaluation of ALOS PALSAR Data for Monitoring of Mining Induced Surface Deformations Using Interferometric Techniques

NASA Astrophysics Data System (ADS)

Walter, Diana; Wegmuller, Urs; Spreckels, Volker; Busch, Wolfgang

2008-11-01

The main objective of the projects "Determination of ground motions in mining areas by interferometric analyses of ALOS data" (ALOS ADEN 3576, ESA) and "Monitoring of mining induced surface deformation" (ALOS-RA-094, JAXA) is to evaluate PALSAR data for surface deformation monitoring, using interferometric techniques. We present monitoring results of surface movements for an active hard coal colliery of the German hard coal mining company RAG Deutsche Steinkohle (RAG). Underground mining activities lead to ground movements at the surface with maximum subsidence rates of about 10cm per month for the test site. In these projects the L-band sensor clearly demonstrates the good potential for deformation monitoring in active mining areas, especially in rural areas. In comparison to C-band sensors we clearly observe advantages in resolving the high deformation gradients that are present in this area and we achieve a more complete spatial coverage than with C-band. Extensive validation data based on levelling data and GPS measurements are available within RAǴs GIS based database "GeoMon" and thus enable an adequate analysis of the quality of the interferometric results. Previous analyses confirm the good accuracy of PALSAR data for deformation monitoring in mining areas. Furthermore, we present results of special investigations like precision geocoding of PALSAR data and corner reflector analysis. At present only DInSAR results are obtained due to the currently available number of PALSAR scenes. For the future we plan to also apply Persistent Scatterer Interferometry (PSI) using longer series of PALSAR data.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

An application of data mining in district heating substations for improving energy performance

NASA Astrophysics Data System (ADS)

Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

2017-11-01

Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

Improvement of a method for positioning of pithead by considering motion of the surface water

NASA Astrophysics Data System (ADS)

Yi, H.; Lee, D. K.

2016-12-01

Underground mining has weakness compared with open pit mining in aspects of efficiency, economy and working environment. However, the method has applied for the development of a deep orebody. Development plan is established when the economic valuation and technical analysis of the deposits is completed through exploration of mineral resources. Development is a process to open a passage from the ground surface to the orebody as one of the steps of mining process. In the planning, there are details such as pithead positioning, mining method selection, and shaft design, etc. Among these, pithead positioning is implemented by considering infrastructures, watershed, geology, and economy. In this study, we propose a method to consider the motion of the surface waters in order to improve the existing pithead positioning techniques. The method contemplates the terrain around the mine and makes the surface water flow information. Then, the drainage treatment cost for each candidate location of pithead is suggested. This study covers the concept and design of the scheme.

Dietary patterns analysis using data mining method. An application to data from the CYKIDS study.

PubMed

Lazarou, Chrystalleni; Karaolis, Minas; Matalas, Antonia-Leda; Panagiotakos, Demosthenes B

2012-11-01

Data mining is a computational method that permits the extraction of patterns from large databases. We applied the data mining approach in data from 1140 children (9-13 years), in order to derive dietary habits related to children's obesity status. Rules emerged via data mining approach revealed the detrimental influence of the increased consumption of soft dinks, delicatessen meat, sweets, fried and junk food. For example, frequent (3-5 times/week) consumption of all these foods increases the risk for being obese by 75%, whereas in children who have a similar dietary pattern, but eat >2 times/week fish and seafood the risk for obesity is reduced by 33%. In conclusion patterns revealed from data mining technique refer to specific groups of children and demonstrate the effect on the risk associated with obesity status when a single dietary habit might be modified. Thus, a more individualized approach when translating public health messages could be achieved. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Simplified process model discovery based on role-oriented genetic mining.

PubMed

Zhao, Weidong; Liu, Xi; Dai, Weihui

2014-01-01

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Characterization of As-polluted soils by laboratory X-ray-based techniques coupled with sequential extractions and electron microscopy: the case of Crocette gold mine in the Monte Rosa mining district (Italy).

PubMed

Allegretta, Ignazio; Porfido, Carlo; Martin, Maria; Barberis, Elisabetta; Terzano, Roberto; Spagnuolo, Matteo

2018-06-24

Arsenic concentration and distribution were studied by combining laboratory X-ray-based techniques (wavelength dispersive X-ray fluorescence (WDXRF), micro X-ray fluorescence (μXRF), and X-ray powder diffraction (XRPD)), field emission scanning electron microscopy equipped with microanalysis (FE-SEM-EDX), and sequential extraction procedure (SEP) coupled to total reflection X-ray fluorescence (TXRF) analysis. This approach was applied to three contaminated soils and one mine tailing collected near the gold extraction plant at the Crocette gold mine (Macugnaga, VB) in the Monte Rosa mining district (Piedmont, Italy). Arsenic (As) concentration, measured with WDXRF, ranged from 145 to 40,200 mg/kg. XRPD analysis evidenced the presence of jarosite and the absence of any As-bearing mineral, suggesting a high weathering grade and strong oxidative conditions. However, small domains of Fe arsenate were identified by combining μXRF with FE-SEM-EDX. SEP results revealed that As was mainly associated to amorphous Fe oxides/hydroxides or hydroxysulfates (50-80%) and the combination of XRPD and FE-SEM-EDX suggested that this phase could be attributed to schwertmannite. On the basis of the reported results, As is scarcely mobile, even if a consistent As fraction (1-3 g As/kg of soil) is still potentially mobilizable. In general, the proposed combination of laboratory X-ray techniques could be successfully employed to unravel environmental issues related to metal(loid) pollution in soil and sediments.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other.

PubMed

Tahmasebian, Shahram; Ghazisaeedi, Marjan; Langarizadeh, Mostafa; Mokhtaran, Mehrshad; Mahdavi-Mazdeh, Mitra; Javadian, Parisa

2017-01-01

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients' medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD.

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other

PubMed Central

Tahmasebian, Shahram; Ghazisaeedi, Marjan; Langarizadeh, Mostafa; Mokhtaran, Mehrshad; Mahdavi-Mazdeh, Mitra; Javadian, Parisa

2017-01-01

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients’ medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD. PMID:28497080

Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

PubMed

Al-Saggaf, Yeslam; Islam, Md Zahidul

2015-08-01

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

Digital Family History Data Mining with Neural Networks: A Pilot Study.

PubMed

Hoyt, Robert; Linnville, Steven; Thaler, Stephen; Moore, Jeffrey

2016-01-01

Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

NASA Astrophysics Data System (ADS)

Uswatun Khasanah, Annisa; Harwati

2017-06-01

Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2011 CFR

2011-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2013 CFR

2013-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2014 CFR

2014-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2012 CFR

2012-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

Effective search for stable segregation configurations at grain boundaries with data-mining techniques

NASA Astrophysics Data System (ADS)

Kiyohara, Shin; Mizoguchi, Teruyasu

2018-03-01

Grain boundary segregation of dopants plays a crucial role in materials properties. To investigate the dopant segregation behavior at the grain boundary, an enormous number of combinations have to be considered in the segregation of multiple dopants at the complex grain boundary structures. Here, two data mining techniques, the random-forests regression and the genetic algorithm, were applied to determine stable segregation sites at grain boundaries efficiently. Using the random-forests method, a predictive model was constructed from 2% of the segregation configurations and it has been shown that this model could determine the stable segregation configurations. Furthermore, the genetic algorithm also successfully determined the most stable segregation configuration with great efficiency. We demonstrate that these approaches are quite effective to investigate the dopant segregation behaviors at grain boundaries.

Blasting Rocks and Blasting Cars: Applied Engineering (LBNL Summer Lecture Series)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hopkins, Deborah

2004-06-30

Summer Lecture Series 2004: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed for auto assembly lines.

Blasting Rocks and Blasting Cars: Applied Engineering (LBNL Summer Lecture Series)

ScienceCinema

Hopkins, Deborah [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Engineering Division

2017-12-09

Summer Lecture Series 2004: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed for auto assembly lines.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Mining Land Subsidence Monitoring Using SENTINEL-1 SAR Data

NASA Astrophysics Data System (ADS)

Yuan, W.; Wang, Q.; Fan, J.; Li, H.

2017-09-01

In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.

Temporal data mining for hospital management

NASA Astrophysics Data System (ADS)

Tsumoto, Shusaku; Hirano, Shoji

2009-04-01

It has passed about twenty years since clinical information are stored electronically as a hospital information system since 1980's. Stored data include from accounting information to laboratory data and even patient records are now started to be accumulated: in other words, a hospital cannot function without the information system, where almost all the pieces of medical information are stored as multimedia databases. In this paper, we applied temporal data mining and exploratory data analysis techniques to hospital management data. The results show several interesting results, which suggests that the reuse of stored data will give a powerful tool for hospial management.

Silos of Academe Thwart Diversity on Campuses

ERIC Educational Resources Information Center

Gilbert, Juan E.

2008-01-01

Although the author is a computer scientist, he has been involved with issues of diversity for many years. He developed an online gamelike environment to teach inner-city kids algebra, using culturally relevant learning technologies, and he has applied data-mining techniques to help universities admit diverse classes without relying on just one…

Monitoring of Soil Remediation Process in the Metal Mining Area

NASA Astrophysics Data System (ADS)

Kim, Kyoung-Woong; Ko, Myoung-Soo; Han, Hyeop-jo; Lee, Sang-Ho; Na, So-Young

2016-04-01

Stabilization using proper additives is an effective soil remediation technique to reduce As mobility in soil. Several researches have reported that Fe-containing materials such as amorphous Fe-oxides, goethite and hematite were effective in As immobilization and therefore acid mine drainage sludge (AMDS) may be potential material for As immobilization. The AMDS is the by-product from electrochemical treatment of acid mine drainage and mainly contains Fe-oxide. The Chungyang area in Korea is located in the vicinity of the huge abandoned Au-Ag Gubong mine which was closed in the 1970s. Large amounts of mine tailings have been remained without proper treatment and the mobilization of mine tailings can be manly occurred during the summer heavy rainfall season. Soil contamination from this mobilization may become an urgent issue because it can cause the contamination of groundwater and crop plants in sequence. In order to reduce the mobilization of the mine tailings, the pilot scale study of in-situ stabilization using AMDS was applied after the batch and column experiments in the lab. For the monitoring of stabilization process, we used to determine the As concentration in crop plants grown on the field site but it is not easily applicable because of time and cost. Therefore, we may need simple monitoring technique to measure the mobility or leachability which can be comparable with As concentration in crop plants. We compared several extraction methods to suggest the representative single extraction method for the monitoring of soil stabilization efficiency. Several selected extraction methods were examined and Mehlich 3 extraction method using the mixture of NH4F, EDTA, NH4NO3, CH3COOH and HNO3 was selected as the best predictor of the leachability or mobility of As in the soil remediation process.

Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

DTIC Science & Technology

1994-09-30

relational versus object oriented DBMS, knowledge discovery, data models, rnetadata, data filtering, clustering techniques, and synthetic data. A secondary...The first was the investigation of Al/ES Lapplications (knowledge discovery, data mining, and clustering ). Here CAST collabo.rated with Dr. Fred Petry...knowledge discovery system based on clustering techniques; implemented an on-line data browser to the DBMS; completed preliminary efforts to apply object

Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state.

PubMed

Kitsos, Christine M; Bhamidipati, Phani; Melnikova, Irena; Cash, Ethan P; McNulty, Chris; Furman, Julia; Cima, Michael J; Levinson, Douglas

2007-01-01

This study examined whether hierarchical clustering could be used to detect cell states induced by treatment combinations that were generated through automation and high-throughput (HT) technology. Data-mining techniques were used to analyze the large experimental data sets to determine whether nonlinear, non-obvious responses could be extracted from the data. Unary, binary, and ternary combinations of pharmacological factors (examples of stimuli) were used to induce differentiation of HL-60 cells using a HT automated approach. Cell profiles were analyzed by incorporating hierarchical clustering methods on data collected by flow cytometry. Data-mining techniques were used to explore the combinatorial space for nonlinear, unexpected events. Additional small-scale, follow-up experiments were performed on cellular profiles of interest. Multiple, distinct cellular profiles were detected using hierarchical clustering of expressed cell-surface antigens. Data-mining of this large, complex data set retrieved cases of both factor dominance and cooperativity, as well as atypical cellular profiles. Follow-up experiments found that treatment combinations producing "atypical cell types" made those cells more susceptible to apoptosis. CONCLUSIONS Hierarchical clustering and other data-mining techniques were applied to analyze large data sets from HT flow cytometry. From each sample, the data set was filtered and used to define discrete, usable states that were then related back to their original formulations. Analysis of resultant cell populations induced by a multitude of treatments identified unexpected phenotypes and nonlinear response profiles.

Data Mining of Macromolecular Structures.

PubMed

van Beusekom, Bart; Perrakis, Anastassis; Joosten, Robbie P

2016-01-01

The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

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

PubMed

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

2006-04-01

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

Convalescing Cluster Configuration Using a Superlative Framework

PubMed Central

Sabitha, R.; Karthik, S.

2015-01-01

Competent data mining methods are vital to discover knowledge from databases which are built as a result of enormous growth of data. Various techniques of data mining are applied to obtain knowledge from these databases. Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments. K-means algorithm is a versatile algorithm among the various approaches used in data clustering. The algorithm and its diverse adaptation methods suffer certain problems in their performance. To overcome these issues a superlative algorithm has been proposed in this paper to perform data clustering. The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids. The generated centroids are fed as input to K-means approach which iteratively segments the data objects into respective clusters. The clustered results are measured for accuracy and validity. Experiments conducted by testing the approach on datasets from the UC Irvine Machine Learning Repository evidently show that the accuracy and validity measure is higher than the other two approaches, namely, simple K-means and Binary Search method. Thus, the proposed approach proves that discretization process will improve the efficacy of descriptive data mining tasks. PMID:26543895

Application of risk management techniques for the remediation of an old mining site in Greece.

PubMed

Panagopoulos, I; Karayannis, A; Adam, K; Aravossis, K

2009-05-01

This article summarizes the project and risk management of a remediation/reclamation project in Lavrion, Greece. In Thoricos the disposal of mining and metallurgical wastes in the past resulted in the contamination with heavy metals and acid mine drainage. The objective of this reclamation project was to transform this coastal zone from a contaminated site to an area suitable for recreation purposes. A separate risk assessment study was performed to provide the basis of determining the relevant environmental contamination and to rate the alternative remedial schemes involved. The study used both existing data available from comprehensive studies, as well as newly collected field data. For considering environmental risk, the isolation and minimization of risk option was selected, and a reclamation scheme, based on environmental criteria, was applied which was comprised of in situ neutralization, stabilization and cover of the potentially acid generating wastes and contaminated soils with a low permeability geochemical barrier. Additional measures were specifically applied in the areas where highly sulphidic wastes existed constituting active acid generation sources, which included the encapsulation of wastes in HDPE liners installed on clay layers.

Measuring MERCI: exploring data mining techniques for examining the neurologic outcomes of stroke patients undergoing endo-vascular therapy at Erlanger Southeast Stroke Center.

PubMed

McNabb, Matthew; Cao, Yu; Devlin, Thomas; Baxter, Blaise; Thornton, Albert

2012-01-01

Mechanical Embolus Removal in Cerebral Ischemia (MERCI) has been supported by medical trials as an improved method of treating ischemic stroke past the safe window of time for administering clot-busting drugs, and was released for medical use in 2004. The importance of analyzing real-world data collected from MERCI clinical trials is key to providing insights on the effectiveness of MERCI. Most of the existing data analysis on MERCI results has thus far employed conventional statistical analysis techniques. To the best of our knowledge, advanced data analytics and data mining techniques have not yet been systematically applied. To address the issue in this thesis, we conduct a comprehensive study on employing state of the art machine learning algorithms to generate prediction criteria for the outcome of MERCI patients. Specifically, we investigate the issue of how to choose the most significant attributes of a data set with limited instance examples. We propose a few search algorithms to identify the significant attributes, followed by a thorough performance analysis for each algorithm. Finally, we apply our proposed approach to the real-world, de-identified patient data provided by Erlanger Southeast Regional Stroke Center, Chattanooga, TN. Our experimental results have demonstrated that our proposed approach performs well.

EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques

NASA Astrophysics Data System (ADS)

Ivanov, Anton; Muller, Jan-Peter; Tao, Yu; Kim, Jung-Rack; Gwinner, Klaus; Van Gasselt, Stephan; Morley, Jeremy; Houghton, Robert; Bamford, Steven; Sidiropoulos, Panagiotis; Fanara, Lida; Waenlish, Marita; Walter, Sebastian; Steinkert, Ralf; Schreiner, Bjorn; Cantini, Federico; Wardlaw, Jessica; Sprinks, James; Giordano, Michele; Marsh, Stuart

2016-07-01

Understanding planetary atmosphere-surface and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to be able to overlay different epochs back in time to the mid 1970s, to examine time-varying changes, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, UCL have developed a fully automated multi-resolution DTM processing chain, called the Co-registration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP), which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed and is being applied to level-1 EDR images taken by the 4 NASA orbital cameras since 1976 using the HRSC map products (both mosaics and orbital strips) as a map-base. The project has also included Mars Radar profiles from Mars Express and Mars Reconnaissance Orbiter missions. A webGIS has been developed for displaying this time sequence of imagery and a demonstration will be shown applied to one of the map-sheets. Automated quality control techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. These data mining techniques are then being employed within a citizen science project within the Zooniverse family to verify the results of these data mining techniques. Examples of data mining and its verification will be presented. We will present a software tool to ease access to co-registered MARSIS and SHARAD radargrams and geometry data such as probing point latitude and longitude and spacecraft altitude. Data are extracted from official ESA and NASA released data using self-developed python classes. Geometrical data and metadata are exposed as WFS layers using a QGIS server, which can be further integrated with other data. Radar geometry data will be available as a part of the iMars WebGIS framework and images will be available via PDS and PSA archives. Acknowledgements The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n˚ 607379 as well as partial funding from the STFC "MSSL Consolidated Grant" ST/K000977/1.

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

PubMed Central

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level. PMID:25830807

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PubMed

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

String Mining in Bioinformatics

NASA Astrophysics Data System (ADS)

Abouelhoda, Mohamed; Ghanem, Moustafa

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nurhandoko, Bagus Endar B.; Wely, Woen; Setiadi, Herlan

It is already known that tomography has a great impact for analyzing and mapping unknown objects based on inversion, travel time as well as waveform inversion. Therefore, tomography has used in wide area, not only in medical but also in petroleum as well as mining. Recently, tomography method is being applied in several mining industries. A case study of tomography imaging has been carried out in DOZ ( Deep Ore Zone ) block caving mine, Tembagapura, Papua. Many researchers are undergoing to investigate the properties of DOZ cave not only outside but also inside which is unknown. Tomography takes amore » part for determining this objective.The sources are natural from the seismic events that caused by mining induced seismicity and rocks deformation activity, therefore it is called as passive seismic. These microseismic travel time data are processed by Simultaneous Iterative Reconstruction Technique (SIRT). The result of the inversion can be used for DOZ cave monitoring. These information must be used for identifying weak zone inside the cave. In addition, these results of tomography can be used to determine DOZ and cave information to support mine activity in PT. Freeport Indonesia.« less

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

PubMed Central

Zou, Wei; She, Jianwen; Tolstikov, Vladimir V.

2013-01-01

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. PMID:24958150

Data mining to support simulation modeling of patient flow in hospitals.

PubMed

Isken, Mark W; Rajagopalan, Balaji

2002-04-01

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.

Using temporarily coherent point interferometric synthetic aperture radar for land subsidence monitoring in a mining region of western China

NASA Astrophysics Data System (ADS)

Fan, Hongdong; Xu, Qiang; Hu, Zhongbo; Du, Sen

2017-04-01

Yuyang mine is located in the semiarid western region of China where, due to serious land subsidence caused by underground coal exploitation, the local ecological environment has become more fragile. An advanced interferometric synthetic aperture radar (InSAR) technique, temporarily coherent point InSAR, is applied to measure surface movements caused by different mining conditions. Fifteen high-resolution TerraSAR-X images acquired between October 2, 2012, and March 27, 2013, were processed to generate time-series data for ground deformation. The results show that the maximum accumulated values of subsidence and velocity were 86 mm and 162 mm/year, respectively; these measurements were taken above the fully mechanized longwall caving faces. Based on the dynamic land subsidence caused by the exploitation of one working face, the land subsidence range was deduced to have increased 38 m in the mining direction with 11 days' coal extraction. Although some mining faces were ceased in 2009, they could also have contributed to a small residual deformation of overlying strata. Surface subsidence of the backfill mining region was quite small, the maximum only 21 mm, so backfill exploitation is an effective method for reducing the land subsidence while coal is mined.

WWW Motivation Mining: Finding Treasures for Teaching Evaluation Skills, Grades 1-6. Professional Growth Series.

ERIC Educational Resources Information Center

Arnone, Marilyn P.; Small, Ruth V.

Designed for elementary or middle school teachers and library media specialists, this book provides educators with practical, easy-to-use ways of applying motivation assessment techniques when selecting World Wide Web sites for inclusion in their lessons and offers concrete examples of how to use Web evaluation with young learners. WebMAC…

A Clustering Methodology of Web Log Data for Learning Management Systems

ERIC Educational Resources Information Center

Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros

2012-01-01

Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…

Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning

ERIC Educational Resources Information Center

Feng, Mingyu; Beck, Joseph E.; Heffernan, Neil T.

2009-01-01

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…

Information Fusion - Methods and Aggregation Operators

NASA Astrophysics Data System (ADS)

Torra, Vicenç

Information fusion techniques are commonly applied in Data Mining and Knowledge Discovery. In this chapter, we will give an overview of such applications considering their three main uses. This is, we consider fusion methods for data preprocessing, model building and information extraction. Some aggregation operators (i.e. particular fusion methods) and their properties are briefly described as well.

Clinical diabetes research using data mining: a Canadian perspective.

PubMed

Shah, Baiju R; Lipscombe, Lorraine L

2015-06-01

With the advent of the digitization of large amounts of information and the computer power capable of analyzing this volume of information, data mining is increasingly being applied to medical research. Datasets created for administration of the healthcare system provide a wealth of information from different healthcare sectors, and Canadian provinces' single-payer universal healthcare systems mean that data are more comprehensive and complete in this country than in many other jurisdictions. The increasing ability to also link clinical information, such as electronic medical records, laboratory test results and disease registries, has broadened the types of data available for analysis. Data-mining methods have been used in many different areas of diabetes clinical research, including classic epidemiology, effectiveness research, population health and health services research. Although methodologic challenges and privacy concerns remain important barriers to using these techniques, data mining remains a powerful tool for clinical research. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

Evaluating data mining algorithms using molecular dynamics trajectories.

PubMed

Tatsis, Vasileios A; Tjortjis, Christos; Tzirakis, Panagiotis

2013-01-01

Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.

Real-World Application of Robust Design Optimization Assisted by Response Surface Approximation and Visual Data-Mining

NASA Astrophysics Data System (ADS)

Shimoyama, Koji; Jeong, Shinkyu; Obayashi, Shigeru

A new approach for multi-objective robust design optimization was proposed and applied to a real-world design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, and resulted in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can markedly reduce the computational time for predictions of robustness. In addition, the use of self-organizing maps as a data-mining technique allows visualization of complicated design information between optimality and robustness in a comprehensible two-dimensional form. Therefore, the extraction and interpretation of trade-off relations between optimality and robustness of design, and also the location of sweet spots in the design space, can be performed in a comprehensive manner.

Monitoring food safety violation reports from internet forums.

PubMed

Kate, Kiran; Negi, Sumit; Kalagnanam, Jayant

2014-01-01

Food-borne illness is a growing public health concern in the world. Government bodies, which regulate and monitor the state of food safety, solicit citizen feedback about food hygiene practices followed by food establishments. They use traditional channels like call center, e-mail for such feedback collection. With the growing popularity of Web 2.0 and social media, citizens often post such feedback on internet forums, message boards etc. The system proposed in this paper applies text mining techniques to identify and mine such food safety complaints posted by citizens on web data sources thereby enabling the government agencies to gather more information about the state of food safety. In this paper, we discuss the architecture of our system and the text mining methods used. We also present results which demonstrate the effectiveness of this system in a real-world deployment.

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

A study of unstable rock failures using finite difference and discrete element methods

NASA Astrophysics Data System (ADS)

Garvey, Ryan J.

Case histories in mining have long described pillars or faces of rock failing violently with an accompanying rapid ejection of debris and broken material into the working areas of the mine. These unstable failures have resulted in large losses of life and collapses of entire mine panels. Modern mining operations take significant steps to reduce the likelihood of unstable failure, however eliminating their occurrence is difficult in practice. Researchers over several decades have supplemented studies of unstable failures through the application of various numerical methods. The direction of the current research is to extend these methods and to develop improved numerical tools with which to study unstable failures in underground mining layouts. An extensive study is first conducted on the expression of unstable failure in discrete element and finite difference methods. Simulated uniaxial compressive strength tests are run on brittle rock specimens. Stable or unstable loading conditions are applied onto the brittle specimens by a pair of elastic platens with ranging stiffnesses. Determinations of instability are established through stress and strain histories taken for the specimen and the system. Additional numerical tools are then developed for the finite difference method to analyze unstable failure in larger mine models. Instability identifiers are established for assessing the locations and relative magnitudes of unstable failure through measures of rapid dynamic motion. An energy balance is developed which calculates the excess energy released as a result of unstable equilibria in rock systems. These tools are validated through uniaxial and triaxial compressive strength tests and are extended to models of coal pillars and a simplified mining layout. The results of the finite difference simulations reveal that the instability identifiers and excess energy calculations provide a generalized methodology for assessing unstable failures within potentially complex mine models. These combined numerical tools may be applied in future studies to design primary and secondary supports in bump-prone conditions, evaluate retreat mining cut sequences, asses pillar de-stressing techniques, or perform backanalyses on unstable failures in select mining layouts.

Automatic mapping of strip mine operations from spacecraft data. [Ohio

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Reed, L. E.; Pettyjohn, W. A.

1974-01-01

The author has identified the following significant results. Computer techniques were applied to process ERTS tapes acquired over coal mining operations in southeastern Ohio on 21 August 1972 and 3 September 1973. ERTS products obtained included geometrically-correct map overlays, at scales from 1:24,000 to 1:250,000, showing stripped earth, partially reclaimed earth, water, and natural vegetation. Computer-generated tables listing the area covered by each land-water category in square kilometers were also produced. By comparing these mapping products, the study demonstrates the capability of ERTS to monitor changes in the extent of stripping and reclamation. NASA C-130 photography acquired on 7 September 1973 when compared with the ERTS products generated from the 3 September 1973 tape established the categorization accuracy to be better than 90%. It is estimated that the stripping and reclamation maps and data were produced from the ERTS CCTs at a tenth of the cost of conventional techniques.

Locating and defining underground goaf caused by coal mining from space-borne SAR interferometry

NASA Astrophysics Data System (ADS)

Yang, Zefa; Li, Zhiwei; Zhu, Jianjun; Yi, Huiwei; Feng, Guangcai; Hu, Jun; Wu, Lixin; Preusse, Alex; Wang, Yunjia; Papst, Markus

2018-01-01

It is crucial to locate underground goafs (i.e., mined-out areas) resulting from coal mining and define their spatial dimensions for effectively controlling the induced damages and geohazards. Traditional geophysical techniques for locating and defining underground goafs, however, are ground-based, labour-consuming and costly. This paper presents a novel space-based method for locating and defining the underground goaf caused by coal extraction using Interferometric Synthetic Aperture Radar (InSAR) techniques. As the coal mining-induced goaf is often a cuboid-shaped void and eight critical geometric parameters (i.e., length, width, height, inclined angle, azimuth angle, mining depth, and two central geodetic coordinates) are capable of locating and defining this underground space, the proposed method reduces to determine the eight geometric parameters from InSAR observations. Therefore, it first applies the Probability Integral Method (PIM), a widely used model for mining-induced deformation prediction, to construct a functional relationship between the eight geometric parameters and the InSAR-derived surface deformation. Next, the method estimates these geometric parameters from the InSAR-derived deformation observations using a hybrid simulated annealing and genetic algorithm. Finally, the proposed method was tested with both simulated and two real data sets. The results demonstrate that the estimated geometric parameters of the goafs are accurate and compatible overall, with averaged relative errors of approximately 2.1% and 8.1% being observed for the simulated and the real data experiments, respectively. Owing to the advantages of the InSAR observations, the proposed method provides a non-contact, convenient and practical method for economically locating and defining underground goafs in a large spatial area from space.

Applying data mining techniques to improve diagnosis in neonatal jaundice.

PubMed

Ferreira, Duarte; Oliveira, Abílio; Freitas, Alberto

2012-12-07

Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa--EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

Fuzzy and rough formal concept analysis: a survey

NASA Astrophysics Data System (ADS)

Poelmans, Jonas; Ignatov, Dmitry I.; Kuznetsov, Sergei O.; Dedene, Guido

2014-02-01

Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

The privacy preserving data mining technique researches have gained much attention in recent years. For data stream systems, wireless networks and mobile devices, the related stream data mining techniques research is still in its' early stage. In this paper, an data mining algorithm dealing with privacy preserving problem in data stream is presented.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2009-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...pilot study to utilize large scale parallel Grid computing to harness the nationwide cluster infrastructure for optimization of medical image ... analysis parameters. Additionally, we investigated the use of cutting edge dataanalysis/ mining techniques as applied to Ultrasound, FFDM, and DCE-MRI Breast

Evaluation of Wheel Loaders in Open Pit Marble Quarrying by Using the AHP and Topsis Approaches / Ocena pracy ładowarki na podwoziu kołowym w odkrywkowej kopalni marmuru w oparciu o metody AHP i topsis

NASA Astrophysics Data System (ADS)

Kun, Mete; Topaloǧlu, Şeyda; Malli, Tahir

2013-03-01

The marble mining in Turkey has been rising since the early 80's. In relation to that, the marble income has become noticeably bigger than those of other mining sectors. In recent years, marble and natural stone export composes half of the total mine export with a value of two billion dollars. This rapid development observed in marble operation has increased the importance of mining economics, income-expenditure balance and cost analysis. The most important cost elements observed in marble quarrying are machinery and equipment, labor costs and geological structures of the field. The aim of this study is to is to propose a multi-criteria decision making (MCDM) approach to evaluate the wheel loader alternatives and select the best loader under multiple criteria. A two-step methodology based on two MCDM methods, which are namely the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are used in the evaluation procedure. More precisely, AHP is applied to determine the relative weights of evaluation criteria and TOPSIS is applied to rank the wheel loader alternatives. The proposed approach also provides a relatively simple and very well suited decision making tool for this type of decision making problems.

Implementation of hospital examination reservation system using data mining technique.

PubMed

Cha, Hyo Soung; Yoon, Tae Sik; Ryu, Ki Chung; Shin, Il Won; Choe, Yang Hyo; Lee, Kyoung Yong; Lee, Jae Dong; Ryu, Keun Ho; Chung, Seung Hyun

2015-04-01

New methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments. In this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface. HERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days). It was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.

Comparative analysis of data mining techniques for business data

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

Application of 57Fe Mössbauer spectroscopy as a tool for mining exploration of bornite (Cu5FeS4) copper ore

NASA Astrophysics Data System (ADS)

Gainov, R. R.; Vagizov, F. G.; Golovanevskiy, V. A.; Ksenofontov, V. A.; Klingelhöfer, G.; Klekovkina, V. V.; Shumilova, T. G.; Pen'kov, I. N.

2014-04-01

Nuclear resonance methods, including Mössbauer spectroscopy,are considered as unique techniques suitable for remote on-line mineralogical analysis. The employment of these methods provides potentially significant commercial benefits for mining industry. As applied to copper sulfide ores, Mössbauer spectroscopy method is suitable for the analysis noted. Bornite (formally Cu5FeS4) is a significant part of copper ore and identification of its properties is important for economic exploitation of commercial copper ore deposits. A series of natural bornite samples was studied by 57Fe Mössbauer spectroscopy. Two aspects were considered: reexamination of 57Fe Mössbauer properties of natural bornite samples and their stability irrespective of origin and potential use of miniaturized Mössbauer spectrometers MIMOS II for in-situ bornite identification. The results obtained show a number of potential benefits of introducing the available portative Mössbauer equipment into the mining industry for express mineralogical analysis. In addition, results of some preliminary 63,65Cu nuclear quadrupole resonance (NQR) studies of bornite are reported and their merits with Mössbauer techniques for bornite detection discussed.

Improved mine blast algorithm for optimal cost design of water distribution systems

NASA Astrophysics Data System (ADS)

Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon

2015-12-01

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.

Cardiac data mining (CDM); organization and predictive analytics on biomedical (cardiac) data

NASA Astrophysics Data System (ADS)

Bilal, M. Musa; Hussain, Masood; Basharat, Iqra; Fatima, Mamuna

2013-10-01

Data mining and data analytics has been of immense importance to many different fields as we witness the evolution of data sciences over recent years. Biostatistics and Medical Informatics has proved to be the foundation of many modern biological theories and analysis techniques. These are the fields which applies data mining practices along with statistical models to discover hidden trends from data that comprises of biological experiments or procedures on different entities. The objective of this research study is to develop a system for the efficient extraction, transformation and loading of such data from cardiologic procedure reports given by Armed Forces Institute of Cardiology. It also aims to devise a model for the predictive analysis and classification of this data to some important classes as required by cardiologists all around the world. This includes predicting patient impressions and other important features.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

NASA Astrophysics Data System (ADS)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

Rules of meridians and acupoints selection in treatment of Parkinson's disease based on data mining techniques.

PubMed

Li, Zhe; Hu, Ying-Yu; Zheng, Chun-Ye; Su, Qiao-Zhen; An, Chang; Luo, Xiao-Dong; Liu, Mao-Cai

2018-01-15

To help selecting appropriate meridians and acupoints in clinical practice and experimental study for Parkinson's disease (PD), the rules of meridians and acupoints selection of acupuncture and moxibustion were analyzed in domestic and foreign clinical treatment for PD based on data mining techniques. Literature about PD treated by acupuncture and moxibustion in China and abroad was searched and selected from China National Knowledge Infrastructure and MEDLINE. Then the data from all eligible articles were extracted to establish the database of acupuncture-moxibustion for PD. The association rules of data mining techniques were used to analyze the rules of meridians and acupoints selection. Totally, 168 eligible articles were included and 184 acupoints were applied. The total frequency of acupoints application was 1,090 times. Those acupoints were mainly distributed in head and neck and extremities. Among all, Taichong (LR 3), Baihui (DU 20), Fengchi (GB 20), Hegu (LI 4) and Chorea-tremor Controlled Zone were the top five acupoints that had been used. Superior-inferior acupoints matching was utilized the most. As to involved meridians, Du Meridian, Dan (Gallbladder) Meridian, Dachang (Large Intestine) Meridian, and Gan (Liver) Meridian were the most popular meridians. The application of meridians and acupoints for PD treatment lay emphasis on the acupoints on the head, attach importance to extinguishing Gan wind, tonifying qi and blood, and nourishing sinews, and make good use of superior-inferior acupoints matching.

Sensitive test for sea mine identification based on polarization-aided image processing.

PubMed

Leonard, I; Alfalou, A; Brosseau, C

2013-12-02

Techniques are widely sought to detect and identify sea mines. This issue is characterized by complicated mine shapes and underwater light propagation dependencies. In a preliminary study we use a preprocessing step for denoising underwater images before applying the algorithm for mine detection. Once a mine is detected, the protocol for identifying it is activated. Among many correlation filters, we have focused our attention on the asymmetric segmented phase-only filter for quantifying the recognition rate because it allows us to significantly increase the number of reference images in the fabrication of this filter. Yet they are not entirely satisfactory in terms of recognition rate and the obtained images revealed to be of low quality. In this report, we propose a way to improve upon this preliminary study by using a single wavelength polarimetric camera in order to denoise the images. This permits us to enhance images and improve depth visibility. We present illustrative results using in situ polarization imaging of a target through a milk-water mixture and demonstrate that our challenging objective of increasing the detection rate and decreasing the false alarm rate has been achieved.

String Mining in Bioinformatics

NASA Astrophysics Data System (ADS)

Abouelhoda, Mohamed; Ghanem, Moustafa

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

Managing equipment innovations in mining: A review.

PubMed

Trudel, Bryan; Nadeau, Sylvie; Zaras, Kazimierz; Deschamps, Isabelle

2015-01-01

Technological innovations in mining equipment have led to increased productivity and occupational health and safety (OHS) performance, but their introduction also brings new risks for workers. The aim of this study is to provide support for mining industry managers who are required to reconcile equipment choices with OHS and productivity. Examination of the literature through interdisciplinary digital databases. Databases were searched using specific combinations of keywords and limited to studies dating back no farther than 1992. The ``snowball'' technique was also used to examining the references listed in research articles initially identified with the databases. A total of 19 contextual factors were identified as having the potential to influence the OHS and productivity leverage of equipment innovations. The most often cited among these factors are the level of training provided to the equipment operators, operator experience and age, supervisor leadership abilities, and maintaining good relations within work crews. Interactions between these factors are not discussed in mining innovation literature. It would be helpful to use a systems thinking approach which incorporates interaction between relevant actors and factors to define properly the most sensitive aspects of innovation management as it applies to mining equipment.

Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems.

PubMed

Wang, Chao; Guo, Xiao-Jing; Xu, Jin-Fang; Wu, Cheng; Sun, Ya-Lin; Ye, Xiao-Fei; Qian, Wei; Ma, Xiu-Qiang; Du, Wen-Min; He, Jia

2012-01-01

The detection of signals of adverse drug events (ADEs) has increased because of the use of data mining algorithms in spontaneous reporting systems (SRSs). However, different data mining algorithms have different traits and conditions for application. The objective of our study was to explore the application of association rule (AR) mining in ADE signal detection and to compare its performance with that of other algorithms. Monte Carlo simulation was applied to generate drug-ADE reports randomly according to the characteristics of SRS datasets. Thousand simulated datasets were mined by AR and other algorithms. On average, 108,337 reports were generated by the Monte Carlo simulation. Based on the predefined criterion that 10% of the drug-ADE combinations were true signals, with RR equaling to 10, 4.9, 1.5, and 1.2, AR detected, on average, 284 suspected associations with a minimum support of 3 and a minimum lift of 1.2. The area under the receiver operating characteristic (ROC) curve of the AR was 0.788, which was equivalent to that shown for other algorithms. Additionally, AR was applied to reports submitted to the Shanghai SRS in 2009. Five hundred seventy combinations were detected using AR from 24,297 SRS reports, and they were compared with recognized ADEs identified by clinical experts and various other sources. AR appears to be an effective method for ADE signal detection, both in simulated and real SRS datasets. The limitations of this method exposed in our study, i.e., a non-uniform thresholds setting and redundant rules, require further research.

Geomechanical analysis applied to geological carbon dioxide sequestration, induced seismicity in deep mines, and detection of stress-induced velocity anisotropy in sub-salt environments

NASA Astrophysics Data System (ADS)

Lucier, Amie Marie

The role of geomechanical analysis in characterizing the feasibility of CO2 sequestration in deep saline aquifers is addressed in two investigations. The first investigation was completed as part of the Ohio River Valley CO2 Storage Project. We completed a geomechanical analysis of the Rose Run Sandstone, a potential injection zone, and its adjacent formations at the American Electric Power's 1.3 GW Mountaineer Power Plant in New Haven, West Virginia. The results of this analysis were then used to evaluate the feasibility of anthropogenic CO2 sequestration in the potential injection zone. First, we incorporated the results of the geomechanical analysis with a geostatistical aquifer model in CO2 injection flow simulations to test the effects of introducing a hydraulic fracture to increase injectivity. Then, we determined that horizontal injection wells at the Mountaineer site are feasible because the high rock strength ensures that such wells would be stable in the local stress state. Finally, we evaluated the potential for injection-induced seismicity. The second investigation concerning CO2 sequestration was motivated by the modeling and fluid flow simulation results from the first study. The geomechanics-based assessment workflow follows a bottom-up approach for evaluating regional deep saline aquifer CO2 injection and storage feasibility. The CO2 storage capacity of an aquifer is a function of its porous volume as well as its CO2 injectivity. For a saline aquifer to be considered feasible in this assessment it must be able to store a specified amount of CO2 at a reasonable cost per ton of CO 2. The proposed assessment workflow has seven steps. The workflow was applied to a case study of the Rose Run sandstone in the eastern Ohio River Valley. We found that it is feasible in this region to inject and store 113 Mt CO2/yr for 30 years at an associated well cost of less than 1.31 US$/t CO2, but only if injectivity enhancement techniques such as hydraulic fracturing and injection induced micro-seismicity are implemented. The second issue to which we apply geomechanical analysis in this thesis is mining-induced stress perturbations and induced seismicity in the TauTona gold mine, which is located in the Witwatersrand Basin of South Africa and is one of the deepest underground mines in the world. In the first investigation, we developed and tested a new technique for determining the virgin stress state near the TauTona gold mine. This technique follows an iterative forward modeling approach that combines observations of drilling induced borehole failures in borehole images, boundary element modeling of the mining-induced stress perturbations, and forward modeling of borehole failures based on the results of the boundary element modeling. The final result was a well constrained range of principal stress orientations and magnitudes that are consistent with all the observed failures and other stress indicators. In the second investigation, we used this constrained stress state to examine the likelihood of faulting to occur both on pre-existing fault planes that are optimally oriented to the virgin stress state and on faults affected by the mining-perturbed stress field, the latter of which is calculated with boundary element modeling. We made several recommendations that could potentially increase safety in deep South African mines as development continues. Finally, the third issue addressed in this thesis is the detection of stress-induced shear wave velocity anisotropy in a sub-salt environment. In this study, we tested a technique proposed by Boness and Zoback (2006) to identify structure-induced velocity anisotropy and isolate possible stress-induced velocity anisotropy. The investigation used cross-dipole sonic data from three deep water sub-salt wells in the Gulf of Mexico. First, we determined the parameters necessary to ensure the quality of the fast azimuth data used in our analysis. We then characterized the quality controlled measured fast directions as either structure-induced or stress-induced based on the results of the Boness and Zoback (2006) technique. We found that this technique supplements the use of dispersion curve analysis for characterizing anisotropy mechanisms. We also find that this technique has the potential to provide information on the stresses that can be used to validate numerical models of salt-related stress perturbations. (Abstract shortened by UMI.)

The Mine Locomotive Wireless Network Strategy Based on Successive Interference Cancellation

PubMed Central

Wu, Liaoyuan; Han, Jianghong; Wei, Xing; Shi, Lei; Ding, Xu

2015-01-01

We consider a wireless network strategy based on successive interference cancellation (SIC) for mine locomotives. We firstly build the original mathematical model for the strategy which is a non-convex model. Then, we examine this model intensively, and figure out that there are certain regulations embedded in it. Based on these findings, we are able to reformulate the model into a new form and design a simple algorithm which can assign each locomotive with a proper transmitting scheme during the whole schedule procedure. Simulation results show that the outcomes obtained through this algorithm are improved by around 50% compared with those that do not apply the SIC technique. PMID:26569240

Summary of Global Ozone Measurements Collected from Field Campaigns

NASA Astrophysics Data System (ADS)

Aguilera, J.; Salazar, V.

2013-12-01

The goal of the NCAR Earth Observing Laboratory data services is to advance science through delivering high-quality project data and meta data in ways that are as transparent, secure, and easily accessible as possible. By using EOL's existing infrastructure and applying data mining techniques, we explored global ozone measurements collected during EOL supported airborne field campaigns. This study highlights ozone concentrations addressing a diverse set of science objectives, and how these timed measurements contribute to the understanding of the state of the atmosphere and evolution of the different measuring techniques.

Evaluating bump control techniques through convergence monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campoli, A.A.

1987-07-01

A coal mine bump is the violent failure of a pillar or pillars due to overstress. Retreat coal mining concentrates stresses on the pillars directly outby gob areas, and the situation becomes critical when mining a coalbed encased in rigid associated strata. Bump control techniques employed by the Olga Mine, McDowell County, WV, were evaluated through convergence monitoring in a Bureau of Mines study. Olga uses a novel pillar splitting mining method to extract 55-ft by 70-ft chain pillars, under 1,100 to 1,550 ft of overburden. Three rows of pillars are mined simultaneously to soften the pillar line and reducemore » strain energy storage capacity. Localized stress reduction (destressing) techniques, auger drilling and shot firing, induced approximately 0.1 in. of roof-to-floor convergence in ''high'' -stress pillars near the gob line. Auger drilling of a ''low''-stress pillar located between two barrier pillars produced no convergence effects.« less

Application of techniques to identify coal-mine and power-generation effects on surface-water quality, San Juan River basin, New Mexico and Colorado

USGS Publications Warehouse

Goetz, C.L.; Abeyta, Cynthia G.; Thomas, E.V.

1987-01-01

Numerous analytical techniques were applied to determine water quality changes in the San Juan River basin upstream of Shiprock , New Mexico. Eight techniques were used to analyze hydrologic data such as: precipitation, water quality, and streamflow. The eight methods used are: (1) Piper diagram, (2) time-series plot, (3) frequency distribution, (4) box-and-whisker plot, (5) seasonal Kendall test, (6) Wilcoxon rank-sum test, (7) SEASRS procedure, and (8) analysis of flow adjusted, specific conductance data and smoothing. Post-1963 changes in dissolved solids concentration, dissolved potassium concentration, specific conductance, suspended sediment concentration, or suspended sediment load in the San Juan River downstream from the surface coal mines were examined to determine if coal mining was having an effect on the quality of surface water. None of the analytical methods used to analyzed the data showed any increase in dissolved solids concentration, dissolved potassium concentration, or specific conductance in the river downstream from the mines; some of the analytical methods used showed a decrease in dissolved solids concentration and specific conductance. Chaco River, an ephemeral stream tributary to the San Juan River, undergoes changes in water quality due to effluent from a power generation facility. The discharge in the Chaco River contributes about 1.9% of the average annual discharge at the downstream station, San Juan River at Shiprock, NM. The changes in water quality detected at the Chaco River station were not detected at the downstream Shiprock station. It was not possible, with the available data, to identify any effects of the surface coal mines on water quality that were separable from those of urbanization, agriculture, and other cultural and natural changes. In order to determine the specific causes of changes in water quality, it would be necessary to collect additional data at strategically located stations. (Author 's abstract)

Estimating the Importance of Terrorists in a Terror Network

NASA Astrophysics Data System (ADS)

Elhajj, Ahmed; Elsheikh, Abdallah; Addam, Omar; Alzohbi, Mohamad; Zarour, Omar; Aksaç, Alper; Öztürk, Orkun; Özyer, Tansel; Ridley, Mick; Alhajj, Reda

While criminals may start their activities at individual level, the same is in general not true for terrorists who are mostly organized in well established networks. The effectiveness of a terror network could be realized by watching many factors, including the volume of activities accomplished by its members, the capabilities of its members to hide, and the ability of the network to grow and to maintain its influence even after the loss of some members, even leaders. Social network analysis, data mining and machine learning techniques could play important role in measuring the effectiveness of a network in general and in particular a terror network in support of the work presented in this chapter. We present a framework that employs clustering, frequent pattern mining and some social network analysis measures to determine the effectiveness of a network. The clustering and frequent pattern mining techniques start with the adjacency matrix of the network. For clustering, we utilize entries in the table by considering each row as an object and each column as a feature. Thus features of a network member are his/her direct neighbors. We maintain the weight of links in case of weighted network links. For frequent pattern mining, we consider each row of the adjacency matrix as a transaction and each column as an item. Further, we map entries into a 0/1 scale such that every entry whose value is greater than zero is assigned the value one; entries keep the value zero otherwise. This way we can apply frequent pattern mining algorithms to determine the most influential members in a network as well as the effect of removing some members or even links between members of a network. We also investigate the effect of adding some links between members. The target is to study how the various members in the network change role as the network evolves. This is measured by applying some social network analysis measures on the network at each stage during the development. We report some interesting results related to two benchmark networks: the first is 9/11 and the second is Madrid bombing.

EU-FP7-iMARS: Analysis of Mars Multi-Resolution Images Using Auto-Coregistration Data Mining and Crowd Source Techniques: Processed Results - a First Look

NASA Astrophysics Data System (ADS)

Muller, Jan-Peter; Tao, Yu; Sidiropoulos, Panagiotis; Gwinner, Klaus; Willner, Konrad; Fanara, Lida; Waehlisch, Marita; van Gasselt, Stephan; Walter, Sebastian; Steikert, Ralf; Schreiner, Bjoern; Ivanov, Anton; Cantini, Federico; Wardlaw, Jessica; Morley, Jeremy; Sprinks, James; Giordano, Michele; Marsh, Stuart; Kim, Jungrack; Houghton, Robert; Bamford, Steven

2016-06-01

Understanding planetary atmosphere-surface exchange and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to overlay image data and derived information from different epochs, back in time to the mid 1970s, to examine changes through time, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, we have developed a fully automated multi-resolution DTM processing chain, called the Coregistration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP) [Tao et al., this conference], which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR [Gwinner et al., 2015] have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed by [Sidiropoulos & Muller, this conference]. Using the HRSC map products (both mosaics and orbital strips) as a map-base it is being applied to many of the 400,000 level-1 EDR images taken by the 4 NASA orbital cameras. In particular, the NASA Viking Orbiter camera (VO), Mars Orbiter Camera (MOC), Context Camera (CTX) as well as the High Resolution Imaging Science Experiment (HiRISE) back to 1976. A webGIS has been developed [van Gasselt et al., this conference] for displaying this time sequence of imagery and will be demonstrated showing an example from one of the HRSC quadrangle map-sheets. Automated quality control [Sidiropoulos & Muller, 2015] techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. For result verification these data mining techniques are then being employed within a citizen science project within the Zooniverse family. Examples of data mining and its verification will be presented.

Biomedical text mining and its applications in cancer research.

PubMed

Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

2013-04-01

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

NASA Astrophysics Data System (ADS)

Bustillo, Andrés; Rodríguez, Juan J.

2014-12-01

Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in the bibliography.

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

PubMed

Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results. Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

Water induced geohazards measured with spaceborne interferometry techniques

NASA Astrophysics Data System (ADS)

Poncos, V.; Serban, F.; Teleaga, D.; Ciocan, V.; Sorin, M.; Caranda, D.; Zamfirescu, F.; Andrei, M.; Copaescu, S.; Radu, M.; Raduca, V.

2012-04-01

Natural and anthropogenic occurrence of groundwater is inducing surficial crustal deformation processes that can be accurately measured with high spatial density from space, regardless of the ground access conditions. The detection of the surface deformation allows uncovering spatial and temporal patterns of subsurface processes such as land subsidence, cave-ins and differential ground settlement related to water content. InSAR measurements combined with ground truth data permit estimation of the mechanical properties of the rocks and the development of models and scenarios to predict disaster events such as cave-ins, landslides and soil liquefaction in the case of an Earthquake. A number of three sites in Romania that suffer of ground instability because of the water component will be presented. The DInSAR, Interferograms Stacking and Persistent Scatterers Interferometry techniques were applied to retrieve as accurate as possible the displacement information. The first studied site is the city of Bucharest; using 7 years of ERS data ground instability was detected on a large area that represents the historical watershed of the Dambovita river. A network of water wells shows that the ground instability is directly proportional to the groundwater depth. The second site is the Ocnele Mari brine extraction area. The exploitation of the Ocnele Mari salt deposit started from the Roman Empire time using the mining technology and from 1954 the salt dissolution technology which involves injecting water into the ground using a well and extracting the brine (water and salt) through another well. The extraction of salt through dissolution led to slow ground subsidence but the flooding and dissolution of the Roman caves led to catastrophic cave-ins and the relocation of an entire village. The water injection technique is still applied and the Roman cave system is an unknown, therefore further catastrophic events are expected. The existing theoretical simulations of the subsidence process are performed using a Finite Element Method (FEM), which calculates the distribution of the state of strains and stresses in the rock masses, in an elasto-plastic behavior. The ground deformation is presently measured with leveling instrumentation and an effort is being made to adopt the InSAR results for a better spatial and temporal coverage that should refine the existing model. The third site is a number of 4 tailing retention ponds at different stages of their life. The tailing ponds are hydrotechnical structures of permeable type designed for the safe storage of mining detritus byproducts and disposal of the water contained in these byproducts. Starting in 1998 approximately 550 mines have been closed and introduced in a conservation process. In order to prevent ecological and human damage, all these mines and storage ponds for mining tailings are required to be under continuous monitoring. Using 15 high-resolution Spotlight TerraSAR-X images, the stability of the storage pond was monitored over a period of 5 months during 2011. Interferometric stacking techniques and PSI analysis were applied in order to generate deformation maps and deformation profiles. In the same time, GPS measurements and Electrical Tomography for water content were used as independent measurements.

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

The stable isotopes of site wide waters at an oil sands mine in northern Alberta, Canada

NASA Astrophysics Data System (ADS)

Baer, Thomas; Barbour, S. Lee; Gibson, John J.

2016-10-01

Oil sands mines have large disturbance footprints and contain a range of new landforms constructed from mine waste such as shale overburden and the byproducts of bitumen extraction such as sand and fluid fine tailings. Each of these landforms are a potential source of water and chemical release to adjacent surface and groundwater, and consequently, the development of methods to track water migration through these landforms is of importance. The stable isotopes of water (i.e. 2H and 18O) have been widely used in hydrology and hydrogeology to characterize surface water/groundwater interactions but have not been extensively applied in mining applications, or specifically to oil sands mining in northern Alberta. A prerequisite for applying these techniques is the establishment of a Local Meteoric Water Line (LMWL) to characterize precipitation at the mine sites as well as the development of a 'catalogue' of the stable water isotope signatures of various mine site waters. This study was undertaken at the Mildred Lake Mine Site, owned and operated by Syncrude Canada Ltd. The LMWL developed from 2 years (2009/2012) of sample collection is shown to be consistent with other LMWLs in western Canada. The results of the study highlight the unique stable water isotope signatures associated with hydraulically placed tailings (sand or fluid fine tailings) and overburden shale dumps relative to natural surface water and groundwater. The signature associated with the snow melt water on reclaimed landscapes was found to be similar to ground water recharge in the region. The isotopic composition of the shale overburden deposits are also distinct and consistent with observations made by other researchers in western Canada on undisturbed shales. The process water associated with the fine and coarse tailings streams has highly enriched 2H and 18O signatures. These signatures are developed through the non-equilibrium fractionation of imported fresh river water during evaporation from cooling towers used within the raw water process circuit. This highly fractionated surface water eventually becomes part of the recycled tailings water circuit, and as a consequence it undergoes further non-equilibrium fractionation as a result of surface evaporation, leading to additional enrichment along local evaporation lines.

Process mining techniques: an application to time management

NASA Astrophysics Data System (ADS)

Khowaja, Ali Raza

2018-04-01

In an environment people have to make sure that all of their work are completed within a given time in accordance with its quality. In order to achieve the real phenomenon of process mining one needs to understand all of these processes in a detailed manner. Personal Information and communication has always been a highlighting issue on internet but for now information and communication tools within factual life refers to their daily schedule, location analysis, environmental analysis and, more generally, social media applications support these systems which makes data available for data analysis generated through event logs, but also for process analysis which combines environmental and location analysis. Process mining can be used to exploit all these real live processes with the help of the event logs which are already available in those datasets through user censored data or may be user labeled data. These processes could be used to redesign a user's flow and understand all these processes in a bit more detailed manner. In order to increase the quality of each of the processes that we go through our daily lives is to give a closer look to each of the processes and after analyzing them, one should make changes to get better results. On the contrarily, we applied process mining techniques on seven different subjects combined in a single dataset collected from Korea. Above all, the following paper comments on the efficiency of processes in the event logs referring to time management's sphere of influence.

Weighting Criteria and Prioritizing of Heat stress indices in surface mining using a Delphi Technique and Fuzzy AHP-TOPSIS Method.

PubMed

Asghari, Mehdi; Nassiri, Parvin; Monazzam, Mohammad Reza; Golbabaei, Farideh; Arabalibeik, Hossein; Shamsipour, Aliakbar; Allahverdy, Armin

2017-01-01

Heat stress as a physical harmful agent can increase the risk of health and safety problems in different workplaces such as mining. Although there are different indices to assess the heat stress imposed on workers, choosing the best index for a specific workplace is so important. Since various criteria affect an index applicability, extracting the most effective ones and determining their weights help to prioritize the existing indices and select the optimal index. In order to achieve this aim, present study compared some heat stress indices using effective methods. The viewpoints of occupational health experts and the qualitative Delphi methods were used to extract the most important criteria. Then, the weights of 11 selected criteria were determined by Fuzzy Analytic Hierarchy Process. Finally, fuzzy TOPSIS technique was applied for choosing the most suitable heat stress index. According to result, simplicity, reliability, being low cost, and comprehensiveness were the most determinative criteria for a heat stress index. Based on these criteria and their weights, the existing indices were prioritized. Eventually, wet bulb glob temperature appropriated the first priority and it was proposed as an applicable index for evaluating the heat stress at outdoor hot environments such as surface mines. The use of these strong methods allows introducing the most simple, precise, and applicable tool for evaluation the heat stress in hot environments. It seems that WBGT acts as an appropriate index for assessing the heat stress in mining activities at outdoors.

Applications of Deep Learning and Reinforcement Learning to Biological Data.

PubMed

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

Monitoring of ground movement in open pit iron mines of Carajás Province (Amazon region) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Silva, Guilherme Gregório; Mura, José Claudio; Paradella, Waldir Renato; Gama, Fabio Furlan; Temporim, Filipe Altoé

2017-04-01

Persistent scatterer interferometry (PSI) analysis of a large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground movement measurements based on a combination of differential SAR interferometry time-series (DTS) and PSI techniques, applied on a large area of extent with open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detecting linear and nonlinear ground movement. These mines have presented a history of instability, and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground-based radar and total station (prisms). Using a priori information regarding the topographic phase error and a phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X (TSX-1) images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multilook unwrapped interferograms using an extension of SVD to obtain the least-square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferograms to perform the PSI analysis. This procedure improved the capability of the PSI analysis for detecting high rates of deformation, as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risk control.

Monitoring of surface movement in a large area of the open pit iron mines (Carajás, Brazil) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Mura, José C.; Paradella, Waldir R.; Gama, Fabio F.; Silva, Guilherme G.

2016-10-01

PSI (Persistent Scatterer Interferometry) analysis of large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground deformation measurements based on a combination of DInSAR Time-Series (DTS) and PSI techniques, applied in a large area of open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detect high rates of linear and nonlinear ground deformation. These mines have presented a historical of instability and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground based radar and total station (prisms). By using a priori information regarding the topographic phase error and phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X-1 images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multi-look unwrapped interferogram using an extension of SVD to obtain the Least-Square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferogram to perform the PSI analysis. This procedure improved the capability of the PSI analysis to detect high rates of deformation as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risks control.

Lessons learned from the U.S. Geological Survey abandoned mine lands initiative: 1997-2002

USGS Publications Warehouse

Kimball, Briant A.; Church, Stan E.; Besser, John M.

2006-01-01

Growth of the United States has been facilitated, in part, by hard-rock mining in the Rocky Mountains. Abandoned and inactive mines cause many significant environmental concerns in hundreds of watersheds. Those who have responsibility to address these environmental concerns must have a basic level of scientific information about mining and mine wastes in a watershed prior to initiating remediation activities. To demonstrate what information is needed and how to obtain that information, the U.S. Geological Survey implemented the Abandoned Mine Lands (AML) Initiative from 1997 to 2002 with demonstration studies in the Boulder River watershed in Montana and the Animas River watershed in Colorado. The AML Initiative included collection and analysis of geologic, hydrologic, geochemical, geophysical, and biological data. The synergy of this interdisciplinary analysis produced a perspective of the environmental concerns that could not have come from a single discipline. Two examples of these perspectives include (1) the combination of hydrological tracer techniques, structural geology, and geophysics help to understand the spatial distribution of loading to the streams in a way that cannot be evaluated by monitoring at a catchment outlet, and (2) the combination of toxicology and hydrology combine to illustrate that seasonal variability of toxicity conditions occurs. Lessons have been learned by listening to and collaborating with land-management agencies to understand their needs and by applying interdisciplinary methods to answer their questions.

Sustainable geoengineering projects for the remediation of mine site

NASA Astrophysics Data System (ADS)

Martínez-Sanchez, Maria Jose; Perez-Sirvent, Carmen; Garcia-Lorenzo, Maria Luz; Martinez-Lopez, Salvadora; Gonzalez, Eva; Perez-Espinosa, Victor; Molina-Ruiz, Jose; Belen Martinez, Lucia; Hernandez, Carmen; Bech, Jaime; Hernandez-Cordoba, Manuel

2015-04-01

A large number of soils are contaminated by heavy metals due to mining activities, generating adverse effects on human health and the environment. In response to these negative effects, a variety of technologies have been developed. In situ immobilization by means of soil amendment is a non-intrusive and cost effective alternative that transforms the highly mobile toxic heavy metals to physico-chemically stable forms. Limestone filler is a good selection for such a purpose, because of its characteristics. In addition, the use of this amendment could revalorize the residues, reducing the costs of the process. The objective of this work was to evaluate the effectiveness of an immobilization technique in sediments contaminated by heavy metals. Two experimental areas, approximately 1 Ha each one, were selected, and technosols were developed as follows: original sediments, sediments mixed with limestone filler in a 1:1 proportion, gravel to avoid capillary and natural soil to allow plant growth. After the remediation technique was applied, monitoring was done in 18 points collecting samples (sediment and water) during a 4 years period at two month intervals. The pH and electrical conductivity as well as the heavy metal (Zn, Pb, Cd, Cu and As) contents were measured. Microtox bioassay was also applied. Sediments before the remediation technique showed acidic pH, high EC values and high trace elements content. The results obtained after the immobilization showed that sediment samples had neutral pH (average value of 8.3) low electrical conductivity (1.32 dS m-1) and low trace elements concentration. It can be concluded that the use of limestone filler is an excellent option in sediments polluted because of the risk for human health or ecosystem disappears or is decreased in a large extent. In addition, the designed experience allows stabilizer proportion to be optimized and may suppose a big cost-saving in the project in areas affected by mining activities.

Statistical studies of selected trace elements with reference to geology and genesis of the Carlin gold deposit, Nevada

USGS Publications Warehouse

Harris, Michael; Radtke, Arthur S.

1976-01-01

Linear regression and discriminant analyses techniques were applied to gold, mercury, arsenic, antimony, barium, copper, molybdenum, lead, zinc, boron, tellurium, selenium, and tungsten analyses from drill holes into unoxidized gold ore at the Carlin gold mine near Carlin, Nev. The statistical treatments employed were used to judge proposed hypotheses on the origin and geochemical paragenesis of this disseminated gold deposit.

Detecting and visualizing weak signatures in hyperspectral data

NASA Astrophysics Data System (ADS)

MacPherson, Duncan James

This thesis evaluates existing techniques for detecting weak spectral signatures from remotely sensed hyperspectral data. Algorithms are presented that successfully detect hard-to-find 'mystery' signatures in unknown cluttered backgrounds. The term 'mystery' is used to describe a scenario where the spectral target and background endmembers are unknown. Sub-Pixel analysis and background suppression are used to find deeply embedded signatures which can be less than 10% of the total signal strength. Existing 'mystery target' detection algorithms are derived and compared. Several techniques are shown to be superior both visually and quantitatively. Detection performance is evaluated using confidence metrics that are developed. A multiple algorithm approach is shown to improve detection confidence significantly. Although the research focuses on remote sensing applications, the algorithms presented can be applied to a wide variety of diverse fields such as medicine, law enforcement, manufacturing, earth science, food production, and astrophysics. The algorithms are shown to be general and can be applied to both the reflective and emissive parts of the electromagnetic spectrum. The application scope is a broad one and the final results open new opportunities for many specific applications including: land mine detection, pollution and hazardous waste detection, crop abundance calculations, volcanic activity monitoring, detecting diseases in food, automobile or airplane target recognition, cancer detection, mining operations, extracting galactic gas emissions, etc.

Application of standard photogeologic techniques to LANDSAT imagery for mineral exploration in the basin and range province of Utah and Nevada

NASA Technical Reports Server (NTRS)

Lattman, L. H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Standard photogeologic techniques were applied to LANDSAT imagery of the basin and range province of Utah and Nevada to relate linear, tonal, textural, drainage, and geomorphic features to known mineralized areas in an attempt to develop criteria for the location of mineral deposits. No consistent correlation was found between lineaments, mapped according to specified criteria, and locations of mines, mining districts, or intrusive outcrops. Tonal and textural patterns were more closely related to geologic outcrop patterns than to mineralization. A statistical study of drainage azimuths of various length classes as measured on LANDSAT showed significant correlation with mineralized districts in the length class of 3-6 km. Alignments of outcrops of basalt, a rock type highly visible on LANDSAT imagery, appear to be colinear with acidic and intermediate intrusive centers in some areas and may assist on the recognition of regional fracture systems for mineral exploration.

Automated strip-mine and reclamation mapping from ERTS

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Reed, L. E.; Pettyjohn, W. A.

1974-01-01

The author has identified the following significant results. Computer processing techniques were applied to ERTS-1 computer-compatible tape (CCT) data acquired in August 1972 on the Ohio Power Company's coal mining operation in Muskingum County, Ohio. Processing results succeeded in automatically classifying, with an accuracy greater than 90%: (1) stripped earth and major sources of erosion; (2) partially reclaimed areas and minor sources of erosion; (3) water with sedimentation; (4) water without sedimentation; and (5) vegetation. Computer-generated tables listing the area in acres and square kilometers were produced for each target category. Processing results also included geometrically corrected map overlays, one for each target category, drawn on a transparent material by a pen under computer control. Each target category is assigned a distinctive color on the overlay to facilitate interpretation. The overlays, drawn at a scale of 1:250,000 when placed over an AMS map of the same area, immediately provided map locations for each target. These mapping products were generated at a tenth of the cost of conventional mapping techniques.

Evaluation of Aster Images for Characterization and Mapping of Amethyst Mining Residues

NASA Astrophysics Data System (ADS)

Markoski, P. R.; Rolim, S. B. A.

2012-07-01

The objective of this work was to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), subsystems VNIR (Visible and Near Infrared) and SWIR (Short Wave Infrared) images, for discrimination and mapping of amethyst mining residues (basalt) in the Ametista do Sul Region, Rio Grande do Sul State, Brazil. This region provides the most part of amethyst mining of the World. The basalt is extracted during the mining process and deposited outside the mine. As a result, mounts of residues (basalt) rise up. These mounts are many times smaller than ASTER pixel size (VNIR - 15 meters and SWIR - 30 meters). Thus, the pixel composition becomes a mixing of various materials, hampering its identification and mapping. Trying to solve this problem, multispectral algorithm Maximum Likelihood (MaxVer) and the hyperspectral technique SAM (Spectral Angle Mapper) were used in this work. Images from ASTER subsystems VNIR and SWIR were used to perform the classifications. SAM technique produced better results than MaxVer algorithm. The main error found by the techniques was the mixing between "shadow" and "mining residues/basalt" classes. With the SAM technique the confusion decreased because it employed the basalt spectral curve as a reference, while the multispectral techniques employed pixels groups that could have spectral mixture with other targets. The results showed that in tropical terrains as the study area, ASTER data can be efficacious for the characterization of mining residues.

EMRlog method for computer security for electronic medical records with logic and data mining.

PubMed

Martínez Monterrubio, Sergio Mauricio; Frausto Solis, Juan; Monroy Borja, Raúl

2015-01-01

The proper functioning of a hospital computer system is an arduous work for managers and staff. However, inconsistent policies are frequent and can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. This paper presents a new method named EMRlog for computer security systems in hospitals. EMRlog is focused on two kinds of security policies: directive and implemented policies. Security policies are applied to computer systems that handle huge amounts of information such as databases, applications, and medical records. Firstly, a syntactic verification step is applied by using predicate logic. Then data mining techniques are used to detect which security policies have really been implemented by the computer systems staff. Subsequently, consistency is verified in both kinds of policies; in addition these subsets are contrasted and validated. This is performed by an automatic theorem prover. Thus, many kinds of vulnerabilities can be removed for achieving a safer computer system.

EMRlog Method for Computer Security for Electronic Medical Records with Logic and Data Mining

PubMed Central

Frausto Solis, Juan; Monroy Borja, Raúl

2015-01-01

The proper functioning of a hospital computer system is an arduous work for managers and staff. However, inconsistent policies are frequent and can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. This paper presents a new method named EMRlog for computer security systems in hospitals. EMRlog is focused on two kinds of security policies: directive and implemented policies. Security policies are applied to computer systems that handle huge amounts of information such as databases, applications, and medical records. Firstly, a syntactic verification step is applied by using predicate logic. Then data mining techniques are used to detect which security policies have really been implemented by the computer systems staff. Subsequently, consistency is verified in both kinds of policies; in addition these subsets are contrasted and validated. This is performed by an automatic theorem prover. Thus, many kinds of vulnerabilities can be removed for achieving a safer computer system. PMID:26495300

Integration of MODIS data and Short Baseline Subset (SBAS) technique for land subsidence monitoring in Datong, China

NASA Astrophysics Data System (ADS)

Zhao, Chao-ying; Zhang, Qin; Yang, Chengsheng; Zou, Weibao

2011-07-01

Datong is located in the north of Shanxi Province, which is famous for its old-fashioned coal-mining preservation in China. Some serious issues such as land subsidence, ground fissures, mining collapse, and earthquake hazards have occurred over this area for a long time resulting in significant damages to buildings and roads. In order to monitor and mitigate these natural man-made hazards, Short Baseline Subsets (SBAS) InSAR technique with ten Envisat ASAR data is applied to detect the surface deformation over an area of thousands of square kilometers. Then, five MODIS data are used to check the atmospheric effects on InSAR interferograms. Finally, nine nonlinear land subsidence cumulative results during September 2004 and February 2008 are obtained. Based on the deformation data, three kinds of land subsidence are clearly detected, caused by mine extraction, underground water withdrawal and construction of new economic zones, respectively. The annual mean velocity of subsidence can reach 1 to 4 cm/year in different subsidence areas. A newly designed high-speed railway (HSR) with speeds of 350 km/h will cross through the Datong hi-tech zone. Special measures should be taken for the long run of this project. In addition, another two subsidence regions need further investigation to mitigate such hazards.

Mining the National Career Assessment Examination Result Using Clustering Algorithm

NASA Astrophysics Data System (ADS)

Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

2018-03-01

Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

The U.S. Geological Survey Coal Hydrology Program and the potential of hydrologic models for impact assessments

USGS Publications Warehouse

Doyle, W. Harry

1981-01-01

A requirement of Public Law 95-87, the Surface Mining Control and Reclamation Act of 1977, is the understanding of the hydrology in actual and proposed surface-mined areas. Surface-water data for small specific-sites and for larger areas such as adjacent and general areas are needed also to satisfy the hydrologic requirements of the Act. The Act specifies that surface-water modeling techniques may be used to generate the data and information. The purpose of this report is to describe how this can be achieved for smaller watersheds. This report also characterizes 12 ' state-of-the-art ' strip-mining assessment models that are to be tested with data from two data-intensive studies involving small watersheds in Tennessee and Indiana. Watershed models are best applied to small watersheds with specific-site data. Extending the use of modeling techniques to larger watersheds remains relatively untested, and to date the upper limits for application have not been established. The U.S. Geological Survey is currently collecting regional hydrologic data in the major coal provinces of the United States and this data will be used to help satisfy the ' general-area ' data requirements of the Act. This program is reviewed and described in this report. (USGS)

76 FR 51274 - Supplemental Nutrition Assistance Program: Major System Failures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

... data mining as necessary to determine if losses are occurring in the process of issuing benefits. It is... further by using data mining techniques on States' data or analyzing QC data for error patterns that may... conjunction with an additional sample of cases. Data mining techniques may be employed when QC data cannot...

Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

NASA Technical Reports Server (NTRS)

Hughes, T. H.; Dillion, A. C., III; White, J. R., Jr.; Drummond, S. E., Jr.; Hooks, W. G.

1975-01-01

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area.

Environmental characterisation of coal mine waste rock in the field: an example from New Zealand

NASA Astrophysics Data System (ADS)

Hughes, J.; Craw, D.; Peake, B.; Lindsay, P.; Weber, P.

2007-08-01

Characterisation of mine waste rock with respect to acid generation potential is a necessary part of routine mine operations, so that environmentally benign waste rock stacks can be constructed for permanent storage. Standard static characterisation techniques, such as acid neutralisation capacity (ANC), maximum potential acidity, and associated acid-base accounting, require laboratory tests that can be difficult to obtain rapidly at remote mine sites. We show that a combination of paste pH and a simple portable carbonate dissolution test, both techniques that can be done in the field in a 15 min time-frame, is useful for distinguishing rocks that are potentially acid-forming from those that are acid-neutralising. Use of these techniques could allow characterisation of mine wastes at the metre scale during mine excavation operations. Our application of these techniques to pyrite-bearing (total S = 1-4 wt%) but variably calcareous coal mine overburden shows that there is a strong correlation between the portable carbonate dissolution technique and laboratory-determined ANC measurements (range of 0-10 wt% calcite equivalent). Paste pH measurements on the same rocks are bimodal, with high-sulphur, low-calcite rocks yielding pH near 3 after 10 min, whereas high-ANC rocks yield paste pH of 7-8. In our coal mine example, the field tests were most effective when used in conjunction with stratigraphy. However, the same field tests have potential for routine use in any mine in which distinction of acid-generating rocks from acid-neutralising rocks is required. Calibration of field-based acid-base accounting characteristics of the rocks with laboratory-based static and/or kinetic tests is still necessary.

Research on preventive technologies for bed-separation water hazard in China coal mines

NASA Astrophysics Data System (ADS)

Gui, Herong; Tong, Shijie; Qiu, Weizhong; Lin, Manli

2018-03-01

Bed-separation water is one of the major water hazards in coal mines. Targeted researches on the preventive technologies are of paramount importance to safe mining. This article studied the restrictive effect of geological and mining factors, such as lithological properties of roof strata, coal seam inclination, water source to bed separations, roof management method, dimensions of mining working face, and mining progress, on the formation of bed-separation water hazard. The key techniques to prevent bed-separation water-related accidents include interception, diversion, destructing the buffer layer, grouting and backfilling, etc. The operation and efficiency of each technique are corroborated in field engineering cases. The results of this study will offer reference to countries with similar mining conditions in the researches on bed-separation water burst and hazard control in coal mines.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

A new approach to preserve privacy data mining based on fuzzy theory in numerical database

NASA Astrophysics Data System (ADS)

Cui, Run; Kim, Hyoung Joong

2014-01-01

With the rapid development of information techniques, data mining approaches have become one of the most important tools to discover the in-deep associations of tuples in large-scale database. Hence how to protect the private information is quite a huge challenge, especially during the data mining procedure. In this paper, a new method is proposed for privacy protection which is based on fuzzy theory. The traditional fuzzy approach in this area will apply fuzzification to the data without considering its readability. A new style of obscured data expression is introduced to provide more details of the subsets without reducing the readability. Also we adopt a balance approach between the privacy level and utility when to achieve the suitable subgroups. An experiment is provided to show that this approach is suitable for the classification without a lower accuracy. In the future, this approach can be adapted to the data stream as the low computation complexity of the fuzzy function with a suitable modification.

Data mining: childhood injury control and beyond.

PubMed

Tepas, Joseph J

2009-08-01

Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or data sets. It has become a major part of modern life and is extensively used in industry, banking, government, and health care delivery. The process requires a data collection system that integrates input from multiple sources containing critical elements that define outcomes of interest. Appropriately designed data mining processes identify and adjust for confounding variables. The statistical modeling used to manipulate accumulated data may involve any number of techniques. As predicted results are periodically analyzed against those observed, the model is consistently refined to optimize precision and accuracy. Whether applying integrated sources of clinical data to inferential probabilistic prediction of risk of ventilator-associated pneumonia or population surveillance for signs of bioterrorism, it is essential that modern health care providers have at least a rudimentary understanding of what the concept means, how it basically works, and what it means to current and future health care.

Chemical named entities recognition: a review on approaches and applications.

PubMed

Eltyeb, Safaa; Salim, Naomie

2014-01-01

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

PubMed

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

2016-09-01

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

Improving entrepreneurial opportunity recognition through web content analytics

NASA Astrophysics Data System (ADS)

Bakar, Muhamad Shahbani Abu; Azmi, Azwiyati

2017-10-01

The ability to recognize and develop an opportunity into a venture defines an entrepreneur. Research in opportunity recognition has been robust and focuses more on explaining the processes involved in opportunity recognition. Factors such as prior knowledge, cognitive and creative capabilities are shown to affect opportunity recognition in entrepreneurs. Prior knowledge in areas such as customer problems, ways to serve the market, and technology has been shows in various studies to be a factor that facilitates entrepreneurs to identify and recognize opportunities. Findings from research also shows that experienced entrepreneurs search and scan for information to discover opportunities. Searching and scanning for information has also been shown to help novice entrepreneurs who lack prior knowledge to narrow this gap and enable them to better identify and recognize opportunities. There is less focus in research on finding empirically proven techniques and methods to develop and enhance opportunity recognition in student entrepreneurs. This is important as the country pushes for more graduate entrepreneurs that can drive the economy. This paper aims to discuss Opportunity Recognition Support System (ORSS), an information support system to help especially student entrepreneurs in identifying and recognizing business opportunities. The ORSS aims to provide the necessary knowledge to student entrepreneurs to be able to better identify and recognize opportunities. Applying design research, theories in opportunity recognition are applied to identify the requirements for the support system and the requirements in turn dictate the design of the support system. The paper proposes the use of web content mining and analytics as two core components and techniques for the support system. Web content mining can mine the vast knowledge repositories available on the internet and analytics can provide entrepreneurs with further insights into the information needed to recognize opportunities in a given market or industry.

Survey of Natural Language Processing Techniques in Bioinformatics.

PubMed

Zeng, Zhiqiang; Shi, Hua; Wu, Yun; Hong, Zhiling

2015-01-01

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Applying data mining methods to the assessment of soil contamination and carbon sequestration under Mediterranean Climate. The case study of Guadiamar basin (SW Spain).

NASA Astrophysics Data System (ADS)

Muñoz Vallés, Sara; Pino-Mejías, Rafael; Blanco-Velázquez, Francisco J.; Anaya-Romero, María

2017-04-01

In the present background of increasing access to vast datasets of soil and environmental records, the application of the newest analytical techniques and approaches for modelling offer excellent opportunities to define recommendations and simulate processes for land degradation and management. In this regard, data mining techniques have been successfully applied in different fields of environmental sciences, performing an innovative tool to explore relevant questions and providing valuable results and useful applications through an efficient management and analysis of large and heterogeneous datasets. Soil Organic matter, pH and trace elements in soil perform close relationships, with ability to alter each other and lead to emerging, synergic properties for soils. In addition, effects associated to climate and land use change promotes mechanisms of feedback that could amplify the negative effects of soil contamination on human health, biodiversity conservation and soil ecosystem services maintenance. The aim of this study was to build and compare several data mining models for the prediction of potential and interrelated functions of soil contamination and carbon sequestration by soils. In this context, under the framework of the EU RECARE project (Preventing and Remediating degradation of Soils in Europe through Land Care), the Guadiamar valley (SW Spain) is used as case study. The area was affected by around four hm3 of acid waters and two hm3 of mud rich in heavy metals, resulting from a mine spill, in 1998, where more than 4,600 ha of agricultural and pasture land were affected. The area was subjected to a large-scale phyto-management project, and consequently protected as "Green Corridor". In this study, twenty environmental variables were taken into account and several base models for supervised classification problems were selected, including linear and quadratic discriminant analysis, logistic regression, neural networks and support vector machines. A database with a size of about 30 Mb of alfa-numeric environmental data from the Guadiamar basin was randomly split into three parts, namely training set (50%), validation set (25%), and test set (25%). The techniques were compared from the viewpoint of their accuracy, robustness of results and applicability, and the best models in terms of overall performance were identified. Finally, results were compared with priorities defined in the current regional and national regulations and policies.

43 CFR 3809.2 - What is the scope of this subpart?

Code of Federal Regulations, 2012 CFR

2012-10-01

... inform the public. (b) This subpart does not apply to lands in the National Park System, National Forest... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS UNDER THE GENERAL MINING LAWS... applies to all operations authorized by the mining laws on public lands where the mineral interest is...

43 CFR 3809.2 - What is the scope of this subpart?

Code of Federal Regulations, 2013 CFR

2013-10-01

... inform the public. (b) This subpart does not apply to lands in the National Park System, National Forest... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS UNDER THE GENERAL MINING LAWS... applies to all operations authorized by the mining laws on public lands where the mineral interest is...

43 CFR 3809.2 - What is the scope of this subpart?

Code of Federal Regulations, 2014 CFR

2014-10-01

... inform the public. (b) This subpart does not apply to lands in the National Park System, National Forest... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS UNDER THE GENERAL MINING LAWS... applies to all operations authorized by the mining laws on public lands where the mineral interest is...

43 CFR 3809.2 - What is the scope of this subpart?

Code of Federal Regulations, 2011 CFR

2011-10-01

... inform the public. (b) This subpart does not apply to lands in the National Park System, National Forest... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS UNDER THE GENERAL MINING LAWS... applies to all operations authorized by the mining laws on public lands where the mineral interest is...

Synthesized af-PFCl and GG-g-P(AN)/TEOS hydrogel composite used in hybridized technique applied for AMD treatment

NASA Astrophysics Data System (ADS)

Fosso-Kankeu, Elvis

2018-06-01

In the present study af-PFCl, GL-g-P(AN) hydrogel and GL-g-P(AN)/TEOS hydrogel composite were synthesized. The hydrogels were characterized using the fourier transformed infra-red (FTIR) and the scanning electron microscope (SEM) techniques. The coagulant af-PFCl and the hydrogels were applied consecutively in flocculation and adsorption processes respectively for the treatment of acid mine drainage (AMD). It was observed that the grafting process increased the amount of binding groups on the hydrogels. The hybridization of the techniques assisted in the removal of anions; while the cations were mostly removed by the adsorption process. The adsorbents behaviour was fittingly expressed by the pseudo-second order model. The adsorption capacities of GL-g-P(AN)/TEOS hydrogel composite for the removal of Al, As and Zn were 3.89, 0.66 and 0.394 respectively; while the adsorption capacities of GL-g-P(AN) for the removal of Al and Mg were 3.47 and 9.66 mg/g respectively. The techniques applied in this study have shown good potential for the removal of specific pollutants from the AMD; it is however, important that the appropriate hybridization of techniques allows to remove all the pollutants and restore acceptable water quality.

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

Witt, R. G.; Bly, B. G.; Campbell, W. J.; Bloemer, H. H. L.; Brumfield, J. O.

1982-01-01

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chironis, N.P.

This book contains a wealth of valuable information carefully selected and compiled from recent issues of Coal Age magazine. Much of the source material has been gathered by Coal Age Editors during their visits to coal mines, research establishments, universities and technical symposiums. Equally important are the articles and data contributed by over 50 top experts, many of whom are well known to the mining industry. Specifically, this easy-to-use handbook is divided into eleven key areas of underground mining. Here you will find the latest information on continuous mining techniques, longwall and shortwall methods and equipment, specialized mining and boringmore » systems, continuous haulage techniques, improved roof control and ventilation methods, mine communications and instrumentation, power systems, fire control methods, and new mining regulations. There is also a section on engineering and management considerations, including the modern use of computer terminals, practical techniques for picking leaders and for encouraging more safety consciousness in employees, factors affecting absenteeism, and some highly important financial considerations. All of this valuable information has been thoroughly indexed to provide immediate access to the specific data needed by the reader.« less

Wideband radar for airborne minefield detection

NASA Astrophysics Data System (ADS)

Clark, William W.; Burns, Brian; Dorff, Gary; Plasky, Brian; Moussally, George; Soumekh, Mehrdad

2006-05-01

Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both antipersonnel and anti-tank landmines. RDECOM CERDEC NVESD is developing an airborne wideband GPR sensor for the detection of minefields including surface and buried mines. In this paper, we describe the as-built system, data and image processing techniques to generate imagery, and current issues with this type of radar. Further, we will display images from a recent field test.

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

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

2000-01-01

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

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

Betancourt, J. L.; Biondi, F.; Bradford, J. B.; Foster, J. R.; Betancourt, J. L.; Foster, J. R.; Biondi, F.; Bradford, J. B.; Henebry, G. M.; Post, E.; Koenig, W.; Hoffman, F. M.; de Beurs, K.; Hoffman, F. M.; Kumar, J.; Hargrove, W. W.; Norman, S. P.; Brooks, B. G.

2016-12-01

Vegetated ecosystems exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and weather disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) every eight days at 250 m resolution for the period 2000-2015 to develop phenological signatures of emergent ecological regimes called phenoregions. We employed a "Big Data" classification approach on a supercomputer, specifically applying an unsupervised data mining technique, to this large collection of NDVI measurements to develop annual maps of phenoregions. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency of occurrence. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. We will present the phenoregions methodology and resulting maps for the CONUS, describe the "label-stealing" technique for ascribing biome characteristics to phenoregions, and introduce a new polar plotting scheme for processing NDVI data by localized seasonality.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Geotechnical characterization of mined clay from Appalachian Ohio: challenges and implications for the clay mining industry.

PubMed

Moran, Anthony R; Hettiarachchi, Hiroshan

2011-07-01

Clayey soil found in coal mines in Appalachian Ohio is often sold to landfills for constructing Recompacted Soil Liners (RSL) in landfills. Since clayey soils possess low hydraulic conductivity, the suitability of mined clay for RSL in Ohio is first assessed by determining its clay content. When soil samples are tested in a laboratory, the same engineering properties are typically expected for the soils originated from the same source, provided that the testing techniques applied are standard, but mined clay from Appalachian Ohio has shown drastic differences in particle size distribution depending on the sampling and/or laboratory processing methods. Sometimes more than a 10 percent decrease in the clay content is observed in the samples collected at the stockpiles, compared to those collected through reverse circulation drilling. This discrepancy poses a challenge to geotechnical engineers who work on the prequalification process of RSL material as it can result in misleading estimates of the hydraulic conductivity of the samples. This paper describes a laboratory investigation conducted on mined clay from Appalachian Ohio to determine how and why the standard sampling and/or processing methods can affect the grain-size distributions. The variation in the clay content was determined to be due to heavy concentrations of shale fragments in the clayey soils. It was also concluded that, in order to obtain reliable grain size distributions from the samples collected at a stockpile of mined clay, the material needs to be processed using a soil grinder. Otherwise, the samples should be collected through drilling.

The LANL/LLNL/AFTAC Black Thunder Coal Mine regional mine monitoring experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearson, D.C.; Stump, B.W.; Baker, D.F.

Cast blasting operations associated with near surface coal recovery provide relatively large explosive sources that generate regional seismograms of interest in monitoring a Comprehensive Test Ban Treaty (CTBT). This paper describes preliminary results of a series of experiments currently being conducted at the Black Thunder Coal Mine in northeast Wyoming as part of the DOE CTBT Research and Development Program. These experiments are intended to provide an integrated set of near-source and regional seismic data for the purposes of quantifying the coupling and source characterization of the explosions. The focus of this paper is on the types of data beingmore » recovered with some preliminary implications. The Black Thunder experiments are designed to assess three major questions: (1) how many mining explosions produce seismograms at regional distances that will have to be detected, located and ultimately identified by the National Data Center and what are the waveform characteristics of these particular mining explosions; (2) can discrimination techniques based on empirical studies be placed on a firm physical basis so that they can be applied to other regions where there is little monitoring experience; (3) can large scale chemical explosions (possibly mining explosions) be used to calibrate source and propagation path effects to regional stations, can source depth of burial and decoupling effects be studied in such a controlled environment? With these key questions in mind and given the cooperation of the Black Thunder Mine, a suite of experiments have been and are currently being conducted. This paper will describe the experiments and their relevance to CTBT issues.« less

Geotechnical Characterization of Mined Clay from Appalachian Ohio: Challenges and Implications for the Clay Mining Industry

PubMed Central

Moran, Anthony R.; Hettiarachchi, Hiroshan

2011-01-01

Clayey soil found in coal mines in Appalachian Ohio is often sold to landfills for constructing Recompacted Soil Liners (RSL) in landfills. Since clayey soils possess low hydraulic conductivity, the suitability of mined clay for RSL in Ohio is first assessed by determining its clay content. When soil samples are tested in a laboratory, the same engineering properties are typically expected for the soils originated from the same source, provided that the testing techniques applied are standard, but mined clay from Appalachian Ohio has shown drastic differences in particle size distribution depending on the sampling and/or laboratory processing methods. Sometimes more than a 10 percent decrease in the clay content is observed in the samples collected at the stockpiles, compared to those collected through reverse circulation drilling. This discrepancy poses a challenge to geotechnical engineers who work on the prequalification process of RSL material as it can result in misleading estimates of the hydraulic conductivity of the samples. This paper describes a laboratory investigation conducted on mined clay from Appalachian Ohio to determine how and why the standard sampling and/or processing methods can affect the grain-size distributions. The variation in the clay content was determined to be due to heavy concentrations of shale fragments in the clayey soils. It was also concluded that, in order to obtain reliable grain size distributions from the samples collected at a stockpile of mined clay, the material needs to be processed using a soil grinder. Otherwise, the samples should be collected through drilling. PMID:21845150

LLNL electro-optical mine detection program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, C.; Aimonetti, W.; Barth, M.

1994-09-30

Under funding from the Advanced Research Projects Agency (ARPA) and the US Marine Corps (USMC), Lawrence Livermore National Laboratory (LLNL) has directed a program aimed at improving detection capabilities against buried mines and munitions. The program has provided a national test facility for buried mines in arid environments, compiled and distributed an extensive data base of infrared (IR), ground penetrating radar (GPR), and other measurements made at that site, served as a host for other organizations wishing to make measurements, made considerable progress in the use of ground penetrating radar for mine detection, and worked on the difficult problem ofmore » sensor fusion as applied to buried mine detection. While the majority of our effort has been concentrated on the buried mine problem, LLNL has worked with the U.S.M.C. on surface mine problems as well, providing data and analysis to support the COBRA (Coastal Battlefield Reconnaissance and Analysis) program. The original aim of the experimental aspect of the program was the utilization of multiband infrared approaches for the detection of buried mines. Later the work was extended to a multisensor investigation, including sensors other than infrared imagers. After an early series of measurements, it was determined that further progress would require a larger test facility in a natural environment, so the Buried Object Test Facility (BOTF) was constructed at the Nevada Test Site. After extensive testing, with sensors spanning the electromagnetic spectrum from the near ultraviolet to radio frequencies, possible paths for improvement were: improved spatial resolution providing better ground texture discrimination; analysis which involves more complicated spatial queueing and filtering; additional IR bands using imaging spectroscopy; the use of additional sensors other than IR and the use of data fusion techniques with multi-sensor data; and utilizing time dependent observables like temperature.« less

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors.

PubMed

Huang, Hongtai; Wang, Aolin; Morello-Frosch, Rachel; Lam, Juleen; Sirota, Marina; Padula, Amy; Woodruff, Tracey J

2018-03-01

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Using natural language processing techniques to inform research on nanotechnology.

PubMed

Lewinski, Nastassja A; McInnes, Bridget T

2015-01-01

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

Object-based image analysis for the assessment of mineral extraction in conflict regions - a case study in the Democratic Republic of the Congo

NASA Astrophysics Data System (ADS)

Kranz, Olaf; Schoepfer, Elisabeth; Spröhnle, Kristin; Lang, Stefan

2016-06-01

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

Application of granular ferric hydroxides for removal elevated concentrations of arsenic from mine waters

NASA Astrophysics Data System (ADS)

Szlachta, Małgorzata; Włodarczyk, Paweł; Wójtowicz, Patryk

2015-04-01

Arsenic is naturally occurring element in the environment. Over three hundred minerals are known to contain some form of arsenic and among them arsenopyrite is the most common one. Arsenic-bearing minerals are frequently associated with ores containing mined metals such as copper, tin, nickel, lead, uranium, zinc, cobalt, platinum and gold. In the aquatic environment arsenic is typically present in inorganic forms, mainly in two oxidation states (+5, +3). As(III) is dominant in more reduced conditions, whereas As(V) is mostly present in an oxidizing environment. However, due to certain human activities the elevated arsenic levels in aquatic ecosystems are arising to a serious environmental problem. High arsenic concentrations found in surface and groundwaters, in some regions originate from mining activities and ore processing. Therefore, the major concern of mining industry is to maintain a good quality of effluents discharged in large volumes. This requires constant monitoring of effluents quality that guarantee the efficient protection of the receiving waters and reacting to possible negative impact of contamination on local communities. A number of proven technologies are available for arsenic removal from waters and wastewaters. In the presented work special attention is given to the adsorption method as a technically feasible, commonly applied and effective technique for the treatment of arsenic rich mine effluents. It is know that arsenic has a strong affinity towards iron rich materials. Thus, in this study the granular ferric hydroxides (CFH 12, provided by Kemira Oyj, Finland) was applied to remove As(III) and As(V) from aqueous solutions. The batch adsorption experiments were carried out to assess the efficiency of the tested Fe-based material under various operating parameters, including composition of treated water, solution pH and temperature. The results obtained from the fixed bed adsorption tests demonstrated the benefits of applying granular ferric hydroxides for treatment As-contaminated waters. This research is a part of the study supported by the National Centre for Research and Development grant (2014-2017) "Sustainable and responsible supply of primary resources - SUSMIN" (http://projects.gtk.fi/susmin), within the EU ERA-NET ERA-MIN program.

A case-based reasoning tool for breast cancer knowledge management with data mining concepts and techniques

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems (HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don't allow managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of mining information from a data set and transform it into an understandable structure for further use. Combining CBR to Data Mining techniques will facilitate diagnosis and decision-making of medical experts.

30 CFR 57.22003 - Mine category or subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Standards for Methane in Metal and Nonmetal Mines Mine Categorization § 57.22003 Mine category or... methane and dusts containing volatile matter. Categories and subcategories are defined as follows: (1) Category I applies to mines that operate within a combustible ore body and either liberate methane or have...

The concept of crosstalk-directed embryological target mining and its application to essential hypertension treatment failures.

PubMed

Sag, Alan Alper; Sal, Oguzhan; Kilic, Yagmur; Onal, Emine Meltem; Kanbay, Mehmet

2017-05-01

This review aims to introduce the novel concept of embryological target mining applied to interorgan crosstalk network genesis, and applies embryological target mining to multidrug-resistant essential hypertension (a prototype, complex, undertreated, multiorgan systemic syndrome) to uncover new treatment targets and critique why existing strategies fail. Briefly, interorgan crosstalk pathways represent the next frontier for target mining in molecular medicine. This is because stereotyped stepwise organogenesis presents a unique opportunity to infer interorgan crosstalk pathways that may be crucial to discovering novel treatment targets. Insights gained from this review will be applied to patient management in a clinician-directed fashion. ©2017 Wiley Periodicals, Inc.

Statistical Inference for Big Data Problems in Molecular Biophysics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Savol, Andrej; Burger, Virginia

2012-01-01

We highlight the role of statistical inference techniques in providing biological insights from analyzing long time-scale molecular simulation data. Technologi- cal and algorithmic improvements in computation have brought molecular simu- lations to the forefront of techniques applied to investigating the basis of living systems. While these longer simulations, increasingly complex reaching petabyte scales presently, promise a detailed view into microscopic behavior, teasing out the important information has now become a true challenge on its own. Mining this data for important patterns is critical to automating therapeutic intervention discovery, improving protein design, and fundamentally understanding the mech- anistic basis of cellularmore » homeostasis.« less

A proposed configurable approach for recommendation systems via data mining techniques

NASA Astrophysics Data System (ADS)

Khedr, Ayman E.; Idrees, Amira M.; Hegazy, Abd El-Fatah; El-Shewy, Samir

2018-02-01

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

Post-mining deterioration of bauxite overburdens in Jamaica: storage methods or subsoil dilution?

NASA Astrophysics Data System (ADS)

Harris, Mark A.; Omoregie, Samson N.

2008-03-01

Rapid degradation of disturbed soil from a karst bauxite mine in Jamaica was recorded. Substantial macronutrient losses were incurred during a short (1 month) or a long (12 months) storage of the replaced topsoils during frequent wet/dry changes. The results suggested very high rates (>70% in the first year) of soil degradation from storage, alongside moderate rates (30%) within the same storage dump. However, higher levels of soil organic matter (SOM) were indicated just below the surface, compared with the surface horizons. It was unlikely that under a high leaching humid tropical rainfall regime, natural degradation processes could have re-emplaced such material firmly intact in the 15-30 cm zone. It was therefore concluded that these SOM anomalies were due to mechanical dilution of surface soil with subsoil material during overburden removal and emplacement rather than from long storage. Increasing the soil organic content during storage could be one corrective approach. However, it is far less costly to exercise greater care to apply more precise overburden removal and emplacement techniques initially, than it is to correct the results of topsoil contamination with subsoil. Although this study was limited to one mine, in the context of imminent large-scale mining expansion and current practices, further investigations are needed to accurately ascertain the proportion of similar subsoil contamination in other bauxite-mined sites.

Data Mining: Going beyond Traditional Statistics

ERIC Educational Resources Information Center

Zhao, Chun-Mei; Luan, Jing

2006-01-01

The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)

Pacific Research Platform - Creation of a West Coast Big Data Freeway System Applied to the CONNected objECT (CONNECT) Data Mining Framework for Earth Science Knowledge Discovery

NASA Astrophysics Data System (ADS)

Sellars, S. L.; Nguyen, P.; Tatar, J.; Graham, J.; Kawsenuk, B.; DeFanti, T.; Smarr, L.; Sorooshian, S.; Ralph, M.

2017-12-01

A new era in computational earth sciences is within our grasps with the availability of ever-increasing earth observational data, enhanced computational capabilities, and innovative computation approaches that allow for the assimilation, analysis and ability to model the complex earth science phenomena. The Pacific Research Platform (PRP), CENIC and associated technologies such as the Flash I/O Network Appliance (FIONA) provide scientists a unique capability for advancing towards this new era. This presentation reports on the development of multi-institutional rapid data access capabilities and data pipeline for applying a novel image characterization and segmentation approach, CONNected objECT (CONNECT) algorithm to study Atmospheric River (AR) events impacting the Western United States. ARs are often associated with torrential rains, swollen rivers, flash flooding, and mudslides. CONNECT is computationally intensive, reliant on very large data transfers, storage and data mining techniques. The ability to apply the method to multiple variables and datasets located at different University of California campuses has previously been challenged by inadequate network bandwidth and computational constraints. The presentation will highlight how the inter-campus CONNECT data mining framework improved from our prior download speeds of 10MB/s to 500MB/s using the PRP and the FIONAs. We present a worked example using the NASA MERRA data to describe how the PRP and FIONA have provided researchers with the capability for advancing knowledge about ARs. Finally, we will discuss future efforts to expand the scope to additional variables in earth sciences.

Mine Water Treatment in Hongai Coal Mines

NASA Astrophysics Data System (ADS)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

Discrimination between landmine and mine-like targets using wavelets and spectral analysis

NASA Astrophysics Data System (ADS)

Mohana, Mahmoud A.; Abbas, Abbas M.; Gomaa, Mohamed L.; Ebrahim, Shereen M.

2013-06-01

Landmine is an explosive apparatus hidden in or on the ground, which blows up when a person or vehicle passes over it. Egypt is one of the countries suffering due to the unexploded ordnance (UXO). Around 2 million UXO are present in the Egyptian soil especially at Al-Alameen province, north of the western desert. Detection of buried landmines is a problem of military and humanitarian importance. Ground penetrating radar (GPR) is a powerful and non-destructive geophysical approach with a wide range of advantages in the field of landmine inspection. In the present paper, we apply different simulation models with Vivaldi antenna and mine-like targets by using the CST Microwave studio program. The field work is carried out by using a GPR device of model SIR 2000 from GSSI (Geophysical Survey Systems Incorporation) connected to 900 MHz antenna where the targets were buried in sand soil. Depending on the fact that the receiving powers (reflected, refracted and scattered) from the different materials are different, we study the spectral power densities for the received power from the different targets. The techniques used in this study are: direct fast Fourier transform, short time Fourier transform (spectrogram), wavelets transform and denoising techniques. Our results ought to be considered as finger prints for different scanned targets during this work. So we can discriminate between landmines and mine-like targets.

Stability Analysis of Railway Subgrade in Mining Area Based on Dinsar

NASA Astrophysics Data System (ADS)

Xu, J.; Hu, J.; Ding, J.

2018-04-01

DInSAR technology have been applied to monitor the mining subsidence and the stability of the railway subgrade. A total of 10 Sentinel-1A images acquired from 2015/9/26 to 2016/2/23 were used in DInSAR analysis. The study mining area is about 13.4 km2. Mining have induced serious land subsidence involve a large area that causing different levels of damages to infrastructures on the land. There is an important railway near the mining area, the DInSAR technology is applied to analyse the subsidence near the railway, which can warn early the possible deformation that may occur during underground mining. The DInSAR results was verified by the field measurement. The results show that the mining did not cause subsidence of railway subgrade and did not affect the stability of railway subgrade.

Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana

PubMed Central

Flamand, Claude; Fabregue, Mickael; Bringay, Sandra; Ardillon, Vanessa; Quénel, Philippe; Desenclos, Jean-Claude; Teisseire, Maguelonne

2014-01-01

Objective To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread. Methods We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana. Results The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4–6-week lag. Discussion We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns. Conclusions Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission. PMID:24549761

The application of knowledge discovery in databases to post-marketing drug safety: example of the WHO database.

PubMed

Bate, A; Lindquist, M; Edwards, I R

2008-04-01

After market launch, new information on adverse effects of medicinal products is almost exclusively first highlighted by spontaneous reporting. As data sets of spontaneous reports have become larger, and computational capability has increased, quantitative methods have been increasingly applied to such data sets. The screening of such data sets is an application of knowledge discovery in databases (KDD). Effective KDD is an iterative and interactive process made up of the following steps: developing an understanding of an application domain, creating a target data set, data cleaning and pre-processing, data reduction and projection, choosing the data mining task, choosing the data mining algorithm, data mining, interpretation of results and consolidating and using acquired knowledge. The process of KDD as it applies to the analysis of spontaneous reports can be exemplified by its routine use on the 3.5 million suspected adverse drug reaction (ADR) reports in the WHO ADR database. Examples of new adverse effects first highlighted by the KDD process on WHO data include topiramate glaucoma, infliximab vasculitis and the association of selective serotonin reuptake inhibitors (SSRIs) and neonatal convulsions. The KDD process has already improved our ability to highlight previously unsuspected ADRs for clinical review in spontaneous reporting, and we anticipate that such techniques will be increasingly used in the successful screening of other healthcare data sets such as patient records in the future.

In-situ identification of anti-personnel mines using acoustic resonant spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perry, R L; Roberts, R S

1999-02-01

A new technique for identifying buried Anti-Personnel Mines is described, and a set of preliminary experiments designed to assess the feasibility of this technique is presented. Analysis of the experimental results indicates that the technique has potential, but additional work is required to bring the technique to fruition. In addition to the experimental results presented here, a technique used to characterize the sensor employed in the experiments is detailed.

Responses of Terrestrial Herpetofauna to Persistent, Novel Ecosystems Resulting from Mountaintop Removal Mining

Treesearch

Jennifer M. Williams; Donald J. Brown; Petra B. Wood

2017-01-01

Mountaintop removal mining is a large-scale surface mining technique that removes entire floral and faunal communities, along with soil horizons located above coal seams. In West Virginia, the majority of this mining occurs on forested mountaintops. However, after mining ceases the land is typically reclaimed to grasslands and shrublands, resulting in novel ecosystems...

Using Open Web APIs in Teaching Web Mining

ERIC Educational Resources Information Center

Chen, Hsinchun; Li, Xin; Chau, M.; Ho, Yi-Jen; Tseng, Chunju

2009-01-01

With the advent of the World Wide Web, many business applications that utilize data mining and text mining techniques to extract useful business information on the Web have evolved from Web searching to Web mining. It is important for students to acquire knowledge and hands-on experience in Web mining during their education in information systems…

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh; Elisevich, Kost; Fotouhi, Farshad

2015-01-01

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases. PMID:26609547

Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery.

PubMed

Li, Jing; Zipper, Carl E; Donovan, Patricia F; Wynne, Randolph H; Oliphant, Adam J

2015-09-01

Surface mining disturbances have attracted attention globally due to extensive influence on topography, land use, ecosystems, and human populations in mineral-rich regions. We analyzed a time series of Landsat satellite imagery to produce a 28-year disturbance history for surface coal mining in a segment of eastern USA's central Appalachian coalfield, southwestern Virginia. The method was developed and applied as a three-step sequence: vegetation index selection, persistent vegetation identification, and mined-land delineation by year of disturbance. The overall classification accuracy and kappa coefficient were 0.9350 and 0.9252, respectively. Most surface coal mines were identified correctly by location and by time of initial disturbance. More than 8 % of southwestern Virginia's >4000-km(2) coalfield area was disturbed by surface coal mining over the 28-year period. Approximately 19.5 % of the Appalachian coalfield surface within the most intensively mined county (Wise County) has been disturbed by mining. Mining disturbances expanded steadily and progressively over the study period. Information generated can be applied to gain further insight concerning mining influences on ecosystems and other essential environmental features.

Data-Mining Technologies for Diabetes: A Systematic Review

PubMed Central

Marinov, Miroslav; Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Boren, Suzanne Austin

2011-01-01

Background The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients. PMID:22226277

The Potential of Sequential Extraction in the Characterisation and Management of Wastes from Steel Processing: A Prospective Review

PubMed Central

Rodgers, Kiri J.; Hursthouse, Andrew; Cuthbert, Simon

2015-01-01

As waste management regulations become more stringent, yet demand for resources continues to increase, there is a pressing need for innovative management techniques and more sophisticated supporting analysis techniques. Sequential extraction (SE) analysis, a technique previously applied to soils and sediments, offers the potential to gain a better understanding of the composition of solid wastes. SE attempts to classify potentially toxic elements (PTEs) by their associations with phases or fractions in waste, with the aim of improving resource use and reducing negative environmental impacts. In this review we explain how SE can be applied to steel wastes. These present challenges due to differences in sample characteristics compared with materials to which SE has been traditionally applied, specifically chemical composition, particle size and pH buffering capacity, which are critical when identifying a suitable SE method. We highlight the importance of delineating iron-rich phases, and find that the commonly applied BCR (The community Bureau of reference) extraction method is problematic due to difficulties with zinc speciation (a critical steel waste constituent), hence a substantially modified SEP is necessary to deal with particular characteristics of steel wastes. Successful development of SE for steel wastes could have wider implications, e.g., for the sustainable management of fly ash and mining wastes. PMID:26393631

The Potential of Sequential Extraction in the Characterisation and Management of Wastes from Steel Processing: A Prospective Review.

PubMed

Rodgers, Kiri J; Hursthouse, Andrew; Cuthbert, Simon

2015-09-18

As waste management regulations become more stringent, yet demand for resources continues to increase, there is a pressing need for innovative management techniques and more sophisticated supporting analysis techniques. Sequential extraction (SE) analysis, a technique previously applied to soils and sediments, offers the potential to gain a better understanding of the composition of solid wastes. SE attempts to classify potentially toxic elements (PTEs) by their associations with phases or fractions in waste, with the aim of improving resource use and reducing negative environmental impacts. In this review we explain how SE can be applied to steel wastes. These present challenges due to differences in sample characteristics compared with materials to which SE has been traditionally applied, specifically chemical composition, particle size and pH buffering capacity, which are critical when identifying a suitable SE method. We highlight the importance of delineating iron-rich phases, and find that the commonly applied BCR (The community Bureau of reference) extraction method is problematic due to difficulties with zinc speciation (a critical steel waste constituent), hence a substantially modified SEP is necessary to deal with particular characteristics of steel wastes. Successful development of SE for steel wastes could have wider implications, e.g., for the sustainable management of fly ash and mining wastes.

Surface mining

Treesearch

Robert Leopold; Bruce Rowland; Reed Stalder

1979-01-01

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

Characterizing Ground-Water Flow Paths in High-Altitude Fractured Rock Settings Impacted by Mining Activities

NASA Astrophysics Data System (ADS)

Wireman, M.; Williams, D.

2003-12-01

The Rocky Mountains of the western USA have tens of thousands of abandoned, inactive and active precious-metal(gold,silver,copper)mine sites. Most of these sites occur in fractured rock hydrogeologic settings. Mining activities often resulted in mobilization and transport of associated heavy metals (zinc,cadmium,lead) which pose a significant threat to aquatic communities in mountain streams.Transport of heavy metals from mine related sources (waste rock piles,tailings impoudments,underground workings, mine pits)can occur along numerous hydrological pathways including complex fracture controlled ground-water pathways. Since 1991, the United States Environmental Protection Agency, the Colorado Division of Minerals and Geology and the University of Colorado (INSTAAR)have been conducting applied hydrologic research at the Mary Murphy underground mine. The mine is in the Chalk Creek mining district which is located on the southwestern flanks of the Mount Princeton Batholith, a Tertiary age intrusive comprised primarily of quartz monzonite.The Mount Princeton batholith comprises a large portion of the southern part of the Collegiate Range west of Buena Vista in Chaffee County, CO. Chalk Creek and its 14 tributaries drain about 24,900 hectares of the eastern slopes of the Range including the mining district. Within the mining district, ground-water flow is controlled by the distribution, orientation and permeability of discontinuities within the bedrock. Important discontinuities include faults, joints and weathered zones. Local and intermediate flow systems are perturbed by extensive underground excavations associated with mining (adits, shafts, stopes, drifts,, etc.). During the past 12 years numerous hydrological investigations have been completed. The investigations have been focused on developing tools for characterizing ground-water flow and contaminant transport in the vicinity of hard-rock mines in fractured-rock settings. In addition, the results from these investigations have been used to develop a sound conceptual model of ground-water flow and transport of heavy metals from the mine workings to Chalk Creek. Ground-water tracing techniques (using organic, fluorescent dyes) have been successfully used to delineate ground-water flow paths. Surface-water tracing techniques have been used to acquire very accurate stream flow measuements and to identify ground-water inflow zones to streams. Stable (O18/D)and radioactive (tritium,sulphur 35) isotope anlysis of waters flowing into and out of underground workings have proved useful for conducting end member mixing analysis to determine which inflows and outflows are most significant with respect to metals loading. Hydrogeologic mapping, inverse geochemical modeling (using MINTEQAK code)and helium 3 analysis of ground water have also proven to useful tools. These tools, used in combination have provided multiple lines of evidence regarding the nature, timing and magnitude of ground-water inflow into underground mine workings and the distribution and types of hydrologic pathways that transport metals from the underground workings to Chalk Creek. This paper presents the results of some of the more important hydrologic investigations completed at the site and a conceptual model of ground-water flow in fractured rock settings that have been impacted by underground mining activites.

A Comparative Study of Data Mining Techniques on Football Match Prediction

NASA Astrophysics Data System (ADS)

Rosli, Che Mohamad Firdaus Che Mohd; Zainuri Saringat, Mohd; Razali, Nazim; Mustapha, Aida

2018-05-01

Data prediction have become a trend in today’s business or organization. This paper is set to predict match outcomes for association football from the perspective of football club managers and coaches. This paper explored different data mining techniques used for predicting the match outcomes where the target class is win, draw and lose. The main objective of this research is to find the most accurate data mining technique that fits the nature of football data. The techniques tested are Decision Trees, Neural Networks, Bayesian Network, and k-Nearest Neighbors. The results from the comparative experiments showed that Decision Trees produced the highest average prediction accuracy in the domain of football match prediction by 99.56%.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

PubMed

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

PubMed Central

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. PMID:22122057

Spectral methods to detect surface mines

NASA Astrophysics Data System (ADS)

Winter, Edwin M.; Schatten Silvious, Miranda

2008-04-01

Over the past five years, advances have been made in the spectral detection of surface mines under minefield detection programs at the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD). The problem of detecting surface land mines ranges from the relatively simple, the detection of large anti-vehicle mines on bare soil, to the very difficult, the detection of anti-personnel mines in thick vegetation. While spatial and spectral approaches can be applied to the detection of surface mines, spatial-only detection requires many pixels-on-target such that the mine is actually imaged and shape-based features can be exploited. This method is unreliable in vegetated areas because only part of the mine may be exposed, while spectral detection is possible without the mine being resolved. At NVESD, hyperspectral and multi-spectral sensors throughout the reflection and thermal spectral regimes have been applied to the mine detection problem. Data has been collected on mines in forest and desert regions and algorithms have been developed both to detect the mines as anomalies and to detect the mines based on their spectral signature. In addition to the detection of individual mines, algorithms have been developed to exploit the similarities of mines in a minefield to improve their detection probability. In this paper, the types of spectral data collected over the past five years will be summarized along with the advances in algorithm development.

Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

PubMed

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

PubMed Central

Wright, Adam; Ricciardi, Thomas N.; Zwick, Martin

2005-01-01

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses. PMID:16779156

Data Mining and Knowledge Management in Higher Education -Potential Applications.

ERIC Educational Resources Information Center

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

Using natural language processing techniques to inform research on nanotechnology

PubMed Central

Lewinski, Nastassja A

2015-01-01

Summary Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics. PMID:26199848

A novel method for predicting kidney stone type using ensemble learning.

PubMed

Kazemi, Yassaman; Mirroshandel, Seyed Abolghasem

2018-01-01

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bayesian model, different types of Decision Trees, Artificial Neural Networks, and Rule-based classifiers were used in these models. We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm. In addition, a novel technique for combining individual classifiers in ensemble learning was proposed. In this technique, for each individual classifier, a weight is assigned based on our proposed genetic algorithm based method. The generated knowledge was evaluated using a 10-fold cross-validation technique based on standard measures. However, the assessment of each feature for building a predictive model was another significant challenge. The predictive strength of each feature for creating a reproducible outcome was also investigated. Regarding the applied models, parameters such as sex, acid uric condition, calcium level, hypertension, diabetes, nausea and vomiting, flank pain, and urinary tract infection (UTI) were the most vital parameters for predicting the chance of nephrolithiasis. The final ensemble-based model (with an accuracy of 97.1%) was a robust one and could be safely applied to future studies to predict the chances of developing nephrolithiasis. This model provides a novel way to study stone disease by deciphering the complex interaction among different biological variables, thus helping in an early identification and reduction in diagnosis time. Copyright © 2017 Elsevier B.V. All rights reserved.

Risk assessment and restoration possibilities of some abandoned mining ponds in Murcia Region, SE Spain

NASA Astrophysics Data System (ADS)

Faz, Angel; Acosta, Jose A.; Martinez-Martinez, Silvia; Carmona, Dora M.; Zornoza, Raul; Kabas, Sebla; Bech, Jaume

2010-05-01

In Murcia Region, SE Spain, there are 85 tailing ponds due to intensive mining activities that occurred during last century, especially in Sierra Minera de Cartagena-La Union. Although mining activity was abandoned several decades ago, those tailing ponds with high amounts of heavy metals still remain in the area. The ponds, due to their composition and location, may create environmental risks of geochemical pollution, negatively affecting soil, water, and plant, animal, and human populations, as well as infrastructures. The main objective of this research is to evaluate the restoration possibilities of two representative mining ponds in order to minimize the risk for human and ecosystems. To achieve this objective, two tailing ponds generated by mining activities were selected, El Lirio and El Gorguel. These ponds are representative of the rest of existent ponds in Sierra Minera de Cartagena-La Unión, with similar problems and characteristics. Several techniques and studies were applied to the tailing ponds for their characterization, including: geophysics, geotechnics, geochemical, geological, hydrological, and vegetation studies. In addition, effects of particulate size in the distribution of heavy metals will be used to assess the risk of dispersion of these metals in finest particles. Once the ponds were characterized, they were divided in several sectors in order to apply different amendments (pig slurry and marble waste) to reduce the risk of metal mobility and improve soil quality for a future phytostabilization. It is known that organic amendments promote soil development processes, microbial diversity, and finally, soil ecosystem restoration to a state of self-sustainability. By comparing the results before and after applications we will be able to evaluate the effect of the different amendments on soil quality and their effectively on risk reduction. Finally, plant metal-tolerant species are used to restore vegetation in the ponds, thereby decreasing the potential migration of contamination through wind erosion, transport of exposed surface soils and leaching of soil contaminants to groundwater.

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data.

PubMed

Luna, Jose Maria; Padillo, Francisco; Pechenizkiy, Mykola; Ventura, Sebastian

2017-09-27

Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing item-set in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3 · 10#x00B9;⁸ transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.

Evaluation of the effect of indigenous mycogenic silver nanoparticles on soil exo-enzymes in barite mine contaminated soils

NASA Astrophysics Data System (ADS)

Gaddam, Durga Prameela; Devamma, Nagalakshmi; Prasad, Tollamadugu Naga Venkata Krishna Vara

2015-04-01

The biosynthesis of nanoparticles has received increasing attention due to the growing need to develop safe, cost-effective and environmentally friendly technologies for nanoscale materials synthesis. In this report, silver nanoparticles (AgNPs) were synthesized by treating aqueous Ag+ ions with the culture supernatants of indigenous fungal species of Fusarium solani isolated from barite mine contaminated soils. The formation of AgNPs might be an enzyme-mediated extracellular reaction process. The localized surface plasmon resonance of the formed AgNPs was recorded using UV-VIS spectrophotometer and was characterized using the techniques transmission electron microscopy, particle size analyzer, Fourier transform-infrared spectroscopy (FT-IR), particle size (dynamic light scattering) and zeta potential. The synthesized AgNPs were stable, polydispersed with the average size of 80 nm. FT-IR spectra reveals that proteins and carboxylic groups present in the fungal secrets might be responsible for the reduction and stabilization of the silver ions. Applied to the barite mine contaminated soils, concentration of AgNPs and incubation period significantly influences the soil exo-enzymatic activities, viz., urease, phosphatase, dehydrogenase and β-glucosidase. To the best of our knowledge, this is the first report on this kind of work in barite mine contaminated soils.

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 the case study of a coal mining region in SW Poland where it has been applied to study characteristics and map mining induced ground deformations in a city in the last two decades of underground coal extraction and in the first decade after the end of mining. The mining subsidence area and its deformation parameters (tilt and curvature) have been calculated and the latter classified and mapped according to the Polish regulations. In addition possible areas of ground deformation have been indicated based on multivariate spatial data analysis of geological and mining operation characteristics with the geographically weighted regression method.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

PubMed

Khalkhali, Hamid Reza; Lotfnezhad Afshar, Hadi; Esnaashari, Omid; Jabbari, Nasrollah

2016-01-01

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

Automated quantitative micro-mineralogical characterization for environmental applications

USGS Publications Warehouse

Smith, Kathleen S.; Hoal, K.O.; Walton-Day, Katherine; Stammer, J.G.; Pietersen, K.

2013-01-01

Characterization of ore and waste-rock material using automated quantitative micro-mineralogical techniques (e.g., QEMSCAN® and MLA) has the potential to complement traditional acid-base accounting and humidity cell techniques when predicting acid generation and metal release. These characterization techniques, which most commonly are used for metallurgical, mineral-processing, and geometallurgical applications, can be broadly applied throughout the mine-life cycle to include numerous environmental applications. Critical insights into mineral liberation, mineral associations, particle size, particle texture, and mineralogical residence phase(s) of environmentally important elements can be used to anticipate potential environmental challenges. Resources spent on initial characterization result in lower uncertainties of potential environmental impacts and possible cost savings associated with remediation and closure. Examples illustrate mineralogical and textural characterization of fluvial tailings material from the upper Arkansas River in Colorado.

Focusing on Environmental Biofilms With Variable-Pressure Scanning Electron Microscopy

NASA Astrophysics Data System (ADS)

Joubert, L.; Wolfaardt, G. M.; Du Plessis, K.

2006-12-01

Since the term biofilm has been coined almost 30 years ago, visualization has formed an integral part of investigations on microbial attachment. Electron microscopic (EM) biofilm studies, however, have been limited by the hydrated extracellular matrix which loses structural integrity with conventional preparative techniques, and under required high-vacuum conditions, resulting in a loss of information on spatial relationships and distribution of biofilm microbes. Recent advances in EM technology enable the application of Variable Pressure Scanning Electron Microscopy (VP SEM) to biofilms, allowing low vacuum and hydrated chamber atmosphere during visualization. Environmental biofilm samples can be viewed in situ, unfixed and fully hydrated, with application of gold-sputter-coating only, to increase image resolution. As the impact of microbial biofilms can be both hazardous and beneficial to man and his environment, recognition of biofilms as a natural form of microbial existence is needed to fully assess the potential role of microbial communities on technology. The integration of multiple techniques to elucidate biofilm processes has become imperative for unraveling complex phenotypic adaptations of this microbial lifestyle. We applied VP SEM as integrative technique with traditional and novel analytical techniques to (1)localize lignocellulosic microbial consortia applied for producing alternative bio-energy sources in the mining wastewater industry, (2) characterize and visualize wetland microbial communities in the treatment of winery wastewater, and (3)determine the impact of recombinant technology on yeast biofilm behavior. Visualization of microbial attachment to a lignocellulose substrate, and degradation of exposed plant tissue, gave insight into fiber degradation and volatile fatty acid production for biological sulphate removal from mining wastewater. Also, the 3D-architecture of complex biofilms developing in constructed wetlands was correlated with molecular fingerprints of wetland communities using tRFLP (Terminal Restriction Fragment Length Polymorphism) - and gave evidence of temporal and spatial variation in a wetland system, to potentially be applied as management tool in wastewater treatment. Visualization of differences in biofilm development by wild and recombinant yeast strains furthermore supported real-time quantitative data of biofilm development by Cryptococcus laurentii and Saccharomyces yeast strains. In all cases VP SEM allowed a more holistic interpretation of biofilm processes than afforded by quantitative empirical data only.

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Indirect estimation of emission factors for phosphate surface mining using air dispersion modeling.

PubMed

Tartakovsky, Dmitry; Stern, Eli; Broday, David M

2016-06-15

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance. Copyright © 2016 Elsevier B.V. All rights reserved.

30 CFR 785.12 - Special bituminous surface coal mining and reclamation operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 3 2013-07-01 2013-07-01 false Special bituminous surface coal mining and... ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL....12 Special bituminous surface coal mining and reclamation operations. (a) This section applies to any...

30 CFR 785.12 - Special bituminous surface coal mining and reclamation operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Special bituminous surface coal mining and... ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL....12 Special bituminous surface coal mining and reclamation operations. (a) This section applies to any...

30 CFR 785.11 - Anthracite surface coal mining and reclamation operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Anthracite surface coal mining and reclamation..., DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION... Anthracite surface coal mining and reclamation operations. (a) This section applies to any person who...

30 CFR 785.12 - Special bituminous surface coal mining and reclamation operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 3 2014-07-01 2014-07-01 false Special bituminous surface coal mining and... ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL....12 Special bituminous surface coal mining and reclamation operations. (a) This section applies to any...

30 CFR 785.11 - Anthracite surface coal mining and reclamation operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 3 2011-07-01 2011-07-01 false Anthracite surface coal mining and reclamation..., DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION... Anthracite surface coal mining and reclamation operations. (a) This section applies to any person who...

30 CFR 785.11 - Anthracite surface coal mining and reclamation operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 3 2014-07-01 2014-07-01 false Anthracite surface coal mining and reclamation..., DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION... Anthracite surface coal mining and reclamation operations. (a) This section applies to any person who...

30 CFR 785.11 - Anthracite surface coal mining and reclamation operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 3 2013-07-01 2013-07-01 false Anthracite surface coal mining and reclamation..., DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION... Anthracite surface coal mining and reclamation operations. (a) This section applies to any person who...

30 CFR 785.11 - Anthracite surface coal mining and reclamation operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 3 2012-07-01 2012-07-01 false Anthracite surface coal mining and reclamation..., DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION... Anthracite surface coal mining and reclamation operations. (a) This section applies to any person who...

A Dictionary of Mining, Mineral and Related Terms.

ERIC Educational Resources Information Center

Thrush, Paul W., Comp.

This dictionary contains about 55,000 terms with approximately 150,000 definitions. These terms are of both a technical and local nature and apply to metal mining, coal mining, quarrying, geology, metallurgy, ceramics and clays, glassmaking, minerals and mineralogy, and general terminology. Petroleum, natural gas, and legal mining terminology,…

36 CFR 292.47 - Mining activities.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 2 2010-07-01 2010-07-01 false Mining activities. 292.47... RECREATION AREAS Hells Canyon National Recreation Area-Federal Lands § 292.47 Mining activities. (a) Other Lands. The standards and guidelines of this section apply to mining activities in the Other Lands...

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Deriving preference order of post-mining land-uses through MLSA framework: application of an outranking technique

NASA Astrophysics Data System (ADS)

Soltanmohammadi, Hossein; Osanloo, Morteza; Aghajani Bazzazi, Abbas

2009-08-01

This study intends to take advantage of a previously developed framework for mined land suitability analysis (MLSA) consisted of economical, social, technical and mine site factors to achieve a partial and also a complete pre-order of feasible post-mining land-uses. Analysis by an outranking multi-attribute decision-making (MADM) technique, called PROMETHEE (preference ranking organization method for enrichment evaluation), was taken into consideration because of its clear advantages on the field of MLSA as compared with MADM ranking techniques. Application of the proposed approach on a mined land can be completed through some successive steps. First, performance of the MLSA attributes is scored locally by each individual decision maker (DM). Then the assigned performance scores are normalized and the deviation amplitudes of non-dominated alternatives are calculated. Weights of the attributes are calculated by another MADM technique namely, analytical hierarchy process (AHP) in a separate procedure. Using the Gaussian preference function beside the weights, the preference indexes of the land-use alternatives are obtained. Calculation of the outgoing and entering flows of the alternatives and one by one comparison of these values will lead to partial pre-order of them and calculation of the net flows, will lead to a ranked preference for each land-use. At the final step, utilizing the PROMETHEE group decision support system which incorporates judgments of all the DMs, a consensual ranking can be derived. In this paper, preference order of post-mining land-uses for a hypothetical mined land has been derived according to judgments of one DM to reveal applicability of the proposed approach.

Application of remote-sensing techniques to hydrologic studies in selected coal-mine areas of southeastern Kansas

USGS Publications Warehouse

Kenny, J.F.; McCauley, J.R.

1983-01-01

Disturbances resulting from intensive coal mining in the Cherry Creek basin of southeastern Kansas were investigated using color and color-infrared aerial photography in conjunction with water-quality data from simultaneously acquired samples. Imagery was used to identify the type and extent of vegetative cover on strip-mined lands and the extent and success of reclamation practices. Drainage patterns, point sources of acid mine drainage, and recharge areas for underground mines were located for onsite inspection. Comparison of these interpretations with water-quality data illustrated differences between the eastern and western parts of the Cherry Creek basin. Contamination in the eastern part is due largely to circulation of water from unreclaimed strip mines and collapse features through the network of underground mines and subsequent discharge of acidic drainage through seeps. Contamination in the western part is primarily caused by runoff and seepage from strip-mined lands in which surfaces have frequently been graded and limed but are generally devoid of mature stands of soil-anchoring vegetation. The successful use of aerial photography in the study of Cherry Creek basin indicates the potential of using remote-sensing techniques in studies of other coal-mined regions. (USGS)

30 CFR 282.28 - Environmental protection measures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... recent research or improved monitoring techniques. (5) When prototype test mining is proposed, the lessee...) The sampling techniques and procedures to be used to acquire the needed data and information; (ii) The... evaluation of the approved Delineation, Testing, or Mining Plan. The Director's review of the air quality...

15 CFR 970.600 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

Effects of organic amendments on water use efficiency evaluated by a stable isotope technique. A case study in experimental mine restoration.

NASA Astrophysics Data System (ADS)

Luna Ramos, Lourdes; Delgado Huertas, Antonio; Miralles Mellado, Isabel; Solé Benet, Albert

2017-04-01

Water deficit and low infiltration reduce restoration success in semiarid post-mine soils, where high mortality of plants has been observed in early years of the restoration. Species that originate from arid and semi-arid regions are often considered appropriate for xeriscaping, but there have been relatively few direct measurements of main water related parameters as water use efficiency (WUE) in restoration strategies. In this respect, the goal of this study was to analyse the efficiency with which native plants use water when organic amendments and mulches are applied in mine soil restorations. The experimental design was established in a calcareous quarry in Almería (SE Spain), under arid climate. We tested two organic amendments (sewage sludge from water treatment plant and compost from vegetable residues) and gravel mulch. Three plant species were planted in 50 m2 experimental plots: Macrochloa tenacissima, Genista umbellata and Anthyllis cytisoides. Soil moisture was monitored at a depth of 0.1 m during 4 years and at the end of this period stable isotope of Carbon (δ13C), considered as an effective method to evaluate the plant intrinsic WUE, was measured. We did not observe significant differences in soil moisture among the different soil restoration treatments. With regard to WUE, species is the factor most important to establish differences. Anthyllis cytisoides showed the lowest mean δ13C values, indicating low WUE. On the contrary, Macrochloa tenacissima presented high δ13C values. Moreover, species showed higher δ13C values when gravel mulch was applied. To increase WUE in restored soils under arid conditions it is necessary to apply water conservation methods and to use the most appropriate species.

A systematic review of data mining and machine learning for air pollution epidemiology.

PubMed

Bellinger, Colin; Mohomed Jabbar, Mohomed Shazan; Zaïane, Osmar; Osornio-Vargas, Alvaro

2017-11-28

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

30 CFR 700.1 - Scope.

Code of Federal Regulations, 2014 CFR

2014-07-01

... the procedures that apply to surface coal mining and reclamation operations conducted on Federal lands...) Subchapter E of this chapter contains regulations that apply to surface coal mining and reclamation... Code of Federal Regulations. (g) Subchapter G governs applications for and decisions on permits for...

30 CFR 700.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... the procedures that apply to surface coal mining and reclamation operations conducted on Federal lands...) Subchapter E of this chapter contains regulations that apply to surface coal mining and reclamation... Code of Federal Regulations. (g) Subchapter G governs applications for and decisions on permits for...

30 CFR 700.1 - Scope.

Code of Federal Regulations, 2013 CFR

2013-07-01

... the procedures that apply to surface coal mining and reclamation operations conducted on Federal lands...) Subchapter E of this chapter contains regulations that apply to surface coal mining and reclamation... Code of Federal Regulations. (g) Subchapter G governs applications for and decisions on permits for...

30 CFR 700.1 - Scope.

Code of Federal Regulations, 2012 CFR

2012-07-01

... the procedures that apply to surface coal mining and reclamation operations conducted on Federal lands...) Subchapter E of this chapter contains regulations that apply to surface coal mining and reclamation... Code of Federal Regulations. (g) Subchapter G governs applications for and decisions on permits for...

30 CFR 700.1 - Scope.

Code of Federal Regulations, 2011 CFR

2011-07-01

... the procedures that apply to surface coal mining and reclamation operations conducted on Federal lands...) Subchapter E of this chapter contains regulations that apply to surface coal mining and reclamation... Code of Federal Regulations. (g) Subchapter G governs applications for and decisions on permits for...

Text mining and its potential applications in systems biology.

PubMed

Ananiadou, Sophia; Kell, Douglas B; Tsujii, Jun-ichi

2006-12-01

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

Application of LANDSAT data to monitor land reclamation progress in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Bloemer, H. H. L.; Brumfield, J. O.; Campbell, W. J.; Witt, R. G.; Bly, B. G.

1981-01-01

Strip and contour mining techniques are reviewed as well as some studies conducted to determine the applicability of LANDSAT and associated digital image processing techniques to the surficial problems associated with mining operations. A nontraditional unsupervised classification approach to multispectral data is considered which renders increased classification separability in land cover analysis of surface mined areas. The approach also reduces the dimensionality of the data and requires only minimal analytical skills in digital data processing.

A Predictive Model of Daily Seismic Activity Induced by Mining, Developed with Data Mining Methods

NASA Astrophysics Data System (ADS)

Jakubowski, Jacek

2014-12-01

The article presents the development and evaluation of a predictive classification model of daily seismic energy emissions induced by longwall mining in sector XVI of the Piast coal mine in Poland. The model uses data on tremor energy, basic characteristics of the longwall face and mined output in this sector over the period from July 1987 to March 2011. The predicted binary variable is the occurrence of a daily sum of tremor seismic energies in a longwall that is greater than or equal to the threshold value of 105 J. Three data mining analytical methods were applied: logistic regression,neural networks, and stochastic gradient boosted trees. The boosted trees model was chosen as the best for the purposes of the prediction. The validation sample results showed its good predictive capability, taking the complex nature of the phenomenon into account. This may indicate the applied model's suitability for a sequential, short-term prediction of mining induced seismic activity.

COMPARISON OF DATA FROM SYNTHETIC LEACHATE AND DIRECT SAMPLING OF ACID DRAINAGE FROM MINE WASTES: IMPLICATIONS FOR MERCURY TRANSPORT AND WASTE MANAGEMENT

EPA Science Inventory

The Sulphur Bank Mercury Mine (SBMM) in Lake County, California operated from the 1860s through the 1950's. Mining for sulfur started with surface operations and progressed to shaft, then open pit techniques to obtain mercury. Mining has resulted in deposition of approximately ...

76 FR 10070 - Division of Coal Mine Workers' Compensation; Proposed Extension of Existing Collection; Comment...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-23

... DEPARTMENT OF LABOR Office of Workers' Compensation Programs Division of Coal Mine Workers... Rereading (CM-933b), Medical History and Examination for Coal Mine Workers' Pneumoconiosis (CM-988), Report... interpretation of x-rays. When a miner applies for benefits, the Division of Coal Mine Workers' Compensation...

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2010 CFR

2010-07-01

... facilities primarily engaged in reproducing text, drawings, plans, maps, or other copy, by blueprinting...)); 212324Kaolin and Ball Clay Mining Limited to facilities operating without a mine or quarry and that are...)); 212393Other Chemical and Fertilizer Mineral Mining Limited to facilities operating without a mine or quarry...

Using Data Mining to Teach Applied Statistics and Correlation

ERIC Educational Resources Information Center

Hartnett, Jessica L.

2016-01-01

This article describes two class activities that introduce the concept of data mining and very basic data mining analyses. Assessment data suggest that students learned some of the conceptual basics of data mining, understood some of the ethical concerns related to the practice, and were able to perform correlations via the Statistical Package for…

43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

Code of Federal Regulations, 2011 CFR

2011-10-01

... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

Code of Federal Regulations, 2012 CFR

2012-10-01

... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

Code of Federal Regulations, 2013 CFR

2013-10-01

... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

Code of Federal Regulations, 2014 CFR

2014-10-01

... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

Data mining for the e-business: developments and directions

NASA Astrophysics Data System (ADS)

Grasso, Alfred; Sleeper, Harry; Thuraisingham, Bhavani M.; Guo, Yike

2000-04-01

This paper describes data mining and e-business and then shows how data mining may be applied to e-business to gather consumer/supplier intelligence so that targeted marketing and merchandising may be carried out.

Comparsion analysis of data mining models applied to clinical research in traditional Chinese medicine.

PubMed

Zhao, Yufeng; Xie, Qi; He, Liyun; Liu, Baoyan; Li, Kun; Zhang, Xiang; Bai, Wenjing; Luo, Lin; Jing, Xianghong; Huo, Ruili

2014-10-01

To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine (TCM) diagnosis and therapy. Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies: symptoms, symptom patterns, herbs, and efficacy. Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes. The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared. By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem.

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

Site investigation report mine research project GUE 70-14.10, Guernsey, Ohio.

DOT National Transportation Integrated Search

2003-06-01

Geophysical investigative techniques can be a valuable supplement to standard subsurface investigations for the : evaluation of abandoned underground coal mine workings and their potential impacts at the ground surface. The GUE : 70 - 14.10 Mine Rese...

Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana.

PubMed

Flamand, Claude; Fabregue, Mickael; Bringay, Sandra; Ardillon, Vanessa; Quénel, Philippe; Desenclos, Jean-Claude; Teisseire, Maguelonne

2014-10-01

To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread. We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana. The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4-6-week lag. We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns. Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Metabolic Analysis

NASA Astrophysics Data System (ADS)

Tolstikov, Vladimir V.

Analysis of the metabolome with coverage of all of the possibly detectable components in the sample, rather than analysis of each individual metabolite at a given time, can be accomplished by metabolic analysis. Targeted and/or nontargeted approaches are applied as needed for particular experiments. Monitoring hundreds or more metabolites at a given time requires high-throughput and high-end techniques that enable screening for relative changes in, rather than absolute concentrations of, compounds within a wide dynamic range. Most of the analytical techniques useful for these purposes use GC or HPLC/UPLC separation modules coupled to a fast and accurate mass spectrometer. GC separations require chemical modification (derivatization) before analysis, and work efficiently for the small molecules. HPLC separations are better suited for the analysis of labile and nonvolatile polar and nonpolar compounds in their native form. Direct infusion and NMR-based techniques are mostly used for fingerprinting and snap phenotyping, where applicable. Discovery and validation of metabolic biomarkers are exciting and promising opportunities offered by metabolic analysis applied to biological and biomedical experiments. We have demonstrated that GC-TOF-MS, HPLC/UPLC-RP-MS and HILIC-LC-MS techniques used for metabolic analysis offer sufficient metabolome mapping providing researchers with confident data for subsequent multivariate analysis and data mining.

Analysis of Hospital Processes with Process Mining Techniques.

PubMed

Orellana García, Arturo; Pérez Alfonso, Damián; Larrea Armenteros, Osvaldo Ulises

2015-01-01

Process mining allows for discovery, monitoring, and improving processes identified in information systems from their event logs. In hospital environments, process analysis has been a crucial factor for cost reduction, control and proper use of resources, better patient care, and achieving service excellence. This paper presents a new component for event logs generation in the Hospital Information System or HIS, developed at University of Informatics Sciences. The event logs obtained are used for analysis of hospital processes with process mining techniques. The proposed solution intends to achieve the generation of event logs in the system with high quality. The performed analyses allowed for redefining functions in the system and proposed proper flow of information. The study exposed the need to incorporate process mining techniques in hospital systems to analyze the processes execution. Moreover, we illustrate its application for making clinical and administrative decisions for the management of hospital activities.

Thermographic inspection of marine composite structures

NASA Astrophysics Data System (ADS)

Jones, Thomas S.; Lindgren, Eric A.

1994-03-01

The marine industry is now facing the problems that were faced by the aircraft industry 20 to 25 years ago: glass-fiber-composite structures do not lend themselves to traditional methods of interrogation. Both the material response and the failure modes of composites are different from traditional materials. Infrared thermographic techniques were investigated for application to composite hull structures and found to be very effective in locating and identifying damage to both solid laminate and sandwich panel construction. The thermographic techniques have been applied to cruising as well as racing yachts with good results. Indicated damage has matched well with the damage discovered during repair operations. More recently, the thermographic techniques have been applied to much thicker solid laminate hull construction used in a new U.S. Navy mine hunter, the MHC-51, U.S.S. Osprey. Thermographic investigations were performed on large test panels used to evaluate different material systems for this vessel and on the vessel itself to provide a baseline thermal characterization. Later this year, shock trials will be performed on the U.S.S. Osprey. Additional thermographic studies are planned following the shock trials.

An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface

NASA Astrophysics Data System (ADS)

Borghgraef, Alexander; Barnich, Olivier; Lapierre, Fabian; Van Droogenbroeck, Marc; Philips, Wilfried; Acheroy, Marc

2010-12-01

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.

Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry.

PubMed

Cheng, Ching-Wu; Leu, Sou-Sen; Cheng, Ying-Mei; Wu, Tsung-Chih; Lin, Chen-Chung

2012-09-01

Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000-2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application of metagenomic techniques in mining enzymes from microbial communities for biofuel synthesis.

PubMed

Xing, Mei-Ning; Zhang, Xue-Zhu; Huang, He

2012-01-01

Feedstock for biofuel synthesis is transitioning to lignocelluosic biomass to address criticism over competition between first generation biofuels and food production. As microbial catalysis is increasingly applied for the conversion of biomass to biofuels, increased import has been placed on the development of novel enzymes. With revolutionary advances in sequencer technology and metagenomic sequencing, mining enzymes from microbial communities for biofuel synthesis is becoming more and more practical. The present article highlights the latest research progress on the special characteristics of metagenomic sequencing, which has been a powerful tool for new enzyme discovery and gene functional analysis in the biomass energy field. Critical enzymes recently developed for the pretreatment and conversion of lignocellulosic materials are evaluated with respect to their activity and stability, with additional explorations into xylanase, laccase, amylase, chitinase, and lipolytic biocatalysts for other biomass feedstocks. Copyright © 2012 Elsevier Inc. All rights reserved.

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

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

2013-12-01

Vegetated ecosystems typically exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and storm disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) at 250 m resolution to develop phenological signatures of emergent ecological regimes called phenoregions. By applying a unsupervised, quantitative data mining technique to NDVI measurements for every eight days over the entire MODIS record, annual maps of phenoregions were developed. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. Utilizing spatial overlays with multiple expert-derived maps, this "label-stealing"' technique exploits the knowledge contained in a collection of maps to identify biome characteristics of our statistically derived phenoregions. Generalized land cover maps were produced by combining phenoregions according to their degree of spatial coincidence with expert-developed land cover or biome regions. Goodness-of-fit maps, which show the strength the spatial correspondence, were also generated.

30 CFR 282.23 - Testing Plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, REGULATION, AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... lessee needs more information to develop a detailed Mining Plan than is obtainable under an approved... techniques or technology or mining equipment, or to determine environmental effects by a pilot test mining...

The Labour Welfare Fund Laws (Amendment) Act, 1987 (No. 15 of 1987), 22 May 1987.

PubMed

1987-01-01

This Act authorizes funds constituted under the Mica Mines Labour Welfare Fund Act, 1946, the Limestone and Dolomite Mines Labour Welfare Fund Act, 1972, the Iron Ore Mines, Manganese Ore Mines and Chrome Mines Labour Welfare Fund Act, 1976, and the Beedi Workers Welfare Fund Act, 1976, to be applied for the provision of family welfare, including family planning education and services. full text

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Martin, Dawn (Elliott); Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Intelligent and integrated techniques for coalbed methane (CBM) recovery and reduction of greenhouse gas emission.

PubMed

Qianting, Hu; Yunpei, Liang; Han, Wang; Quanle, Zou; Haitao, Sun

2017-07-01

Coalbed methane (CBM) recovery is a crucial approach to realize the exploitation of a clean energy and the reduction of the greenhouse gas emission. In the past 10 years, remarkable achievements on CBM recovery have been obtained in China. However, some key difficulties still exist such as long borehole drilling in complicated geological condition, and poor gas drainage effect due to low permeability. In this study, intelligent and integrated techniques for CBM recovery are introduced. These integrated techniques mainly include underground CBM recovery techniques and ground well CBM recovery techniques. The underground CBM recovery techniques consist of the borehole formation technique, gas concentration improvement technique, and permeability enhancement technique. According to the division of mining-induced disturbance area, the ground well arrangement area and well structure type in mining-induced disturbance developing area and mining-induced disturbance stable area are optimized to significantly improve the ground well CBM recovery. Besides, automatic devices such as drilling pipe installation device are also developed to achieve remote control of data recording, which makes the integrated techniques intelligent. These techniques can provide key solutions to some long-term difficulties in CBM recovery.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

2017-06-01

We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

The Evaluation of Land Ecological Safety of Chengchao Iron Mine Based on PSR and MEM

NASA Astrophysics Data System (ADS)

Jin, Xiangdong; Chen, Yong

2018-01-01

Land ecological security is of vital importance to local security and sustainable development of mining activities. The study has analyzed the potential causal chains between the land ecological security of Iron Mine mining environment, mine resource and the social-economic background. On the base of Pressure-State-Response model, the paper set up a matter element evaluation model of land ecological security, and applies it in Chengchao iron mine. The evaluation result proves to be effective in land ecological evaluation.

The Role of Economic Uncertainty on the Block Economic Value - a New Valuation Approach / Rola Czynnika Niepewności Przy Obliczaniu Wskaźnika Rentowności - Nowe Podejście

NASA Astrophysics Data System (ADS)

Dehghani, H.; Ataee-Pour, M.

2012-12-01

The block economic value (EV) is one of the most important parameters in mine evaluation. This parameter can affect significant factors such as mining sequence, final pit limit and net present value. Nowadays, the aim of open pit mine planning is to define optimum pit limits and an optimum life of mine production scheduling that maximizes the pit value under some technical and operational constraints. Therefore, it is necessary to calculate the block economic value at the first stage of the mine planning process, correctly. Unrealistic block economic value estimation may cause the mining project managers to make the wrong decision and thus may impose inexpiable losses to the project. The effective parameters such as metal price, operating cost, grade and so forth are always assumed certain in the conventional methods of EV calculation. While, obviously, these parameters have uncertain nature. Therefore, usually, the conventional methods results are far from reality. In order to solve this problem, a new technique is used base on an invented binomial tree which is developed in this research. This method can calculate the EV and project PV under economic uncertainty. In this paper, the EV and project PV were initially determined using Whittle formula based on certain economic parameters and a multivariate binomial tree based on the economic uncertainties such as the metal price and cost uncertainties. Finally the results were compared. It is concluded that applying the metal price and cost uncertainties causes the calculated block economic value and net present value to be more realistic than certain conditions.

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Moment tensor clustering: a tool to monitor mining induced seismicity

NASA Astrophysics Data System (ADS)

Cesca, Simone; Dahm, Torsten; Tolga Sen, Ali

2013-04-01

Automated moment tensor inversion routines have been setup in the last decades for the analysis of global and regional seismicity. Recent developments could be used to analyse smaller events and larger datasets. In particular, applications to microseismicity, e.g. in mining environments, have then led to the generation of large moment tensor catalogues. Moment tensor catalogues provide a valuable information about the earthquake source and details of rupturing processes taking place in the seismogenic region. Earthquake focal mechanisms can be used to discuss the local stress field, possible orientations of the fault system or to evaluate the presence of shear and/or tensile cracks. Focal mechanism and moment tensor solutions are typically analysed for selected events, and quick and robust tools for the automated analysis of larger catalogues are needed. We propose here a method to perform cluster analysis for large moment tensor catalogues and identify families of events which characterize the studied microseismicity. Clusters include events with similar focal mechanisms, first requiring the definition of distance between focal mechanisms. Different metrics are here proposed, both for the case of pure double couple, constrained moment tensor and full moment tensor catalogues. Different clustering approaches are implemented and discussed. The method is here applied to synthetic and real datasets from mining environments to demonstrate its potential: the proposed cluserting techniques prove to be able to automatically recognise major clusters. An important application for mining monitoring concerns the early identification of anomalous rupture processes, which is relevant for the hazard assessment. This study is funded by the project MINE, which is part of the R&D-Programme GEOTECHNOLOGIEN. The project MINE is funded by the German Ministry of Education and Research (BMBF), Grant of project BMBF03G0737.

SEASONAL VARIATIONS OF DISSOLVED MERCURY CONCENTRATIONS AT THE SULPHUR BANK MERCURY MINE, CLEAR LAKE, CALIFORNIA: IMPLICATIONS FOR MINE DRAINAGE MONITORING

EPA Science Inventory

The Sulphur Bank Mercury Mine in Lake County, California (SBMM) was operated from the 1860s through the 1950s. Mining for sulfur started with surface operations and then progressed to shaft and later open pit techniques to obtain mercury. SBMM is located adjacent to the shore o...

Data Mining in Social Media

NASA Astrophysics Data System (ADS)

Barbier, Geoffrey; Liu, Huan

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

40 CFR 434.55 - New source performance standards (NSPS).

Code of Federal Regulations, 2014 CFR

2014-07-01

... performance standards shall be achieved for the discharge of any acid or ferruginous mine drainage subject to... following new source performance standards shall apply to the post-mining areas of all new source coal mines... new source coal mines until SMCRA bond release. Except as provided in 40 CFR 401.17 and §§ 434.61 and...

40 CFR 434.55 - New source performance standards (NSPS).

Code of Federal Regulations, 2011 CFR

2011-07-01

... standards shall be achieved for the discharge of any acid or ferruginous mine drainage subject to this... source performance standards shall apply to the post-mining areas of all new source coal mines: (a... coal mines until SMCRA bond release. Except as provided in 40 CFR 401.17 and §§ 434.61 and 434.63 (d)(2...

40 CFR 434.55 - New source performance standards (NSPS).

Code of Federal Regulations, 2012 CFR

2012-07-01

... performance standards shall be achieved for the discharge of any acid or ferruginous mine drainage subject to... following new source performance standards shall apply to the post-mining areas of all new source coal mines... new source coal mines until SMCRA bond release. Except as provided in 40 CFR 401.17 and §§ 434.61 and...

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques.

PubMed

Mobasheri, Amin; Huang, Haosheng; Degrossi, Lívia Castro; Zipf, Alexander

2018-02-08

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Recommendation in Higher Education Using Data Mining Techniques

ERIC Educational Resources Information Center

Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro

2009-01-01

One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…

Journal of Air Transportation, Volume 9, No. 1. Volume 9, No. 1

NASA Technical Reports Server (NTRS)

Bowen, Brent D. (Editor)

2004-01-01

The articles in this issue include: 1) Are Four Year Universities Better than Two-Year Colleges at Preparing Students to Pass the FAA Aircraft Mechanic Certification Written Examinations? 2) Assessing Perceived Risk of Consumers in Internet Airline Reservations Services; 3) Perceptions of Communication Training Among Collegiate Flight Educators; 4) Ethics Education in University Aviation Management Programs in the U.S.: Part Three - Qualitative Analysis and Recommendations; 5) Airline Flight Operations Internships Perspectives; 6) Applying Data Mining Techniques to Forecast Number of Airline Passengers in Saudi Arabia (Domestic and International Travels).

An Integrated Approach for Gear Health Prognostics

NASA Technical Reports Server (NTRS)

He, David; Bechhoefer, Eric; Dempsey, Paula; Ma, Jinghua

2012-01-01

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

Uranium aqueous speciation in the vicinity of the former uranium mining sites using the diffusive gradients in thin films and ultrafiltration techniques.

PubMed

Drozdzak, Jagoda; Leermakers, Martine; Gao, Yue; Elskens, Marc; Phrommavanh, Vannapha; Descostes, Michael

2016-03-24

The performance of the Diffusive Gradients in Thin films (DGT) technique with Chelex(®)-100, Metsorb™ and Diphonix(®) as binding phases was evaluated in the vicinity of the former uranium mining sites of Chardon and L'Ecarpière (Loire-Atlantique department in western France). This is the first time that the DGT technique with three different binding agents was employed for the aqueous U determination in the context of uranium mining environments. The fractionation and speciation of uranium were investigated using a multi-methodological approach using filtration (0.45 μm, 0.2 μm), ultrafiltration (500 kDa, 100 kDa and 10 kDa) coupled to geochemical speciation modelling (PhreeQC) and the DGT technique. The ultrafiltration data showed that at each sampling point uranium was present mostly in the 10 kDa truly dissolved fraction and the geochemical modelling speciation calculations indicated that U speciation was markedly predominated by CaUO2(CO3)3(2-). In natural waters, no significant difference was observed in terms of U uptake between Chelex(®)-100 and Metsorb™, while similar or inferior U uptake was observed on Diphonix(®) resin. In turn, at mining influenced sampling spots, the U accumulation on DGT-Diphonix(®) was higher than on DGT-Chelex(®)-100 and DGT-Metsorb™, probably because their performance was disturbed by the extreme composition of the mining waters. The use of Diphonix(®) resin leads to a significant advance in the application and development of the DGT technique for determination of U in mining influenced environments. This investigation demonstrated that such multi-technique approach provides a better picture of U speciation and enables to assess more accurately the potentially bioavailable U pool. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of multivariate analysis to investigate the trace element contamination in top soil of coal mining district in Jorong, South Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Pujiwati, Arie; Nakamura, K.; Watanabe, N.; Komai, T.

2018-02-01

Multivariate analysis is applied to investigate geochemistry of several trace elements in top soils and their relation with the contamination source as the influence of coal mines in Jorong, South Kalimantan. Total concentration of Cd, V, Co, Ni, Cr, Zn, As, Pb, Sb, Cu and Ba was determined in 20 soil samples by the bulk analysis. Pearson correlation is applied to specify the linear correlation among the elements. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to observe the classification of trace elements and contamination sources. The results suggest that contamination loading is contributed by Cr, Cu, Ni, Zn, As, and Pb. The elemental loading mostly affects the non-coal mining area, for instances the area near settlement and agricultural land use. Moreover, the contamination source is classified into the areas that are influenced by the coal mining activity, the agricultural types, and the river mixing zone. Multivariate analysis could elucidate the elemental loading and the contamination sources of trace elements in the vicinity of coal mine area.

Remote Sensing Applications with High Reliability in Changjiang Water Resource Management

NASA Astrophysics Data System (ADS)

Ma, L.; Gao, S.; Yang, A.

2018-04-01

Remote sensing technology has been widely used in many fields. But most of the applications cannot get the information with high reliability and high accuracy in large scale, especially for the applications using automatic interpretation methods. We have designed an application-oriented technology system (PIR) composed of a series of accurate interpretation techniques,which can get over 85 % correctness in Water Resource Management from the view of photogrammetry and expert knowledge. The techniques compose of the spatial positioning techniques from the view of photogrammetry, the feature interpretation techniques from the view of expert knowledge, and the rationality analysis techniques from the view of data mining. Each interpreted polygon is accurate enough to be applied to the accuracy sensitive projects, such as the Three Gorge Project and the South - to - North Water Diversion Project. In this paper, we present several remote sensing applications with high reliability in Changjiang Water Resource Management,including water pollution investigation, illegal construction inspection, and water conservation monitoring, etc.

30 CFR 740.11 - Applicability.

Code of Federal Regulations, 2010 CFR

2010-07-01

... jurisdiction. (e) This subchapter shall not apply to surface coal mining and reclamation operations within a... Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR FEDERAL LANDS PROGRAM GENERAL REQUIREMENTS FOR SURFACE COAL MINING AND RECLAMATION OPERATIONS ON FEDERAL LANDS § 740.11...

PERFORMING QUALITY FLOW MEASUREMENTS AT MINE SITES

EPA Science Inventory

Accurate flow measurement data is vital to research, monitoring, and remediation efforts at mining sites. This guidebook has been prepared to provide a summary of information relating to the performance of low measurements, and how this information can be applied at mining sites....

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

From IHE Audit Trails to XES Event Logs Facilitating Process Mining.

PubMed

Paster, Ferdinand; Helm, Emmanuel

2015-01-01

Recently Business Intelligence approaches like process mining are applied to the healthcare domain. The goal of process mining is to gain process knowledge, compliance and room for improvement by investigating recorded event data. Previous approaches focused on process discovery by event data from various specific systems. IHE, as a globally recognized basis for healthcare information systems, defines in its ATNA profile how real-world events must be recorded in centralized event logs. The following approach presents how audit trails collected by the means of ATNA can be transformed to enable process mining. Using the standardized audit trails provides the ability to apply these methods to all IHE based information systems.

Unsupervised text mining for assessing and augmenting GWAS results.

PubMed

Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

2016-04-01

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

Mining the SDSS SkyServer SQL queries log

NASA Astrophysics Data System (ADS)

Hirota, Vitor M.; Santos, Rafael; Raddick, Jordan; Thakar, Ani

2016-05-01

SkyServer, the Internet portal for the Sloan Digital Sky Survey (SDSS) astronomic catalog, provides a set of tools that allows data access for astronomers and scientific education. One of SkyServer data access interfaces allows users to enter ad-hoc SQL statements to query the catalog. SkyServer also presents some template queries that can be used as basis for more complex queries. This interface has logged over 330 million queries submitted since 2001. It is expected that analysis of this data can be used to investigate usage patterns, identify potential new classes of queries, find similar queries, etc. and to shed some light on how users interact with the Sloan Digital Sky Survey data and how scientists have adopted the new paradigm of e-Science, which could in turn lead to enhancements on the user interfaces and experience in general. In this paper we review some approaches to SQL query mining, apply the traditional techniques used in the literature and present lessons learned, namely, that the general text mining approach for feature extraction and clustering does not seem to be adequate for this type of data, and, most importantly, we find that this type of analysis can result in very different queries being clustered together.

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It’s difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

NASA Astrophysics Data System (ADS)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..

Exploring patterns of epigenetic information with data mining techniques.

PubMed

Aguiar-Pulido, Vanessa; Seoane, José A; Gestal, Marcos; Dorado, Julián

2013-01-01

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

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

Land subsidence susceptibility and hazard mapping: the case of Amyntaio Basin, Greece

NASA Astrophysics Data System (ADS)

Tzampoglou, P.; Loupasakis, C.

2017-09-01

Landslide susceptibility and hazard mapping has been applying for more than 20 years succeeding the assessment of the landslide risk and the mitigation the phenomena. On the contrary, equivalent maps aiming to study and mitigate land subsidence phenomena caused by the overexploitation of the aquifers are absent from the international literature. The current study focuses at the Amyntaio basin, located in West Macedonia at Florina prefecture. As proved by numerous studies the wider area has been severely affected by the overexploitation of the aquifers, caused by the mining and the agricultural activities. The intensive ground water level drop has triggered extensive land subsidence phenomena, especially at the perimeter of the open pit coal mine operating at the site, causing damages to settlements and infrastructure. The land subsidence susceptibility and risk maps were produced by applying the semi-quantitative WLC (Weighted Linear Combination) method, especially calibrated for this particular catastrophic event. The results were evaluated by using detailed field mapping data referring to the spatial distribution of the surface ruptures caused by the subsidence. The high correlation between the produced maps and the field mapping data, have proved the great value of the maps and of the applied technique on the management and the mitigation of the phenomena. Obviously, these maps can be safely used by decision-making authorities for the future urban safety development.

Treatment and prevention of ARD using silica micro encapsulation[Acid Rock Drainage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, P.; Rybock, J.; Wheaton, A.

1999-07-01

In response to the known drawbacks of liming and the ever-increasing regulatory demands on the mining industry, KEECO has developed a silica micro encapsulation (SME) process. SME is a cost-effective, high performance reagent that is utilized in conjunction with simple chemical delivery systems. By encapsulating metals in a silica matrix formation and rapidly precipitating them into a sand-like sludge, it offers all the advantages of liming without the negative drawbacks. Utilizing an injection technique via a high shear mixing device, a slurry form of the SME product called KB-1{trademark} was applied to ARD at the Bunker Hill Mine in Idahomore » and to ARD pumped from collection ponds at a remote mine site in the Sierra Nevada Mountains. Flow rates at both sites ranged form 500 to 800 gallons per minute. Treated water from the Bunker Hill Mine operation achieved the site's NPDES criteria for all evaluated metals and US Drinking Water quality for arsenic, cadmium, chromium, lead and zinc with a dosage rate of 1.34 grams KB-1{trademark} per liter. Treated water from the Sierra Nevada project focused on the control of aluminum, arsenic, copper, iron and nickel. All water samples displayed a >99.5% reduction in these metals, as well as an 84%--87% reduction in the concentration of sulfate. Testing on sludge generated form both operations achieved TCLP Action Limits. The SME process is currently under evaluation as a means to coat the pyrite surfaces of newly generated mine tailings to prevent oxidation and future acid generation.« less

Comparing data mining methods on the VAERS database.

PubMed

Banks, David; Woo, Emily Jane; Burwen, Dale R; Perucci, Phil; Braun, M Miles; Ball, Robert

2005-09-01

Data mining may enhance traditional surveillance of vaccine adverse events by identifying events that are reported more commonly after administering one vaccine than other vaccines. Data mining methods find signals as the proportion of times a condition or group of conditions is reported soon after the administration of a vaccine; thus it is a relative proportion compared across vaccines, and not an absolute rate for the condition. The Vaccine Adverse Event Reporting System (VAERS) contains approximately 150 000 reports of adverse events that are possibly associated with vaccine administration. We studied four data mining techniques: empirical Bayes geometric mean (EBGM), lower-bound of the EBGM's 90% confidence interval (EB05), proportional reporting ratio (PRR), and screened PRR (SPRR). We applied these to the VAERS database and compared the agreement among methods and other performance properties, particularly focusing on the vaccine-event combinations with the highest numerical scores in the various methods. The vaccine-event combinations with the highest numerical scores varied substantially among the methods. Not all combinations representing known associations appeared in the top 100 vaccine-event pairs for all methods. The four methods differ in their ranking of vaccine-COSTART pairs. A given method may be superior in certain situations but inferior in others. This paper examines the statistical relationships among the four estimators. Determining which method is best for public health will require additional analysis that focuses on the true alarm and false alarm rates using known vaccine-event associations. Evaluating the properties of these data mining methods will help determine the value of such methods in vaccine safety surveillance. (c) 2005 John Wiley & Sons, Ltd.

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

ERIC Educational Resources Information Center

Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Restoration of tropical moist forest on bauxite mined lands in the Brazilian Amazon

Treesearch

John A Parrotta; Oliver H. Knowles

1999-01-01

We evaluated forest structure and composition in 9- to 13-year-old stands established on a bauxite-mined site at Trombetas (Pará), Brazil, using four different reforestation techniques following initial site preparation and topsoil replacement. These techniques included reliance on natural forest regeneration, mixed commercial species plantings of mostly exotic timber...

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

2017-01-01

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Federal program. (c) The rules in this part apply to all surface coal mining operations in Oregon... more stringent environmental control and regulation of surface coal mining operations than do the... extent they provide for regulation of surface coal mining and reclamation operations which are exempt...

Use of natural and applied tracers to guide targeted remediation efforts in an acid mine drainage system, Colorado Rockies, USA

USGS Publications Warehouse

Cowie, Rory; Williams, Mark W.; Wireman, Mike; Runkel, Robert L.

2014-01-01

Stream water quality in areas of the western United States continues to be degraded by acid mine drainage (AMD), a legacy of hard-rock mining. The Rico-Argentine Mine in southwestern Colorado consists of complex multiple-level mine workings connected to a drainage tunnel discharging AMD to passive treatment ponds that discharge to the Dolores River. The mine workings are excavated into the hillslope on either side of a tributary stream with workings passing directly under the stream channel. There is a need to define hydrologic connections between surface water, groundwater, and mine workings to understand the source of both water and contaminants in the drainage tunnel discharge. Source identification will allow targeted remediation strategies to be developed. To identify hydrologic connections we employed a combination of natural and applied tracers including isotopes, ionic tracers, and fluorescent dyes. Stable water isotopes (δ18O/δD) show a well-mixed hydrological system, while tritium levels in mine waters indicate a fast flow-through system with mean residence times of years not decades or longer. Addition of multiple independent tracers indicated that water is traveling through mine workings with minimal obstructions. The results from a simultaneous salt and dye tracer application demonstrated that both tracer types can be successfully used in acidic mine water conditions.

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

Illustrated surface mining methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1979-01-01

This manual provides a visual synopsis of surface coal mining methods in the United States. The manual presents various surface mining methods and techniques through artist renderings and appropriate descriptions. The productive coal fields of the United States were divided into four regions according to geology and physiography. A glossay of terminology is included. (DP)

A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

ERIC Educational Resources Information Center

Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

2013-01-01

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

A Data Warehouse Architecture for DoD Healthcare Performance Measurements.

DTIC Science & Technology

1999-09-01

design, develop, implement, and apply statistical analysis and data mining tools to a Data Warehouse of healthcare metrics. With the DoD healthcare...framework, this thesis defines a methodology to design, develop, implement, and apply statistical analysis and data mining tools to a Data Warehouse...21 F. INABILITY TO CONDUCT HELATHCARE ANALYSIS

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Testing the Reviewed Event Bulletin of the International Data Centre Using Waveform Cross Correlation: Repeat Events at Aitik Copper Mine, Sweden

NASA Astrophysics Data System (ADS)

Kitov, I. O.; Rozhkov, N.; Bobrov, D.; Rozhkov, M.; Yedlin, M. J.

2016-12-01

The quality of the Reviewed Event Bulletin (REB) issued by the International Data Centre (IDC) of the Comprehensive Nuclear-Test- Ban Treaty Organization (CTBTO) is crucial for the Member States as well as for the seismological community. One of the most efficient methods to test the REB quality is using repeat events having very accurate absolute locations. Hundreds of quarry blasts detonated at Aitik copper mine (the central point of active mining - 67.08N, 20.95E) were recorded by several seismic arrays of the International Monitoring System (IMS), found by IDC automatic processing and then confirmed by analysts as REB events. The size of the quarry is approximately 1 km and one can consider that the uncertainty in absolute coordinates of the studied events is less than 0.5 km as measured from the central point. In the REB, the corresponding epicenters are almost uniformly scattered over the territory 67.0N to 67.3N, and 20.7E to 21.5E. These REB locations are based on the measured arrival times as well as azimuth and slowness estimates at several IMS stations with the main input from ARCES, NOA, FINES, and HFS. The higher scattering of REB locations is caused by the uncertainty in measurements and velocity model. Seismological methods based on waveform cross correlation allow very accurate relative location of repeat events. Here we test the level of similarity between signals from these events. It was found that IMS primary array station ARCES demonstrates the highest similarity as expressed by cross correlation coefficient (CC) and signal-to-noise ratio (SNR) calculated at the CC traces. Small-aperture array FINES is the second best and large-aperture array NOA demonstrating mediocre performance likely due its size and the loss of coherency between high-frequency and relatively low-velocity signals from the mine. During the last five years station ARCES has been upgraded from a vertical array to a 3-C one. This transformation has improved the performance of CC-technique as applied to the Aitik mine events. We have also applied a Principal Component Analysis to estimate the level of variability in the signals as well as to build the best waveform template for effective detection and identification of all blasts conducted at Aitik mine.

Haneş and Valea Vinului (Romania) closed mines Acid Mine Drainages (AMDs)--actual condition and passive treatment remediation proposal.

PubMed

Măicăneanu, Andrada; Bedelean, Horea; Ardelean, Marius; Burcă, Silvia; Stanca, Maria

2013-10-01

Acid Mine Drainages (AMDs) from Haneş and Valea Vinului (Romania) closed mines were considered for characterization and treatment using a local zeolitic volcanic tuff, ZVT, (Măcicaş, Cluj County, Romania). Water samples were collected from two locations, before and after discharging point in case of Haneş mine, and on three horizons in case of Valea Vinului mine. Physico-chemical (pH, total solid, heavy metal ions concentration) analyses showed that the environment is strongly affected by these AMD discharges even if the mines were closed years ago. Iron, manganese and zinc were the main pollutants identified in Haneş mine AMD, while zinc is the one mainly present in case of Valea Vinului AMD. A batch technique (no stirring) in which the ZVT was put in contact with the AMD sample was proposed as a passive remediation technique. ZVT successfully remove heavy metal ion from AMD. According to heavy metal ion concentrations, removal efficiencies are reaching 100%, varying as follows, Fe(2+)>Zn(2+)>Mn(2+). When the ZVT was compared with two cationic resins (strong, SAR and weak acid, WAR) the following series was depicted, SAR>ZVT>WAR. Copyright © 2013 Elsevier Ltd. All rights reserved.

Coal Mining Machinery Development As An Ecological Factor Of Progressive Technologies Implementation

NASA Astrophysics Data System (ADS)

Efremenkov, A. B.; Khoreshok, A. A.; Zhironkin, S. A.; Myaskov, A. V.

2017-01-01

At present, a significant amount of energy spent for the work of mining machines and coal mining equipment on coal mines and open pits goes to the coal grinding in the process of its extraction in mining faces. Meanwhile, the increase of small fractions in mined coal does not only reduce the profitability of its production, but also causes a further negative impact on the environment and degrades labor conditions for miners. The countermeasure to the specified processes is possible with the help of coal mining equipment development. However, against the background of the technological decrease of coal mine equipment applied in Russia the negative impact on the environment is getting reinforced.

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

PubMed

Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping

2018-04-27

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

PubMed

Oztekin, Asil; Delen, Dursun; Kong, Zhenyu James

2009-12-01

Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival. The collection of methods known as 'data mining' offers significant advantages over conventional statistical techniques in dealing with the latter's limitations such as normality assumption of observations, independence of observations from each other, and linearity of the relationship between the observations and the output measure(s). There are statistical methods that overcome these limitations. Yet, they are computationally more expensive and do not provide fast and flexible solutions as do data mining techniques in large datasets. The main objective of this study is to improve the prediction of outcomes following combined heart-lung transplantation by proposing an integrated data-mining methodology. A large and feature-rich dataset (16,604 cases with 283 variables) is used to (1) develop machine learning based predictive models and (2) extract the most important predictive factors. Then, using three different variable selection methods, namely, (i) machine learning methods driven variables-using decision trees, neural networks, logistic regression, (ii) the literature review-based expert-defined variables, and (iii) common sense-based interaction variables, a consolidated set of factors is generated and used to develop Cox regression models for heart-lung graft survival. The predictive models' performance in terms of 10-fold cross-validation accuracy rates for two multi-imputed datasets ranged from 79% to 86% for neural networks, from 78% to 86% for logistic regression, and from 71% to 79% for decision trees. The results indicate that the proposed integrated data mining methodology using Cox hazard models better predicted the graft survival with different variables than the conventional approaches commonly used in the literature. This result is validated by the comparison of the corresponding Gains charts for our proposed methodology and the literature review based Cox results, and by the comparison of Akaike information criteria (AIC) values received from each. Data mining-based methodology proposed in this study reveals that there are undiscovered relationships (i.e. interactions of the existing variables) among the survival-related variables, which helps better predict the survival of the heart-lung transplants. It also brings a different set of variables into the scene to be evaluated by the domain-experts and be considered prior to the organ transplantation.

The nature of fracturing and stress distribution in quartzite around the 1128-M (3700-FT) level of the crescent mine, Coeur d'Alene mining district, Idaho

USGS Publications Warehouse

Miller, C.H.; Skinner, E.H.

1980-01-01

Silver and copper are the principal ores mined from the quartzite at the Crescent mine. Both the main ore-bearing veins and foliation in the quartzite are parallel to the nearly vertical formational contacts. Anisotropy of the quartzite is indicated by both dynamic and static tests. Disking and breakage of core from holes perpendicular to the foliation are about twice what they are in core from holes parallel to foliation. Natural cleavage as well as slabbing and blasting fractures around the tunnels are also controlled by the foliation. Extensive overcore deformation measurements indicate that most of the influence of the tunnels on the "free" stress field is between the rib and a depth of 2.7 m (1 tunnel diameter). The maximum principal stress axis in the free field is nearly horizontal; its magnitude is not much greater than the vertical component and calculations indicate a nearly hydrostatic free stress field. Stress considerably greater than the free field was measured between about 0.3-2.7 m behind the rib and is caused by a transfer of load from above the tunnel opening. Peak stress is in the vertical direction and about 1.7 m behind the rib. An air-injection survey shows that high permeabilities are confined to the highly fractured annulus around a tunnel to a depth of at least 0.6 m. Air-injection measurements could be taken in the interval of about 0.6-1.8 m, but more fractures with high permeabilities may also be present in the annulus from about 0.6-1.2 m. Permeabilities measured deeper than about 1.8 m by the air-injection technique are either very low or nonexistent. The absence of open and noncontinuous fractures beyond about 1.8 m is also indicated by very low porosities and permeabilities of core, very high stresses (which presumably would close fractures), the lack of stains or secondary fillings in disking fractures, a conspicuous lack of ground water in the tunnels, and the fact that fractures encountered in an experimental 0.9-m tunnel did not extend into the 1.8-m tunnel that was mined over it. Air-injection techniques exceed the accuracy of any field deformation measurement now in use, and they are sensitive to permeabilities as small as one microdarcy and to fracture widths as small as 250 nanometers. This technique was applied for future reference in mining design and, perhaps, to be used later to detect microfracturing prior to rockbursts. ?? 1980 Elsevier Scientific Publishing Company.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

Product Recommendation System Based on Personal Preference Model Using CAM

NASA Astrophysics Data System (ADS)

Murakami, Tomoko; Yoshioka, Nobukazu; Orihara, Ryohei; Furukawa, Koichi

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.

A data mining approach to predict in situ chlorinated ethene detoxification potential

NASA Astrophysics Data System (ADS)

Lee, J.; Im, J.; Kim, U.; Loeffler, F. E.

2015-12-01

Despite major advances in physicochemical remediation technologies, in situ biostimulation and bioaugmentation treatment aimed at stimulating Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. In practice, selecting the best remedial strategy is challenging due to uncertainties associated with the microbiology (e.g., presence and activity of Dhc) and geochemical factors influencing Dhc activity. Extensive groundwater datasets collected over decades of monitoring exist, but have not been systematically analyzed. In the present study, geochemical and microbial data sets collected from 35 wells at 5 contaminated sites were used to develop a predictive empirical model using a machine learning algorithm (i) to rank the relative importance of parameters that affect in situ reductive dechlorination potential, and (ii) to provide recommendations for selecting the optimal remediation strategy at a specific site. Classification and regression tree (CART) analysis was applied, and a representative classification tree model was developed that allowed short-term prediction of dechlorination potential. Indirect indicators for low dissolved oxygen (e.g., low NO3-and NO2-, high Fe2+ and CH4) were the most influential factors for predicting dechlorination potential, followed by total organic carbon content (TOC) and Dhc cell abundance. These findings indicate that machine learning-based data mining techniques applied to groundwater monitoring data can lead to the development of predictive groundwater remediation models. A major need for improving the predictive capabilities of the data mining approach is a curated, up-to-date and comprehensive collection of groundwater monitoring data.

Data mining and visualization techniques

DOEpatents

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

[Analysis of syndrome discipline of generalized anxiety disorder using data mining techniques].

PubMed

Tang, Qi-sheng; Sun, Wen-jun; Qu, Miao; Guo, Dong-fang

2012-09-01

To study the use of data mining techniques in analyzing the syndrome discipline of generalized anxiety disorder (GAD). From August 1, 2009 to July 31, 2010, 705 patients with GAD in 10 hospitals of Beijing were investigated over one year. Data mining techniques, such as Bayes net and cluster analysis, were used to analyze the syndrome discipline of GAD. A total of 61 symptoms of GAD were screened out. By using Bayes net, nine syndromes of GAD were abstracted based on the symptoms. Eight syndromes were abstracted by cluster analysis. After screening for duplicate syndromes and combining the experts' experience and traditional Chinese medicine theory, six syndromes of GAD were defined. These included depressed liver qi transforming into fire, phlegm-heat harassing the heart, liver depression and spleen deficiency, heart-kidney non-interaction, dual deficiency of the heart and spleen, and kidney deficiency and liver yang hyperactivity. Based on the results, the draft of Syndrome Diagnostic Criteria for Generalized Anxiety Disorder was developed. Data mining techniques such as Bayes net and cluster analysis have certain future potential for establishing syndrome models and analyzing syndrome discipline, thus they are suitable for the research of syndrome differentiation.

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

30 CFR 75.1314 - Sheathed explosive units.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Sheathed explosive units. 75.1314 Section 75.1314 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND... damaged or deteriorated. (d) Except in anthracite mines, rock dust shall be applied to the roof, ribs and...

Applying WEPP technologies to western alkaline surface coal mines

Treesearch

J. Q. Wu; S. Dun; H. Rhee; X. Liu; W. J. Elliot; T. Golnar; J. R. Frankenberger; D. C. Flanagan; P. W. Conrad; R. L. McNearny

2011-01-01

One aspect of planning surface mining operations, regulated by the National Pollutant Discharge Elimination System (NPDES), is estimating potential environmental impacts during mining operations and the reclamation period that follows. Practical computer simulation tools are effective for evaluating site-specific sediment control and reclamation plans for the NPDES....

40 CFR 434.55 - New source performance standards (NSPS).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 29 2010-07-01 2010-07-01 false New source performance standards (NSPS... PERFORMANCE STANDARDS Post-Mining Areas § 434.55 New source performance standards (NSPS). The following new source performance standards shall apply to the post-mining areas of all new source coal mines: (a...

Data mining in child welfare.

PubMed

Schoech, D; Quinn, A; Rycraft, J R

2000-01-01

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Reforestation of mined land in the northeastern and north-central U.S.

Treesearch

Walter H. Davidson; Russell J. Hutnik; Delbert E. Parr

1984-01-01

This paper reviews the state of the art of surface mine reclamation for forestry in Pennsylvania, Maryland, West Virginia, Ohio, Indiana, and Illinois. Legislative constraints, socioeconomic issues, factors limiting the success of reforestation efforts, post-mining land-use trends, species options, and establishment techniques are discussed. Sources of assistance to...

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Increasing the Reliability of the Work of Artificial Filtering Arrays for the Purification of Quarry Waste Water

NASA Astrophysics Data System (ADS)

Tyulenev, Maxim; Lesin, Yury; Litvin, Oleg; Maliukhina, Elena; Abay, Asmelash

2017-11-01

Features of geological structure of the Kuznetsk coal basin stipulate the application of a low-cost open technique of coal mining, which is more advantageous both from the economic standpoint, and by safety criteria of mining. However, open mining affects significantly the water resources of region. Intensive pollution of reservoirs and water courses, exhaustion of the underground water-bearing layers, violation of a hydrographic network, etc. be-long to the main disadvantages of an open technique of coal mining. Besides, the volume of the water coming into the mining producers exceeds signi-ficantly the needed quantity. According to the data of annual reports of ecology and natural resources department, 348.277 million m3 of water were ta-ken away during production of soft coal, brown coal and lignum fossil from waters of Kemerovo region in 2013 (mostly from underground water objects (96,5%) when draining of mine openings). At the same time, only 87.018 million m3 of water (25%) has been used within a year.

Methods and costs of thin-seam mining. Final report, 25 September 1977-24 January 1979. [Thin seam in association with a thick seam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finch, T.E.; Fidler, E.L.

1981-02-01

This report defines the state of the art (circa 1978) in removing thin coal seams associated with vastly thicker seams found in the surface coal mines of the western United States. New techniques are evaluated and an innovative method and machine is proposed. Western states resource recovery regulations are addressed and representative mining operations are examined. Thin seam recovery is investigated through its effect on (1) overburden removal, (2) conventional seam extraction methods, and (3) innovative techniques. Equations and graphs are used to accommodate the variable stratigraphic positions in the mining sequence on which thin seams occur. Industrial concern andmore » agency regulations provided the impetus for this study of total resource recovery. The results are a compendium of thin seam removal methods and costs. The work explains how the mining industry recovers thin coal seams in western surface mines where extremely thick seams naturally hold the most attention. It explains what new developments imply and where to look for new improvements and their probable adaptability.« less

Field test of an alternative longwall gate road design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cox, R.M.; Vandergrift, T.L.; McDonnell, J.P.

1994-01-01

The US Bureau of Mines (USBM) MULSIM/ML modeling technique has been used to analyze anticipated stress distributions for a proposed alternative longwall gate road design for a western Colorado coal mine. The model analyses indicated that the alternative gate road design would reduce stresses in the headgate entry. To test the validity of the alternative gate road design under actual mining conditions, a test section of the alternative system was incorporated into a subsequent set of gate roads developed at the mine. The alternative gate road test section was instrumented with borehole pressure cells, as part of an ongoing USBMmore » research project to monitor ground pressure changes as longwall mining progressed. During the excavation of the adjacent longwall panels, the behavior of the alternative gate road system was monitored continuously using the USBM computer-assisted Ground Control Management System. During these field tests, the alternative gate road system was first monitored and evaluated as a headgate, and later monitored and evaluated as a tailgate. The results of the field tests confirmed the validity of using the MULSIM/NL modeling technique to evaluate mine designs.« less

All-Optical Fibre Networks For Coal Mines

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1987-09-01

A topic of the paper is fiber-optic integrated network (FOIN) suited to the most hostile environments existing in coal mines. The use of optical fibres for transmission of mine instrumentation data offers the prospects of improved safety and immunity to electromagnetic interference (EMI). The feasibility of optically powered sensors has opened up new opportunities for research into optical signal processing architectures. This article discusses a new fibre-optic sensor network involving a time domain multiplexing(TDM)scheme and optical signal processing techniques. The pros and cons of different FOIN topologies with respect to coal mine applications are considered. The emphasis has been placed on a recently developed all-optical fibre network using spread spectrum code division multiple access (COMA) techniques. The all-optical networks have applications in explosive environments where electrical isolation is required.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

Visual analytics techniques for large multi-attribute time series data

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.

2008-01-01

Time series data commonly occur when variables are monitored over time. Many real-world applications involve the comparison of long time series across multiple variables (multi-attributes). Often business people want to compare this year's monthly sales with last year's sales to make decisions. Data warehouse administrators (DBAs) want to know their daily data loading job performance. DBAs need to detect the outliers early enough to act upon them. In this paper, two new visual analytic techniques are introduced: The color cell-based Visual Time Series Line Charts and Maps highlight significant changes over time in a long time series data and the new Visual Content Query facilitates finding the contents and histories of interesting patterns and anomalies, which leads to root cause identification. We have applied both methods to two real-world applications to mine enterprise data warehouse and customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH ...

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

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH CONVEYORS. JIM WALTER RESOURCES INC. MINING DIVISION OPERATES FOUR UNDERGROUND COAL MINES IN THE BLUE CREEK COAL FIELD OF BIRMINGHAM DISTRICT, THREE IN TUSCALOOSA COUNTY AND ONE IN JEFFERSON COUNTY. TOTAL ANNUAL PRODUCTION IS 8,000,000 TONS. AT 2,300 DEEP, JIM WALTER'S BROOKWOOD MINES ARE THE DEEPEST UNDERGROUND COAL MINES IN NORTH AMERICA. THEY PRODUCE A HIGH-GRADE MEDIUM VOLATILE LOW SULPHUR METALLURGICAL COAL. THE BROOKWOOD NO. 5 MINE (PICTURED IN THIS PHOTOGRAPH) EMPLOYS THE LONGWALL MINING TECHNIQUES WITH BELTS CONVEYING COAL FROM UNDERGROUND OPERATIONS TO THE SURFACE. - JIm Walter Resources, Incorporated, Brookwood No. 5 Mine, 12972 Lock 17 Road, Brookwood, Tuscaloosa County, AL

[Study on composing prescription laws of treating aplastic anemia by Chinese medicine using applying data mining technique].

PubMed

Xiang, Yang; Yu, Jing-Wei; Cheng, Yu-Bin; Li, Hai-Xi; Chang, Xiao-Hui; Zhou, Da-Wei; Sun, Feng; Fang, Yong-Guang

2013-07-01

To explore composing prescription laws of treating aplastic anemia (AA) by Chinese medicine (CM). The literatures on treating AA by CM were recruited from various medical periodicals at home from 1979 to 2009 including China National Knowledge Infrastructure (CNKI), VIP information network, and Wangfang data knowledge service platform. The database correlated to CM features was established using the technique of computer data bank. The data mining (DM) technique was applied to analyze drugs sorts, frequency of drug application, and association degree. Three hundred and eleven pertinent literatures including 677 prescriptions and 254 Chinese herbs (CHs) were screened. There were 69 CHs for invigorating deficiency, 42 for heat clearing, 20 for promoting blood circulation and removing blood stasis, 16 for arresting bleeding, and 16 for relieving exterior syndrome, which occupied the top 5. The frequency of drug application of 254 CHs amounted to 7 547, in which the frequency of drug application of Mongolian milkvetch root, Rehmannia root, Suberect spatholobus stem, Hairyvein agrimonia herb, and Chinese thorowax root were 379, 248, 167, 85, and 13 respectively, and they occupied the first place of CHs for invigorating deficiency, heat clearing, promoting blood circulation and removing blood stasis, arresting bleeding, and relieving exterior syndrome, respectively. The number of the prescriptions containing 12, 10, and 11 CHs was occupied the top 3. The coverage rate of the prescription including Mongolian milkvetch root and Chinese angelica was 60%, and thus 4 core drugs groups were established covering invigorating qi and enriching the blood, reinforcing Shen and supporting yang, replenishing yin to tonify Shen, tonifying Shen to replenish essence, and invigorating qi and enriching blood respectively. Summarized were six potential composing prescription laws covering invigorating qi and enriching blood, reinforcing Shen and supporting yang, replenishing yin to tonify Shen, strengthening Pi and harmonizing Wei, tonifying the blood and promoting blood circulation, clearing away heat and toxic materials, and removing heat from the blood to stop bleeding. Applying DM technique, the fundamental core drugs groups consisting of Mongolian milkvetch root and Chinese angelica were discovered. The 4 core drugs groups established were in accordance with the realization of modern CM for the pathomechanism of AA. The 6 composing prescription laws summarized revealed the rules of drug application.

Use of an automatic earth resistivity system for detection of abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.

1982-04-01

Under the sponsorship of the US Bureau of Mines, a surface-operated automatic high resolution earth resistivity system and associated computer data processing techniques have been designed and constructed for use as a potential means of detecting abandoned coal mine workings. The hardware and software aspects of the new system are described together with applications of the method to the survey and mapping of abandoned mine workings.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei

2017-01-01

Background: We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. Methods: We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. Results: For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Conclusion: Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. PMID:28828316

Design of material management system of mining group based on Hadoop

NASA Astrophysics Data System (ADS)

Xia, Zhiyuan; Tan, Zhuoying; Qi, Kuan; Li, Wen

2018-01-01

Under the background of persistent slowdown in mining market at present, improving the management level in mining group has become the key link to improve the economic benefit of the mine. According to the practical material management in mining group, three core components of Hadoop are applied: distributed file system HDFS, distributed computing framework Map/Reduce and distributed database HBase. Material management system of mining group based on Hadoop is constructed with the three core components of Hadoop and SSH framework technology. This system was found to strengthen collaboration between mining group and affiliated companies, and then the problems such as inefficient management, server pressure, hardware equipment performance deficiencies that exist in traditional mining material-management system are solved, and then mining group materials management is optimized, the cost of mining management is saved, the enterprise profit is increased.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Analysis on Heavy Metal Distribution in Overlying Deposit and Pollution Characteristics in Rivers around Dahongshan Fe&Cu Mine in Yunnan Province, China

NASA Astrophysics Data System (ADS)

Huang, Qianrui; Cheng, Xianfeng; Qi, Wufu; Xu, Jun; Yang, Shuran

2017-12-01

Dahongshan Fe&Cu mine in Yunnan Province was endowed with the title of “National Green Mine Pilots” by Chinese Ministry of Land and Resources in April 2013. In order to verify the implementation effects of the green mine and better drive the construction of the green mine by other mine enterprises in Yunnan, the project team investigated overlying deposit in rivers around the Dahongshan mine in the wet season (August) of 2016, investigated mine enterprises, and applied the Potential Ecological Risk Index to evaluate potential ecological hazards of heavy metal pollution in overlying deposit. The results showed that all sampling points were less than 105, indicating the lower ecological hazard degree.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS

USGS Publications Warehouse

Chirico, Peter G.; Dewitt, Jessica D.

2017-01-01

This study evaluates the use of a small unmanned aerial system (UAS) to collect imagery over artisanal mining sites in West Africa. The purpose of this study is to consider how very high-resolution imagery and digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetric techniques from a small UAS can fill the gap in geospatial data collection between satellite imagery and data gathered during field work to map and monitor informal mining sites in tropical environments. The study compares both wide-angle and narrow field of view camera systems in the collection and analysis of high-resolution orthoimages and DSMs of artisanal mining pits. The results of the study indicate that UAS imagery and SfM photogrammetric techniques permit DSMs to be produced with a high degree of precision and relative accuracy, but highlight the challenges of mapping small artisanal mining pits in remote and data sparse terrain.

30 CFR 57.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Definitions. 57.2 Section 57.2 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES General § 57.2 Definitions. The following definitions apply to this part. In...

30 CFR 57.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Definitions. 57.2 Section 57.2 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES General § 57.2 Definitions. The following definitions apply to this part. In...

40 CFR 436.31 - Specialized definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... this chapter shall apply to this subpart. (b) The term “mine dewatering” shall mean any water that is... efforts of the mine operator. This term shall also include wet pit overflows caused solely by direct rainfall and ground water seepage. However, if a mine is also used for treatment of process generated waste...

40 CFR 436.31 - Specialized definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... this chapter shall apply to this subpart. (b) The term “mine dewatering” shall mean any water that is... efforts of the mine operator. This term shall also include wet pit overflows caused solely by direct rainfall and ground water seepage. However, if a mine is also used for treatment of process generated waste...

40 CFR 436.41 - Specialized definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... shall apply to this subpart. (b) The term “mine dewatering” shall mean any water that is impounded or... efforts of the mine operator. This term shall also include wet pit overflows caused solely by direct rainfall and ground water seepage. However, if a mine is also used for the treatment of process generated...

40 CFR 436.41 - Specialized definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... shall apply to this subpart. (b) The term “mine dewatering” shall mean any water that is impounded or... efforts of the mine operator. This term shall also include wet pit overflows caused solely by direct rainfall and ground water seepage. However, if a mine is also used for the treatment of process generated...

5 CFR 5201.105 - Additional rules for Mine Safety and Health Administration employees.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Health Administration employees. 5201.105 Section 5201.105 Administrative Personnel DEPARTMENT OF LABOR... for Mine Safety and Health Administration employees. The rules in this section apply to employees of the Mine Safety and Health Administration (MSHA) and are in addition to §§ 5201.101, 5201.102, and...

A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases.

PubMed

Pérez, Joaquín; Iturbide, Emmanuel; Olivares, Víctor; Hidalgo, Miguel; Martínez, Alicia; Almanza, Nelva

2015-11-01

It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50% or up to 70% of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.

Computer-aided visual assessment in mine planning and design

Treesearch

Michael Hatfield; A. J. LeRoy Balzer; Roger E. Nelson

1979-01-01

A computer modeling technique is described for evaluating the visual impact of a proposed surface mine located within the viewshed of a national park. A computer algorithm analyzes digitized USGS baseline topography and identifies areas subject to surface disturbance visible from the park. Preliminary mine and reclamation plan information is used to describe how the...

Learner Typologies Development Using OIndex and Data Mining Based Clustering Techniques

ERIC Educational Resources Information Center

Luan, Jing

2004-01-01

This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…

A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success

ERIC Educational Resources Information Center

Huebner, Richard A.

2017-01-01

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Hot mill process parameters impacting on hot mill tertiary scale formation

NASA Astrophysics Data System (ADS)

Kennedy, Jonathan Ian

For high end steel applications surface quality is paramount to deliver a suitable product. A major cause of surface quality issues is from the formation of tertiary scale. The scale formation depends on numerous factors such as thermo-mechanical processing routes, chemical composition, thickness and rolls used. This thesis utilises a collection of data mining techniques to better understand the influence of Hot Mill process parameters on scale formation at Port Talbot Hot Strip Mill in South Wales. The dataset to which these data mining techniques were applied was carefully chosen to reduce process variation. There are several main factors that were considered to minimise this variability including time period, grade and gauge investigated. The following data mining techniques were chosen to investigate this dataset: Partial Least Squares (PLS); Logit Analysis; Principle Component Analysis (PCA); Multinomial Logistical Regression (MLR); Adaptive Neuro Inference Fuzzy Systems (ANFIS). The analysis indicated that the most significant variable for scale formation is the temperature entering the finishing mill. If the temperature is controlled on entering the finishing mill scale will not be formed. Values greater than 1070 °C for the average Roughing Mill and above 1050 °C for the average Crop Shear temperature are considered high, with values greater than this increasing the chance of scale formation. As the temperature increases more scale suppression measures are required to limit scale formation, with high temperatures more likely to generate a greater amount of scale even with fully functional scale suppression systems in place. Chemistry is also a significant factor in scale formation, with Phosphorus being the most significant of the chemistry variables. It is recommended that the chemistry specification for Phosphorus be limited to a maximum value of 0.015 % rather than 0.020 % to limit scale formation. Slabs with higher values should be treated with particular care when being processed through the Hot Mill to limit scale formation.

Monitorization of technosols in old mining sites treated with calcareous fillers

NASA Astrophysics Data System (ADS)

Martínez-Sanchez, MJose; Perez-Sirvent, Carmen; Garcia-Lorenzo, MariLuz; Gonzalez, Eva; Perez-Espinosa, Victor; Martínez-Lopez, Salvadora; Hernandez, Carmen; Molina, Jose; Martínez, Lucia B.

2014-05-01

A large number of soils around the world are contaminated by heavy metals due to mining activities, generating adverse effects on human health and the environment. In response to these negative effects, a variety of technologies to remediate soils affected by heavy metals have been developed. Among them, in situ immobilization by means of soil amendment is a non-intrusive and cost effective alternative, that transforms the highly mobile toxic heavy metals to physico-chemically stable forms, reducing their mobility and environmental risks. Limestone filler is a good selection for such a purpose, because of its low permeability and low solubility, due to its high degree of physical-chemical stability and because is a non-toxic material with a high finely divided calcium carbonate content. In addition, the use of this amendment could revalorize the residues, reducing the costs of the process. The objective of this work was to evaluate the effectiveness of a immobilization technique in sediments contaminated by heavy metals as a results of mining activities. The study area was Portman bay, located close to the mining region of La Unión and subjected to mining from the time of the Roman Empire to 1991. Wastes from mining activities mainly consisted in ore materials (galena, pyrite and sphalerite), phyllosilicates, in addition to siderite, iron oxides and sometimes alteration products such as jarosite, alunite, kaolinite and greenalite. These materials have suffered a concentration process by floatation with sea water and, as a result of the discharge, the whole of the bay has filled up with wastes which also extend into the Mediterranean Sea. Two experimental areas, approximately 1 Ha each one, were selected and technosols were developed as follows: original sediments from the bay, sediments mixed with limestone filler in a 1:1 proportion, gravel to avoid capillary and natural soil to allow plant growth. After the remediation technique was applied, monitorization of experimental areas was done in 18 sampling points in which sediment and water samples were collected and analyzed. Monitorization was carried out during a 4 years period, samples being obtained at two month intervals. The pH and the electrical conductivity were determined, in naddition to the heavy metal concentration. The Zn content was determined by flame atomic absorption spectrometry. The Pb, Cd and Cu content was determined by electrothermal atomization atomic absorption spectrometry. The As content was measured by atomic fluorescence spectrometry using an automated continuous flow hydride generation spectrometer. In addition, Microtox bioassay was applied in order to study ecotoxicity of collected water samples. Sediments before the remediation technique showed acidic pH, high EC values and high trace elements content. The results obtained after the immobilization showed that sediment samples had neutral pH (average value of 8.3) low electrical conductivity (1.32 dS m-1) and low trace elements concentration, in some cases below the detection limit. When water samples obtained in the piezometers were evaluated, the results indicated that these samples correspond to rainfall waters and were characterized by neutral pH and trace elements concentration below the detection limit. In addition, none of them showed toxicity when submitted to the selected bioassay Then, we can conclude that the use of limestone filler constitutes an excellent option in sediments polluted by trace elements, because of risk for human health or ecosystem does not exist or is decreased in a large extent after the intervention. In addition, the designed experience allows stabilizer proportion to be optimized and may suppose a big cost-saving in the project in areas affected by mining activities.

From Data to Equations: Inferring the Laws governing Saturn's Ring Temperature

NASA Astrophysics Data System (ADS)

Altobelli, N.; Lopez-Paz, D.; Spilker, L.; Pilorz, S.

2011-10-01

Six years after Saturn Orbit Insertion (SOI), the Composite Infrared Spectrometer (CIRS) on-board the Cassini Spacecraft has been performing a thermal mapping of Saturn's main rings, by measuring the thermal radiance in the far-infrared ( [10-600] cm-1 ) for different viewing geometries. So far, more than 2.5 millions individual spectra have been recorded, from Saturn's northern winter solstice till Saturn's northern spring. We present a first attempt of treating the data set globally by applying numerical data mining techniques inherited from the field of artificial intelligence, such as neural networks and genetic programing.

Modern methods of surveyor observations in opencast mining under complex hydrogeological conditions.

NASA Astrophysics Data System (ADS)

Usoltseva, L. A.; Lushpei, V. P.; Mursin, VA

2017-10-01

The article considers the possibility of linking the modern methods of surveying security of open mining works to improve industrial safety in the Primorsky Territory, as well as their use in the educational process. Industrial Safety in the management of Surface Mining depends largely on the applied assessment methods and methods of stability of pit walls and slopes of dumps in the complex mining and hydro-geological conditions.

Feature selection and classification model construction on type 2 diabetic patients' data.

PubMed

Huang, Yue; McCullagh, Paul; Black, Norman; Harper, Roy

2007-11-01

Diabetes affects between 2% and 4% of the global population (up to 10% in the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial public health and health economics issues in the 21st century. The aim of this research was to identify significant factors influencing diabetes control, by applying feature selection to a working patient management system to assist with ranking, classification and knowledge discovery. The classification models can be used to determine individuals in the population with poor diabetes control status based on physiological and examination factors. The diabetic patients' information was collected by Ulster Community and Hospitals Trust (UCHT) from year 2000 to 2004 as part of clinical management. In order to discover key predictors and latent knowledge, data mining techniques were applied. To improve computational efficiency, a feature selection technique, feature selection via supervised model construction (FSSMC), an optimisation of ReliefF, was used to rank the important attributes affecting diabetic control. After selecting suitable features, three complementary classification techniques (Naïve Bayes, IB1 and C4.5) were applied to the data to predict how well the patients' condition was controlled. FSSMC identified patients' 'age', 'diagnosis duration', the need for 'insulin treatment', 'random blood glucose' measurement and 'diet treatment' as the most important factors influencing blood glucose control. Using the reduced features, a best predictive accuracy of 95% and sensitivity of 98% was achieved. The influence of factors, such as 'type of care' delivered, the use of 'home monitoring', and the importance of 'smoking' on outcome can contribute to domain knowledge in diabetes control. In the care of patients with diabetes, the more important factors identified: patients' 'age', 'diagnosis duration' and 'family history', are beyond the control of physicians. Treatment methods such as 'insulin', 'diet' and 'tablets' (a variety of oral medicines) may be controlled. However lifestyle indicators such as 'body mass index' and 'smoking status' are also important and may be controlled by the patient. This further underlines the need for public health education to aid awareness and prevention. More subtle data interactions need to be better understood and data mining can contribute to the clinical evidence base. The research confirms and to a lesser extent challenges current thinking. Whilst fully appreciating the requirement for clinical verification and interpretation, this work supports the use of data mining as an exploratory tool, particularly as the domain is suffering from a data explosion due to enhanced monitoring and the (potential) storage of this data in the electronic health record. FSSMC has proved a useful feature estimator for large data sets, where processing efficiency is an important factor.

ArcView Coal Evaluation User's Guide

USGS Publications Warehouse

Watson, William

2007-01-01

Purpose: The objective of the ArcView Coal Evaluation (ACE) is to estimate the amount and location of coal available to be mined by various coal mining technologies, based on the geologic coverages developed in the National Coal Resource Assessment (NCRA) which are the starting coverages used in the Geographic Information Systems (GIS) evaluation of coal resources. The ACE Users Guide provides many examples of how to apply technical limits based upon mining technology. The methods, which are iterative for any given mining technology, should transfer directly by mining technology to other coal beds.

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Application of Modern Tools and Techniques for Mine Safety & Disaster Management

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj

2016-04-01

The implementation of novel systems and adoption of improvised equipment in mines help mining companies in two important ways: enhanced mine productivity and improved worker safety. There is a substantial need for adoption of state-of-the-art automation technologies in the mines to ensure the safety and to protect health of mine workers. With the advent of new autonomous equipment used in the mine, the inefficiencies are reduced by limiting human inconsistencies and error. The desired increase in productivity at a mine can sometimes be achieved by changing only a few simple variables. Significant developments have been made in the areas of surface and underground communication, robotics, smart sensors, tracking systems, mine gas monitoring systems and ground movements etc. Advancement in information technology in the form of internet, GIS, remote sensing, satellite communication, etc. have proved to be important tools for hazard reduction and disaster management. This paper is mainly focused on issues pertaining to mine safety and disaster management and some of the recent innovations in the mine automations that could be deployed in mines for safe mining operations and for avoiding any unforeseen mine disaster.

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

PubMed Central

Tameem, Hussain Z.; Sinha, Usha S.

2011-01-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features. PMID:21785520

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

NASA Astrophysics Data System (ADS)

Tameem, Hussain Z.; Sinha, Usha S.

2007-11-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.

30 CFR 903.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities, applies to surface...

30 CFR 903.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities, applies to surface...

30 CFR 903.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities, applies to surface...

30 CFR 903.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities, applies to surface...

Privacy Is Become with, Data Perturbation

NASA Astrophysics Data System (ADS)

Singh, Er. Niranjan; Singhai, Niky

2011-06-01

Privacy is becoming an increasingly important issue in many data mining applications that deal with health care, security, finance, behavior and other types of sensitive data. Is particularly becoming important in counterterrorism and homeland security-related applications. We touch upon several techniques of masking the data, namely random distortion, including the uniform and Gaussian noise, applied to the data in order to protect it. These perturbation schemes are equivalent to additive perturbation after the logarithmic Transformation. Due to the large volume of research in deriving private information from the additive noise perturbed data, the security of these perturbation schemes is questionable Many artificial intelligence and statistical methods exist for data analysis interpretation, Identifying and measuring the interestingness of patterns and rules discovered, or to be discovered is essential for the evaluation of the mined knowledge and the KDD process as a whole. While some concrete measurements exist, assessing the interestingness of discovered knowledge is still an important research issue. As the tool for the algorithm implementations we chose the language of choice in industrial world MATLAB.

Chemical named entities recognition: a review on approaches and applications

PubMed Central

2014-01-01

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to “text mine” these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted. PMID:24834132

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

PubMed

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges

PubMed Central

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area. PMID:28401016

Mainstreaming gender and promoting intersectionality in Papua New Guinea's health policy: a triangulated analysis applying data-mining and content analytic techniques.

PubMed

Lamprell, G; Braithwaite, J

2017-04-20

Gender mainstreaming is an approach to policy and planning that emphasizes equality between the sexes. It is the stated policy for gender equity in Papua New Guinea's (PNG) health sector, as well as all other sectors, and is enshrined in the policies of its biggest aid givers. However, there is criticism that gender mainstreaming's application has too often been technocratic and lacking in conceptual clarity not only in PNG but elsewhere. In the health sector this is further exacerbated by a traditional bio-medical approach, which is often paternalistic and insufficiently patient- and family-centered. This study analyses the policy attitudes toward gender in PNG's health sector using both data-mining and a traditional, summative content analysis. Our results show that gender is rarely mentioned. When it is, it is most often mentioned in relation to programs such as maternity and childcare for women, and elsewhere is applied technocratically. For PNG to promote greater levels of equity, the focus should first be on conceptualizing gender in a way that is meaningful for Papuans, taking into account the diversity of experiences and setting. Second, there should be greater focus on activists and civil society groups as the stakeholders most likely to make a difference in gender equity.

Application of the Deformation Information System for automated analysis and mapping of mining terrain deformations - case study from SW Poland

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Grzempowski, Piotr; Milczarek, Wojciech; Nowacka, Anna

2015-04-01

Monitoring, mapping and modelling of mining induced terrain deformations are important tasks for quantifying and minimising threats that arise from underground extraction of useful minerals and affect surface infrastructure, human safety, the environment and security of the mining operation itself. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and expanding with the progress in geographical information technologies. These include for example: terrestrial geodetic measurements, Global Navigation Satellite Systems, remote sensing, GIS based modelling and spatial statistics, finite element method modelling, geological modelling, empirical modelling using e.g. the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The presentation shows the results of numerical modelling and mapping of mining terrain deformations for two cases of underground mining sites in SW Poland, hard coal one (abandoned) and copper ore (active) using the functionalities of the Deformation Information System (DIS) (Blachowski et al, 2014 @ http://meetingorganizer.copernicus.org/EGU2014/EGU2014-7949.pdf). The functionalities of the spatial data modelling module of DIS have been presented and its applications in modelling, mapping and visualising mining terrain deformations based on processing of measurement data (geodetic and GNSS) for these two cases have been characterised and compared. These include, self-developed and implemented in DIS, automation procedures for calculating mining terrain subsidence with different interpolation techniques, calculation of other mining deformation parameters (i.e. tilt, horizontal displacement, horizontal strain and curvature), as well as mapping mining terrain categories based on classification of the values of these parameters as used in Poland. Acknowledgments. This work has been financed from the National Science Centre Project "Development of a numerical method of mining ground deformation modelling in complex geological and mining conditions" UMO-2012/07/B/ST10/04297 executed at the Faculty of Geoengineering, Mining and Geology of the Wroclaw University of Technology (Poland).

Time-lapse seismic tomography using the data of microseismic monitoring network and analysis of mine-induced events, seismic tomography results and technological data in Pyhäsalmi mine, Finland

NASA Astrophysics Data System (ADS)

Nevalainen, Jouni; Kozlovskaya, Elena

2016-04-01

We present results of a seismic travel-time tomography applied to microseismic data from the Pyhäsalmi mine, Finland. The data about microseismic events in the mine is recorded since 2002 when the passive microseismic monitoring network was installed in the mine. Since that over 130000 microseismic events have been observed. The first target of our study was to test can the passive microseismic monitoring data be used with travel-time tomography. In this data set the source-receiver geometry is based on non-even distribution of natural and mine-induced events inside and in the vicinity of the mine and hence, is a non-ideal one for the travel-time tomography. The tomographic inversion procedure was tested with the synthetic data and real source-receiver geometry from Pyhäsalmi mine and with the real travel-time data of the first arrivals of P-waves from the microseismic events. The results showed that seismic tomography is capable to reveal differences in seismic velocities in the mine area corresponding to different rock types. For example, the velocity contrast between the ore body and surrounding rock is detectable. The velocity model recovered agrees well with the known geological structures in the mine area. The second target of the study was to apply the travel-time tomography to microseismic monitoring data recorded during different time periods in order to track temporal changes in seismic velocities within the mining area as the excavation proceeds. The result shows that such a time-lapse travel-time tomography can recover such changes. In order to obtain good ray coverage and good resolution, the time interval for a single tomography round need to be selected taking into account the number of events and their spatial distribution. The third target was to compare and analyze mine-induced event locations, seismic tomography results and mining technological data (for example, mine excavation plans) in order to understand the influence of mining technology to mining-induced seismicity. Acknowledgements: This study has been supported by ERDF SEISLAB project and Pyhäsalmi Mine Ltd.

30 CFR 922.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 922.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 947.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 941.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 922.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 912.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 905.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 910.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 905.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 905.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 941.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 912.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 941.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 922.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 947.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 947.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 941.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 910.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 910.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 910.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 905.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitions, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 941.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 912.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 947.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 947.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 922.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

30 CFR 912.764 - Process for designating areas unsuitable for surface coal mining operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Mining Operations, pertaining to petitioning, initial processing, hearing requirements, decisions, data base and inventory systems, public information, and regulatory responsibilities shall apply to surface...

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

Occupancy schedules learning process through a data mining framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'Oca, Simona; Hong, Tianzhen

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

Mapping Environmental Contaminants at Ray Mine, AZ

NASA Technical Reports Server (NTRS)

McCubbin, Ian; Lang, Harold

2000-01-01

Airborne Visible and InfraRed Imaging Spectrometer (AVIRIS) data was collected over Ray Mine as part of a demonstration project for the Environmental Protection Agency (EPA) through the Advanced Measurement Initiative (AMI). The overall goal of AMI is to accelerate adoption and application of advanced measurement technologies for cost effective environmental monitoring. The site was selected to demonstrate the benefit to EPA in using advanced remote sensing technologies for the detection of environmental contaminants due to the mineral extraction industry. The role of the Jet Propulsion Laboratory in this pilot study is to provide data as well as performing calibration, data analysis, and validation of the AVIRIS results. EPA is also interested in developing protocols that use commercial software to perform such work on other high priority EPA sites. Reflectance retrieval was performed using outputs generated by the MODTRAN radiative transfer model and field spectra collected for the purpose of calibration. We are presenting advanced applications of the ENVI software package using n-Dimensional Partial Unmixing to identify image-derived endmembers that best match target materials reference spectra from multiple spectral libraries. Upon identification of the image endmembers the Mixture Tuned Match Filter algorithm was applied to map the endmembers within each scene. Using this technique it was possible to map four different mineral classes that are associated with mine generated acid waste.

Occupancy schedules learning process through a data mining framework

DOE PAGES

D'Oca, Simona; Hong, Tianzhen

2014-12-17

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

PubMed Central

Zhang, Lei; Li, Yin; Guo, Xinfeng; May, Brian H.; Xue, Charlie C. L.; Yang, Lihong; Liu, Xusheng

2014-01-01

Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1) identification of candidate ancient terms; (2) systemic search and assessment of medical records written in classical Chinese; (3) preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction) was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae. PMID:24744808

Preventing spontaneous combustion after mine closing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewicki, G.

1987-11-01

The author explains how the Northern Coal Company and a Houston-based firefighting firm developed an innovative technique to reduce the risk of spontaneous combustion after mine closing in its Rienau number2 Mine. The ''Light Water TM'' ATC series of firefighting foam concentrates were designed for extinguishing flammable liquid fires. By slightly altering the chemicals, the concentrates could be used to seal the coal ribs, floor, and roof, reducing the risk of combustion. Subsequent monitoring of the mine has identified no signs of heating.

Artisanal and Small-Scale Gold Mining Without Mercury

EPA Pesticide Factsheets

Mercury-free techniques are safer for miners, their families and local communities. They can also help miners qualify for certification under fair-mined standards, potentially allowing them to market their gold at higher prices.

The Pollution Detectives: Part II. Lead and Zinc Mining.

ERIC Educational Resources Information Center

Sanderson, P. L.

1988-01-01

Describes a field trip taken to an old mining area to study water pollution. Discussed are methods for silt analysis, reagent preparation, color charts, techniques, fieldwork, field results, and a laboratory study. (CW)

30 CFR 886.27 - What special procedures apply to Indian lands not subject to an approved Tribal reclamation program?

Code of Federal Regulations, 2010 CFR

2010-07-01

... OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR ABANDONED MINE LAND... mitigate emergency situations or extreme danger situations arising from past mining practices and begin... Indian tribe and the Bureau of Indian Affairs office having jurisdiction over the Indian lands. (d) If a...

An integrated environment monitoring system for underground coal mines--Wireless Sensor Network subsystem with multi-parameter monitoring.

PubMed

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-07-21

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

40 CFR 440.14 - New source performance standards (NSPS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... reduction attainable by applying the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines operated to obtain iron ore shall not exceed: Effluent...