Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

PubMed Central

Men, Hong; Shi, Yan; Fu, Songlin; Jiao, Yanan; Qiao, Yu; Liu, Jingjing

2017-01-01

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. PMID:28753917

SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

NASA Astrophysics Data System (ADS)

Xu, Xiwei; Zhang, Changhai

2013-12-01

Now some sequential patterns mining algorithms generate too many candidate sequences, and increase the processing cost of support counting. Therefore, we present an effective and scalable algorithm called SPMBR (Sequential Patterns Mining based on Bitmap Representation) to solve the problem of mining the sequential patterns for large databases. Our method differs from previous related works of mining sequential patterns. The main difference is that the database of sequential patterns is represented by bitmaps, and a simplified bitmap structure is presented firstly. In this paper, First the algorithm generate candidate sequences by SE(Sequence Extension) and IE(Item Extension), and then obtain all frequent sequences by comparing the original bitmap and the extended item bitmap .This method could simplify the problem of mining the sequential patterns and avoid the high processing cost of support counting. Both theories and experiments indicate that the performance of SPMBR is predominant for large transaction databases, the required memory size for storing temporal data is much less during mining process, and all sequential patterns can be mined with feasibility.

Mine Planning for Asteroid Orebodies

NASA Astrophysics Data System (ADS)

Gertsch, L. S.; Gertsch, R. E.

2000-01-01

Given that an asteroid (or comet) has been determined to contain sufficient material of value to be potentially economic to exploit, a mining method must be selected and implemented. This paper discusses the engineering necessary to bring a mine online, and the opportunities and challenges inherent in asteroid mineral prospects. The very important step of orebody characterization is discussed elsewhere. The mining methods discussed here are based on enclosing the asteroid within a bag in some fashion, whether completely or partially. In general, asteroid mining methods based on bags will consist of the following steps. Not all will be required in every case, nor necessarily in this particular sequence. Some steps will be performed simultaneously. Their purpose is to extract the valuable material from the body of the asteroid in the most efficient, cost-effective manner possible. In approximate order of initiation, if not of conclusion, the steps are: 1. Tether anchoring to the asteroid. 2. Asteroid motion control. 3. Body/fragment restraint system placement. 4. Operations platform construction. 5. Bag construction. 6. Auxiliary and support equipment placement. 7. Mining operations. 8. Processing operations. 9. Product transport to markets.

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.

Leaching characteristics, ecotoxicity, and risk assessment based management of mine wastes

NASA Astrophysics Data System (ADS)

Kim, J.; Ju, W. J.; Jho, E. H.; Nam, K.; Hong, J. K.

2016-12-01

Mine wastes generated during mining activities in metal mines generally contain high concentrations of metals that may impose toxic effects to surrounding environment. Thus, it is necessary to properly assess the mining-impacted landscapes for management. The study investigated leaching characteristics, potential environmental effects, and human health risk of mine wastes from three different metal mines in South Korea (molybdenum mine, lead-zinc mine, and magnetite mine). The heavy metal concentrations in the leachates obtained by using the Korean Standard Test Method for Solid Wastes (STM), Toxicity Characteristics Leaching Procedure (TCLP), and Synthetic Precipitation Leaching Procedure (SPLP) met the Korea Waste Control Act and the USEPA region 3 regulatory levels accordingly, even though the mine wastes contained high concentrations of metals. Assuming that the leachates may get into nearby water sources, the leachate toxicity was tested using Daphnia Magna. The toxic unit (TU) values after 24 h and 48 h exposure of all the mine wastes tested met the Korea Allowable Effluent Water Quality Standards (TU<1). The column leaching test showed that the lead-zinc mine waste may have long-term toxic effects (TU>1 for the eluent at L/S of 30) implying that the long-term effect of mine wastes left in mining areas need to be assessed. Considering reuse of mine wastes as a way of managing mine wastes, the human health risk assessment of reusing the lead-zinc mine waste in industrial areas was carried out using the bioavailable fraction of the heavy metals contained in the mine wastes, which was determined by using the Solubility/Bioavailability Research Consortium method. There may be potential carcinogenic risk (9.7E-05) and non-carcinogenic risk (HI, Hazard Index of 1.0E+00) as CR≧1.0E-05 has carcinogenic risk and HI≧1.0E+00 has non-carcinogenic risk. Overall, this study shows that not only the concentration-based assessment but ecological toxic effect and human health risk based assessments can be utilized for mining-impacted landscapes management.

Scenario-Based Systems Engineering Application to Mine Warfare

DTIC Science & Technology

2015-12-01

hunter- killer capabilities to find, classify, and destroy moored and bottom mines with sonar and video systems, cable cutting devices, and mine...Method: Serial 3 LCS MH-60s LCS: RMS with AN/AQS-20, MH-60s: Archerfish Hunt Method: Serial 1M MK18 MH-60s LCS: MK18 Mod 2, MH-60s...Archerfish Hunt Method: Serial 2M MK18 MH-60s LCS: MK18 Mod 2, MH-60s: Archerfish Hunt Method: Serial 3M MK18 MH-60s LCS: MK18

Power System Transient Stability Based on Data Mining Theory

NASA Astrophysics Data System (ADS)

Cui, Zhen; Shi, Jia; Wu, Runsheng; Lu, Dan; Cui, Mingde

2018-01-01

In order to study the stability of power system, a power system transient stability based on data mining theory is designed. By introducing association rules analysis in data mining theory, an association classification method for transient stability assessment is presented. A mathematical model of transient stability assessment based on data mining technology is established. Meanwhile, combining rule reasoning with classification prediction, the method of association classification is proposed to perform transient stability assessment. The transient stability index is used to identify the samples that cannot be correctly classified in association classification. Then, according to the critical stability of each sample, the time domain simulation method is used to determine the state, so as to ensure the accuracy of the final results. The results show that this stability assessment system can improve the speed of operation under the premise that the analysis result is completely correct, and the improved algorithm can find out the inherent relation between the change of power system operation mode and the change of transient stability degree.

Mining algorithm for association rules in big data based on Hadoop

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Wang, Xiaojing; Zhang, Lijun; Qiao, Liying

2018-04-01

In order to solve the problem that the traditional association rules mining algorithm has been unable to meet the mining needs of large amount of data in the aspect of efficiency and scalability, take FP-Growth as an example, the algorithm is realized in the parallelization based on Hadoop framework and Map Reduce model. On the basis, it is improved using the transaction reduce method for further enhancement of the algorithm's mining efficiency. The experiment, which consists of verification of parallel mining results, comparison on efficiency between serials and parallel, variable relationship between mining time and node number and between mining time and data amount, is carried out in the mining results and efficiency by Hadoop clustering. Experiments show that the paralleled FP-Growth algorithm implemented is able to accurately mine frequent item sets, with a better performance and scalability. It can be better to meet the requirements of big data mining and efficiently mine frequent item sets and association rules from large dataset.

Real -time dispatching modelling for trucks with different capacities in open pit mines / Modelowanie w czasie rzeczywistym przewozów ciężarówek o różnej ładowności w kopalni odkrywkowej

NASA Astrophysics Data System (ADS)

Ahangaran, Daryoush Kaveh; Yasrebi, Amir Bijan; Wetherelt, Andy; Foster, Patrick

2012-10-01

Application of fully automated systems for truck dispatching plays a major role in decreasing the transportation costs which often represent the majority of costs spent on open pit mining. Consequently, the application of a truck dispatching system has become fundamentally important in most of the world's open pit mines. Recent experiences indicate that by decreasing a truck's travelling time and the associated waiting time of its associated shovel then due to the application of a truck dispatching system the rate of production will be considerably improved. Computer-based truck dispatching systems using algorithms, advanced and accurate software are examples of these innovations. Developing an algorithm of a computer- based program appropriated to a specific mine's conditions is considered as one of the most important activities in connection with computer-based dispatching in open pit mines. In this paper the changing trend of programming and dispatching control algorithms and automation conditions will be discussed. Furthermore, since the transportation fleet of most mines use trucks with different capacities, innovative methods, operational optimisation techniques and the best possible methods for developing the required algorithm for real-time dispatching are selected by conducting research on mathematical-based planning methods. Finally, a real-time dispatching model compatible with the requirement of trucks with different capacities is developed by using two techniques of flow networks and integer programming.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

PubMed

Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

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…

An Evaluation of Practical Applicability of Multi-Assortment Production Break-Even Analysis based on Mining Companies

NASA Astrophysics Data System (ADS)

Fuksa, Dariusz; Trzaskuś-Żak, Beata; Gałaś, Zdzisław; Utrata, Arkadiusz

2017-03-01

In the practice of mining companies, the vast majority of them produce more than one product. The analysis of the break-even, which is referred to as CVP (Cost-Volume-Profit) analysis (Wilkinson, 2005; Czopek, 2003) in their case is significantly constricted, given the necessity to include multi-assortment structure in the analysis, which may have more than 20 types of assortments (depending on the grain size) in their offer, as in the case of open-pit mines. The article presents methods of evaluation of break-even (volume and value) for both a single-assortment production and a multi-assortment production. The complexity of problem of break-even evaluation for multi-assortment production has resulted in formation of many methods, and, simultaneously, various approaches to its analysis, especially differences in accounting fixed costs, which may be either totally accounted for among particular assortments, relating to the whole company or partially accounted for among particular assortments and partially relating to the company, as a whole. The evaluation of the chosen methods of break-even analysis, given the availability of data, was based on two examples of mining companies: an open-pit mine of rock materials and an underground hard coal mine. The selection of methods was set by the available data provided by the companies. The data for the analysis comes from internal documentation of the mines - financial statements, breakdowns and cost calculations.

[Research of bleeding volume and method in blood-letting acupuncture therapy based on data mining].

PubMed

Liu, Xin; Jia, Chun-Sheng; Wang, Jian-Ling; Du, Yu-Zhu; Zhang, Xiao-Xu; Shi, Jing; Li, Xiao-Feng; Sun, Yan-Hui; Zhang, Shen; Zhang, Xuan-Ping; Gang, Wei-Juan

2014-03-01

Through computer-based technology and data mining method, with treatment in cases of bloodletting acupuncture therapy in collected literature as sample data, the association rule in data mining was applied. According to self-built database platform, the data was input, arranged and summarized, and eventually required data was acquired to perform the data mining of bleeding volume and method in blood-letting acupuncture therapy, which summarized its application rules and clinical values to provide better guide for clinical practice. There were 9 kinds of blood-letting tools in the literature, in which the frequency of three-edge needle was the highest, accounting for 84.4% (1239/1468). The bleeding volume was classified into six levels, in which less volume (less than 0.1 mL) had the highest frequency (401 times). According to the results of the data mining, blood-letting acupuncture therapy was widely applied in clinical practice of acupuncture, in which use of three-edge needle and less volume (less than 0.1 mL) of blood were the most common, however, there was no central tendency in general.

Mining and beneficiation: A review of possible lunar applications

NASA Technical Reports Server (NTRS)

Chamberlain, Peter G.

1991-01-01

Successful exploration of Mars and outer space may require base stations strategically located on the Moon. Such bases must develop a certain self-sufficiency, particularly in the critical life support materials, fuel components, and construction materials. Technology is reviewed for the first steps in lunar resource recovery-mining and beneficiation. The topic is covered in three main categories: site selection; mining; and beneficiation. It will also include (in less detail) in-situ processes. The text described mining technology ranging from simple diggings and hauling vehicles (the strawman) to more specialized technology including underground excavation methods. The section of beneficiation emphasizes dry separation techniques and methods of sorting the ore by particle size. In-situ processes, chemical and thermal, are identified to stimulate further thinking by future researchers.

Edu-Mining for Book Recommendation for Pupils

ERIC Educational Resources Information Center

Nagata, Ryo; Takeda, Keigo; Suda, Koji; Kakegawa, Junichi; Morihiro, Koichiro

2009-01-01

This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan histories (pupil ID, book ID, date of loan) whereas the conventional methods require additional information such as taste information from a great number of users which…

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.

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Evaluation of a Cubature Kalman Filtering-Based Phase Unwrapping Method for Differential Interferograms with High Noise in Coal Mining Areas

PubMed Central

Liu, Wanli; Bian, Zhengfu; Liu, Zhenguo; Zhang, Qiuzhao

2015-01-01

Differential interferometric synthetic aperture radar has been shown to be effective for monitoring subsidence in coal mining areas. Phase unwrapping can have a dramatic influence on the monitoring result. In this paper, a filtering-based phase unwrapping algorithm in combination with path-following is introduced to unwrap differential interferograms with high noise in mining areas. It can perform simultaneous noise filtering and phase unwrapping so that the pre-filtering steps can be omitted, thus usually retaining more details and improving the detectable deformation. For the method, the nonlinear measurement model of phase unwrapping is processed using a simplified Cubature Kalman filtering, which is an effective and efficient tool used in many nonlinear fields. Three case studies are designed to evaluate the performance of the method. In Case 1, two tests are designed to evaluate the performance of the method under different factors including the number of multi-looks and path-guiding indexes. The result demonstrates that the unwrapped results are sensitive to the number of multi-looks and that the Fisher Distance is the most suitable path-guiding index for our study. Two case studies are then designed to evaluate the feasibility of the proposed phase unwrapping method based on Cubature Kalman filtering. The results indicate that, compared with the popular Minimum Cost Flow method, the Cubature Kalman filtering-based phase unwrapping can achieve promising results without pre-filtering and is an appropriate method for coal mining areas with high noise. PMID:26153776

Comparison of different static methods for assessment of AMD generation potential in mining waste dumps in the Muteh Gold Mines, Iran.

PubMed

Mohammadi, Zohreh; Modabberi, Soroush; Jafari, Mohammad Reza; Ajayebi, Kimia Sadat

2015-06-01

Acid mine drainage (AMD) gives rise to several problems in sulfide-bearing mineral deposits whether in an ore body or in the mining wastes and tailings. Hence, several methods and parameters have been proposed to evaluate the acid-producing and acid-neutralizing potential of a material. This research compares common static methods for evaluation of acid-production potential of mining wastes in the Muteh gold mines by using 62 samples taken from six waste dumps around Senjedeh and Chah-Khatoun mines. According to a detailed mineralogical study, the waste materials are composed of mica-schist and quartz veins with a high amount of pyrite and are supposed to be susceptible to acid production, and upon a rainfall, they release acid drainage. All parameters introduced in different methods were calculated and compared in this research in order to predict the acid-generating and neutralization potential, including APP, NNP, MPA, NPR, and NAGpH. Based on the analytical results and calculation of different parameters, all methods are in a general consensus that DWS-02 and DWS-03 waste dumps are acid-forming which is clearly attributed to high content of pyrite in samples. DWS-04 is considered as non-acid forming in all methods except method 8 which is uncertain about its acid-forming potential and method 7 which considers a low potential for it. DWC-01 is acid-forming based on all methods except 8, 9, 10, and 11 which are also uncertain about its potential. The methods used are not reached to a compromise on DWS-01 and DWC-02 waste dumps. It is supposed that method 7 gives the conservationist results in all cases. Method 8 is unable to decide on some cases. It is recommended to use and rely on results provided by methods 1, 2, 3, and 12 for taking decisions for further studies. Therefore, according to the static tests used, the aforementioned criteria in selected methods can be used with much confidence as a rule of thumb estimation.

Acid-base accounting to predict post-mining drainage quality on surface mines.

PubMed

Skousen, J; Simmons, J; McDonald, L M; Ziemkiewicz, P

2002-01-01

Acid-base accounting (ABA) is an analytical procedure that provides values to help assess the acid-producing and acid-neutralizing potential of overburden rocks prior to coal mining and other large-scale excavations. This procedure was developed by West Virginia University scientists during the 1960s. After the passage of laws requiring an assessment of surface mining on water quality, ABA became a preferred method to predict post-mining water quality, and permitting decisions for surface mines are largely based on the values determined by ABA. To predict the post-mining water quality, the amount of acid-producing rock is compared with the amount of acid-neutralizing rock, and a prediction of the water quality at the site (whether acid or alkaline) is obtained. We gathered geologic and geographic data for 56 mined sites in West Virginia, which allowed us to estimate total overburden amounts, and values were determined for maximum potential acidity (MPA), neutralization potential (NP), net neutralization potential (NNP), and NP to MPA ratios for each site based on ABA. These values were correlated to post-mining water quality from springs or seeps on the mined property. Overburden mass was determined by three methods, with the method used by Pennsylvania researchers showing the most accurate results for overburden mass. A poor relationship existed between MPA and post-mining water quality, NP was intermediate, and NNP and the NP to MPA ratio showed the best prediction accuracy. In this study, NNP and the NP to MPA ratio gave identical water quality prediction results. Therefore, with NP to MPA ratios, values were separated into categories: <1 should produce acid drainage, between 1 and 2 can produce either acid or alkaline water conditions, and >2 should produce alkaline water. On our 56 surface mined sites, NP to MPA ratios varied from 0.1 to 31, and six sites (11%) did not fit the expected pattern using this category approach. Two sites with ratios <1 did not produce acid drainage as predicted (the drainage was neutral), and four sites with a ratio >2 produced acid drainage when they should not have. These latter four sites were either mined very slowly, had nonrepresentative ABA data, received water from an adjacent underground mine, or had a surface mining practice that degraded the water. In general, an NP to MPA ratio of <1 produced mostly acid drainage sites, between 1 and 2 produced mostly alkaline drainage sites, while NP to MPA ratios >2 produced alkaline drainage with a few exceptions. Using these values, ABA is a good tool to assess overburden quality before surface mining and to predict post-mining drainage quality after mining. The interpretation from ABA values was correct in 50 out of 52 cases (96%), excluding the four anomalous sites, which had acid water for reasons other than overburden quality.

Chapter 2: The forestry reclamation approach

Treesearch

Jim Burger; Don Graves; Patrick Angel; Vic Davis; Carl Zipper

2017-01-01

The Forestry Reclamation Approach (FRA) is a method for reclaiming coal-mined land to forest under the federal Surface Mining Control and Reclamation Act of 1977 (SMCRA). The FRA is based on knowledge gained from both scientific research and experience (Fig. 2-1). The FRA can achieve cost-effective regulatory compliance for mine operators while creating productive...

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.

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.

Defining the Cubature Changes of Historic St. Kinga Chamber in Bochnia Salt Mine, Using Laser Scanning Technology

NASA Astrophysics Data System (ADS)

Szafarczyk, Anna; Gawałkiewicz, Rafał

2018-03-01

In Poland, there are many mining enterprises, of historic character registered in the UNESCO World Heritage List. One of the oldest mining enterprises in Poland is the Salt Mine in Bochnia. The processes inside the rock mass require that surveying services carry out regular geometric control of the cavities. A particular attention should be paid (due to its sacral function) on St. Kinga Chamber, located 195 metres below the surface, on the mine level "August". So far measurement technologies have been connected with the studies on changes in the geometry of cavities and based on linear bases used to measure convergence. This only provides discrete information (in a point) and not always presents a real state of deformation. In the scanning method, in practice a three dimension image of changes (structural deformations) is obtained, impossible to determine with the application of measurement methods, applied to measure the value of linear convergence (the method with a limited number of bases). Laser scanning, apart from determining the value of volume convergence, gives also the possibility of the visualization of 3D cavern. Moreover, it provides direct information to update mining numerical maps and make it possible to generate various cross-sections through the cavern. The authors analysed the possibility of the application of laser scanning (scanner Faro Focus 3D), as a modern tool allowing the measuring of the value of volume convergence.

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.

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.

Industrial chimney monitoring - contemporary methods

NASA Astrophysics Data System (ADS)

Kaszowska, Olga; Gruchlik, Piotr; Mika, Wiesław

2018-04-01

The paper presents knowledge acquired during the monitoring of a flue-gas stack, performed as part of technical and scientific surveillance of mining activity and its impact on industrial objects. The chimney is located in an area impacted by mining activity since the 1970s, from a coal mine which is no longer in existence. In the period of 2013-16, this area was subject to mining carried out by a mining entrepreneur who currently holds a license to excavate hard coal. Periodic measurements of the deflection of the 113-meter chimney are performed using conventional geodetic methods. The GIG used 3 methods to observe the stack: landbased 3D laser scanning, continuous deflection monitoring with a laser sensor, and drone-based visual inspections. The drone offered the possibility to closely inspect the upper sections of the flue-gas stack, which are difficult to see from the ground level.

Blasting preparation for selective mining of complex structured ore deposition

NASA Astrophysics Data System (ADS)

Marinin, M. A.; Dolzhikov, V. V.

2017-10-01

Technological features of ore mining in the open pit development for processing of complex structured ore deposit of steeply falling occurrence have been considered. The technological schemes of ore bodies mining under different conditions of occurrence, consistency and capacity have been considered and offered in the paper. These technologies permit to reduce losses and dilution, but to increase the completeness and quality of mined ore. A method of subsequent selective excavation of ore bodies has been proposed. The method is based on the complex use of buffer-blasting technology for the muck mass and the principle of trim blasting at ore-rock junctions.

Evaluation and selection of decision-making methods to assess landfill mining projects.

PubMed

Hermann, Robert; Baumgartner, Rupert J; Vorbach, Stefan; Ragossnig, Arne; Pomberger, Roland

2015-09-01

For the first time in Austria, fundamental technological and economic studies on recovering secondary raw materials from large landfills have been carried out, based on the 'LAMIS - Landfill Mining Austria' pilot project. A main focus of the research - and the subject of this article - was to develop an assessment or decision-making procedure that allows landfill owners to thoroughly examine the feasibility of a landfill mining project in advance. Currently there are no standard procedures that would sufficiently cover all the multiple-criteria requirements. The basic structure of the multiple attribute decision making process was used to narrow down on selection, conceptual design and assessment of suitable procedures. Along with a breakdown into preliminary and main assessment, the entire foundation required was created, such as definitions of requirements to an assessment method, selection and accurate description of the various assessment criteria and classification of the target system for the present 'landfill mining' vs. 'retaining the landfill in after-care' decision-making problem. Based on these studies, cost-utility analysis and the analytical-hierarchy process were selected from the range of multiple attribute decision-making procedures and examined in detail. Overall, both methods have their pros and cons with regard to their use for assessing landfill mining projects. Merging these methods or connecting them with single-criteria decision-making methods (like the net present value method) may turn out to be reasonable and constitute an appropriate assessment method. © The Author(s) 2015.

A Principal Component Analysis/Fuzzy Comprehensive Evaluation for Rockburst Potential in Kimberlite

NASA Astrophysics Data System (ADS)

Pu, Yuanyuan; Apel, Derek; Xu, Huawei

2018-02-01

Kimberlite is an igneous rock which sometimes bears diamonds. Most of the diamonds mined in the world today are found in kimberlite ores. Burst potential in kimberlite has not been investigated, because kimberlite is mostly mined using open-pit mining, which poses very little threat of rock bursting. However, as the mining depth keeps increasing, the mines convert to underground mining methods, which can pose a threat of rock bursting in kimberlite. This paper focuses on the burst potential of kimberlite at a diamond mine in northern Canada. A combined model with the methods of principal component analysis (PCA) and fuzzy comprehensive evaluation (FCE) is developed to process data from 12 different locations in kimberlite pipes. Based on calculated 12 fuzzy evaluation vectors, 8 locations show a moderate burst potential, 2 locations show no burst potential, and 2 locations show strong and violent burst potential, respectively. Using statistical principles, a Mahalanobis distance is adopted to build a comprehensive fuzzy evaluation vector for the whole mine and the final evaluation for burst potential is moderate, which is verified by a practical rockbursting situation at mine site.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

PubMed Central

2014-01-01

Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies. PMID:25350277

Research on Customer Value Based on Extension Data Mining

NASA Astrophysics Data System (ADS)

Chun-Yan, Yang; Wei-Hua, Li

Extenics is a new discipline for dealing with contradiction problems with formulize model. Extension data mining (EDM) is a product combining Extenics with data mining. It explores to acquire the knowledge based on extension transformations, which is called extension knowledge (EK), taking advantage of extension methods and data mining technology. EK includes extensible classification knowledge, conductive knowledge and so on. Extension data mining technology (EDMT) is a new data mining technology that mining EK in databases or data warehouse. Customer value (CV) can weigh the essentiality of customer relationship for an enterprise according to an enterprise as a subject of tasting value and customers as objects of tasting value at the same time. CV varies continually. Mining the changing knowledge of CV in databases using EDMT, including quantitative change knowledge and qualitative change knowledge, can provide a foundation for that an enterprise decides the strategy of customer relationship management (CRM). It can also provide a new idea for studying CV.

Combined mining: discovering informative knowledge in complex data.

PubMed

Cao, Longbing; Zhang, Huaifeng; Zhao, Yanchang; Luo, Dan; Zhang, Chengqi

2011-06-01

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

Influence of the Roof Movement Control Method on the Stability of Remnant

NASA Astrophysics Data System (ADS)

Adach-Pawelus, Karolina

2017-12-01

In the underground mines, there are geological and mining situations that necessitate leaving behind remnants in the mining field. Remnants, in the form of small, irregular parcels, are usually separated in the case of: significant problems with maintaining roof stability, high rockburst hazard, the occurrence of complex geological conditions and for random reasons (ore remnants), as well as for economic reasons (undisturbed rock remnants). Remnants left in the mining field become sites of high stress values concentration and may affect the rock in their vicinity. The values of stress inside the remnant and its vicinity, as well as the stability of the remnant, largely depend on the roof movement control method used in the mining field. The article presents the results of the numerical analysis of the influence of roof movement control method on remnant stability and the geomechanical situation in the mining field. The numerical analysis was conducted for the geological and mining conditions characteristic of Polish underground copper mines owned by KGHM Polska Miedz S.A. Numerical simulations were performed in a plane strain state by means of Phase 2 v. 8.0 software, based on the finite element method. The behaviour of remnant and rock mass in its vicinity was simulated in the subsequent steps of the room and pillar mining system for three types of roof movement control method: roof deflection, dry backfill and hydraulic backfill. The parameters of the rock mass accepted for numerical modelling were calculated by means of RocLab software on the basis of the Hoek-Brown classification. The Mohr-Coulomb strength criterion was applied.

A Volterra series-based method for extracting target echoes in the seafloor mining environment.

PubMed

Zhao, Haiming; Ji, Yaqian; Hong, Yujiu; Hao, Qi; Ma, Liyong

2016-09-01

The purpose of this research was to evaluate the applicability of the Volterra adaptive method to predict the target echo of an ultrasonic signal in an underwater seafloor mining environment. There is growing interest in mining of seafloor minerals because they offer an alternative source of rare metals. Mining the minerals cause the seafloor sediments to be stirred up and suspended in sea water. In such an environment, the target signals used for seafloor mapping are unable to be detected because of the unavoidable presence of volume reverberation induced by the suspended sediments. The detection of target signals in reverberation is currently performed using a stochastic model (for example, the autoregressive (AR) model) based on the statistical characterisation of reverberation. However, we examined a new method of signal detection in volume reverberation based on the Volterra series by confirming that the reverberation is a chaotic signal and generated by a deterministic process. The advantage of this method over the stochastic model is that attributions of the specific physical process are considered in the signal detection problem. To test the Volterra series based method and its applicability to target signal detection in the volume reverberation environment derived from the seafloor mining process, we simulated the real-life conditions of seafloor mining in a water filled tank of dimensions of 5×3×1.8m. The bottom of the tank was covered with 10cm of an irregular sand layer under which 5cm of an irregular cobalt-rich crusts layer was placed. The bottom was interrogated by an acoustic wave generated as 16μs pulses of 500kHz frequency. This frequency is demonstrated to ensure a resolution on the order of one centimetre, which is adequate in exploration practice. Echo signals were collected with a data acquisition card (PCI 1714 UL, 12-bit). Detection of the target echo in these signals was performed by both the Volterra series based model and the AR model. The results obtained confirm that the Volterra series based method is more efficient in the detection of the signal in reverberation than the conventional AR model (the accuracy is 80% for the PIM-Volterra prediction model versus 40% for the AR model). Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Research of land resources comprehensive utilization of coal mining in plain area based on GIS: case of Panyi Coal Mine of Huainan Mining Group Corp.

NASA Astrophysics Data System (ADS)

Dai, Chunxiao; Wang, Songhui; Sun, Dian; Chen, Dong

2007-06-01

The result of land use in coalfield is important to sustainable development in resourceful city. For surface morphology being changed by subsidence, the mining subsidence becomes the main problem to land use with the negative influence of ecological environment, production and steadily develop in coal mining areas. Taking Panyi Coal Mine of Huainan Mining Group Corp as an example, this paper predicted and simulated the mining subsidence in Matlab environment on the basis of the probability integral method. The change of land use types of early term, medium term and long term was analyzed in accordance with the results of mining subsidence prediction with GIS as a spatial data management and spatial analysis tool. The result of analysis showed that 80% area in Panyi Coal Mine be affected by mining subsidence and 52km2 perennial waterlogged area was gradually formed. The farmland ecosystem was gradually turned into wetland ecosystem in most study area. According to the economic and social development and natural conditions of mining area, calculating the ecological environment, production and people's livelihood, this paper supplied the plan for comprehensive utilization of land resource. In this plan, intervention measures be taken during the coal mining and the mining subsidence formation and development, and this method can solve the problems of Land use at the relative low cost.

Target-Based Maintenance of Privacy Preserving Association Rules

ERIC Educational Resources Information Center

Ahluwalia, Madhu V.

2011-01-01

In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This research fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving…

Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature

PubMed Central

Joudaki, Hossein; Rashidian, Arash; Minaei-Bidgoli, Behrouz; Mahmoodi, Mahmood; Geraili, Bijan; Nasiri, Mahdi; Arab, Mohammad

2015-01-01

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims. PMID:25560347

Mining of Business-Oriented Conversations at a Call Center

NASA Astrophysics Data System (ADS)

Takeuchi, Hironori; Nasukawa, Tetsuya; Watanabe, Hideo

Recently it has become feasible to transcribe textual records from telephone conversations at call centers by using automatic speech recognition. In this research, we extended a text mining system for call summary records and constructed a conversation mining system for the business-oriented conversations at the call center. To acquire useful business insights from the conversational data through the text mining system, it is critical to identify appropriate textual segments and expressions as the viewpoints to focus on. In the analysis of call summary data using a text mining system, some experts defined the viewpoints for the analysis by looking at some sample records and by preparing the dictionaries based on frequent keywords in the sample dataset. However with conversations it is difficult to identify such viewpoints manually and in advance because the target data consists of complete transcripts that are often lengthy and redundant. In this research, we defined a model of the business-oriented conversations and proposed a mining method to identify segments that have impacts on the outcomes of the conversations and can then extract useful expressions in each of these identified segments. In the experiment, we processed the real datasets from a car rental service center and constructed a mining system. With this system, we show the effectiveness of the method based on the defined conversation model.

Integrated Positioning for Coal Mining Machinery in Enclosed Underground Mine Based on SINS/WSN

PubMed Central

Hui, Jing; Wu, Lei; Yan, Wenxu; Zhou, Lijuan

2014-01-01

To realize dynamic positioning of the shearer, a new method based on SINS/WSN is studied in this paper. Firstly, the shearer movement model is built and running regularity of the shearer in coal mining face has been mastered. Secondly, as external calibration of SINS using GPS is infeasible in enclosed underground mine, WSN positioning strategy is proposed to eliminate accumulative error produced by SINS; then the corresponding coupling model is established. Finally, positioning performance is analyzed by simulation and experiment. Results show that attitude angle and position of the shearer can be real-timely tracked by integrated positioning strategy based on SINS/WSN, and positioning precision meet the demand of actual working condition. PMID:24574891

Development of management information system for land in mine area based on MapInfo

NASA Astrophysics Data System (ADS)

Wang, Shi-Dong; Liu, Chuang-Hua; Wang, Xin-Chuang; Pan, Yan-Yu

2008-10-01

MapInfo is current a popular GIS software. This paper introduces characters of MapInfo and GIS second development methods offered by MapInfo, which include three ones based on MapBasic, OLE automation, and MapX control usage respectively. Taking development of land management information system in mine area for example, in the paper, the method of developing GIS applications based on MapX has been discussed, as well as development of land management information system in mine area has been introduced in detail, including development environment, overall design, design and realization of every function module, and simple application of system, etc. The system uses MapX 5.0 and Visual Basic 6.0 as development platform, takes SQL Server 2005 as back-end database, and adopts Matlab 6.5 to calculate number in back-end. On the basis of integrated design, the system develops eight modules including start-up, layer control, spatial query, spatial analysis, data editing, application model, document management, results output. The system can be used in mine area for cadastral management, land use structure optimization, land reclamation, land evaluation, analysis and forecasting for land in mine area and environmental disruption, thematic mapping, and so on.

Theoretical approaches to creation of robotic coal mines based on the synthesis of simulation technologies

NASA Astrophysics Data System (ADS)

Fryanov, V. N.; Pavlova, L. D.; Temlyantsev, M. V.

2017-09-01

Methodological approaches to theoretical substantiation of the structure and parameters of robotic coal mines are outlined. The results of mathematical and numerical modeling revealed the features of manifestation of geomechanical and gas dynamic processes in the conditions of robotic mines. Technological solutions for the design and manufacture of technical means for robotic mine are adopted using the method of economic and mathematical modeling and in accordance with the current regulatory documents. For a comparative performance evaluation of technological schemes of traditional and robotic mines, methods of cognitive modeling and matrix search for subsystem elements in the synthesis of a complex geotechnological system are applied. It is substantiated that the process of technical re-equipment of a traditional mine with a phased transition to a robotic mine will reduce unit costs by almost 1.5 times with a significant social effect due to a reduction in the number of personnel engaged in hazardous work.

Methodology of Estimation of Methane Emissions from Coal Mines in Poland

NASA Astrophysics Data System (ADS)

Patyńska, Renata

2014-03-01

Based on a literature review concerning methane emissions in Poland, it was stated in 2009 that the National Greenhouse Inventory 2007 [13] was published. It was prepared firstly to meet Poland's obligations resulting from point 3.1 Decision no. 280/2004/WE of the European Parliament and of the Council of 11 February 2004, concerning a mechanism for monitoring community greenhouse gas emissions and for implementing the Kyoto Protocol and secondly, for the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol. The National Greenhouse Inventory states that there are no detailed data concerning methane emissions in collieries in the Polish mining industry. That is why the methane emission in the methane coal mines of Górnośląskie Zagłębie Węglowe - GZW (Upper Silesian Coal Basin - USCB) in Poland was meticulously studied and evaluated. The applied methodology for estimating methane emission from the GZW coal mining system was used for the four basic sources of its emission. Methane emission during the mining and post-mining process. Such an approach resulted from the IPCC guidelines of 2006 [10]. Updating the proposed methods (IPCC2006) of estimating the methane emissions of hard coal mines (active and abandoned ones) in Poland, assumes that the methane emission factor (EF) is calculated based on methane coal mine output and actual values of absolute methane content. The result of verifying the method of estimating methane emission during the mining process for Polish coal mines is the equation of methane emission factor EF.

A Tools-Based Approach to Teaching Data Mining Methods

ERIC Educational Resources Information Center

Jafar, Musa J.

2010-01-01

Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…

Research on target tracking in coal mine based on optical flow method

NASA Astrophysics Data System (ADS)

Xue, Hongye; Xiao, Qingwei

2015-03-01

To recognize, track and count the bolting machine in coal mine video images, a real-time target tracking method based on the Lucas-Kanade sparse optical flow is proposed in this paper. In the method, we judge whether the moving target deviate from its trajectory, predicate and correct the position of the moving target. The method solves the problem of failure to track the target or lose the target because of the weak light, uneven illumination and blocking. Using the VC++ platform and Opencv lib we complete the recognition and tracking. The validity of the method is verified by the result of the experiment.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Data Mining for Web-Based Support Systems: A Case Study in e-Custom Systems

NASA Astrophysics Data System (ADS)

Razmerita, Liana; Kirchner, Kathrin

This chapter provides an example of a Web-based support system (WSS) used to streamline trade procedures, prevent potential security threats, and reduce tax-related fraud in cross-border trade. The architecture is based on a service-oriented architecture that includes smart seals and Web services. We discuss the implications and suggest further enhancements to demonstrate how such systems can move toward a Web-based decision support system with the support of data mining methods. We provide a concrete example of how data mining can help to analyze the vast amount of data collected while monitoring the container movements along its supply chain.

Screening and prioritisation of chemical risks from metal mining operations, identifying exposure media of concern.

PubMed

Pan, Jilang; Oates, Christopher J; Ihlenfeld, Christian; Plant, Jane A; Voulvoulis, Nikolaos

2010-04-01

Metals have been central to the development of human civilisation from the Bronze Age to modern times, although in the past, metal mining and smelting have been the cause of serious environmental pollution with the potential to harm human health. Despite problems from artisanal mining in some developing countries, modern mining to Western standards now uses the best available mining technology combined with environmental monitoring, mitigation and remediation measures to limit emissions to the environment. This paper develops risk screening and prioritisation methods previously used for contaminated land on military and civilian sites and engineering systems for the analysis and prioritisation of chemical risks from modern metal mining operations. It uses hierarchical holographic modelling and multi-criteria decision making to analyse and prioritise the risks from potentially hazardous inorganic chemical substances released by mining operations. A case study of an active platinum group metals mine in South Africa is used to demonstrate the potential of the method. This risk-based methodology for identifying, filtering and ranking mining-related environmental and human health risks can be used to identify exposure media of greatest concern to inform risk management. It also provides a practical decision-making tool for mine acquisition and helps to communicate risk to all members of mining operation teams.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

PubMed

He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

2014-01-01

Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies.

The performance evaluation model of mining project founded on the weight optimization entropy value method

NASA Astrophysics Data System (ADS)

Mao, Chao; Chen, Shou

2017-01-01

According to the traditional entropy value method still have low evaluation accuracy when evaluating the performance of mining projects, a performance evaluation model of mineral project founded on improved entropy is proposed. First establish a new weight assignment model founded on compatible matrix analysis of analytic hierarchy process (AHP) and entropy value method, when the compatibility matrix analysis to achieve consistency requirements, if it has differences between subjective weights and objective weights, moderately adjust both proportions, then on this basis, the fuzzy evaluation matrix for performance evaluation. The simulation experiments show that, compared with traditional entropy and compatible matrix analysis method, the proposed performance evaluation model of mining project based on improved entropy value method has higher accuracy assessment.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

NASA Astrophysics Data System (ADS)

Tripathy, Debi Prasad; Ala, Charan Kumar

2018-04-01

Fatal accidents are occurring every year as regular events in Indian coal mining industry. To increase the safety conditions, it has become a prerequisite to performing a risk assessment of various operations in mines. However, due to uncertain accident data, it is hard to conduct a risk assessment in mines. The object of this study is to present a method to assess safety risks in underground coalmines. The assessment of safety risks is based on the fuzzy reasoning approach. Mamdani fuzzy logic model is developed in the fuzzy logic toolbox of MATLAB. A case study is used to demonstrate the applicability of the developed model. The summary of risk evaluation in case study mine indicated that mine fire has the highest risk level among all the hazard factors. This study could help the mine management to prepare safety measures based on the risk rankings obtained.

Particle damping applied research on mining dump truck vibration control

NASA Astrophysics Data System (ADS)

Song, Liming; Xiao, Wangqiang; Guo, Haiquan; Yang, Zhe; Li, Zeguang

2018-05-01

Vehicle vibration characteristics has become an important evaluation indexes of mining dump truck. In this paper, based on particle damping technology, mining dump truck vibration control was studied by combining the theoretical simulation with actual testing, particle damping technology was successfully used in mining dump truck cab vibration control. Through testing results analysis, with a particle damper, cab vibration was reduced obviously, the methods and basis were provided for vehicle vibration control research and particle damping technology application.

Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation.

PubMed

Wang, Gang; Zhao, Zhikai; Ning, Yongjie

2018-05-28

As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

Flooded Underground Coal Mines: A Significant Source of Inexpensive Geothermal Energy

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

Watzlaf, G.R.; Ackman, T.E.

2007-04-01

Many mining regions in the United States contain extensive areas of flooded underground mines. The water within these mines represents a significant and widespread opportunity for extracting low-grade, geothermal energy. Based on current energy prices, geothermal heat pump systems using mine water could reduce the annual costs for heating to over 70 percent compared to conventional heating methods (natural gas or heating oil). These same systems could reduce annual cooling costs by up to 50 percent over standard air conditioning in many areas of the country. (Formatted full-text version is released by permission of publisher)

Monitoring of Surface Subsidence of the Mining Area Based on Sbas

NASA Astrophysics Data System (ADS)

Zhu, Y.; Zhou, S.; Zang, D.; Lu, T.

2018-05-01

This paper has collected 7 scenes of L band PALSAR sensor radar data of a mine in FengCheng city, jiangxi province, using the Small-baseline Subset (SBAS) method to invert the surface subsidence of the mine. Baselines of interference less than 800m has been chosen to constitute short baseline differential interference atlas, using pixels whose average coherent coefficient was larger than or equal to 0.3 as like high coherent point target, using singular value decomposition (SVD) method to calculate deformation phase sequence based on these high coherent points, and the accumulation of settlements of study area of different period had been obtained, so as to reflect the ground surface settlement evolution of the settlement of the area. The results of the study has showed that: SBAS technology has overcome coherent problem of the traditionality D-InSAR technique, continuous deformation field of surface mining in time dimension of time could been obtained, characteristics of ground surface settlement of mining subsidence in different period has been displayed, so to improve the accuracy and reliability of the monitoring results.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Introduction to Space Resource Mining

NASA Technical Reports Server (NTRS)

Mueller, Robert P.

2013-01-01

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

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Optical method for continuous monitoring of dust deposition in mine's entry / Optyczna metoda ciągłego pomiaru intensywności osiadania pyłu węglowego w wyrobisku górniczym

NASA Astrophysics Data System (ADS)

2012-12-01

The paper presents factors determining dust explosion hazards occurring in underground hard coal mines. The authors described the mechanism of transport and deposition of dust in mines entries and previous research on this topic. The paper presents a method of determination of depositing dust distribution during mining and presents the way to use it to assess coal dust explosion risk. The presented method of calculating the intensity of coal dust deposition is based on continuous monitoring of coal dust concentrations with use of optical sensors. Mathematical model of the distribution of the average coal dust concentration was created. Presented method allows to calculate the intensity of coal dust deposition in a continuous manner. Additionally, the authors presented the PŁ-2 stationary optical dust sampler, used in the study, connected to the monitoring system in the mine. The article features the results of studies conducted in the return air courses of the active longwalls, and the results of calculations of dust deposition intensity carried out with the use of the presented method.

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 .

Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

PubMed

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

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.

Chapter 3: Selecting materials for mine soil construction when establishing forests on Appalachian mined lands

Treesearch

Jeff Skousen; Carl Zipper; Jim Burger; Christopher Barton; Patrick. Angel

2017-01-01

The Forestry Reclamation Approach (FRA), a method for reclaiming coal-mined land to forest (Chapter 2, this volume), is based on research, knowledge, and experience of forest soil scientists and reclamation practitioners. Step 1 of the FRA is to create a suitable rooting medium for good tree growth that is no less than 4 feet deep and consists of topsoil, weathered...

Gravimetric surveys for assessing rock mass condition around a mine shaft

NASA Astrophysics Data System (ADS)

Madej, Janusz

2017-06-01

The fundamentals of use of vertical gravimetric surveying method in mine shafts are presented in the paper. The methods of gravimetric measurements and calculation of interval and complex density are discussed in detail. The density calculations are based on an original method accounting for the gravity influence of the mine shaft thus guaranteeing closeness of calculated and real values of density of rocks beyond the shaft lining. The results of many gravimetric surveys performed in shafts are presented and interpreted. As a result, information about the location of heterogeneous zones of work beyond the shaft lining is obtained. In many cases, these zones used to threaten the safe operation of machines and utilities in the shaft.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

An evaluation of processing InSAR Sentinel-1A/B data for correlation of mining subsidence with mining induced tremors in the Upper Silesian Coal Basin (Poland)

NASA Astrophysics Data System (ADS)

Krawczyk, Artur; Grzybek, Radosław

2018-01-01

The Satellite Radar Interferometry is one of the common methods that allow to measure the land subsidence caused by the underground black coal excavation. The interferometry images processed from the repeat-pass Synthetic Aperture Radar (SAR) systems give the spatial image of the terrain subjected to the surface subsidence over mining areas. Until now, the InSAR methods using data from the SAR Systems like ERS-1/ERS-2 and Envisat-1 were limited to a repeat-pass cycle of 35-day only. Recently, the ESA launched Sentinel-1A and 1B, and together they can provide the InSAR coverage in a 6-day repeat cycle. The studied area was the Upper Silesian Coal Basin in Poland, where the underground coal mining causes continuous subsidence of terrain surface and mining tremors (mine-induced seismicity). The main problem was with overlapping the subsidence caused by the mining exploitation with the epicentre tremors. Based on the Sentinel SAR images, research was done in regard to the correlation between the short term ground subsidence range border and the mine-induced seismicity epicentres localisation.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Web-video-mining-supported workflow modeling for laparoscopic surgeries.

PubMed

Liu, Rui; Zhang, Xiaoli; Zhang, Hao

2016-11-01

As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems. The objective of this research is to solve the knowledge scalability problem in surgical workflow modeling with a low cost and labor efficient way. A novel web-video-mining-supported surgical workflow modeling (webSWM) method is developed. A novel video quality analysis method based on topic analysis and sentiment analysis techniques is developed to select high-quality videos from abundant and noisy web videos. A statistical learning method is then used to build the workflow model based on the selected videos. To test the effectiveness of the webSWM method, 250 web videos were mined to generate a surgical workflow for the robotic cholecystectomy surgery. The generated workflow was evaluated by 4 web-retrieved videos and 4 operation-room-recorded videos, respectively. The evaluation results (video selection consistency n-index ≥0.60; surgical workflow matching degree ≥0.84) proved the effectiveness of the webSWM method in generating robust and reliable SWM knowledge by mining web videos. With the webSWM method, abundant web videos were selected and a reliable SWM was modeled in a short time with low labor cost. Satisfied performances in mining web videos and learning surgery-related knowledge show that the webSWM method is promising in scaling knowledge for intelligent surgical systems. Copyright © 2016 Elsevier B.V. All rights reserved.

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

PubMed

Xue, Yun; Liao, Zhengling; Li, Meihang; Luo, Jie; Kuang, Qiuhua; Hu, Xiaohui; Li, Tiechen

2015-01-01

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

Preference Mining Using Neighborhood Rough Set Model on Two Universes.

PubMed

Zeng, Kai

2016-01-01

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

Research on position and orientation measurement method for roadheader based on vision/INS

NASA Astrophysics Data System (ADS)

Yang, Jinyong; Zhang, Guanqin; Huang, Zhe; Ye, Yaozhong; Ma, Bowen; Wang, Yizhong

2018-01-01

Roadheader which is a kind of special equipment for large tunnel excavation has been widely used in Coal Mine. It is one of the main mechanical-electrical equipment for mine production and also has been regarded as the core equipment for underground tunnel driving construction. With the deep application of the rapid driving system, underground tunnel driving methods with higher automation level are required. In this respect, the real-time position and orientation measurement technique for roadheader is one of the most important research contents. For solving the problem of position and orientation measurement automatically in real time for roadheaders, this paper analyses and compares the features of several existing measuring methods. Then a new method based on the combination of monocular vision and strap down inertial navigation system (SINS) would be proposed. By realizing five degree-of-freedom (DOF) measurement of real-time position and orientation of roadheader, this method has been verified by the rapid excavation equipment in Daliuta coal mine. Experiment results show that the accuracy of orientation measurement is better than 0.1°, the standard deviation of static drift is better than 0.25° and the accuracy of position measurement is better than 1cm. It is proved that this method can be used in real-time position and orientation measurement application for roadheader which has a broad prospect in coal mine engineering.

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

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.

Efficient discovery of risk patterns in medical data.

PubMed

Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul

2009-01-01

This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.

Untangling Topic Threads in Chat-Based Communication: A Case Study

DTIC Science & Technology

2011-08-01

learning techniques such as clustering are very popular for analyzing text for topic identification (Anjewierden,, Kollöffel and Hulshof 2007; Adams...Anjewierden, A., Kollöffel, B., and Hulshof , C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to

Perfection Of Methods Of Mathematical Analysis For Increasing The Completeness Of Subsoil Development

NASA Astrophysics Data System (ADS)

Fokina, Mariya

2017-11-01

The economy of Russia is based around the mineral-raw material complex to the highest degree. The mining industry is a prioritized and important area. Given the high competitiveness of businesses in this sector, increasing the efficiency of completed work and manufactured products will become a central issue. Improvement of planning and management in this sector should be based on multivariant study and the optimization of planning decisions, the appraisal of their immediate and long-term results, taking the dynamic of economic development into account. All of this requires the use of economic mathematic models and methodsApplying an economic-mathematic model to determine optimal ore mine production capacity, we receive a figure of 4,712,000 tons. The production capacity of the Uchalinsky ore mine is 1560 thousand tons, and the Uzelginsky ore mine - 3650 thousand. Conducting a corresponding analysis of the production of OAO "Uchalinsky Gok", an optimal production plan was received: the optimal production of copper - 77961,4 rubles; the optimal production of zinc - 17975.66 rubles. The residual production volume of the two main ore mines of OAO "UGOK" is 160 million tons of ore.

30 CFR 75.203 - Mining methods.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... faulty pillar recovery methods. Pillar dimensions shall be compatible with effective control of the roof...

30 CFR 75.203 - Mining methods.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... faulty pillar recovery methods. Pillar dimensions shall be compatible with effective control of the roof...

30 CFR 75.203 - Mining methods.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... faulty pillar recovery methods. Pillar dimensions shall be compatible with effective control of the roof...

30 CFR 75.203 - Mining methods.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... faulty pillar recovery methods. Pillar dimensions shall be compatible with effective control of the roof...

30 CFR 75.203 - Mining methods.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mining methods. 75.203 Section 75.203 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.203 Mining methods. (a) The method of mining... faulty pillar recovery methods. Pillar dimensions shall be compatible with effective control of the roof...

Sustainability Activities In The Mining Sector: Current Status And Challenges Ahead Limestone Mining In Nusakambangan

NASA Astrophysics Data System (ADS)

Ayuningrum, Theresia Vika; Purnaweni, Hartuti

2018-02-01

Potential Karst area in Nusakambangan has an important role in maintaining the balance of nature. But with the existence of mining activities, will automatically change the environmental conditions there. In order for the utilization of resources to meet the rules of optimization between the interests of mining and sustainability of the environment so in every mining sector activities required a variety of environmental studies. The purpose of this study is to find out how the analysis of environmental management due to limestone mining activities in Nusakambangan so that it can be known the management of mining areas are optimal, wise based on ecological principles, and sustainability. In qualitative research methods, data analysis using description percentage, with the type of data collected in the form of primary data and secondary data.

Chapter 16: text mining for translational bioinformatics.

PubMed

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

PubMed Central

Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

2014-01-01

Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

An Environmental Unit for the Social Studies.

ERIC Educational Resources Information Center

Kroll, Claudia J.

Based on the inquiry method of learning, this instructional unit attempts to encourage students to discover for themselves the facts, problems, values, conflicts, and potential solutions of an environmental issue. Specifically, it deals with surface mining in the United States, with special focus on surface mining in Illinois. Materials and…

Remote sensing of Northern mines: supporting operation and environmental monitoring in cold conditions

NASA Astrophysics Data System (ADS)

Tuomela, Anne; Davids, Corine; Knutsson, Sven; Knutsson, Roger; Rauhala, Anssi; Rossi, Pekka M.; Rouyet, Line

2017-04-01

Northern areas of Finland, Sweden and Norway have mineral-rich deposits. There are several active mines in the area but also closed ones and deposits with plans for future mining. With increasing demand for environmental protection in the sensitive Northern conditions, there is a need for more comprehensive monitoring of the mining environment. In our study, we aim to develop new opportunities to use remote sensing data from satellites and unmanned aerial vehicles (UAVs) in improving mining safety and monitoring, for example in the case of mine waste storage facilities. Remote sensing methods have evolved fast, and could in many cases enable precise, reliable, and cost-efficient data collection over large areas. The study has focused on four mining areas in Northern Fennoscandia. Freely available medium-resolution (e.g. Sentinel-1), commercial high-resolution (e.g. TerraSAR-X) and Synthetic Aperture Radar (SAR) data has been collected during 2015-2016 to study how satellite remote sensing could be used e.g. for displacement monitoring using SAR Interferometry (InSAR). Furthermore, UAVs have been utilized in similar data collection in a local scale, and also in collection of thermal infrared data for hydrological monitoring of the areas. The development and efficient use of the methods in mining areas requires experts from several fields. In addition, the Northern conditions with four distinct seasons bring their own challenges for the efficient use of remote sensing, and further complicate their integration as standardised monitoring methods for mine environments. Based on the initial results, remote sensing could especially enhance the monitoring of large-scale structures in mine areas such as tailings impoundments.

Ultrasound-assisted extraction for total sulphur measurement in mine tailings.

PubMed

Khan, Adnan Hossain; Shang, Julie Q; Alam, Raquibul

2012-10-15

A sample preparation method for percentage recovery of total sulphur (%S) in reactive mine tailings based on ultrasound-assisted digestion (USAD) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) was developed. The influence of various methodological factors was screened by employing a two-level and three-factor (2(3)) full factorial design and using KZK-1, a sericite schist certified reference material (CRM), to find the optimal combination of studied factors and %S. Factors such as the sonication time, temperature and acid combination were studied, with the best result identified as 20 min of sonication, 80°C temperature and 1 ml of HNO(3):1 ml of HCl, which can achieve 100% recovery for the selected CRM. Subsequently a fraction of the 2(3) full factorial design was applied to mine tailings. The percentage relative standard deviation (%RSD) for the ultrasound method is less than 3.0% for CRM and less than 6% for the mine tailings. The investigated method was verified by X-ray diffraction analysis. The USAD method compared favorably with existing methods such as hot plate assisted digestion method, X-ray fluorescence and LECO™-CNS method. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Research on Occupational Safety, Health Management and Risk Control Technology in Coal Mines.

PubMed

Zhou, Lu-Jie; Cao, Qing-Gui; Yu, Kai; Wang, Lin-Lin; Wang, Hai-Bin

2018-04-26

This paper studies the occupational safety and health management methods as well as risk control technology associated with the coal mining industry, including daily management of occupational safety and health, identification and assessment of risks, early warning and dynamic monitoring of risks, etc.; also, a B/S mode software (Geting Coal Mine, Jining, Shandong, China), i.e., Coal Mine Occupational Safety and Health Management and Risk Control System, is developed to attain the aforementioned objectives, namely promoting the coal mine occupational safety and health management based on early warning and dynamic monitoring of risks. Furthermore, the practical effectiveness and the associated pattern for applying this software package to coal mining is analyzed. The study indicates that the presently developed coal mine occupational safety and health management and risk control technology and the associated software can support the occupational safety and health management efforts in coal mines in a standardized and effective manner. It can also control the accident risks scientifically and effectively; its effective implementation can further improve the coal mine occupational safety and health management mechanism, and further enhance the risk management approaches. Besides, its implementation indicates that the occupational safety and health management and risk control technology has been established based on a benign cycle involving dynamic feedback and scientific development, which can provide a reliable assurance to the safe operation of coal mines.

Research on Occupational Safety, Health Management and Risk Control Technology in Coal Mines

PubMed Central

Zhou, Lu-jie; Cao, Qing-gui; Yu, Kai; Wang, Lin-lin; Wang, Hai-bin

2018-01-01

This paper studies the occupational safety and health management methods as well as risk control technology associated with the coal mining industry, including daily management of occupational safety and health, identification and assessment of risks, early warning and dynamic monitoring of risks, etc.; also, a B/S mode software (Geting Coal Mine, Jining, Shandong, China), i.e., Coal Mine Occupational Safety and Health Management and Risk Control System, is developed to attain the aforementioned objectives, namely promoting the coal mine occupational safety and health management based on early warning and dynamic monitoring of risks. Furthermore, the practical effectiveness and the associated pattern for applying this software package to coal mining is analyzed. The study indicates that the presently developed coal mine occupational safety and health management and risk control technology and the associated software can support the occupational safety and health management efforts in coal mines in a standardized and effective manner. It can also control the accident risks scientifically and effectively; its effective implementation can further improve the coal mine occupational safety and health management mechanism, and further enhance the risk management approaches. Besides, its implementation indicates that the occupational safety and health management and risk control technology has been established based on a benign cycle involving dynamic feedback and scientific development, which can provide a reliable assurance to the safe operation of coal mines. PMID:29701715

Text mining approach to predict hospital admissions using early medical records from the emergency department.

PubMed

Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

2017-04-01

Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Sentiment topic mining based on comment tags

NASA Astrophysics Data System (ADS)

Zhang, Daohai; Liu, Xue; Li, Juan; Fan, Mingyue

2018-03-01

With the development of e-commerce, various comments based on tags are generated, how to extract valuable information from these comment tags has become an important content of business management decisions. This study takes HUAWEI mobile phone tags as an example using the sentiment analysis and topic LDA mining method. The first step is data preprocessing and classification of comment tag topic mining. And then make the sentiment classification for comment tags. Finally, mine the comments again and analyze the emotional theme distribution under different sentiment classification. The results show that HUAWEI mobile phone has a good user experience in terms of fluency, cost performance, appearance, etc. Meanwhile, it should pay more attention to independent research and development, product design and development. In addition, battery and speed performance should be enhanced.

Prioritizing abandoned coal mine reclamation projects within the contiguous United States using geographic information system extrapolation.

PubMed

Gorokhovich, Yuri; Reid, Matthew; Mignone, Erica; Voros, Andrew

2003-10-01

Coal mine reclamation projects are very expensive and require coordination of local and federal agencies to identify resources for the most economic way of reclaiming mined land. Location of resources for mine reclamation is a spatial problem. This article presents a methodology that allows the combination of spatial data on resources for the coal mine reclamation and uses GIS analysis to develop a priority list of potential mine reclamation sites within contiguous United States using the method of extrapolation. The extrapolation method in this study was based on the Bark Camp reclamation project. The mine reclamation project at Bark Camp, Pennsylvania, USA, provided an example of the beneficial use of fly ash and dredged material to reclaim 402,600 sq mi of a mine abandoned in the 1980s. Railroads provided transportation of dredged material and fly ash to the site. Therefore, four spatial elements contributed to the reclamation project at Bark Camp: dredged material, abandoned mines, fly ash sources, and railroads. Using spatial distribution of these data in the contiguous United States, it was possible to utilize GIS analysis to prioritize areas where reclamation projects similar to Bark Camp are feasible. GIS analysis identified unique occurrences of all four spatial elements used in the Bark Camp case for each 1 km of the United States territory within 20, 40, 60, 80, and 100 km radii from abandoned mines. The results showed the number of abandoned mines for each state and identified their locations. The federal or state governments can use these results in mine reclamation planning.

Concentrated Brine Treatment using New Energy in Coal Mine Evaporation Ponds

NASA Astrophysics Data System (ADS)

Li, Ting; Li, Jingfeng

2017-12-01

Recently, more and more coal mine water is being advanced treated and reused in China. The concentrated brine that results from advanced treatment methods can only be evaporated in an evaporation pond. Because of limited treatment capabilities and winter freezing, evaporation ponds often overflow, causing environment contamination. In this paper, based on analysis of brine water quality and economic-technical feasibility, we present a suitable treatment method for brine in evaporation ponds as electrodialysis using solar energy. In addition, we propose a new system to treat brine in coal mine evaporation ponds, which is powered by solar and wind. The operating efficiency of this treatment system proposed in this paper can meet the concentrated brine treatment demands in most coal mines in western mining areas of China and it places the photovoltaic power generation plates on the surface of the evaporation pond on a fixed floating island, which reduces any risk associated with land acquisition. This system can enhance brine treatment efficiency, requires a reduced evaporation pond area, increases the utilization of coal mine water, and minimizes the risk of environment contamination.

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Mining method selection by integrated AHP and PROMETHEE method.

PubMed

Bogdanovic, Dejan; Nikolic, Djordje; Ilic, Ivana

2012-03-01

Selecting the best mining method among many alternatives is a multicriteria decision making problem. The aim of this paper is to demonstrate the implementation of an integrated approach that employs AHP and PROMETHEE together for selecting the most suitable mining method for the "Coka Marin" underground mine in Serbia. The related problem includes five possible mining methods and eleven criteria to evaluate them. Criteria are accurately chosen in order to cover the most important parameters that impact on the mining method selection, such as geological and geotechnical properties, economic parameters and geographical factors. The AHP is used to analyze the structure of the mining method selection problem and to determine weights of the criteria, and PROMETHEE method is used to obtain the final ranking and to make a sensitivity analysis by changing the weights. The results have shown that the proposed integrated method can be successfully used in solving mining engineering problems.

Applying operational research and data mining to performance based medical personnel motivation system.

PubMed

Niaksu, Olegas; Zaptorius, Jonas

2014-01-01

This paper presents the methodology suitable for creation of a performance related remuneration system in healthcare sector, which would meet requirements for efficiency and sustainable quality of healthcare services. Methodology for performance indicators selection, ranking and a posteriori evaluation has been proposed and discussed. Priority Distribution Method is applied for unbiased performance criteria weighting. Data mining methods are proposed to monitor and evaluate the results of motivation system.We developed a method for healthcare specific criteria selection consisting of 8 steps; proposed and demonstrated application of Priority Distribution Method for the selected criteria weighting. Moreover, a set of data mining methods for evaluation of the motivational system outcomes was proposed. The described methodology for calculating performance related payment needs practical approbation. We plan to develop semi-automated tools for institutional and personal performance indicators monitoring. The final step would be approbation of the methodology in a healthcare facility.

Intertransaction Class Association Rule Mining Based on Genetic Network Programming and Its Application to Stock Market Prediction

NASA Astrophysics Data System (ADS)

Yang, Yuchen; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

Intertransaction association rules have been reported to be useful in many fields such as stock market prediction, but still there are not so many efficient methods to dig them out from large data sets. Furthermore, how to use and measure these more complex rules should be considered carefully. In this paper, we propose a new intertransaction class association rule mining method based on Genetic Network Programming (GNP), which has the ability to overcome some shortages of Apriori-like based intertransaction association methods. Moreover, a general classifier model for intertransaction rules is also introduced. In experiments on the real world application of stock market prediction, the method shows its efficiency and ability to obtain good results and can bring more benefits with a suitable classifier considering larger interval span.

Predicting ground-water movement in large mine spoil areas in the Appalachian Plateau

USGS Publications Warehouse

Wunsch, D.R.; Dinger, J.S.; Graham, C.D.R.

1999-01-01

Spoil created by surface mining can accumulate large quantities of ground-water, which can create geotechnical or regulatory problems, as well as flood active mine pits. A current study at a large (4.1 km2), thick, (up to 90 m) spoil body in eastern Kentucky reveals important factors that control the storage and movement of water. Ground-water recharge occurs along the periphery of the spoil body where surface-water drainage is blocked, as well as from infiltration along the spoil-bedrock contact, recharge from adjacent bedrock, and to a minor extent, through macropores at the spoil's surface. Based on an average saturated thickness of 6.4 m for all spoil wells, and assuming an estimated porosity of 20%, approximately 5.2 x 106 m3 of water is stored within the existing 4.1 km2 of reclaimed spoil. A conceptual model of ground-water flow, based on data from monitoring wells, dye-tracing data, discharge from springs and ponds, hydraulic gradients, chemical data, field reconnaissance, and aerial photographs indicate that three distinct but interconnected saturated zones have been established: one in the spoil's interior, and others in the valley fills that surround the main spoil body at lower elevations. Ground-water movement is sluggish in the spoil's interior, but moves quickly through the valley fills. The conceptual model shows that a prediction of ground-water occurrence, movement, and quality can be made for active or abandoned spoil areas if all or some of the following data are available: structural contour of the base of the lowest coal seam being mined, pre-mining topography, documentation of mining methods employed throughout the mine, overburden characteristics, and aerial photographs of mine progression.Spoil created by surface mining can accumulate large quantities of ground-water, which can create geotechnical or regulatory problems, as well as flood active mine pits. A current study at a large (4.1 km2), thick, (up to 90 m) spoil body in eastern Kentucky reveals important factors that control the storage and movement of water. Ground-water recharge occurs along the periphery of the spoil body where surface-water drainage is blocked, as well as from infiltration along the spoil-bedrock contact, recharge from adjacent bedrock, and to a minor extent, through macropores at the spoil's surface. Based on an average saturated thickness of 6.4 m for all spoil wells, and assuming an estimated porosity of 20%, approximately 5.2 ?? 106 m3 of water is stored within the existing 4.1 km2 of reclaimed spoil. A conceptual model of ground-water flow, based on data from monitoring wells, dye-tracing data, discharge from springs and ponds, hydraulic gradients, chemical data, field reconnaissance, and aerial photographs indicate that three distinct but interconnected saturated zones have been established: one in the spoil's interior, and others in the valley fills that surround the main spoil body at lower elevations. Ground-water movement is sluggish in the spoil's interior, but moves quickly through the valley fills. The conceptual model shows that a prediction of ground-water occurrence, movement, and quality can be made for active or abandoned spoil areas if all or some of the following data are available: structural contour of the base of the lowest coal seam being mined, pre-mining topography, documentation of mining methods employed throughout the mine, overburden characteristics, and aerial photographs of mine progression.

Environmental impacts of unmanaged solid waste at a former base metal mining and ore processing site (Kirki, Greece).

PubMed

Liakopoulos, Alexandros; Lemière, Bruno; Michael, Konstantinos; Crouzet, Catherine; Laperche, Valérie; Romaidis, Ioannis; Drougas, Iakovos; Lassin, Arnault

2010-11-01

The Kirki project aimed to identify, among the mining waste abandoned at a mine and processing plant, the most critical potential pollution sources, the exposed milieus and the main pathways for contamination of a littoral area. This was accompanied by the definition of a monitoring network and remedial options. For this purpose, field analytical methods were extensively used to allow a more precise identification of the source, to draw relevant conceptual models and outline a monitoring network. Data interpretation was based on temporal series and on a geographical model. A classification method for mining waste was established, based on data on pollutant contents and emissions, and their long-term pollution potential. Mining waste present at the Kirki mine and plant sites comprises (A) extraction waste, mainly metal sulfide-rich rocks; (B) processing waste, mainly tailings, with iron and sulfides, sulfates or other species, plus residues of processing reagents; and (C) other waste, comprising leftover processing reagents and Pb-Zn concentrates. Critical toxic species include cadmium and cyanide. The stormy rainfall regime and hilly topography favour the flush release of large amounts of pollutants. The potential impacts and remedial options vary greatly. Type C waste may generate immediate and severe chemical hazards, and should be dealt with urgently by careful removal, as it is localised in a few spots. Type B waste has significant acid mine drainage potential and contains significant amounts of bioavailable heavy metals and metalloids, but they may also be released in solid form into the surface water through dam failure. The most urgent action is thus dams consolidation. Type A waste is by far the most bulky, and it cannot be economically removed. Unfortunately, it is also the most prone to acid mine drainage (seepage pH 1 to 2). This requires neutralisation to prevent acid water accelerating heavy metals and metalloids transfer. All waste management options require the implementation of a monitoring network for the design of a remediation plan, efficiency control, and later, community alert in case of accidental failure of mitigation/remediation measures. A network design strategy based on field measurements, laboratory validation and conceptual models is proposed.

Land use-based landscape planning and restoration in mine closure areas.

PubMed

Zhang, Jianjun; Fu, Meichen; Hassani, Ferri P; Zeng, Hui; Geng, Yuhuan; Bai, Zhongke

2011-05-01

Landscape planning and restoration in mine closure areas is not only an inevitable choice to sustain mining areas but also an important path to maximize landscape resources and to improve ecological function in mine closure areas. The analysis of the present mine development shows that many mines are unavoidably facing closures in China. This paper analyzes the periodic impact of mining activities on landscapes and then proposes planning concepts and principles. According to the landscape characteristics in mine closure areas, this paper classifies available landscape resources in mine closure areas into the landscape for restoration, for limited restoration and for protection, and then summarizes directions for their uses. This paper establishes the framework of spatial control planning and design of landscape elements from "macro control, medium allocation and micro optimization" for the purpose of managing and using this kind of special landscape resources. Finally, this paper applies the theories and methods to a case study in Wu'an from two aspects: the construction of a sustainable land-use pattern on a large scale and the optimized allocation of typical mine landscape resources on a small scale.

Presentation to Office of Surface Mines about use of LEAF in evaluating use of CCRs in mine reclamation

EPA Science Inventory

Providing information on new EPA methods that predict environmental release based on changing environmental conditions. The purpose is to determine if OSM needs support on their ongoing rulemaking. According to OSWER, there is a need and OSM is anxious to meet with us particula...

Nephelometry and turbidimetry to assess concentration and dispersion of coal dust in mines

NASA Astrophysics Data System (ADS)

Yushchenko, VP; Legky, VN; Demidov, DE

2018-03-01

The article considers the model of the optical instrument to measure coal dust concentration in mines based on the turbidimetric and nephelometric methods. The calculated data on the intensity of transmitted and scattered waves depending on coal dust concentration and on the size of coal dust particles are presented.

Use of bioassays for testing soils and/or sediments contaminated by mining activities

NASA Astrophysics Data System (ADS)

Pérez-Sirvent, C.; Martínez-Sánchez, M. J.; García-Lorenzo, M. L.; Molina, J.

2009-04-01

Ecotoxicity tests measure the bioavailability of the contaminants and the effects of the chemically not measured toxic compounds on the members of the soil community. Therefore, ecotoxicological testing may be a useful approach for assessing the toxicity as a complement to chemical analysis. They are solid phase tests based on terrestrial methods and tests performed on water extracts using aquatic test protocols. The extent and degree of heavy metal contamination around mines may vary depending on geochemical characteristics, the mineralization of tailings, physico-chemical conditions and the processes used to extract metals. Portman Bay was subject to mining from the time of the Roman Empire to 1991 when the activity ceased. Since 1957, the wastes from mining operations were discharged directly into the sea. These wastes mainly consisted of clay, quartz, siderite, magnetite, remains of sphalerite, pyrite and galena and residues of the chemical reagents used in floatation. In our study two methods of environmental toxicological tests were compared and applied to sediments of the Portman Bay (SE, Spain): the standardized toxicological test based on inhibition of luminescence employing Microtox

Improving the Method of Roof Fall Susceptibility Assessment based on Fuzzy Approach

NASA Astrophysics Data System (ADS)

Ghasemi, Ebrahim; Ataei, Mohammad; Shahriar, Kourosh

2017-03-01

Retreat mining is always accompanied by a great amount of accidents and most of them are due to roof fall. Therefore, development of methodologies to evaluate the roof fall susceptibility (RFS) seems essential. Ghasemi et al. (2012) proposed a systematic methodology to assess the roof fall risk during retreat mining based on risk assessment classic approach. The main defect of this method is ignorance of subjective uncertainties due to linguistic input value of some factors, low resolution, fixed weighting, sharp class boundaries, etc. To remove this defection and improve the mentioned method, in this paper, a novel methodology is presented to assess the RFS using fuzzy approach. The application of fuzzy approach provides an effective tool to handle the subjective uncertainties. Furthermore, fuzzy analytical hierarchy process (AHP) is used to structure and prioritize various risk factors and sub-factors during development of this method. This methodology is applied to identify the susceptibility of roof fall occurrence in main panel of Tabas Central Mine (TCM), Iran. The results indicate that this methodology is effective and efficient in assessing RFS.

The exploration and prevention of mine water invasion in Feicheng area based on RS

NASA Astrophysics Data System (ADS)

Zheng, Yong-Guo; Wang, Ping; Ting, He

2004-10-01

Recently, when the ninth and tenth were mined in Feiching city mining area, several mine wells occurred on water invasion. Based on systematic interpretation of TMimages in Fei Cheng mining area, authors find that there are five zones of NS trending lineaments, which nearly distribute in radial in TM images. Image processing can be divided into three types, they are spectrum enhancement, spatial filtering and data fusion, the useful methods in this area are auto-adaptive enhancement, density slicing and K-L transform. With ninth and tenth seam coals mined, three mines of east area have broken out serious accidents of water. Statistical materials and the test of water quality drawing off five limestone indicates water-yielding zone near NS, NNE, and NW trending faults, or near intersection point of its and others. In order to solve the problem, using remote sensing and other techniques, we try to find some influential factors on mine flow. Further analyses, such as, the exploration of geology on earth, and microcosmic from rock slice, the authors find that there are some reasons which lead to water invasion such as geological structure, karsts, index and so on, in which the main reason might be north-south deep fracture which is the pathway of well water's distribution, migration and recharge of mine water. There being more complicate geologic structure in the west of mine area, at last, with RS authors point out important zone of mine water invasion which the prevention-control of hazards from mine water and some measures to avoid water blast in future.

Adaptive semantic tag mining from heterogeneous clinical research texts.

PubMed

Hao, T; Weng, C

2015-01-01

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

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.

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud.

PubMed

Kamdar, Maulik R; Musen, Mark A

2017-01-01

Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug-ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7-0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

Process mining in oncology using the MIMIC-III dataset

NASA Astrophysics Data System (ADS)

Prima Kurniati, Angelina; Hall, Geoff; Hogg, David; Johnson, Owen

2018-03-01

Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research.

Risk Assessment of Heavy Metals in Abandoned Mine Lands as Signifcant Contamination Problem in Romania

NASA Astrophysics Data System (ADS)

Horvath, E.; Jordan, G.; Fugedi, U.; Bartha, A.; Kuti, L.; Heltai, G.; Kalmar, J.; Waldmann, I.; Napradean, I.; Damian, G.

2009-04-01

INTRODUCTION Wide-spread environmental contamination associated with historic mining in Europe has triggered social responses to improve related environmental legislation, the environmental assessment and management methods for the mining industry. Pollution by acid mine drainage (AMD) from ore and coal mining is the outstanding and most important source of mining-induced environmental pollution. Younger et al. (2002) estimates that watercourses polluted by coal mine drainage could be in the order of 2,000 to 3,000 km, and 1,000 to 1,500 km polluted by metal mine discharges for the EU 15 Member States (Younger et al. 2002). Significance of contamination risk posed by mining is also highlighted by mine accidents such as those in Baia Mare, Romania in 2002 and in Aznalcollar, Spain in 1999 (Jordan and D'Alessandro 2004). The new EU Mine Waste Directive (Directive 2006/21/EC) requires the risk-based inventory of abandoned mines in the EU. The cost-effective implementation of the inventory is especially demanding in countries with extensive historic mining and great number of abandoned mine sites, like Romania. The problem is further complicated in areas with trans-boundary effects. The objective of this investigation to carry out the risk-based contamination assessment of a mine site with possible trans-boundary effects in Romania. Assessment follows the source-pathway-receptor chain with a special attention to heavy metal leaching from waste dumps as sources and to transport modelling along surface water pathways. STUDY AREA In this paper the Baiut mine catchment located in the Gutai Mts., Romania, close to the Hungarian border is studied. The polymetallic deposites in the Tertiary Inner-Carpathian Volcanic Arc are exposed by a series of abandoned Zn and Pb mines first operated in the 14th century. Elevation in the high relief catchment ranges from 449m to 1044m. Geology is characterised by andesites hosting the ore deposits and paleogene sediments dominating at the lower topographic elevations. Several mine adits, waste rock dumps are located along the main stream and a large tailings dump is found next to village Baiut just above the receiving floodplain. Predominant land cover is coniferous and mixed forests with agricultural lands on the downstream floodplain. METHODS Six samples at vaious depths were collected from the two major waste rock dumps in the headwater area, and the large tailings dump was also sampled for heavy metal source characterisation. 11 stream sediment samples were collected along the main surface water contamination transport pathway, and a further 11 soil samples were collected in 2 boreholes in the receptor floodplain in October 2008. Besides background stream sediment samples, samples from the exposed rock formations were also collected in order to capture natural background geochemistry in the studied mineralised area. The collected waste rock, stream sediment, soil and rock samples are analysed for total chemical composition (major elements and heavy metals) by ICP-MS spectroscopy, and XRD is used for the determination of mineralogical composition. Rock sample mineralogy is further investigated in thin-sections by petrological microscopy. According to EU legislation expectations, a special emphasis is taken on the determination of metal mobility from the waste rock dumps and various leaching tests are performed and compared including US EPA, USGS and ISO methods. A simple cathcment-based distributed sediment transport model (Jordan et al, 2005; Jordan et al. 2005, 2008) is used to decribe the pathways and quantities of particle-bound contamination. RESULTS AND CONCLUSIONS Results show that (1) sediments are an efficient means for the preliminary inventory of mine contamination as a preparation for the more detailed hydrological sampling and assessment, and (2) the risk-based contamination assessment of mining sites often located in diverse geological, hydrological and landcover environment requires careful and successive sampling design and a tiered assessment approach. Leaching tests are shown cost-efficient and informative methods for source (hazard) characterisation. REFERENCES Directive 2006/21/EC the European Parliament and of the Council on the management of waste from extractive industries and amending Directive 2004/35/EC. Commission of the European Communities, Brussels. Jordan G. and D'Alessandro M. (eds) (2004) Mining, Mining Waste and Related Environmental Issues: Problems and Solutions in the Central and Eastern European Candidate Countries. Joint Research Centre of the European Commission, Ispra. LB-NA-20868-EN-C. Jordan G., van Rompaey A., Szilassi P., Csillag G., Mannaerts C. and Woldai T. (2005) Historical land use changes and their impact on sediment fluxes in the Balaton basin (Hungary). Agriculture, Ecosystems and Environment, 108, 119-133. Jordan G., van Rompaey A., Somody A., Fügedi U., Bats M. and Farsang A. (2008) Spatial Modelling of Contamination in a Catchment Area Impacted by Mining: a Case Study for the Recsk Copper Mines, Hungary. Journal of Land Contamination and Reclamation (in press). Younger P.L., Banwart S.A., Hedin R.S. (2002) Mine water. Hydrology, pollution, remediation. Kluwer Academic Publishers, Dodrecht.

Model of environmental life cycle assessment for coal mining operations.

PubMed

Burchart-Korol, Dorota; Fugiel, Agata; Czaplicka-Kolarz, Krystyna; Turek, Marian

2016-08-15

This paper presents a novel approach to environmental assessment of coal mining operations, which enables assessment of the factors that are both directly and indirectly affecting the environment and are associated with the production of raw materials and energy used in processes. The primary novelty of the paper is the development of a computational environmental life cycle assessment (LCA) model for coal mining operations and the application of the model for coal mining operations in Poland. The LCA model enables the assessment of environmental indicators for all identified unit processes in hard coal mines with the life cycle approach. The proposed model enables the assessment of greenhouse gas emissions (GHGs) based on the IPCC method and the assessment of damage categories, such as human health, ecosystems and resources based on the ReCiPe method. The model enables the assessment of GHGs for hard coal mining operations in three time frames: 20, 100 and 500years. The model was used to evaluate the coal mines in Poland. It was demonstrated that the largest environmental impacts in damage categories were associated with the use of fossil fuels, methane emissions and the use of electricity, processing of wastes, heat, and steel supports. It was concluded that an environmental assessment of coal mining operations, apart from direct influence from processing waste, methane emissions and drainage water, should include the use of electricity, heat and steel, particularly for steel supports. Because the model allows the comparison of environmental impact assessment for various unit processes, it can be used for all hard coal mines, not only in Poland but also in the world. This development is an important step forward in the study of the impacts of fossil fuels on the environment with the potential to mitigate the impact of the coal industry on the environment. Copyright © 2016 Elsevier B.V. All rights reserved.

Cinnamon gulch revisited: Another look at separating natural and mining-impacted contributions to instream metal load

USGS Publications Warehouse

Runkel, Robert L.; Verplanck, Philip; Kimball, Briant; Walton-Day, Katie

2018-01-01

Baseline, premining data for streams draining abandoned mine lands is virtually non existent, and indirect methods for estimating premining conditions are needed to establish realistic, cost effective cleanup goals. One such indirect method is the proximal analog approach, in which premining conditions are estimated using data from nearby mineralized areas that are unaffected by mining. In this paper, we combine the proximal analog approach with a quantitative mass balance framework using data from a spatially-detailed synoptic sampling campaign. The combined approach is applied to Cinnamon Gulch, a headwater stream with numerous draining adits. Synoptic sampling results indicate that three of the top five metal sources are affected by mining activities, and stream segments draining these sources account for a large percentage of overall metal loading within the study reach. These initial calculations overestimate the effects of mining, as the affected stream segments were likely acidic and metal rich prior to mining. Premining loads and concentrations were therefore determined through a replacement approach in which the chemistry of each mining-affected stream segment is revised based on proximal analog concentrations. The revised loading profiles indicate that 15–17% of the Al, Cd, Cu, Mn, Ni, and Zn loads are attributable to mining, whereas the mining contribution for Pb is 40%. Premining concentrations of Al, Cd, Cu, Mn, and Zn are estimated to be in excess of aquatic life standards over the length of the study reach.

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.

Testing contamination risk assessment methods for toxic elements from mine waste sites

NASA Astrophysics Data System (ADS)

Abdaal, A.; Jordan, G.; Szilassi, P.; Kiss, J.; Detzky, G.

2012-04-01

Major incidents involving mine waste facilities and poor environmental management practices have left a legacy of thousands of contaminated sites like in the historic mining areas in the Carpathian Basin. Associated environmental risks have triggered the development of new EU environmental legislation to prevent and minimize the effects of such incidents. The Mine Waste Directive requires the risk-based inventory of all mine waste sites in Europe by May 2012. In order to address the mining problems a standard risk-based Pre-selection protocol has been developed by the EU Commission. This paper discusses the heavy metal contamination in acid mine drainage (AMD) for risk assessment (RA) along the Source-Pathway-Receptor chain using decision support methods which are intended to aid national and regional organizations in the inventory and assessment of potentially contaminated mine waste sites. Several recognized methods such as the European Environmental Agency (EEA) standard PRAMS model for soil contamination, US EPA-based AIMSS and Irish HMS-IRC models for RA of abandoned sites are reviewed, compared and tested for the mining waste environment. In total 145 ore mine waste sites have been selected for scientific testing using the EU Pre-selection protocol as a case study from Hungary. The proportion of uncertain to certain responses for a site and for the total number of sites may give an insight of specific and overall uncertainty in the data we use. The Pre-selection questions are efficiently linked to a GIS system as database inquiries using digital spatial data to directly generate answers. Key parameters such as distance to the nearest surface and ground water bodies, to settlements and protected areas are calculated and statistically evaluated using STATGRAPHICS® in order to calibrate the RA models. According to our scientific research results, of the 145 sites 11 sites are the most risky having foundation slope >20o, 57 sites are within distance <500m to the nearest surface water bodies, and 33 sites are within distance <680m to the nearest settlements. Moreover 25 sites lie directly above the 'poor status' ground water bodies and 91 sites are located in the protected Natura2000 sites (distance =0). Analysis of the total score of all sites was performed, resulting in six risk classes, as follows: <21 (class I, 4 sites), 21-31 (class II, 16 sites), 31-42 (class III, 27 sites), 42-54 (class II, 38 sites), 54-66 (class V, 40 sites) and >66 (class VI, 20 sites). The total risk scores and key parameters are provided in separate tables and GIS maps, in order to facilitate interpretation and comparison. Results of the Pre-selection protocol are consistent with those of the screening PRAMS model. KEY WORDS contamination risk assessment, Mine Waste Directive, Pre-selection Protocol, PRA.MS, AIMSS, abandoned mine sites, GIS

Radon measurements and dose estimate of workers in a manganese ore mine.

PubMed

Shahrokhi, Amin; Vigh, Tamás; Németh, Csaba; Csordás, Anita; Kovács, Tibor

2017-06-01

In the new European Basic Safety Standard (EU-BSS), a new reference level for indoor radon concentration in workplaces has recommended that the annual average activity concentration of indoor radon shall not be higher than 300Bqm -3 . This paper describes the radon concentration level in an underground workplace (manganese ore mine) over long time intervals (4 years). Several common radon monitors devices - including NRPB and Raduet (as a passive method based on CR-39), AlphaGUARD PQ 2000Pro, SARAD EQF3220, TESLA and Pylon WLX (as active methods) - were used for continuous radon measurements. The output results were used, first, to comprised the result of each device, based on conditions present in underground mines; Second, to have comprehensive measurements about all factors that cause workers exposure to radiation (each monitoring device specified for a unique measurement). The results indicate that the mine's staff had successful efforts to reach the strict requirement of the new EU-BSS, and the average annual radon activity concentrations during the working hours were below 300Bqm -3 in the investigated period. The paper presents the effective dose calculations; applying different equilibrium factors suggested by the literature and calculated basing on our measurements at the site, concluding that the differences could be about threefold. Copyright © 2017 Elsevier Ltd. All rights reserved.

Commonality of drug-associated adverse events detected by 4 commonly used data mining algorithms.

PubMed

Sakaeda, Toshiyuki; Kadoyama, Kaori; Minami, Keiko; Okuno, Yasushi

2014-01-01

Data mining algorithms have been developed for the quantitative detection of drug-associated adverse events (signals) from a large database on spontaneously reported adverse events. In the present study, the commonality of signals detected by 4 commonly used data mining algorithms was examined. A total of 2,231,029 reports were retrieved from the public release of the US Food and Drug Administration Adverse Event Reporting System database between 2004 and 2009. The deletion of duplicated submissions and revision of arbitrary drug names resulted in a reduction in the number of reports to 1,644,220. Associations with adverse events were analyzed for 16 unrelated drugs, using the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM). All EBGM-based signals were included in the PRR-based signals as well as IC- or ROR-based ones, and PRR- and IC-based signals were included in ROR-based ones. The PRR scores of PRR-based signals were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the EBGM method, as were the IC scores of IC-based signals for all drugs; however, no such effect was observed in the ROR scores of ROR-based signals. The EBGM method was the most conservative among the 4 methods examined, which suggested its better suitability for pharmacoepidemiological studies. Further examinations should be performed on the reproducibility of clinical observations, especially for EBGM-based signals.

Principles of adoption of the successful environmental practices used in developed countries into mining industry in developing countries.

NASA Astrophysics Data System (ADS)

Masaitis, Alexandra

2013-04-01

The successful implementation of the environmental practices in the mining industry is of a paramount importance, as it not only prevents both local and trans-border pollution but also guarantees clean and healthy environment for the people regardless of their place of habitation. It is especially important to encourage the progress of the environmental practices implementation in developing countries because such countries have resource-oriented economy based on exploitation of nonrenewable resources. Poor environmental practices in developing countries will lead to local environmental crises that could eventually spill into surrounding countries including the most economically advanced. This abstract is a summary of a two-year research project attempted (1) to determine deficiencies of the mining sector ecological practices in developing countries and (2) to suggest substitute practices from developed countries that could be adapted to the developing countries reality. The following research methods were used: 1. The method of the system analysis, where the system is an interaction of the sets of environmental practices with the global mining sector; 2. The comparative method of inquiry, where the comparison was made between environmental protection practices as implemented in the US (developed country) and the developing countries such as RF, Mongolia mining sectors; 3. Quantitative date analysis, where date was collected from "The collection of statistic data", Russian Geographic Society Annual reports, the US EPA open reports, and the USGS Reports; The following results were obtained: Identified the systemic crisis of the ecological environmental policies and practices in the mining sector in developing countries based on the exploitation of nonrenewable resources, absence of the ecological interest by the mining companies that lack mechanisms of environmental and public health protection, the lack of insurance policy, the lack of risk assistance, and in the presence of the audit and monitoring that do not address the local conditions of the mining operations. Based on the above the following concepts were thought of to improve the environmental conditions in mining sector: 1. Was developed the Regional Environmental Management principle based on the local conditions such as physiographic region, local population, and socioeconomic conditions of the area; 2. Devised were criteria for the risk assessment for developing countries. Where the fundamental principals were public health, both near and far from the operation, environmental and biodiversity impact, waste management, long- and short- term rehabilitation plans, compliance with international standards and norms. 3. Developed was the mechanism of the economic motivation to make mining operations "environmentally friendly" that includes defrayal of expenses from both direct and indirect damages. 4. Identified were spheres of possible cooperation between mining companies, government organizations, and the NGOs. These include development of international standards for Good Neighbor Agreement, exchange of environmental information, exchange of successful environmental, health, and safety practices between mining operations from developed and developing countries. The study showed the dire necessity for the mining industry that operates in developing countries to adopt the successful environmental practices used in developed countries. To achieve this goal the Regional Environmental Management principle, the risk assessment criteria, the mechanism of the economic motivation and the principles for international cooperation can play an extremely important role.

Model for the prediction of subsurface strata movement due to underground mining

NASA Astrophysics Data System (ADS)

Cheng, Jianwei; Liu, Fangyuan; Li, Siyuan

2017-12-01

The problem of ground control stability due to large underground mining operations is often associated with large movements and deformations of strata. It is a complicated problem, and can induce severe safety or environmental hazards either at the surface or in strata. Hence, knowing the subsurface strata movement characteristics, and making any subsidence predictions in advance, are desirable for mining engineers to estimate any damage likely to affect the ground surface or subsurface strata. Based on previous research findings, this paper broadly applies a surface subsidence prediction model based on the influence function method to subsurface strata, in order to predict subsurface stratum movement. A step-wise prediction model is proposed, to investigate the movement of underground strata. The model involves a dynamic iteration calculation process to derive the movements and deformations for each stratum layer; modifications to the influence method function are also made for more precise calculations. The critical subsidence parameters, incorporating stratum mechanical properties and the spatial relationship of interest at the mining level, are thoroughly considered, with the purpose of improving the reliability of input parameters. Such research efforts can be very helpful to mining engineers’ understanding of the moving behavior of all strata over underground excavations, and assist in making any damage mitigation plan. In order to check the reliability of the model, two methods are carried out and cross-validation applied. One is to use a borehole TV monitor recording to identify the progress of subsurface stratum bedding and caving in a coal mine, the other is to conduct physical modelling of the subsidence in underground strata. The results of these two methods are used to compare with theoretical results calculated by the proposed mathematical model. The testing results agree well with each other, and the acceptable accuracy and reliability of the proposed prediction model are thus validated.

Uncovering text mining: A survey of current work on web-based epidemic intelligence

PubMed Central

Collier, Nigel

2012-01-01

Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project. PMID:22783909

Development of computerized stocktaking system in mine surveying for ore mineral volume calculation in covered storehouses

NASA Astrophysics Data System (ADS)

Valdman, V. V.; Gridnev, S. O.

2017-10-01

The article examines into the vital issues of measuring and calculating the raw stock volumes in covered storehouses at mining and processing plants. The authors bring out two state-of-the-art high-technology solutions: 1 - to use the ground-based laser scanning system (the method is reasonably accurate and dependable, but costly and time consuming; it also requires the stoppage of works in the storehouse); 2 - to use the fundamentally new computerized stocktaking system in mine surveying for the ore mineral volume calculation, based on the profile digital images. These images are obtained via vertical projection of the laser plane onto the surface of the stored raw materials.

Unsupervised user similarity mining in GSM sensor networks.

PubMed

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.

Prediction of Floor Water Inrush: The Application of GIS-Based AHP Vulnerable Index Method to Donghuantuo Coal Mine, China

NASA Astrophysics Data System (ADS)

Wu, Qiang; Liu, Yuanzhang; Liu, Donghai; Zhou, Wanfang

2011-09-01

Floor water inrush represents a geohazard that can pose significant threat to safe operations for instance in coal mines in China and elsewhere. Its occurrence is controlled by many factors, and the processes are often not amenable to mathematical expressions. To evaluate the water inrush risk, the paper proposes the vulnerability index approach by coupling the analytic hierarchy process (AHP) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case study in China (Donghuantuo Coal Mine). The powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the six factors that control the water inrush, and the contribution weights of each factor was determined with the AHP method. The established AHP evaluation model was used to determine the threshold value for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into five regions with different vulnerability levels which served as general guidelines for the mine operations. The prediction results were further corroborated with the actual mining data, and the evaluation result is satisfactory.

Model of load distribution for earth observation satellite

NASA Astrophysics Data System (ADS)

Tu, Shumin; Du, Min; Li, Wei

2017-03-01

For the system of multiple types of EOS (Earth Observing Satellites), it is a vital issue to assure that each type of payloads carried by the group of EOS can be used efficiently and reasonably for in astronautics fields. Currently, most of researches on configuration of satellite and payloads focus on the scheduling for launched satellites. However, the assignments of payloads for un-launched satellites are bit researched, which are the same crucial as the scheduling of tasks. Moreover, the current models of satellite resources scheduling lack of more general characteristics. Referring the idea about roles-based access control (RBAC) of information system, this paper brings forward a model based on role-mining of RBAC to improve the generality and foresight of the method of assignments of satellite-payload. By this way, the assignment of satellite-payload can be mapped onto the problem of role-mining. A novel method will be introduced, based on the idea of biclique-combination in graph theory and evolutionary algorithm in intelligence computing, to address the role-mining problem of satellite-payload assignments. The simulation experiments are performed to verify the novel method. Finally, the work of this paper is concluded.

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

Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

PubMed Central

Lu, Hsueh-Yi; Huang, Chen-Yuan; Su, Chwen-Tzeng; Lin, Chen-Chiang

2014-01-01

Objectives Rotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone. Methods In this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into “tear” and “no tear” groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models. Results Our proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear). Conclusions Our predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears. PMID:24733553

Study of the Rock Mass Failure Process and Mechanisms During the Transformation from Open-Pit to Underground Mining Based on Microseismic Monitoring

NASA Astrophysics Data System (ADS)

Zhao, Yong; Yang, Tianhong; Bohnhoff, Marco; Zhang, Penghai; Yu, Qinglei; Zhou, Jingren; Liu, Feiyue

2018-05-01

To quantitatively understand the failure process and failure mechanism of a rock mass during the transformation from open-pit mining to underground mining, the Shirengou Iron Mine was selected as an engineering project case study. The study area was determined using the rock mass basic quality classification method and the kinematic analysis method. Based on the analysis of the variations in apparent stress and apparent volume over time, the rock mass failure process was analyzed. According to the recent research on the temporal and spatial change of microseismic events in location, energy, apparent stress, and displacement, the migration characteristics of rock mass damage were studied. A hybrid moment tensor inversion method was used to determine the rock mass fracture source mechanisms, the fracture orientations, and fracture scales. The fracture area can be divided into three zones: Zone A, Zone B, and Zone C. A statistical analysis of the orientation information of the fracture planes orientations was carried out, and four dominant fracture planes were obtained. Finally, the slip tendency analysis method was employed, and the unstable fracture planes were obtained. The results show: (1) The microseismic monitoring and hybrid moment tensor analysis can effectively analyze the failure process and failure mechanism of rock mass, (2) during the transformation from open-pit to underground mining, the failure type of rock mass is mainly shear failure and the tensile failure is mostly concentrated in the roof of goafs, and (3) the rock mass of the pit bottom and the upper of goaf No. 18 have the possibility of further damage.

Accidents in Coal Mining from Perspective of Risk Theory

NASA Astrophysics Data System (ADS)

Khamidullina, E. A.; Timofeeva, S. S.; Smirnov, G. I.

2017-11-01

Introduction. The indicators of the safety system quality in the technosphere include risk indicators. The purpose of this work is to assess the social risk of coal mining since coal mining is associated with specific working conditions, and any emergency situation immediately jeopardizes thelives of many people at the same time. Methods. The work is based on the analysis of statistical information. Results and discussion. The F/N curve of coal mining for the 70-year period (1943-2012) was constructed, and the normative values of the social risk of Russia and other industrialized countries were discussed. Judging by the F/N diagram, only the frequency of accidents with a large number of deaths can correspond to the normative level indicating an exceptionally high level of coal mining risk.

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

NASA Astrophysics Data System (ADS)

Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

Data mining and computationally intensive methods: summary of Group 7 contributions to Genetic Analysis Workshop 13.

PubMed

Costello, Tracy J; Falk, Catherine T; Ye, Kenny Q

2003-01-01

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations. Copyright 2003 Wiley-Liss, Inc.

Remote Sensing Extraction of Stopes and Tailings Ponds in AN Ultra-Low Iron Mining Area

NASA Astrophysics Data System (ADS)

Ma, B.; Chen, Y.; Li, X.; Wu, L.

2018-04-01

With the development of economy, global demand for steel has accelerated since 2000, and thus mining activities of iron ore have become intensive accordingly. An ultra-low-grade iron has been extracted by open-pit mining and processed massively since 2001 in Kuancheng County, Hebei Province. There are large-scale stopes and tailings ponds in this area. It is important to extract their spatial distribution information for environmental protection and disaster prevention. A remote sensing method of extracting stopes and tailings ponds is studied based on spectral characteristics by use of Landsat 8 OLI imagery and ground spectral data. The overall accuracy of extraction is 95.06 %. In addition, tailings ponds are distinguished from stopes based on thermal characteristics by use of temperature image. The results could provide decision support for environmental protection, disaster prevention, and ecological restoration in the ultra-low-grade iron ore mining area.

Study on Mine Emergency Mechanism based on TARP and ICS

NASA Astrophysics Data System (ADS)

Xi, Jian; Wu, Zongzhi

2018-01-01

By analyzing the experiences and practices of mine emergency in China and abroad, especially the United States and Australia, normative principle, risk management principle and adaptability principle of constructing mine emergency mechanism based on Trigger Action Response Plans (TARP) and Incident Command System (ICS) are summarized. Classification method, framework, flow and subject of TARP and ICS which are suitable for the actual situation of domestic mine emergency are proposed. The system dynamics model of TARP and ICS is established. The parameters such as evacuation ratio, response rate, per capita emergency capability and entry rate of rescuers are set up. By simulating the operation process of TARP and ICS, the impact of these parameters on the emergency process are analyzed, which could provide a reference and basis for building emergency capacity, formulating emergency plans and setting up action plans in the emergency process.

Quantitative Analysis of Critical Factors for the Climate Impact of Landfill Mining.

PubMed

Laner, David; Cencic, Oliver; Svensson, Niclas; Krook, Joakim

2016-07-05

Landfill mining has been proposed as an innovative strategy to mitigate environmental risks associated with landfills, to recover secondary raw materials and energy from the deposited waste, and to enable high-valued land uses at the site. The present study quantitatively assesses the importance of specific factors and conditions for the net contribution of landfill mining to global warming using a novel, set-based modeling approach and provides policy recommendations for facilitating the development of projects contributing to global warming mitigation. Building on life-cycle assessment, scenario modeling and sensitivity analysis methods are used to identify critical factors for the climate impact of landfill mining. The net contributions to global warming of the scenarios range from -1550 (saving) to 640 (burden) kg CO2e per Mg of excavated waste. Nearly 90% of the results' total variation can be explained by changes in four factors, namely the landfill gas management in the reference case (i.e., alternative to mining the landfill), the background energy system, the composition of the excavated waste, and the applied waste-to-energy technology. Based on the analyses, circumstances under which landfill mining should be prioritized or not are identified and sensitive parameters for the climate impact assessment of landfill mining are highlighted.

Detecting Underground Mine Voids Using Complex Geophysical Techniques

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

Kaminski, V. F.; Harbert, W. P.; Hammack, R. W.

2006-12-01

In July 2006, the National Energy Technology Laboratory in collaboration with Department of Geology and Planetary Science, University of Pittsburgh conducted complex ground geophysical surveys of an area known to be underlain by shallow coal mines. Geophysical methods including electromagnetic induction, DC resistivity and seismic reflection were conducted. The purpose of these surveys was to: 1) verify underground mine voids based on a century-old mine map that showed subsurface mine workings georeferenced to match with present location of geophysical test-site located on the territory of Bruceton research center in Pittsburgh, PA, 2) deliniate mine workings that may be potentially filledmore » with electrically conductive water filtrate emerging from adjacent groundwater collectors and 3) establish an equipment calibration site for geophysical instruments. Data from electromagnetic and resistivity surveys were further processed and inverted using EM1DFM, EMIGMA or Earthimager 2D capablilities in order to generate conductivity/depth images. Anomaly maps were generated, that revealed the locations of potential mine openings.« less

A review of soil heavy metal pollution from mines in China: pollution and health risk assessment.

PubMed

Li, Zhiyuan; Ma, Zongwei; van der Kuijp, Tsering Jan; Yuan, Zengwei; Huang, Lei

2014-01-15

Heavy metal pollution has pervaded many parts of the world, especially developing countries such as China. This review summarizes available data in the literature (2005-2012) on heavy metal polluted soils originating from mining areas in China. Based on these obtained data, this paper then evaluates the soil pollution levels of these collected mines and quantifies the risks these pollutants pose to human health. To assess these potential threat levels, the geoaccumulation index was applied, along with the US Environmental Protection Agency (USEPA) recommended method for health risk assessment. The results demonstrate not only the severity of heavy metal pollution from the examined mines, but also the high carcinogenic and non-carcinogenic risks that soil heavy metal pollution poses to the public, especially to children and those living in the vicinity of heavily polluted mining areas. In order to provide key management targets for relevant government agencies, based on the results of the pollution and health risk assessments, Cd, Pb, Cu, Zn, Hg, As, and Ni are selected as the priority control heavy metals; tungsten, manganese, lead-zinc, and antimony mines are selected as the priority control mine categories; and southern provinces and Liaoning province are selected as the priority control provinces. This review, therefore, provides a comprehensive assessment of soil heavy metal pollution derived from mines in China, while identifying policy recommendations for pollution mitigation and environmental management of these mines. © 2013.

Supporting technology of roadside in gob-side entry in 110 longwall mining method

NASA Astrophysics Data System (ADS)

He, Manchao; Guo, Pengfei; Chen, Shangyuan; Gao, Yubing; Wang, Yajun

2017-05-01

To get better results of shaping roadside in 110 longwall mining method, the roadside support can be reasonably choose and designed through theoretical analysis, engineering test and other methods. The roadway support need to be designed based on the mining height and influence of mining pressure, and it is necessary to consider the "limited deformation" but also "given deformation". Because of the small mining high and short time under mining pressure effect in thin coal seam, roadside support can meet the requirements of block rock from gob using I-steel, but I-steel can't satisfy the deformation of roadway roof and easily lead to I-steel flexural buckling. In that condition we should use the U-steel that can compatible deformation with subsidence of roadway roof and enough torque in overlapping part between tow U-steel should be given when the U-steel is used to support gangue from gob and the U steel assembling two cards can coordinal deformation in dynamic pressure area keeping constant resistance with the deformation of roadway roof and can get a good effect. Through field test, due to the great impact force of the gangue from gob, single props and I-steel and U-steel are easily knocked down when the mining height is more than 4m. For large mining height, gangue blocking hydraulic support is designed and developed which can guarantee the stability and integrity of the roadway roof in the dynamic pressure area and can prevent the impact of gangue from gob. So it has better effect of forming roadway side using gangue from gob. According to above classification, the field experiments were carried out and obtained satisfactory results.

A novel method for estimating methane emissions from underground coal mines: The Yanma coal mine, China

NASA Astrophysics Data System (ADS)

Ji, Zhong-Min; Chen, Zhi-Jian; Pan, Jie-Nan; Niu, Qing-He

2017-12-01

As the world's largest coal producer and consumer, China accounts for a relatively high proportion of methane emissions from coal mines. Several estimation methods had been established for the coal mine methane (CMM) emission. However, with large regional differences, various reservoir formation types of coalbed methane (CBM) and due to the complicated geological conditions in China, these methods may be deficient or unsuitable for all the mining areas (e.g. Jiaozuo mining area). By combing the CMM emission characteristics and considering the actual situation of methane emissions from underground coal mine, we found that the methane pre-drainage is a crucial reason creating inaccurate evaluating results for most estimation methods. What makes it so essential is the extensive pre-drainage quantity and its irrelevance with annual coal production. Accordingly, the methane releases were divided into two categories: methane pre-drainage and methane release during mining. On this basis, a pioneering method for estimating CMM emissions was proposed. Taking the Yanma coal mine in the Jiaozuo mining area as a study case, the evaluation method of the pre-drainage methane quantity was established after the correlation analysis between the pre-drainage rate and time. Thereafter, the mining activity influence factor (MAIF) was first introduced to reflect the methane release from the coal and rock seams around where affected by mining activity, and the buried depth was adopted as the predictor of the estimation for future methane emissions. It was verified in the six coal mines of Jiaozuo coalfield (2011) that the new estimation method has the minimum errors of 12.11%, 9.23%, 5.77%, -5.20%, -8.75% and 4.92% respectively comparing with other methods. This paper gives a further insight and proposes a more accurate evaluation method for the CMM emissions, especially for the coal seams with low permeability and strong tectonic deformation in methane outburst coal mines.

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.

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.

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.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics.

PubMed

Jeffryes, James G; Colastani, Ricardo L; Elbadawi-Sidhu, Mona; Kind, Tobias; Niehaus, Thomas D; Broadbelt, Linda J; Hanson, Andrew D; Fiehn, Oliver; Tyo, Keith E J; Henry, Christopher S

2015-01-01

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

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…

Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats

Treesearch

Richard Trans Mills; Forrest M Hoffman; Jitendra Kumar; William W. Hargrove

2011-01-01

We investigate methods for geospatiotemporal data mining of multi-year land surface phenology data (250 m2 Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectrometer (MODIS) in this study) for the conterminous United States (CONUS) as part of an early warning system for detecting threats to forest ecosystems. The...

Opinion data mining based on DNA method and ORA software

NASA Astrophysics Data System (ADS)

Tian, Ru-Ya; Wu, Lei; Liang, Xiao-He; Zhang, Xue-Fu

2018-01-01

Public opinion, especially the online public opinion is a critical issue when it comes to mining its characteristics. Because it can be formed directly and intensely in a short time, and may lead to the outbreak of online group events, and the formation of online public opinion crisis. This may become the pushing hand of a public crisis event, or even have negative social impacts, which brings great challenges to the government management. Data from the mass media which reveal implicit, previously unknown, and potentially valuable information, can effectively help us to understand the evolution law of public opinion, and provide a useful reference for rumor intervention. Based on the Dynamic Network Analysis method, this paper uses ORA software to mine characteristics of public opinion information, opinion topics, and public opinion agents through a series of indicators, and quantitatively analyzed the relationships between them. The results show that through the analysis of the 8 indexes associating with opinion data mining, we can have a basic understanding of the public opinion characteristics of an opinion event, such as who is important in the opinion spreading process, the information grasping condition, and the opinion topics release situation.

Mining dynamic noteworthy functions in software execution sequences.

PubMed

Zhang, Bing; Huang, Guoyan; Wang, Yuqian; He, Haitao; Ren, Jiadong

2017-01-01

As the quality of crucial entities can directly affect that of software, their identification and protection become an important premise for effective software development, management, maintenance and testing, which thus contribute to improving the software quality and its attack-defending ability. Most analysis and evaluation on important entities like codes-based static structure analysis are on the destruction of the actual software running. In this paper, from the perspective of software execution process, we proposed an approach to mine dynamic noteworthy functions (DNFM)in software execution sequences. First, according to software decompiling and tracking stack changes, the execution traces composed of a series of function addresses were acquired. Then these traces were modeled as execution sequences and then simplified so as to get simplified sequences (SFS), followed by the extraction of patterns through pattern extraction (PE) algorithm from SFS. After that, evaluating indicators inner-importance and inter-importance were designed to measure the noteworthiness of functions in DNFM algorithm. Finally, these functions were sorted by their noteworthiness. Comparison and contrast were conducted on the experiment results from two traditional complex network-based node mining methods, namely PageRank and DegreeRank. The results show that the DNFM method can mine noteworthy functions in software effectively and precisely.

The Problem of Multiple Criteria Selection of the Surface Mining Haul Trucks

NASA Astrophysics Data System (ADS)

Bodziony, Przemysław; Kasztelewicz, Zbigniew; Sawicki, Piotr

2016-06-01

Vehicle transport is a dominant type of technological processes in rock mines, and its profit ability is strictly dependent on overall cost of its exploitation, especially on diesel oil consumption. Thus, a rational design of transportation system based on haul trucks should result from thorough analysis of technical and economic issues, including both cost of purchase and its further exploitation, having a crucial impact on the cost of minerals extraction. Moreover, off-highway trucks should be selected with respect to all specific exploitation conditions and even the user's preferences and experience. In this paper a development of universal family of evaluation criteria as well as application of evaluation method for haul truck selection process for a specific exploitation conditions in surface mining have been carried out. The methodology presented in the paper is based on the principles of multiple criteria decision aiding (MCDA) using one of the ranking method, i.e. ELECTRE III. The applied methodology has been allowed for ranking of alternative solution (variants), on the considered set of haul trucks. The result of the research is a universal methodology, and it consequently may be applied in other surface mines with similar exploitation parametres.

A decision support system using analytical hierarchy process (AHP) for the optimal environmental reclamation of an open-pit mine

NASA Astrophysics Data System (ADS)

Bascetin, A.

2007-04-01

The selection of an optimal reclamation method is one of the most important factors in open-pit design and production planning. It also affects economic considerations in open-pit design as a function of plan location and depth. Furthermore, the selection is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by applying a systematic and logical approach to assess the priorities based on the inputs of several specialists from different functional areas within the mine company. The analytical hierarchy process (AHP) can be very useful in involving several decision makers with different conflicting objectives to arrive at a consensus decision. In this paper, the selection of an optimal reclamation method using an AHP-based model was evaluated for coal production in an open-pit coal mine located at Seyitomer region in Turkey. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a reclamation method that satisfies optimal specifications. Also, it is found that the decision process is systematic and using the proposed model can reduce the time taken to select a optimal method.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining.

PubMed

Xu, Xiao; Jin, Tao; Wei, Zhijie; Wang, Jianmin

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

PubMed Central

Xu, Xiao; Wei, Zhijie

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model. PMID:29065617

Investigation of the Mechanism of Roof Caving in the Jinchuan Nickel Mine, China

NASA Astrophysics Data System (ADS)

Ding, Kuo; Ma, Fengshan; Guo, Jie; Zhao, Haijun; Lu, Rong; Liu, Feng

2018-04-01

On 13 March 2016, a sudden, violent roof caving event with a collapse area of nearly 11,000 m2 occurred in the Jinchuan Nickel Mine and accompanied by air blasts, loud noises and ground vibrations. This collapse event coincided with related, conspicuous surface subsidence across an area of nearly 19,000 m2. This article aims to analyse this collapse event. In previous studies, various mining-induced collapses have been studied, but collapse accidents associated with the filling mining method are very rare and have not been thoroughly studied. The filling method has been regarded as a safe mining method for a long time, so research on associated collapse mechanisms is of considerable significance. In this study, a detailed field investigation of roadway damage was performed, and GPS monitoring results were used to analyse the surface failure. In addition, a numerical model was constructed based on the geometry of the ore body and a major fault. The analysis of the model revealed three failure mechanisms acting during different stages of destruction: double-sided embedded beam deformation, fault activation, and cantilever-articulated rock beam failure. The fault activation and the specific filling method are the key factors of this collapse event. To gain a better understanding of these factors, the shear stress and normal stress along the fault plane were monitored to determine the variation in stress at different failure stages. Discrete element models were established to study two filling methods and to analyse the stability of different filling structures.

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

PubMed Central

Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

2013-01-01

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems. PMID:24351646

Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges.

PubMed

Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

2013-12-17

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

ENVIRONMENTAL IMPACT ON PHYSIOLOGICAL RESPONSES OF UNDERGROUND COAL MINERS IN THE EASTERN PART OF INDIA.

PubMed

Dey, Netai Chandra; Nath, Suva; Sharma, Gourab Dhara; Mallik, Avijit

2014-12-01

Coal in India is extracted generally by semi-mechanized and mechanized underground mining methods. The Bord and Pillar (B & P) mining method still continues to be popular where deployment of manual miners is more than that of other mining methods. The study is conducted at haulage based mine of Eastern Coalfields of West Bengal. Underground miners confront with a lot of hazards like extreme hostile environment, awkward working posture, dust, noise as well as low luminosity. It is difficult to delay the onset of fatigue. In order to study the physiological responses of trammers, various parameters like working heart rates, net cardiac cost and relative cardiac cost including recovery heart rate patterns are recorded during their work at site. Workload classification of trammers has been done following various scales of heaviness. The effect of environment on the physiological responses has been observed and suitable recommendations are made. The work tasks are bound to induce musculoskeletal problems and those problems could be better managed through rationalizing the work-rest scheduling.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Applications of Geomatics in Surface Mining

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Górniak-Zimroz, Justyna; Milczarek, Wojciech; Pactwa, Katarzyna

2017-12-01

In terms of method of extracting mineral from deposit, mining can be classified into: surface, underground, and borehole mining. Surface mining is a form of mining, in which the soil and the rock covering the mineral deposits are removed. Types of surface mining include mainly strip and open-cast methods, as well as quarrying. Tasks associated with surface mining of minerals include: resource estimation and deposit documentation, mine planning and deposit access, mine plant development, extraction of minerals from deposits, mineral and waste processing, reclamation and reclamation of former mining grounds. At each stage of mining, geodata describing changes occurring in space during the entire life cycle of surface mining project should be taken into consideration, i.e. collected, analysed, processed, examined, distributed. These data result from direct (e.g. geodetic) and indirect (i.e. remote or relative) measurements and observations including airborne and satellite methods, geotechnical, geological and hydrogeological data, and data from other types of sensors, e.g. located on mining equipment and infrastructure, mine plans and maps. Management of such vast sources and sets of geodata, as well as information resulting from processing, integrated analysis and examining such data can be facilitated with geomatic solutions. Geomatics is a discipline of gathering, processing, interpreting, storing and delivering spatially referenced information. Thus, geomatics integrates methods and technologies used for collecting, management, processing, visualizing and distributing spatial data. In other words, its meaning covers practically every method and tool from spatial data acquisition to distribution. In this work examples of application of geomatic solutions in surface mining on representative case studies in various stages of mine operation have been presented. These applications include: prospecting and documenting mineral deposits, assessment of land accessibility for a potential large-scale surface mining project, modelling mineral deposit (granite) management, concept of a system for management of conveyor belt network technical condition, project of a geoinformation system of former mining terrains and objects, and monitoring and control of impact of surface mining on mine surroundings with satellite radar interferometry.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

PubMed Central

2012-01-01

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. PMID:22595088

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials.

PubMed

Korkontzelos, Ioannis; Mu, Tingting; Ananiadou, Sophia

2012-04-30

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.

Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients.

PubMed

Pourhoseingholi, Mohamad Amin; Kheirian, Sedigheh; Zali, Mohammad Reza

2017-12-01

Colorectal cancer (CRC) is one of the most common malignancies and cause of cancer mortality worldwide. Given the importance of predicting the survival of CRC patients and the growing use of data mining methods, this study aims to compare the performance of models for predicting 5-year survival of CRC patients using variety of basic and ensemble data mining methods. The CRC dataset from The Shahid Beheshti University of Medical Sciences Research Center for Gastroenterology and Liver Diseases were used for prediction and comparative study of the base and ensemble data mining techniques. Feature selection methods were used to select predictor attributes for classification. The WEKA toolkit and MedCalc software were respectively utilized for creating and comparing the models. The obtained results showed that the predictive performance of developed models was altogether high (all greater than 90%). Overall, the performance of ensemble models was higher than that of basic classifiers and the best result achieved by ensemble voting model in terms of area under the ROC curve (AUC= 0.96). AUC Comparison of models showed that the ensemble voting method significantly outperformed all models except for two methods of Random Forest (RF) and Bayesian Network (BN) considered the overlapping 95% confidence intervals. This result may indicate high predictive power of these two methods along with ensemble voting for predicting 5-year survival of CRC patients.

Stakeholders' Engagement Methods for the Mining Social Responsibility Practice: Determination of Local Issues and Concerns Related to the Mines Operations in Northwest of the US.

NASA Astrophysics Data System (ADS)

Masaitis, A.

2014-12-01

Every year, all around the world, global environmental change affects the human habitat. This is effect enhanced by the mining operation, and creates new challenges in relationship between the mining and local community. The purpose of this project are developed the Stakeholders engagement evaluation plan which is currently developed in University of Nevada, Reno for the Emigrant mining project, located in the central Nevada, USA, and belong to the Newmont Mining Corporation, one of the gold production leader worldwide. The needs for this project is to create the open dialog between Newmont mining company and all interested parties which have social or environmental impacts from the Emigrant mine. Identification of the stakeholders list is first and one of the most difficult steps in the developing of mine social responsibility. Stakeholders' engagement evaluation plan must be based on the timing and available resources of the mining company, understanding the goals for the engagement, and on analyzes of the possible risks from engagement. In conclusion, the Stakeholders engagement evaluation plan includes: first, determinations of the stakeholders list, which must include any interested or effected by the mine projects groups, for example: state and local government representatives, people from local communities, business partners, environmental NGOs, indigenous people, and academic groups. The contacts and availability for communication is critical for Stakeholders engagement. Next, is to analyze characteristics of all these parties and determinate the level of interest and level of their influence on the project. The next step includes the Stakeholders matrix and mapping development, where all these information will be put together.After that, must be chosen the methods for stakeholders' engagement. The methods usually depends from the goals of engagement (create the dialog lines, collect the data, determinations of the local issues and concerns, or establish the negotiation process) and available resources as a time, people, budget. Is it very important here to recognize the possible risks from the engagement and establish the key massage for stakeholders. Finally, the engagement plan should be evaluated and can be implementing for the new social responsibility practice development.

Developing image processing meta-algorithms with data mining of multiple metrics.

PubMed

Leung, Kelvin; Cunha, Alexandre; Toga, A W; Parker, D Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

A data mining based approach to predict spatiotemporal changes in satellite images

NASA Astrophysics Data System (ADS)

Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

2011-06-01

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

Preliminary studies of the CHIM electrogeochemical method at the Kokomo Mine, Russell Gulch, Colorado

USGS Publications Warehouse

Smith, D.B.; Hoover, D.B.; Sanzolone, R.F.

1993-01-01

The CHIM electrogeochemical exploration technique was developed in the former Soviet Union about 20 years ago and is claimed to be effective in exploration for concealed mineral deposits that are not detectable by other geochemical or geophysical techniques. The method involves providing a high-voltage direct current to an anode and an array of special collector cathodes. Cations mobile in the electric field are collected at the cathodes and their concentrations determined. The U.S. Geological Survey started a study of the CHIM method by conducting tests over a precious- and base-metal-bearing quartz vein covered with 3 m of colluvial soil and weathered bedrock near the Kokomo Mine, Colorado. The tests show that the CHIM method gives better definition of the vein than conventional soil geochemistry based on a total-dissolution technique. The CHIM technique gives reproducible geochemical anomaly patterns, but the absolute concentrations depend on local site variability as well as temporal variations. Weak partial dissolutions of soils at the Kokomo Mine by an enzyme leach, a dilute acetic acid leach, and a dilute hydrochloric acid leach show results comparable to those from the CHIM method. This supports the idea that the CHIM technique is essentially a weak in-situ partial extraction involving only ions able to move in a weak electric field. ?? 1993.

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

Rockburst Disaster Prediction of Isolated Coal Pillar by Electromagnetic Radiation Based on Frictional Effect

PubMed Central

Zhao, Tongbin; Yin, Yanchun; Xiao, Fukun; Tan, Yunliang; Zou, Jianchao

2014-01-01

Based on the understanding that charges generated during coal cracking are due to coal particle friction, a microstructure model was developed by considering four different variation laws of friction coefficient. Firstly, the frictional energy release of coal sample during uniaxial compressive tests was investigated and discussed. Then electromagnetic radiation method was used to predict the potential rockburst disaster in isolated coal pillar mining face, Muchengjian Colliery. The results indicate that the friction coefficient of coal particles decreases linearly with the increase of axial loading force. In predicting the strain-type rockburst, the high stress state of coal must be closely monitored. Field monitoring shows that electromagnetic radiation signal became abnormal before the occurrence of rockburst during isolated coal pillar mining. Furthermore, rockburst tends to occur at the early and ending stages of isolated coal pillar extraction. Mine-site investigation shows the occurrence zone of rockburst is consistent with the prediction, proving the reliability of the electromagnetic radiation method to predict strain-type rockburst disaster. PMID:25054186

Rockburst disaster prediction of isolated coal pillar by electromagnetic radiation based on frictional effect.

PubMed

Zhao, Tongbin; Yin, Yanchun; Xiao, Fukun; Tan, Yunliang; Zou, Jianchao

2014-01-01

Based on the understanding that charges generated during coal cracking are due to coal particle friction, a microstructure model was developed by considering four different variation laws of friction coefficient. Firstly, the frictional energy release of coal sample during uniaxial compressive tests was investigated and discussed. Then electromagnetic radiation method was used to predict the potential rockburst disaster in isolated coal pillar mining face, Muchengjian Colliery. The results indicate that the friction coefficient of coal particles decreases linearly with the increase of axial loading force. In predicting the strain-type rockburst, the high stress state of coal must be closely monitored. Field monitoring shows that electromagnetic radiation signal became abnormal before the occurrence of rockburst during isolated coal pillar mining. Furthermore, rockburst tends to occur at the early and ending stages of isolated coal pillar extraction. Mine-site investigation shows the occurrence zone of rockburst is consistent with the prediction, proving the reliability of the electromagnetic radiation method to predict strain-type rockburst disaster.

Zoning method for environmental engineering geological patterns in underground coal mining areas.

PubMed

Liu, Shiliang; Li, Wenping; Wang, Qiqing

2018-09-01

Environmental engineering geological patterns (EEGPs) are used to express the trend and intensity of eco-geological environment caused by mining in underground coal mining areas, a complex process controlled by multiple factors. A new zoning method for EEGPs was developed based on the variable-weight theory (VWT), where the weights of factors vary with their value. The method was applied to the Yushenfu mining area, Shaanxi, China. First, the mechanism of the EEGPs caused by mining was elucidated, and four types of EEGPs were proposed. Subsequently, 13 key control factors were selected from mining conditions, lithosphere, hydrosphere, ecosphere, and climatic conditions; their thematic maps were constructed using ArcGIS software and remote-sensing technologies. Then, a stimulation-punishment variable-weight model derived from the partition of basic evaluation unit of study area, construction of partition state-variable-weight vector, and determination of variable-weight interval was built to calculate the variable weights of each factor. On this basis, a zoning mathematical model of EEGPs was established, and the zoning results were analyzed. For comparison, the traditional constant-weight theory (CWT) was also applied to divide the EEGPs. Finally, the zoning results obtained using VWT and CWT were compared. The verification of field investigation indicates that VWT is more accurate and reliable than CWT. The zoning results are consistent with the actual situations and the key of planning design for the rational development of coal resources and protection of eco-geological environment. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Decision support methods for the environmental assessment of contamination at mining sites.

PubMed

Jordan, Gyozo; Abdaal, Ahmed

2013-09-01

Polluting mine accidents and widespread environmental contamination associated with historic mining in Europe and elsewhere has triggered the improvement of related environmental legislation and of the environmental assessment and management methods for the mining industry. Mining has some unique features such as natural background pollution associated with natural mineral deposits, industrial activities and contamination located in the three-dimensional sub-surface space, the problem of long-term remediation after mine closure, problem of secondary contaminated areas around mine sites and abandoned mines in historic regions like Europe. These mining-specific problems require special tools to address the complexity of the environmental problems of mining-related contamination. The objective of this paper is to review and evaluate some of the decision support methods that have been developed and applied to mining contamination. In this paper, only those methods that are both efficient decision support tools and provide a 'holistic' approach to the complex problem as well are considered. These tools are (1) landscape ecology, (2) industrial ecology, (3) landscape geochemistry, (4) geo-environmental models, (5) environmental impact assessment, (6) environmental risk assessment, (7) material flow analysis and (8) life cycle assessment. This unique inter-disciplinary study should enable both the researcher and the practitioner to obtain broad view on the state-of-the-art of decision support methods for the environmental assessment of contamination at mine sites. Documented examples and abundant references are also provided.

Integrated approach of environmental impact and risk assessment of Rosia Montana Mining Area, Romania.

PubMed

Stefănescu, Lucrina; Robu, Brînduşa Mihaela; Ozunu, Alexandru

2013-11-01

The environmental impact assessment of mining sites represents nowadays a large interest topic in Romania. Historical pollution in the Rosia Montana mining area of Romania caused extensive damage to environmental media. This paper has two goals: to investigate the environmental pollution induced by mining activities in the Rosia Montana area and to quantify the environmental impacts and associated risks by means of an integrated approach. Thus, a new method was developed and applied for quantifying the impact of mining activities, taking account of the quality of environmental media in the mining area, and used as case study in the present paper. The associated risks are a function of the environmental impacts and the probability of their occurrence. The results show that the environmental impacts and quantified risks, based on quality indicators to characterize the environmental quality, are of a higher order, and thus measures for pollution remediation and control need to be considered in the investigated area. The conclusion drawn is that an integrated approach for the assessment of environmental impact and associated risks is a valuable and more objective method, and is an important tool that can be applied in the decision-making process for national authorities in the prioritization of emergency action.

Patent data mining method and apparatus

DOEpatents

Boyack, Kevin W.; Grafe, V. Gerald; Johnson, David K.; Wylie, Brian N.

2002-01-01

A method of data mining represents related patents in a multidimensional space. Distance between patents in the multidimensional space corresponds to the extent of relationship between the patents. The relationship between pairings of patents can be expressed based on weighted combinations of several predicates. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the patents.

Review of Recent Development of Dynamic Wind Farm Equivalent Models Based on Big Data Mining

NASA Astrophysics Data System (ADS)

Wang, Chenggen; Zhou, Qian; Han, Mingzhe; Lv, Zhan’ao; Hou, Xiao; Zhao, Haoran; Bu, Jing

2018-04-01

Recently, the big data mining method has been applied in dynamic wind farm equivalent modeling. In this paper, its recent development with present research both domestic and overseas is reviewed. Firstly, the studies of wind speed prediction, equivalence and its distribution in the wind farm are concluded. Secondly, two typical approaches used in the big data mining method is introduced, respectively. For single wind turbine equivalent modeling, it focuses on how to choose and identify equivalent parameters. For multiple wind turbine equivalent modeling, the following three aspects are concentrated, i.e. aggregation of different wind turbine clusters, the parameters in the same cluster, and equivalence of collector system. Thirdly, an outlook on the development of dynamic wind farm equivalent models in the future is discussed.

Prediction of subsurface fracture in mining zone of Papua using passive seismic tomography based on Fresnel zone

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

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

Fracture prediction in a block cave of underground mine is very important to monitor the structure of the fracture that can be harmful to the mining activities. Many methods can be used to obtain such information, such as TDR (Time Domain Relectometry) and open hole. Both of them have limitations in range measurement. Passive seismic tomography is one of the subsurface imaging method. It has advantage in terms of measurements, cost, and rich of rock physical information. This passive seismic tomography studies using Fresnel zone to model the wavepath by using frequency parameter. Fresnel zone was developed by Nurhandoko inmore » 2000. The result of this study is tomography of P and S wave velocity which can predict position of fracture. The study also attempted to use sum of the wavefronts to obtain position and time of seismic event occurence. Fresnel zone tomography and the summation wavefront can predict location of geological structure of mine area as well.« less

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.

Estimation of m.w.e (meter water equivalent) depth of the salt mine of Slanic Prahova, Romania

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

Mitrica, B.; Margineanu, R.; Stoica, S.

2010-11-24

A new mobile detector was developed in IFIN-HH, Romania, for measuring muon flux at surface and in underground. The measurements have been performed in the salt mines of Slanic Prahova, Romania. The muon flux was determined for 2 different galleries of the Slanic mine at different depths. In order to test the stability of the method, also measurements of the muon flux at surface at different altitudes were performed. Based on the results, the depth of the 2 galleries was established at 610 and 790 m.w.e. respectively.

Alexandra Masaitis, UNR NV, USA. PhD Grad St. Principles of Adoption of the Successful Environmental Policies and Practices Used in Developed Countries into the Russian Mining Industry

NASA Astrophysics Data System (ADS)

Masaitis, A.

2012-12-01

The successful implementation of the environmental policies in the mining industry is of a paramount importance, as it not only prevents both local and trans-border pollution but also guarantees a clean and healthy environment for the people regardless of their place of habitation. It is especially important to encourage the progress of the environmental policy implementation in less regulated countries such as the Russia because such countries have resource-oriented economy based on development of nonrenewable resources. Poor environmental practices in such countries will lead to local environmental crises that could eventually spill into surrounding countries including the most economically advanced. This abstract is a summary of a two-year research project attempted (1) to determine deficiencies of the Russian mining sector ecological policies and (2) to suggest substitute policies from developed countries that could be adapted to the Russian reality. The following research methods were used: 1. The method of the system analysis, where the system is an interaction of the sets of environmental policies; 2. The comparative method of inquiry, 3. Quantitative data analysis, where data was collected from "The collection of statistic data", the US EPA open reports, and the USGS Reports; 4. Review of the Norilsk Nickel Company annual reports. The following results were obtained: Identified the systemic crisis of the ecological environmental policies in the Russian mining sector based on the development of nonrenewable resources, in the absence of the ecological interest by the mining companies that lack mechanisms of environmental and public health protection, the lack of insurance policy, the lack of risk assessment, and in the presence of the audit and monitoring that do not address the local conditions. Based on the above, the following concepts were thought of to improve the environmental conditions in the Russian mining sector: 1. Was developed the Regional Environmental Management principle based on the local conditions; 2. Devised were criteria for the risk assessment for mining operations in Russia. Where the fundamental principals were public health, environmental and biodiversity impact, long- and short- term rehabilitation plans, compliance with international standards and norms. Every criterion has grade level of probability that directly affects quarterly fees. 3. Developed was the mechanism of the economic motivation to make mining operations "environmentally friendly" that includes defrayal of expenses from both direct and indirect damages. 4. Identified were spheres of possible cooperation between mining companies, government organizations, and the NGOs. These include development of standards for Good Neighbor Agreement, exchange of environmental information, international exchange of successful environmental, health, and safety practices. The study showed the necessity for the Russian Federation mining industry to adopt the more successful environmental policies and practices used in developed countries. To achieve this goal the Regional Environmental Management principle, the risk assessment criteria, the mechanism of the economic motivation, and the principles for international cooperation can play an extremely important role.

Unsupervised User Similarity Mining in GSM Sensor Networks

PubMed Central

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining. PMID:23576905

Increasing the technical level of mining haul trucks

NASA Astrophysics Data System (ADS)

Voronov, Yuri; Voronov, Artyom; Grishin, Sergey; Bujankin, Alexey

2017-11-01

Theoretical and methodological fundamentals of mining haul trucks optimal design are articulated. Methods based on the systems approach to integrated assessment of truck technical level and methods for optimization of truck parameters depending on performance standards are provided. The results of using these methods are given. The developed method allows not only assessing the truck technical levels but also choosing the most promising models and providing quantitative evaluations of the decisions to be made at the design stage. These areas are closely connected with the problem of improvement in the industrial output quality, which, being a part of the widely spread in Western world "total quality control" ideology, is one of the major issues for the Russian economy.

[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.

Environmental effects of hydrothermal alteration and historical mining on water and sediment quality in Central Colorado

USGS Publications Warehouse

Church, S.E.; Fey, D. L.; Klein, T.L.; Schmidt, T.S.; Wanty, R.B.; deWitt, E.H.; Rockwell, B.W.; San, Juan C.A.

2009-01-01

The U.S. Geological Survey conducted an environmental assessment of 198 catchments in a 54,000-km2 area of central Colorado, much of which is on Federal land. The Colorado Mineral Belt, a northeast-trending zone of historical base- and precious-metal mining, cuts diagonally across the study area. The investigation was intended to test the hypothesis that degraded water and sediment quality are restricted to catchments in which historical mining has occurred. Water, streambed sediment, and aquatic insects were collected from (1) catchments underlain by single lithogeochemical units, some of which were hydrothermally altered, that had not been prospected or mined; (2) catchments that contained evidence of prospecting, most of which contain hydrothermally altered rock, but no historical mining; and (3) catchments, all of which contain hydrothermally altered rock, where historical but now inactive mines occur. Geochemical data determined from catchments that did not contain hydrothermal alteration or historical mines met water quality criteria and sediment quality guidelines. Base-metal concentrations from these types of catchments showed small geochemical variations that reflect host lithology. Hydrothermal alteration and mineralization typically are associated with igneous rocks that have intruded older bedrock in a catchment. This alteration was regionally mapped and characterized primarily through the analysis of remote sensing data acquired by the ASTER satellite sensor. Base-metal concentrations among unaltered rock types showed small geochemical variations that reflect host lithology. Base-metal concentrations were elevated in sediment from catchments underlain by hydrothermally altered rock. Classification of catchments on the basis of mineral deposit types proved to be an efficient and accurate method for discriminating catchments that have degraded water and sediment quality. Only about 4.5 percent of the study area has been affected by historical mining, whereas a larger part of the study area is underlain by hydrothermally altered rock that has weathered to produce water and sediment with naturally elevated geochemical baselines. 

Privacy Preserving Nearest Neighbor Search

NASA Astrophysics Data System (ADS)

Shaneck, Mark; Kim, Yongdae; Kumar, Vipin

Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this chapter we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. We prove the security of these algorithms under the semi-honest adversarial model, and describe methods that can be used to optimize their performance. Keywords: Privacy Preserving Data Mining, Nearest Neighbor Search, Outlier Detection, Clustering, Classification, Secure Multiparty Computation

A novel approach to generating CER hypotheses based on mining clinical data.

PubMed

Zhang, Shuo; Li, Lin; Yu, Yiqin; Sun, Xingzhi; Xu, Linhao; Zhao, Wei; Teng, Xiaofei; Pan, Yue

2013-01-01

Comparative effectiveness research (CER) is a scientific method of investigating the effectiveness of alternative intervention methods. In a CER study, clinical researchers typically start with a CER hypothesis, and aim to evaluate it by applying a series of medical statistical methods. Traditionally, the CER hypotheses are defined manually by clinical researchers. This makes the task of hypothesis generation very time-consuming and the quality of hypothesis heavily dependent on the researchers' skills. Recently, with more electronic medical data being collected, it is highly promising to apply the computerized method for discovering CER hypotheses from clinical data sets. In this poster, we proposes a novel approach to automatically generating CER hypotheses based on mining clinical data, and presents a case study showing that the approach can facilitate clinical researchers to identify potentially valuable hypotheses and eventually define high quality CER studies.

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.

An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

Hematite Core Nanoparticles with Carbon Shell: Potential for Environmentally Friendly Production from Iron Mining Sludge

NASA Astrophysics Data System (ADS)

Stević, Dragana; Mihajlović, Dijana; Kukobat, Radovan; Hattori, Yoshiyuki; Sagisaka, Kento; Kaneko, Katsumi; Atlagić, Suzana Gotovac

2016-08-01

Hematite nanoparticles with amorphous, yet relatively uniform carbon shell, were produced based exclusively on the waste sludge from the iron mine as the raw material. The procedure for acid digestion-based purification of the sludge with the full recovery of acid vapors and the remaining non-toxic rubble is described. Synthesis of the hematite nanoparticles was performed by the arrested precipitation method with cationic surfactant. The particles were thoroughly characterized and the potential of their economical production for the battery industry is indicated.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Design of Meter-Scale Antenna and Signal Detection System for Underground Magnetic Resonance Sounding in Mines.

PubMed

Yi, Xiaofeng; Zhang, Jian; Fan, Tiehu; Tian, Baofeng; Jiang, Chuandong

2018-03-13

Magnetic resonance sounding (MRS) is a novel geophysical method to detect groundwater directly. By applying this method to underground projects in mines and tunnels, warning information can be provided on water bodies that are hidden in front prior to excavation and thus reduce the risk of casualties and accidents. However, unlike its application to ground surfaces, the application of MRS to underground environments is constrained by the narrow space, quite weak MRS signal, and complex electromagnetic interferences with high intensities in mines. Focusing on the special requirements of underground MRS (UMRS) detection, this study proposes the use of an antenna with different turn numbers, which employs a separated transmitter and receiver. We designed a stationary coil with stable performance parameters and with a side length of 2 m, a matching circuit based on a Q-switch and a multi-stage broad/narrowband mixed filter that can cancel out most electromagnetic noise. In addition, noises in the pass-band are further eliminated by adopting statistical criteria and harmonic modeling and stacking, all of which together allow weak UMRS signals to be reliably detected. Finally, we conducted a field case study of the UMRS measurement in the Wujiagou Mine in Shanxi Province, China, with known water bodies. Our results show that the method proposed in this study can be used to obtain UMRS signals in narrow mine environments, and the inverted hydrological information generally agrees with the actual situation. Thus, we conclude that the UMRS method proposed in this study can be used for predicting hazardous water bodies at a distance of 7-9 m in front of the wall for underground mining projects.

Design of Meter-Scale Antenna and Signal Detection System for Underground Magnetic Resonance Sounding in Mines

PubMed Central

Yi, Xiaofeng; Fan, Tiehu; Tian, Baofeng

2018-01-01

Magnetic resonance sounding (MRS) is a novel geophysical method to detect groundwater directly. By applying this method to underground projects in mines and tunnels, warning information can be provided on water bodies that are hidden in front prior to excavation and thus reduce the risk of casualties and accidents. However, unlike its application to ground surfaces, the application of MRS to underground environments is constrained by the narrow space, quite weak MRS signal, and complex electromagnetic interferences with high intensities in mines. Focusing on the special requirements of underground MRS (UMRS) detection, this study proposes the use of an antenna with different turn numbers, which employs a separated transmitter and receiver. We designed a stationary coil with stable performance parameters and with a side length of 2 m, a matching circuit based on a Q-switch and a multi-stage broad/narrowband mixed filter that can cancel out most electromagnetic noise. In addition, noises in the pass-band are further eliminated by adopting statistical criteria and harmonic modeling and stacking, all of which together allow weak UMRS signals to be reliably detected. Finally, we conducted a field case study of the UMRS measurement in the Wujiagou Mine in Shanxi Province, China, with known water bodies. Our results show that the method proposed in this study can be used to obtain UMRS signals in narrow mine environments, and the inverted hydrological information generally agrees with the actual situation. Thus, we conclude that the UMRS method proposed in this study can be used for predicting hazardous water bodies at a distance of 7–9 m in front of the wall for underground mining projects. PMID:29534007

Survey of nine surface mines in North America. [Nine different mines in USA and Canada

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

Hayes, L.G.; Brackett, R.D.; Floyd, F.D.

This report presents the information gathered by three mining engineers in a 1980 survey of nine surface mines in the United States and Canada. The mines visited included seven coal mines, one copper mine, and one tar sands mine selected as representative of present state of the art in open pit, strip, and terrace pit mining. The purpose of the survey was to investigate mining methods, equipment requirements, operating costs, reclamation procedures and costs, and other aspects of current surface mining practices in order to acquire basic data for a study comparing conventional and terrace pit mining methods, particularly inmore » deeper overburdens. The survey was conducted as part of a project under DOE Contract No. DE-AC01-79ET10023 titled The Development of Optimal Terrace Pit Coal Mining Systems.« less

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

PubMed Central

Cunha, Alexandre; Toga, A. W.; Parker, D. Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation. PMID:24653748

Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

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

Parwatiningtyas, Diyan, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com; Ambarsari, Erlin Windia, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com; Marlina, Dwi, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com

Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method ofmore » calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.« less

Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

NASA Astrophysics Data System (ADS)

Parwatiningtyas, Diyan; Ambarsari, Erlin Windia; Marlina, Dwi; Wiratomo, Yogi

2014-03-01

Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method of calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.

Comprehensive evaluation of ecological security in mining area based on PSR-ANP-GRAY.

PubMed

He, Gang; Yu, Baohua; Li, Shuzhou; Zhu, Yanna

2017-09-06

With the large exploitation of mineral resources, a series of problems have appeared in the ecological environment of the mining area. Therefore, evaluating the ecological security of mining area is of great significance to promote its healthy development. In this paper, the evaluation index system of ecological security in mining area was constructed from three dimensions of nature, society and economy, combined with Pressure-State-Response framework model. Then network analytic hierarchy process and GRAY relational analysis method were used to evaluate the ecological security of the region, and the weighted correlation degree of ecological security was calculated through the index data of a coal mine from 2012 to 2016 in China. The results show that the ecological security in the coal mine area is on the rise as a whole, though it alternatively rose and dropped from 2012 to 2016. Among them, the ecological security of the study mining area is at the general security level from 2012 to 2015, and at a relatively safe level in 2016. It shows that the ecological environment of the study mining area can basically meet the requirement of the survival and development of the enterprises.

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

PubMed

Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products.

PubMed

Kang, Hahk-Soo

2017-02-01

Genomics-based methods are now commonplace in natural products research. A phylogeny-guided mining approach provides a means to quickly screen a large number of microbial genomes or metagenomes in search of new biosynthetic gene clusters of interest. In this approach, biosynthetic genes serve as molecular markers, and phylogenetic trees built with known and unknown marker gene sequences are used to quickly prioritize biosynthetic gene clusters for their metabolites characterization. An increase in the use of this approach has been observed for the last couple of years along with the emergence of low cost sequencing technologies. The aim of this review is to discuss the basic concept of a phylogeny-guided mining approach, and also to provide examples in which this approach was successfully applied to discover new natural products from microbial genomes and metagenomes. I believe that the phylogeny-guided mining approach will continue to play an important role in genomics-based natural products research.

A new genome-mining tool redefines the lasso peptide biosynthetic landscape

PubMed Central

Tietz, Jonathan I.; Schwalen, Christopher J.; Patel, Parth S.; Maxson, Tucker; Blair, Patricia M.; Tai, Hua-Chia; Zakai, Uzma I.; Mitchell, Douglas A.

2016-01-01

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts. PMID:28244986

Mining dynamic noteworthy functions in software execution sequences

PubMed Central

Huang, Guoyan; Wang, Yuqian; He, Haitao; Ren, Jiadong

2017-01-01

As the quality of crucial entities can directly affect that of software, their identification and protection become an important premise for effective software development, management, maintenance and testing, which thus contribute to improving the software quality and its attack-defending ability. Most analysis and evaluation on important entities like codes-based static structure analysis are on the destruction of the actual software running. In this paper, from the perspective of software execution process, we proposed an approach to mine dynamic noteworthy functions (DNFM)in software execution sequences. First, according to software decompiling and tracking stack changes, the execution traces composed of a series of function addresses were acquired. Then these traces were modeled as execution sequences and then simplified so as to get simplified sequences (SFS), followed by the extraction of patterns through pattern extraction (PE) algorithm from SFS. After that, evaluating indicators inner-importance and inter-importance were designed to measure the noteworthiness of functions in DNFM algorithm. Finally, these functions were sorted by their noteworthiness. Comparison and contrast were conducted on the experiment results from two traditional complex network-based node mining methods, namely PageRank and DegreeRank. The results show that the DNFM method can mine noteworthy functions in software effectively and precisely. PMID:28278276

Multisource geological data mining and its utilization of uranium resources exploration

NASA Astrophysics Data System (ADS)

Zhang, Jie-lin

2009-10-01

Nuclear energy as one of clear energy sources takes important role in economic development in CHINA, and according to the national long term development strategy, many more nuclear powers will be built in next few years, so it is a great challenge for uranium resources exploration. Research and practice on mineral exploration demonstrates that utilizing the modern Earth Observe System (EOS) technology and developing new multi-source geological data mining methods are effective approaches to uranium deposits prospecting. Based on data mining and knowledge discovery technology, this paper uses multi-source geological data to character electromagnetic spectral, geophysical and spatial information of uranium mineralization factors, and provides the technical support for uranium prospecting integrating with field remote sensing geological survey. Multi-source geological data used in this paper include satellite hyperspectral image (Hyperion), high spatial resolution remote sensing data, uranium geological information, airborne radiometric data, aeromagnetic and gravity data, and related data mining methods have been developed, such as data fusion of optical data and Radarsat image, information integration of remote sensing and geophysical data, and so on. Based on above approaches, the multi-geoscience information of uranium mineralization factors including complex polystage rock mass, mineralization controlling faults and hydrothermal alterations have been identified, the metallogenic potential of uranium has been evaluated, and some predicting areas have been located.

Quantification of proportions of different water sources in a mining operation.

PubMed

Scheiber, Laura; Ayora, Carlos; Vázquez-Suñé, Enric

2018-04-01

The water drained in mining operations (galleries, shafts, open pits) usually comes from different sources. Evaluating the contribution of these sources is very often necessary for water management. To determine mixing ratios, a conventional mass balance is often used. However, the presence of more than two sources creates uncertainties in mass balance applications. Moreover, the composition of the end-members is not commonly known with certainty and/or can vary in space and time. In this paper, we propose a powerful tool for solving such problems and managing groundwater in mining sites based on multivariate statistical analysis. This approach was applied to the Cobre Las Cruces mining complex, the largest copper mine in Europe. There, the open pit water is a mixture of three end-members: runoff (RO), basal Miocene (Mb) and Paleozoic (PZ) groundwater. The volume of water drained from the Miocene base aquifer must be determined and compensated via artificial recharging to comply with current regulations. Through multivariate statistical analysis of samples from a regional field campaign, the compositions of PZ and Mb end-members were firstly estimated, and then used for mixing calculations at the open pit scale. The runoff end-member was directly determined from samples collected in interception trenches inside the open pit. The application of multivariate statistical methods allowed the estimation of mixing ratios for the hydrological years 2014-2015 and 2015-2016. Open pit water proportions have changed from 15% to 7%, 41% to 36%, and 44% to 57% for runoff, Mb and PZ end-members, respectively. An independent estimation of runoff based on the curve method yielded comparable results. Copyright © 2017 Elsevier B.V. All rights reserved.

The Mining Minds digital health and wellness framework.

PubMed

Banos, Oresti; Bilal Amin, Muhammad; Ali Khan, Wajahat; Afzal, Muhammad; Hussain, Maqbool; Kang, Byeong Ho; Lee, Sungyong

2016-07-15

The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people's conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems. This work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people's lifestyles and provide a variety of smart coaching and support services. This paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework. Mining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.

Comprehensive Fractal Description of Porosity of Coal of Different Ranks

PubMed Central

Ren, Jiangang; Zhang, Guocheng; Song, Zhimin; Liu, Gaofeng; Li, Bing

2014-01-01

We selected, as the objects of our research, lignite from the Beizao Mine, gas coal from the Caiyuan Mine, coking coal from the Xiqu Mine, and anthracite from the Guhanshan Mine. We used the mercury intrusion method and the low-temperature liquid nitrogen adsorption method to analyze the structure and shape of the coal pores and calculated the fractal dimensions of different aperture segments in the coal. The experimental results show that the fractal dimension of the aperture segment of lignite, gas coal, and coking coal with an aperture of greater than or equal to 10 nm, as well as the fractal dimension of the aperture segment of anthracite with an aperture of greater than or equal to 100 nm, can be calculated using the mercury intrusion method; the fractal dimension of the coal pore, with an aperture range between 2.03 nm and 361.14 nm, can be calculated using the liquid nitrogen adsorption method, of which the fractal dimensions bounded by apertures of 10 nm and 100 nm are different. Based on these findings, we defined and calculated the comprehensive fractal dimensions of the coal pores and achieved the unity of fractal dimensions for full apertures of coal pores, thereby facilitating, overall characterization for the heterogeneity of the coal pore structure. PMID:24955407

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.

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.

Subsidence from underground mining; environmental analysis and planning considerations

USGS Publications Warehouse

Lee, Fitzhugh T.; Abel, John F.

1983-01-01

Subsidence, a universal process that occurs in response to the voids created by extracting solids or liquids from beneath the Earth's surface, is controlled by many factors including mining methods, depth of extraction, thickness of deposit, and topography, as well as the in situ properties of the rock mass above the deposit. The impacts of subsidence are potentially severe in terms of damage to surface utility lines and structures, changes in surface-water and ground-water conditions, and effects on vegetation and animals. Although subsidence cannot be eliminated, it can be reduced or controlled in areas where deformation of the ground surface would produce dangerous or costly effects. Subsidence prediction is highly developed in Europe where there are comparatively uniform mining conditions and a long history of field measurements. Much of this mining has been carried out beneath crowded urban and industrial areas where accurate predictions have facilitated use of the surface and reduced undesirable impacts. Concerted efforts to understand subsidence processes in the United States are recent. Empirical methods of subsidence analysis and prediction based on local conditions seem better suited to the current state of knowledge of the varied geologic and topographic conditions in domestic coal mining regions than do theoretical/mathematical approaches. In order to develop broadly applicable subsidence prediction methods and models for the United States, more information is needed on magnitude and timing of ground movements and geologic properties.

Analysis on composition rules of Chinese patent drugs treating pain-related diseases based on data mining method.

PubMed

Tang, Shi-Huan; Shen, Dan; Yang, Hong-Jun

2017-08-24

To analyze the composition rules of oral prescriptions in the treatment of headache, stomachache and dysmenorrhea recorded in National Standard for Chinese Patent Drugs (NSCPD) enacted by Ministry of Public Health of China and then make comparison between them to better understand pain treatment in different regions of human body. Constructed NSCPD database had been constructed in 2014. Prescriptions treating the three pain-related diseases were searched and screened from the database. Then data mining method such as association rules analysis and complex system entropy method integrated in the data mining software Traditional Chinese Medicine Inheritance Support System (TCMISS) were applied to process the data. Top 25 drugs with high frequency in the treatment of each disease were selected, and 51, 33 and 22 core combinations treating headache, stomachache and dysmenorrhea respectively were mined out as well. The composition rules of the oral prescriptions for treating headache, stomachache and dysmenorrhea recorded in NSCPD has been summarized. Although there were similarities between them, formula varied according to different locations of pain. It can serve as an evidence and reference for clinical treatment and new drug development.

Prediction of acid mine drainage generation potential of various lithologies using static tests: Etili coal mine (NW Turkey) as a case study.

PubMed

Yucel, Deniz Sanliyuksel; Baba, Alper

2016-08-01

The Etili neighborhood in Can County (northwestern Turkey) has large reserves of coal and has been the site of many small- to medium-scale mining operations since the 1980s. Some of these have ceased working while others continue to operate. Once activities cease, the mining facilities and fields are usually abandoned without rehabilitation. The most significant environmental problem is acid mine drainage (AMD). This study was carried out to determine the acid generation potential of various lithological units in the Etili coal mine using static test methods. Seventeen samples were selected from areas with high acidic water concentrations: from different alteration zones belonging to volcanic rocks, from sedimentary rocks, and from coals and mine wastes. Static tests (paste pH, standard acid-base accounting, and net acid generation tests) were performed on these samples. The consistency of the static test results showed that oxidation of sulfide minerals, especially pyrite-which is widely found not only in the alteration zones of volcanic rocks but also in the coals and mine wastes-is the main factor controlling the generation of AMD in this mine. Lack of carbonate minerals in the region also increases the occurrence of AMD.

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

NASA Astrophysics Data System (ADS)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

Intelligent Scheduling for Underground Mobile Mining Equipment.

PubMed

Song, Zhen; Schunnesson, Håkan; Rinne, Mikael; Sturgul, John

2015-01-01

Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

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.

Comparison of ALE and SPH Methods for Simulating Mine Blast Effects on Structures

DTIC Science & Technology

2010-12-01

Comparison of ALE and SPH methods for simulating mine blast effects on struc- tures Geneviève Toussaint Amal Bouamoul DRDC Valcartier Defence R&D...Canada – Valcartier Technical Report DRDC Valcartier TR 2010-326 December 2010 Comparison of ALE and SPH methods for simulating mine blast...Valcartier TR 2010-326 iii Executive summary Comparison of ALE and SPH methods for simulating mine blast effects on structures

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

A Case Study on the Strata Movement Mechanism and Surface Deformation Regulation in Chengchao Underground Iron Mine

NASA Astrophysics Data System (ADS)

Cheng, Guanwen; Chen, Congxin; Ma, Tianhui; Liu, Hongyuan; Tang, Chunan

2017-04-01

The regular pattern of surface deformation and the mechanism of underground strata movement, especially in iron mines constructed with the block caving method, have a great influence on infrastructure on the surface, so they are an important topic for research. Based on the engineering geology conditions and the surface deformation and fracture features in Chengchao Iron Mine, the mechanism of strata movement and the regular pattern of surface deformation in the footwall were studied by the geomechanical method, and the following conclusions can be drawn: I. The surface deformation process is divided into two stages over time, i.e., the chimney caving development stage and the post-chimney deformation stage. Currently, the surface deformation in Chengchao Iron Mine is at the post-chimney deformation stage. II. At the post-chimney deformation stage, the surface deformation and geological hazards in Chengchao Iron Mine are primarily controlled by the NWW-trending joints, with the phenomenon of toppling deformation and failure on the surface. Based on the surface deformation characteristics in Chengchao Iron Mine, the surface deformation area can be divided into the following four zones: the fracture extension zone, the fracture closure zone, the fracture formation zone and the deformation accumulation zone. The zones on the surface can be determined by the surface deformation characteristics. III. The cantilever beams near the chimney caving area, caused by the NWW-trending joints, have been subjected to toppling failure. This causes the different deformation and failure mechanisms in different locations of the deep rock mass. The deep rock can be divided into four zones, i.e., the fracture zone, fracture transition zone, deformation zone and undisturbed zone, according to the different deformation and failure mechanisms. The zones in the deep rock are the reason for the zones on the surface, so they can be determined by the zones on the surface. Through these findings, the degree of damage to the infrastructure in different locations can be determined based on the surface deformation zones. As the mining continues deeper, the development regulation of the zones on the surface and in deep rock mass can be further studied based on the zones in the deep rock.

Physical-chemical property based sequence motifs and methods regarding same

DOEpatents

Braun, Werner [Friendswood, TX; Mathura, Venkatarajan S [Sarasota, FL; Schein, Catherine H [Friendswood, TX

2008-09-09

A data analysis system, program, and/or method, e.g., a data mining/data exploration method, using physical-chemical property motifs. For example, a sequence database may be searched for identifying segments thereof having physical-chemical properties similar to the physical-chemical property motifs.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Experimental Study on Comprehensive Performance of Full Tailings Paste Filling in Jiaojia Gold Mine.

NASA Astrophysics Data System (ADS)

Zhang, Z. H.; Zou, Q. B.; Wang, P. Z.

2017-11-01

Filling mining method is the main method of modern underground mining. High concentration cementation is carried out using coarse tailing of +37 μm, and the mine has maturely used classified tailings paste filling technology. The gold mine studied on the performance of full tailings paste filling in order to maximize the use of tailings, reduce -37 μm fine tailings discharged into the tailing pond, reduce mining cost and eliminate security risks. The results show that: comprehensive index of full tailings paste filling is higher than that of classified tailings high concentration cementation filling, and the full tailings paste filling of 76% mass concentration has the best comprehensive index of slump, expansibility, yield stress and viscosity to meet the mining method requirements, which can effectively reduce the mining loss rate and dilution rate.

Attributed community mining using joint general non-negative matrix factorization with graph Laplacian

NASA Astrophysics Data System (ADS)

Chen, Zigang; Li, Lixiang; Peng, Haipeng; Liu, Yuhong; Yang, Yixian

2018-04-01

Community mining for complex social networks with link and attribute information plays an important role according to different application needs. In this paper, based on our proposed general non-negative matrix factorization (GNMF) algorithm without dimension matching constraints in our previous work, we propose the joint GNMF with graph Laplacian (LJGNMF) to implement community mining of complex social networks with link and attribute information according to different application needs. Theoretical derivation result shows that the proposed LJGNMF is fully compatible with previous methods of integrating traditional NMF and symmetric NMF. In addition, experimental results show that the proposed LJGNMF can meet the needs of different community minings by adjusting its parameters, and the effect is better than traditional NMF in the community vertices attributes entropy.

Remediation strategies for historical mining and smelting sites.

PubMed

Dybowska, Agnieszka; Farago, Margaret; Valsami-Jones, Eugenia; Thornton, Iain

2006-01-01

The environmental, social and economic problems associated with abandoned mine sites are serious and global. Environmental damage arising from polluted waters and dispersal of contaminated waste is a feature characteristic of many old mines in North America, Australia, Europe and elsewhere. Today, because of the efficiency of mining operations and legal requirements in many countries for prevention of environmental damage from mining operations, the release of metals to the environment from modern mining is low. However, many mineralized areas that were extensively worked in the 18th and 19th centuries and left abandoned after mining had ceased, have left a legacy of metal contaminated land. Unlike organic chemicals and plastics, metals cannot be degraded chemically or biologically into non-toxic and environmentally neutral constituents. Thus sites contaminated with toxic metals present a particular challenge for remediation. Soil remediation has been the subject of a significant amount of research work in the past decade; this has resulted in a number of remediation options currently available or being developed. Remediation strategies for metal/metalloid contaminated historical mining sites are reviewed and summarized in this article. It focuses on the current applications of in situ remediation with the use of soil amendments (adsorption and precipitation based methods are discussed) and phytoremediation (in situ plant based technology for environmental clean up and restoration). These are promising alternative technologies to traditional options of excavation and ex situ treatment, offering an advantage of being non-invasive and low cost. In particular, they have been shown to be effective in remediation of mining and smelting contaminated sites, although the long-term durability of these treatments cannot be predicted.

Net alkalinity and net acidity 1: Theoretical considerations

USGS Publications Warehouse

Kirby, C.S.; Cravotta, C.A.

2005-01-01

Net acidity and net alkalinity are widely used, poorly defined, and commonly misunderstood parameters for the characterization of mine drainage. The authors explain theoretical expressions of 3 types of alkalinity (caustic, phenolphthalein, and total) and acidity (mineral, CO2, and total). Except for rarely-invoked negative alkalinity, theoretically defined total alkalinity is closely analogous to measured alkalinity and presents few practical interpretation problems. Theoretically defined "CO 2-acidity" is closely related to most standard titration methods with an endpoint pH of 8.3 used for determining acidity in mine drainage, but it is unfortunately named because CO2 is intentionally driven off during titration of mine-drainage samples. Using the proton condition/mass- action approach and employing graphs to illustrate speciation with changes in pH, the authors explore the concept of principal components and how to assign acidity contributions to aqueous species commonly present in mine drainage. Acidity is defined in mine drainage based on aqueous speciation at the sample pH and on the capacity of these species to undergo hydrolysis to pH 8.3. Application of this definition shows that the computed acidity in mg L -1 as CaCO3 (based on pH and analytical concentrations of dissolved FeII, FeIII, Mn, and Al in mg L -1):aciditycalculated=50{1000(10-pH)+[2(FeII)+3(FeIII)]/56+2(Mn)/ 55+3(Al)/27}underestimates contributions from HSO4- and H+, but overestimates the acidity due to Fe3+ and Al3+. However, these errors tend to approximately cancel each other. It is demonstrated that "net alkalinity" is a valid mathematical construction based on theoretical definitions of alkalinity and acidity. Further, it is shown that, for most mine-drainage solutions, a useful net alkalinity value can be derived from: (1) alkalinity and acidity values based on aqueous speciation, (2) measured alkalinity minus calculated acidity, or (3) taking the negative of the value obtained in a standard method "hot peroxide" acidity titration, provided that labs report negative values. The authors recommend the third approach; i.e., net alkalinity = -Hot Acidity. ?? 2005 Elsevier Ltd. All rights reserved.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Method and apparatus for recovering a gas from a gas hydrate located on the ocean floor

DOEpatents

Wyatt, Douglas E.

2001-01-01

A method and apparatus for recovering a gas from a gas hydrate on the ocean floor includes a flexible cover, a plurality of steerable base members secured to the cover, and a steerable mining module. A suitable source for inflating the cover over the gas hydrate deposit is provided. The mining module, positioned on the gas hydrate deposit, is preferably connected to the cover by a control cable. A gas retrieval conduit or hose extends upwardly from the cover to be connected to a support ship on the ocean surface.

Unsupervised iterative detection of land mines in highly cluttered environments.

PubMed

Batman, Sinan; Goutsias, John

2003-01-01

An unsupervised iterative scheme is proposed for land mine detection in heavily cluttered scenes. This scheme is based on iterating hybrid multispectral filters that consist of a decorrelating linear transform coupled with a nonlinear morphological detector. Detections extracted from the first pass are used to improve results in subsequent iterations. The procedure stops after a predetermined number of iterations. The proposed scheme addresses several weaknesses associated with previous adaptations of morphological approaches to land mine detection. Improvement in detection performance, robustness with respect to clutter inhomogeneities, a completely unsupervised operation, and computational efficiency are the main highlights of the method. Experimental results reveal excellent performance.

Toxic element mobility assessment and modeling for regional geo-scientific survey to support Risk Assessment in a European Union context

NASA Astrophysics Data System (ADS)

Abdaal, Ahmed; Jordan, Gyozo; Bartha, Andras; Fugedi, Ubul

2013-04-01

The Mine Waste Directive 2006/21/EC requires the risk-based inventory of all mine waste sites in Europe. The geochemical documentation concerning inert classification and ranking of the mine wastes requires detailed field study and laboratory testing and analyses of waste material to assess the Acid Mine Drainage potential and toxic element mobility. The procedure applied in this study used a multi-level decision support scheme including: 1) expert judgment, 2) data review, 3) representative field sampling and laboratory analysis of formations listed in the Inert Mining Waste List, and 4) requesting available laboratory analysis data from selected operating mines. Based on expert judgment, the listed formations were classified into three categories. A: inert B: probably inert, but has to be checked, C: probably not inert, has to be examined. This paper discusses the heavy metal contamination risk assessment (RA) in leached quarry-mine waste sites in Hungary. In total 34 mine waste sites (including tailing lagoons and heaps of both abandoned mines and active quarries) have been selected for scientific testing using the EU Pre-selection Protocol. Over 93 field samples have been collected from the mine sites including Ore (Andesite and Ryolite), Coal (Lignite, black and brown coals), Peat, Alginite, Bauxite, Clay and Limestone. Laboratory analyses of the total toxic element content (aqua regia extraction), the mobile toxic element content (deionized water leaching) and the analysis of different forms of sulfur (sulfuric acid potential) ) on the base of Hungarian GKM Decree No. 14/2008. (IV. 3) concerning mining waste management. A detailed geochemical study together with spatial analysis and GIS has been performed to derive a geochemically sound contamination RA of the mine waste sites. Key parameters such as heavy metal and sulphur content, in addition to the distance to the nearest surface and ground water bodies, or to sensitive receptors such as settlements and protected areas are calculated and statistically evaluated using STATGRAPHICS® in order to calibrate the RA methods. Results show that some of the waste rock materials assumed to be inert were found non/inert. Thus, regional RA needs more spatial and petrological examination with special care to rock and mineral deposit genetics.

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

Automated data mining: an innovative and efficient web-based approach to maintaining resident case logs.

PubMed

Bhattacharya, Pratik; Van Stavern, Renee; Madhavan, Ramesh

2010-12-01

Use of resident case logs has been considered by the Residency Review Committee for Neurology of the Accreditation Council for Graduate Medical Education (ACGME). This study explores the effectiveness of a data-mining program for creating resident logs and compares the results to a manual data-entry system. Other potential applications of data mining to enhancing resident education are also explored. Patient notes dictated by residents were extracted from the Hospital Information System and analyzed using an unstructured mining program. History, examination and ICD codes were obtained and compared to the existing manual log. The automated data History, examination, and ICD codes were gathered for a 30-day period and compared to manual case logs. The automated method extracted all resident dictations with the dates of encounter and transcription. The automated data-miner processed information from all 19 residents, while only 4 residents logged manually. The manual method identified only broad categories of diseases; the major categories were stroke or vascular disorder 53 (27.6%), epilepsy 28 (14.7%), and pain syndromes 26 (13.5%). In the automated method, epilepsy 114 (21.1%), cerebral atherosclerosis 114 (21.1%), and headache 105 (19.4%) were the most frequent primary diagnoses, and headache 89 (16.5%), seizures 94 (17.4%), and low back pain 47 (9%) were the most common chief complaints. More detailed patient information such as tobacco use 227 (42%), alcohol use 205 (38%), and drug use 38 (7%) were extracted by the data-mining method. Manual case logs are time-consuming, provide limited information, and may be unpopular with residents. Data mining is a time-effective tool that may aid in the assessment of resident experience or the ACGME core competencies or in resident clinical research. More study of this method in larger numbers of residency programs is needed.

Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications

PubMed Central

2011-01-01

Background The advent of ChIP-seq technology has made the investigation of epigenetic regulatory networks a computationally tractable problem. Several groups have applied statistical computing methods to ChIP-seq datasets to gain insight into the epigenetic regulation of transcription. However, methods for estimating enrichment levels in ChIP-seq data for these computational studies are understudied and variable. Since the conclusions drawn from these data mining and machine learning applications strongly depend on the enrichment level inputs, a comparison of estimation methods with respect to the performance of statistical models should be made. Results Various methods were used to estimate the gene-wise ChIP-seq enrichment levels for 20 histone methylations and the histone variant H2A.Z. The Multivariate Adaptive Regression Splines (MARS) algorithm was applied for each estimation method using the estimation of enrichment levels as predictors and gene expression levels as responses. The methods used to estimate enrichment levels included tag counting and model-based methods that were applied to whole genes and specific gene regions. These methods were also applied to various sizes of estimation windows. The MARS model performance was assessed with the Generalized Cross-Validation Score (GCV). We determined that model-based methods of enrichment estimation that spatially weight enrichment based on average patterns provided an improvement over tag counting methods. Also, methods that included information across the entire gene body provided improvement over methods that focus on a specific sub-region of the gene (e.g., the 5' or 3' region). Conclusion The performance of data mining and machine learning methods when applied to histone modification ChIP-seq data can be improved by using data across the entire gene body, and incorporating the spatial distribution of enrichment. Refinement of enrichment estimation ultimately improved accuracy of model predictions. PMID:21834981

Activity Recognition for Personal Time Management

NASA Astrophysics Data System (ADS)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

[Text mining, a method for computer-assisted analysis of scientific texts, demonstrated by an analysis of author networks].

PubMed

Hahn, P; Dullweber, F; Unglaub, F; Spies, C K

2014-06-01

Searching for relevant publications is becoming more difficult with the increasing number of scientific articles. Text mining as a specific form of computer-based data analysis may be helpful in this context. Highlighting relations between authors and finding relevant publications concerning a specific subject using text analysis programs are illustrated graphically by 2 performed examples. © Georg Thieme Verlag KG Stuttgart · New York.

Sustainable mineral resources management: from regional mineral resources exploration to spatial contamination risk assessment of mining

NASA Astrophysics Data System (ADS)

Jordan, Gyozo

2009-07-01

Wide-spread environmental contamination associated with historic mining in Europe has triggered social responses to improve related environmental legislation, the environmental assessment and management methods for the mining industry. Mining has some unique features such as natural background contamination associated with mineral deposits, industrial activities and contamination in the three-dimensional subsurface space, problem of long-term remediation after mine closure, problem of secondary contaminated areas around mine sites, land use conflicts and abandoned mines. These problems require special tools to address the complexity of the environmental problems of mining-related contamination. The objective of this paper is to show how regional mineral resources mapping has developed into the spatial contamination risk assessment of mining and how geological knowledge can be transferred to environmental assessment of mines. The paper provides a state-of-the-art review of the spatial mine inventory, hazard, impact and risk assessment and ranking methods developed by national and international efforts in Europe. It is concluded that geological knowledge on mineral resources exploration is essential and should be used for the environmental contamination assessment of mines. Also, sufficient methodological experience, knowledge and documented results are available, but harmonisation of these methods is still required for the efficient spatial environmental assessment of mine contamination.

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

PubMed Central

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. PMID:25143989

Personalized privacy-preserving frequent itemset mining using randomized response.

PubMed

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

New method for the direct determination of dissolved Fe(III) concentration in acid mine waters

USGS Publications Warehouse

To, T.B.; Nordstrom, D. Kirk; Cunningham, K.M.; Ball, J.W.; McCleskey, R. Blaine

1999-01-01

A new method for direct determination of dissolved Fe(III) in acid mine water has been developed. In most present methods, Fe(III) is determined by computing the difference between total dissolved Fe and dissolved Fe(II). For acid mine waters, frequently Fe(II) >> Fe(III); thus, accuracy and precision are considerably improved by determining Fe(III) concentration directly. The new method utilizes two selective ligands to stabilize Fe(III) and Fe(II), thereby preventing changes in Fe reduction-oxidation distribution. Complexed Fe(II) is cleanly removed using a silica-based, reversed-phase adsorbent, yielding excellent isolation of the Fe(III) complex. Iron(III) concentration is measured colorimetrically or by graphite furnace atomic absorption spectrometry (GFAAS). The method requires inexpensive commercial reagents and simple procedures that can be used in the field. Calcium(II), Ni(II), Pb(II), AI(III), Zn(II), and Cd(II) cause insignificant colorimetric interferences for most acid mine waters. Waters containing >20 mg of Cu/L could cause a colorimetric interference and should be measured by GFAAS. Cobalt(II) and Cr(III) interfere if their molar ratios to Fe(III) exceed 24 and 5, respectively. Iron(II) interferes when its concentration exceeds the capacity of the complexing ligand (14 mg/L). Because of the GFAAS elemental specificity, only Fe(II) is a potential interferent in the GFAAS technique. The method detection limit is 2 ??g/L (40 nM) using GFAAS and 20 ??g/L (0.4 ??M) by colorimetry.A new method for direct determination of dissolved Fe(III) in acid mine water has been developed. In most present methods, Fe(III) is determined by computing the difference between total dissolved Fe and dissolved Fe(II). For acid mine waters, frequently Fe(II)???Fe(III); thus, accuracy and precision are considerably improved by determining Fe(III) concentration directly. The new method utilizes two selective ligands to stabilize Fe(III) and Fe(II), thereby preventing changes in Fe reduction-oxidation distribution. Complexed Fe(II) is cleanly removed using a silica-based, reversed-phase adsorbent, yielding excellent isolation of the Fe(III) complex. Iron(III) concentration is measured colorimetrically or by graphite furnace atomic absorption spectrometry (GFAAS). The method requires inexpensive commercial reagents and simple procedures that can be used in the field. Calcium(II), Ni(II), Pb(II), Al(III), Zn(II), and Cd(II) cause insignificant colorimetric interferences for most acid mine waters. Waters containing >20 mg of Cu/L could cause a colorimetric interference and should be measured by GFAAS. Cobalt(II) and Cr(III) interfere if their molar ratios to Fe(III) exceed 24 and 5, respectively. Iron(II) interferes when its concentration exceeds the capacity of the complexing ligand (14 mg/L). Because of the GFAAS elemental specificity, only Fe(II) is a potential interferent in the GFAAS technique. The method detection limit is 2/??g/L (40 nM) using GFAAS and 20 ??g/L (0.4 ??M) by colorimetry.

Personalised Information Services Using a Hybrid Recommendation Method Based on Usage Frequency

ERIC Educational Resources Information Center

Kim, Yong; Chung, Min Gyo

2008-01-01

Purpose: This paper seeks to describe a personal recommendation service (PRS) involving an innovative hybrid recommendation method suitable for deployment in a large-scale multimedia user environment. Design/methodology/approach: The proposed hybrid method partitions content and user into segments and executes association rule mining,…

Integrated method of RS and GPR for monitoring the changes in the soil moisture and groundwater environment due to underground coal mining

NASA Astrophysics Data System (ADS)

Bian, Zhengfu; Lei, Shaogang; Inyang, Hilary I.; Chang, Luqun; Zhang, Richen; Zhou, Chengjun; He, Xiao

2009-03-01

Mining affects the environment in different ways depending on the physical context in which the mining occurs. In mining areas with an arid environment, mining affects plants’ growth by changing the amount of available water. This paper discusses the effects of mining on two important determinants of plant growth—soil moisture and groundwater table (GWT)—which were investigated using an integrated approach involving a field sampling investigation with remote sensing (RS) and ground-penetrating radar (GPR). To calculate and map the distribution of soil moisture for a target area, we initially analyzed four models for regression analysis between soil moisture and apparent thermal inertia and finally selected a linear model for modeling the soil moisture at a depth 10 cm; the relative error of the modeled soil moisture was about 6.3% and correlation coefficient 0.7794. A comparison of mined and unmined areas based on the results of limited field sampling tests or RS monitoring of Landsat 5-thermatic mapping (TM) data indicated that soil moisture did not undergo remarkable changes following mining. This result indicates that mining does not have an effect on soil moisture in the Shendong coal mining area. The coverage of vegetation in 2005 was compared with that in 1995 by means of the normalized difference vegetation index (NDVI) deduced from TM data, and the results showed that the coverage of vegetation in Shendong coal mining area has improved greatly since 1995 because of policy input RMB¥0.4 per ton coal production by Shendong Coal Mining Company. The factor most affected by coal mining was GWT, which dropped from a depth of 35.41 m before mining to a depth of 43.38 m after mining at the Bulianta Coal Mine based on water well measurements. Ground-penetrating radar at frequencies of 25 and 50 MHz revealed that the deepest GWT was at about 43.4 m. There was a weak water linkage between the unsaturated zone and groundwater, and the decline of water table primarily resulted from the well pumping for mining safety rather than the movement of cracking strata. This result is in agreement with the measurements of the water wells. The roots of nine typical plants in the study area were investigated. Populus was found to have the deepest root system with a depth of about 26 m. Based on an assessment of plant growth demands and the effect of mining on environmental factors, we concluded that mining will have less of an effect on plant growth at those sites where the primary GWT depth before mining was deep enough to be unavailable to plants. If the primary GWT was available for plant growth before mining, especially to those plants with deeper roots, mining will have a significant effect on the growth of plants and the mechanism of this effect will include the loss of water to roots and damage to the root system.

Continuous Rating for Diggability Assessment in Surface Mines

NASA Astrophysics Data System (ADS)

IPHAR, Melih

2016-10-01

The rocks can be loosened either by drilling-blasting or direct excavation using powerful machines in opencast mining operations. The economics of rock excavation is considered for each method to be applied. If blasting operation is not preferred and also the geological structures and rock mass properties in site are convenient (favourable ground conditions) for ripping or direct excavation method by mining machines, the next step is to determine which machine or excavator should be selected for the excavation purposes. Many researchers have proposed several diggability or excavatability assessment methods for deciding on excavator type to be used in the field. Most of these systems are generally based on assigning a rating for the parameters having importance in rock excavation process. However, the sharp transitions between the two adjacent classes for a given parameter can lead to some uncertainties. In this paper, it has been proposed that varying rating should be assigned for a given parameter called as “continuous rating” instead of giving constant rating for a given class.

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

Rockbursting Potential of Kimberlite: A Case Study of Diavik Diamond Mine

NASA Astrophysics Data System (ADS)

Leveille, Paul; Sepehri, Mohammadali; Apel, Derek B.

2017-12-01

The research described in this paper provides information about the rockbursting potential of kimberlite. Kimberlite is a diamond-bearing rock found in deposits around the world including northern Canada. This paper outlines three methods for the prediction of rockbursts based on the properties of a rock. The methods include the: strain energy index, strain energy density, and rock brittleness. Kimberlite samples collected from Diavik, a diamond mine in northern Canada, were tested to define the rock's uniaxial compressive strength, tensile strength, and hysteresis loop. The samples were separated into sub-rock types based on their descriptions from the mine geologists. The results indicate that it is possible to produce rockbursts in kimberlite. It was also observed that the sub-rock types had a range of rockbursting properties. Some types of kimberlite exhibited little to no potential for producing bursts, while other types potentially could produce violent bursts. The diverse nature of kimberlite indicates that the rockbursting properties of the rock should not be generalized and are dependent on the sub-rock type being encountered.

TCGA4U: A Web-Based Genomic Analysis Platform To Explore And Mine TCGA Genomic Data For Translational Research.

PubMed

Huang, Zhenzhen; Duan, Huilong; Li, Haomin

2015-01-01

Large-scale human cancer genomics projects, such as TCGA, generated large genomics data for further study. Exploring and mining these data to obtain meaningful analysis results can help researchers find potential genomics alterations that intervene the development and metastasis of tumors. We developed a web-based gene analysis platform, named TCGA4U, which used statistics methods and models to help translational investigators explore, mine and visualize human cancer genomic characteristic information from the TCGA datasets. Furthermore, through Gene Ontology (GO) annotation and clinical data integration, the genomic data were transformed into biological process, molecular function, cellular component and survival curves to help researchers identify potential driver genes. Clinical researchers without expertise in data analysis will benefit from such a user-friendly genomic analysis platform.

Method of gas emission control for safe working of flat gassy coal seams

NASA Astrophysics Data System (ADS)

Vinogradov, E. A.; Yaroshenko, V. V.; Kislicyn, M. S.

2017-10-01

The main problems at intensive flat gassy coal seam longwall mining are considered. For example, mine Kotinskaja JSC “SUEK-Kuzbass” shows that when conducting the work on the gassy coal seams, methane emission control by means of ventilation, degassing and insulated drain of methane-air mixture is not effective and stable enough. It is not always possible to remove the coal production restrictions by the gas factor, which leads to financial losses because of incomplete using of longwall equipment and the reduction of the technical and economic indicators of mining. To solve the problems, the authors used a complex method that includes the compilation and analysis of the theory and practice of intensive flat gassy coal seam longwall mining. Based on the results of field and numerical researches, the effect of parameters of technological schemes on efficiency of methane emission control on longwall panels, the non-linear dependence of the permissible according to gas factor longwall productivity on parameters of technological schemes, ventilation and degassing during intensive mining flat gassy coal seams was established. The number of recommendations on the choice of the location and the size of the intermediate section of coal heading to control gassing in the mining extracted area, and guidelines for choosing the parameters of ventilation of extracted area with the help of two air supply entries and removal of isolated methane-air mixture are presented in the paper. The technological scheme, using intermediate entry for fresh air intake, ensuring effective management gassing and allowing one to refuse from drilling wells from the surface to the mined-out space for mining gas-bearing coal seams, was developed.

Intelligent Scheduling for Underground Mobile Mining Equipment

PubMed Central

Song, Zhen; Schunnesson, Håkan; Rinne, Mikael; Sturgul, John

2015-01-01

Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine. PMID:26098934

30 CFR 77.703-1 - Approved methods of grounding.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Approved methods of grounding. 77.703-1 Section 77.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE... COAL MINES Grounding § 77.703-1 Approved methods of grounding. The methods of grounding stated in § 77...

30 CFR 77.703-1 - Approved methods of grounding.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Approved methods of grounding. 77.703-1 Section 77.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE... COAL MINES Grounding § 77.703-1 Approved methods of grounding. The methods of grounding stated in § 77...

30 CFR 77.703-1 - Approved methods of grounding.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approved methods of grounding. 77.703-1 Section 77.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE... COAL MINES Grounding § 77.703-1 Approved methods of grounding. The methods of grounding stated in § 77...

Use of the method for addressing the challenges of resources procurement management at a mining enterprise

NASA Astrophysics Data System (ADS)

Petrova, T. V.; Strekalov, S. V.; Novichikhin, A. V.

2017-09-01

The article is devoted to the analysis of possible application of the total cost of ownership method for the purchase of resources at a mining enterprise. The description of the total cost of ownership method and experience of using this method in other spheres is provided. The article identifies the essential components needed to calculate the total cost of ownership of a resource. Particular attention is paid to the ratio of the price of the purchased resource and the total cost of ownership. To justify the relevance of application of this method at a mining enterprise for resources purchase, the technical and economic conditions of mining enterprises have been analyzed, which are quite specific and force to introduce certain adjustments to the application of the considered method and opens up new possibilities for its use. Specific spheres for application of this method at a mining enterprise are determined. The main result of the study is the proposed practical recommendations for the introduction and extension of the practice of using the method when a mining enterprise purchases resources.

Visual information mining in remote sensing image archives

NASA Astrophysics Data System (ADS)

Pelizzari, Andrea; Descargues, Vincent; Datcu, Mihai P.

2002-01-01

The present article focuses on the development of interactive exploratory tools for visually mining the image content in large remote sensing archives. Two aspects are treated: the iconic visualization of the global information in the archive and the progressive visualization of the image details. The proposed methods are integrated in the Image Information Mining (I2M) system. The images and image structure in the I2M system are indexed based on a probabilistic approach. The resulting links are managed by a relational data base. Both the intrinsic complexity of the observed images and the diversity of user requests result in a great number of associations in the data base. Thus new tools have been designed to visualize, in iconic representation the relationships created during a query or information mining operation: the visualization of the query results positioned on the geographical map, quick-looks gallery, visualization of the measure of goodness of the query, visualization of the image space for statistical evaluation purposes. Additionally the I2M system is enhanced with progressive detail visualization in order to allow better access for operator inspection. I2M is a three-tier Java architecture and is optimized for the Internet.

Statistical sensor fusion analysis of near-IR polarimetric and thermal imagery for the detection of minelike targets

NASA Astrophysics Data System (ADS)

Weisenseel, Robert A.; Karl, William C.; Castanon, David A.; DiMarzio, Charles A.

1999-02-01

We present an analysis of statistical model based data-level fusion for near-IR polarimetric and thermal data, particularly for the detection of mines and mine-like targets. Typical detection-level data fusion methods, approaches that fuse detections from individual sensors rather than fusing at the level of the raw data, do not account rationally for the relative reliability of different sensors, nor the redundancy often inherent in multiple sensors. Representative examples of such detection-level techniques include logical AND/OR operations on detections from individual sensors and majority vote methods. In this work, we exploit a statistical data model for the detection of mines and mine-like targets to compare and fuse multiple sensor channels. Our purpose is to quantify the amount of knowledge that each polarimetric or thermal channel supplies to the detection process. With this information, we can make reasonable decisions about the usefulness of each channel. We can use this information to improve the detection process, or we can use it to reduce the number of required channels.

The Mine Safety and Health Administration's criterion threshold value policy increases miners' risk of pneumoconiosis.

PubMed

Weeks, James L

2006-06-01

The Mine Safety and Health Administration (MSHA) proposes to issue citations for non-compliance with the exposure limit for respirable coal mine dust when measured exposure exceeds the exposure limit with a "high degree of confidence." This criterion threshold value (CTV) is derived from the sampling and analytical error of the measurement method. This policy is based on a combination of statistical and legal reasoning: the one-tailed 95% confidence limit of the sampling method, the apparent principle of due process and a standard of proof analogous to "beyond a reasonable doubt." This policy raises the effective exposure limit, it is contrary to the precautionary principle, it is not a fair sharing of the burden of uncertainty, and it employs an inappropriate standard of proof. Its own advisory committee and NIOSH have advised against this policy. For longwall mining sections, it results in a failure to issue citations for approximately 36% of the measured values that exceed the statutory exposure limit. Citations for non-compliance with the respirable dust standard should be issued for any measure exposure that exceeds the exposure limit.

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.

Greenhouse gas emissions from Australian open-cut coal mines: contribution from spontaneous combustion and low-temperature oxidation.

PubMed

Day, Stuart J; Carras, John N; Fry, Robyn; Williams, David J

2010-07-01

Spontaneous combustion and low-temperature oxidation of waste coal and other carbonaceous material at open-cut coal mines are potentially significant sources of greenhouse gas emissions. However, the magnitude of these emissions is largely unknown. In this study, emissions from spontaneous combustion and low-temperature oxidation were estimated for six Australian open-cut coal mines with annual coal production ranging from 1.7 to more than 16 Mt. Greenhouse emissions from all other sources at these mines were also estimated and compared to those from spontaneous combustion and low-temperature oxidation. In all cases, fugitive emission of methane was the largest source of greenhouse gas; however, in some mines, spontaneous combustion accounted for almost a third of all emissions. For one mine, it was estimated that emissions from spontaneous combustion were around 250,000 t CO(2)-e per annum. The contribution from low-temperature oxidation was generally less than about 1% of the total for all six mines. Estimating areas of spoil affected by spontaneous combustion by ground-based surveys was prone to under-report the area. Airborne infrared imaging appears to be a more reliable method.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Study on characteristics of EMR signals induced from fracture of rock samples and their application in rockburst prediction in copper mine

NASA Astrophysics Data System (ADS)

Liu, Xiaofei; Wang, Enyuan

2018-06-01

A rockburst is a dynamic disaster that occurs during underground excavation or mining which has been a serious threat to safety. Rockburst prediction and control are as important as any other underground engineering in deep mines. For this paper, we tested electromagnetic radiation (EMR) signals generated during the deformation and fracture of rock samples from a copper mine under uniaxial compression, tension, and cycle-loading experiments, analyzed the changes in the EMR intensity, pulse number, and frequency corresponding to the loading, and a high correlation between these EMR parameters and the applied loading was observed. EMR apparently reflects the deformation and fracture status to the loaded rock. Based on this experimental work, we invented the KBD5-type EMR monitor and used it to test EMR signals generated in the rock surrounding the Hongtoushan copper mine. From the test results, it is determined the responding characteristics of EMR signals generated by changes in mine-generated stresses and stress concentrations and it is proposed that this EMR monitoring method can be used to provide early warning for rockbursts.

[An Introduction to Methods for Evaluating Health Care Technology].

PubMed

Lee, Ting-Ting

2015-06-01

The rapid and continual advance of healthcare technology makes ensuring that this technology is used effectively to achieve its original goals a critical issue. This paper presents three methods that may be applied by healthcare professionals in the evaluation of healthcare technology. These methods include: the perception/experiences of users, user work-pattern changes, and chart review or data mining. The first method includes two categories: using interviews to explore the user experience and using theory-based questionnaire surveys. The second method applies work sampling to observe the work pattern changes of users. The last method conducts chart reviews or data mining to analyze the designated variables. In conclusion, while evaluative feedback may be used to improve the design and development of healthcare technology applications, the informatics competency and informatics literacy of users may be further explored in future research.

The improved business valuation model for RFID company based on the community mining method.

PubMed

Li, Shugang; Yu, Zhaoxu

2017-01-01

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company's net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies.

The improved business valuation model for RFID company based on the community mining method

PubMed Central

Li, Shugang; Yu, Zhaoxu

2017-01-01

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company’s net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies. PMID:28459815

Spatial forecast of landslides in three gorges based on spatial data mining.

PubMed

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

PubMed Central

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. PMID:22573999

Lunar surface mining for automated acquisition of helium-3: Methods, processes, and equipment

NASA Technical Reports Server (NTRS)

Li, Y. T.; Wittenberg, L. J.

1992-01-01

In this paper, several techniques considered for mining and processing the regolith on the lunar surface are presented. These techniques have been proposed and evaluated based primarily on the following criteria: (1) mining operations should be relatively simple; (2) procedures of mineral processing should be few and relatively easy; (3) transferring tonnages of regolith on the Moon should be minimized; (4) operations outside the lunar base should be readily automated; (5) all equipment should be maintainable; and (6) economic benefit should be sufficient for commercial exploitation. The economic benefits are not addressed in this paper; however, the energy benefits have been estimated to be between 250 and 350 times the mining energy. A mobile mining scheme is proposed that meets most of the mining objectives. This concept uses a bucket-wheel excavator for excavating the regolith, several mechanical electrostatic separators for beneficiation of the regolith, a fast-moving fluidized bed reactor to heat the particles, and a palladium diffuser to separate H2 from the other solar wind gases. At the final stage of the miner, the regolith 'tailings' are deposited directly into the ditch behind the miner and cylinders of the valuable solar wind gases are transported to a central gas processing facility. During the production of He-3, large quantities of valuable H2, H2O, CO, CO2, and N2 are produced for utilization at the lunar base. For larger production of He-3 the utilization of multiple-miners is recommended rather than increasing their size. Multiple miners permit operations at more sites and provide redundancy in case of equipment failure.

Lunar surface mining for automated acquisition of helium-3: Methods, processes, and equipment

NASA Astrophysics Data System (ADS)

Li, Y. T.; Wittenberg, L. J.

1992-09-01

In this paper, several techniques considered for mining and processing the regolith on the lunar surface are presented. These techniques have been proposed and evaluated based primarily on the following criteria: (1) mining operations should be relatively simple; (2) procedures of mineral processing should be few and relatively easy; (3) transferring tonnages of regolith on the Moon should be minimized; (4) operations outside the lunar base should be readily automated; (5) all equipment should be maintainable; and (6) economic benefit should be sufficient for commercial exploitation. The economic benefits are not addressed in this paper; however, the energy benefits have been estimated to be between 250 and 350 times the mining energy. A mobile mining scheme is proposed that meets most of the mining objectives. This concept uses a bucket-wheel excavator for excavating the regolith, several mechanical electrostatic separators for beneficiation of the regolith, a fast-moving fluidized bed reactor to heat the particles, and a palladium diffuser to separate H2 from the other solar wind gases. At the final stage of the miner, the regolith 'tailings' are deposited directly into the ditch behind the miner and cylinders of the valuable solar wind gases are transported to a central gas processing facility. During the production of He-3, large quantities of valuable H2, H2O, CO, CO2, and N2 are produced for utilization at the lunar base. For larger production of He-3 the utilization of multiple-miners is recommended rather than increasing their size. Multiple miners permit operations at more sites and provide redundancy in case of equipment failure.

Mechanism of Rock Burst Occurrence in Specially Thick Coal Seam with Rock Parting

NASA Astrophysics Data System (ADS)

Wang, Jian-chao; Jiang, Fu-xing; Meng, Xiang-jun; Wang, Xu-you; Zhu, Si-tao; Feng, Yu

2016-05-01

Specially thick coal seam with complex construction, such as rock parting and alternative soft and hard coal, is called specially thick coal seam with rock parting (STCSRP), which easily leads to rock burst during mining. Based on the stress distribution of rock parting zone, this study investigated the mechanism, engineering discriminant conditions, prevention methods, and risk evaluation method of rock burst occurrence in STCSRP through setting up a mechanical model. The main conclusions of this study are as follows. (1) When the mining face moves closer to the rock parting zone, the original non-uniform stress of the rock parting zone and the advancing stress of the mining face are combined to intensify gradually the shearing action of coal near the mining face. When the shearing action reaches a certain degree, rock burst easily occurs near the mining face. (2) Rock burst occurrence in STCSRP is positively associated with mining depth, advancing stress concentration factor of the mining face, thickness of rock parting, bursting liability of coal, thickness ratio of rock parting to coal seam, and difference of elastic modulus between rock parting and coal, whereas negatively associated with shear strength. (3) Technologies of large-diameter drilling, coal seam water injection, and deep hole blasting can reduce advancing stress concentration factor, thickness of rock parting, and difference of elastic modulus between rock parting and coal to lower the risk of rock burst in STCSRP. (4) The research result was applied to evaluate and control the risk of rock burst occurrence in STCSRP.

Heavy Metal Contamination Assessment and Partition for Industrial and Mining Gathering Areas

PubMed Central

Guan, Yang; Shao, Chaofeng; Ju, Meiting

2014-01-01

Industrial and mining activities have been recognized as the major sources of soil heavy metal contamination. This study introduced an improved Nemerow index method based on the Nemerow and geo-accumulation index. Taking a typical industrial and mining gathering area in Tianjin (China) as example, this study then analyzed the contamination sources as well as the ecological and integrated risks. The spatial distribution of the contamination level and ecological risk were determined using Geographic Information Systems. The results are as follows: (1) Zinc showed the highest contaminant level in the study area; the contamination levels of the other seven heavy metals assessed were relatively lower. (2) The combustion of fossil fuels and emissions from industrial and mining activities were the main sources of contamination in the study area. (3) The overall contamination level of heavy metals in the study area ranged from heavily contaminated to extremely contaminated and showed an uneven distribution. (4) The potential ecological risk showed an uneven distribution, and the overall ecological risk level ranged from low to moderate. This study also emphasized the importance of partition in industrial and mining areas, the extensive application of spatial analysis methods, and the consideration of human health risks in future studies. PMID:25032743

A review of contrast pattern based data mining

NASA Astrophysics Data System (ADS)

Zhu, Shiwei; Ju, Meilong; Yu, Junfeng; Cai, Binlei; Wang, Aiping

2015-07-01

Contrast pattern based data mining is concerned with the mining of patterns and models that contrast two or more datasets. Contrast patterns can describe similarities or differences between the datasets. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust clusters and classifiers. The increasing use of contrast pattern data mining has initiated a great deal of research and development attempts in the field of data mining. A comprehensive revision on the existing contrast pattern based data mining research is given in this paper. They are generally categorized into background and representation, definitions and mining algorithms, contrast pattern based classification, clustering, and other applications, the research trends in future. The primary of this paper is to server as a glossary for interested researchers to have an overall picture on the current contrast based data mining development and identify their potential research direction to future investigation.

Remote health monitoring of heart failure with data mining via CART method on HRV features.

PubMed

Pecchia, Leandro; Melillo, Paolo; Bracale, Marcello

2011-03-01

Disease management programs, which use no advanced information and computer technology, are as effective as telemedicine but more efficient because less costly. We proposed a platform to enhance effectiveness and efficiency of home monitoring using data mining for early detection of any worsening in patient's condition. These worsenings could require more complex and expensive care if not recognized. In this letter, we briefly describe the remote health monitoring platform we designed and realized, which supports heart failure (HF) severity assessment offering functions of data mining based on the classification and regression tree method. The system developed achieved accuracy and a precision of 96.39% and 100.00% in detecting HF and of 79.31% and 82.35% in distinguishing severe versus mild HF, respectively. These preliminary results were achieved on public databases of signals to improve their reproducibility. Clinical trials involving local patients are still running and will require longer experimentation.

Remote sensing of strippable coal reserves and mine inventory in part of the Warrior Coal Field in Alabama

NASA Technical Reports Server (NTRS)

Joiner, T. J.; Copeland, C. W., Jr.; Russell, D. D.; Evans, F. E., Jr.; Sapp, C. D.; Boone, P. A.

1978-01-01

Methods by which estimates of the remaining reserves of strippable coal in Alabama could be made were developed. Information acquired from NASA's Earth Resources Office was used to analyze and map existing surface mines in a four-quadrangle area in west central Alabama. Using this information and traditional methods for mapping coal reserves, an estimate of remaining strippable reserves was derived. Techniques for the computer analysis of remotely sensed data and other types of available coal data were developed to produce an estimate of strippable coal reserves for a second four-quadrangle area. Both areas lie in the Warrior coal field, the most prolific and active of Alabama's coal fields. They were chosen because of the amount and type of coal mining in the area, their location relative to urban areas, and the amount and availability of base data necessary for this type of study.

A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network

NASA Astrophysics Data System (ADS)

Qin, Li; Li, Bing; Pan, Wei-Feng; Peng, Tao

The number of online software services based on SaaS paradigm is increasing. However, users usually find it hard to get the exact software services they need. At present, tags are widely used to annotate specific software services and also to facilitate the searching of them. Currently these tags are arbitrary and ambiguous since mostly of them are generated manually by service developers. This paper proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity between the terms, we construct a software similarity network where nodes represent software services, edges denote the similarity relationship between software services, and the weights of the edges are the similarity degrees. The hierarchical clustering algorithm is used for community detection in this software similarity network. At the final stage, tags are mined for each of the communities and stored as ontology.

An empirical method for estimating instream pre-mining pH and dissolved Cu concentration in catchments with acidic drainage and ferricrete

USGS Publications Warehouse

Nimick, D.A.; Gurrieri, J.T.; Furniss, G.

2009-01-01

Methods for assessing natural background water quality of streams affected by historical mining are vigorously debated. An empirical method is proposed in which stream-specific estimation equations are generated from relationships between either pH or dissolved Cu concentration in stream water and the Fe/Cu concentration ratio in Fe-precipitates presently forming in the stream. The equations and Fe/Cu ratios for pre-mining deposits of alluvial ferricrete then were used to reconstruct estimated pre-mining longitudinal profiles for pH and dissolved Cu in three acidic streams in Montana, USA. Primary assumptions underlying the proposed method are that alluvial ferricretes and modern Fe-precipitates share a common origin, that the Cu content of Fe-precipitates remains constant during and after conversion to ferricrete, and that geochemical factors other than pH and dissolved Cu concentration play a lesser role in determining Fe/Cu ratios in Fe-precipitates. The method was evaluated by applying it in a fourth, naturally acidic stream unaffected by mining, where estimated pre-mining pH and Cu concentrations were similar to present-day values, and by demonstrating that inflows, particularly from unmined areas, had consistent effects on both the pre-mining and measured profiles of pH and Cu concentration. Using this method, it was estimated that mining has affected about 480 m of Daisy Creek, 1.8 km of Fisher Creek, and at least 1 km of Swift Gulch. Mean values of pH decreased by about 0.6 pH units to about 3.2 in Daisy Creek and by 1-1.5 pH units to about 3.5 in Fisher Creek. In Swift Gulch, mining appears to have decreased pH from about 5.5 to as low as 3.6. Dissolved Cu concentrations increased due to mining almost 40% in Daisy Creek to a mean of 11.7 mg/L and as much as 230% in Fisher Creek to 0.690 mg/L. Uncertainty in the fate of Cu during the conversion of Fe-precipitates to ferricrete translates to potential errors in pre-mining estimates of as much as 0.25 units for pH and 22% for dissolved Cu concentration. The method warrants further testing in other mined and unmined watersheds. Comparison of pre-mining water-quality estimates derived from the ferricrete and other methods in single watersheds would be particularly valuable. The method has potential for use in monitoring remedial efforts at mine sites with ferricrete deposits. A reasonable remediation objective might be realized when the downstream pattern of Fe/Cu ratios in modern streambed Fe-precipitates corresponds to the pattern in pre-mining alluvial ferricrete deposits along a stream valley.

Stratification-Based Outlier Detection over the Deep Web.

PubMed

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Information Gain Based Dimensionality Selection for Classifying Text Documents

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

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Stratification-Based Outlier Detection over the Deep Web

PubMed Central

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. PMID:27313603

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Remote Sensing based multi-temporal observation of North Korea mining activities : A case study of Rakyeon mine

NASA Astrophysics Data System (ADS)

Lim, J. H.; Yu, J.; Koh, S. M.; Lee, G.

2017-12-01

Mining is a major industrial business of North Korea accounting for significant portion of an export for North Korean economy. However, due to its veiled political system, details of mining activities of North Korea is rarely known. This study investigated mining activities of Rakyeon Au-Ag mine, North Korea based on remote sensing based multi-temporal observation. To monitor the mining activities, CORONA data acquired in 1960s and 1970s, SPOT and Landsat data acquired in 1980s and 1990s and KOMPSAT-2 data acquired in 2010s are utilized. The results show that mining activities of Rakyeon mine continuously carried out for the observation period expanding tailing areas of the mine. However, its expanding rate varies between the period related to North Korea's economic and political situations.

Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

NASA Astrophysics Data System (ADS)

Zhao, Dekang; Wu, Qiang; Cui, Fangpeng; Xu, Hua; Zeng, Yifan; Cao, Yufei; Du, Yuanze

2018-04-01

Coal-floor water-inrush incidents account for a large proportion of coal mine disasters in northern China, and accurate risk assessment is crucial for safe coal production. A novel and promising assessment model for water inrush is proposed based on random forest (RF), which is a powerful intelligent machine-learning algorithm. RF has considerable advantages, including high classification accuracy and the capability to evaluate the importance of variables; in particularly, it is robust in dealing with the complicated and non-linear problems inherent in risk assessment. In this study, the proposed model is applied to Panjiayao Coal Mine, northern China. Eight factors were selected as evaluation indices according to systematic analysis of the geological conditions and a field survey of the study area. Risk assessment maps were generated based on RF, and the probabilistic neural network (PNN) model was also used for risk assessment as a comparison. The results demonstrate that the two methods are consistent in the risk assessment of water inrush at the mine, and RF shows a better performance compared to PNN with an overall accuracy higher by 6.67%. It is concluded that RF is more practicable to assess the water-inrush risk than PNN. The presented method will be helpful in avoiding water inrush and also can be extended to various engineering applications.

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.

Mapping extent and change in surface mines within the United States for 2001 to 2006

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Stehman, Stephen V.; Parker, Owen P.

2016-01-01

A complete, spatially explicit dataset illustrating the 21st century mining footprint for the conterminous United States does not exist. To address this need, we developed a semi-automated procedure to map the country's mining footprint (30-m pixel) and establish a baseline to monitor changes in mine extent over time. The process uses mine seed points derived from the U.S. Energy Information Administration (EIA), U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), and USGS National Land Cover Dataset (NLCD) and recodes patches of barren land that meet a “distance to seed” requirement and a patch area requirement before mapping a pixel as mining. Seed points derived from EIA coal points, an edited MRDS point file, and 1992 NLCD mine points were used in three separate efforts using different distance and patch area parameters for each. The three products were then merged to create a 2001 map of moderate-to-large mines in the United States, which was subsequently manually edited to reduce omission and commission errors. This process was replicated using NLCD 2006 barren pixels as a base layer to create a 2006 mine map and a 2001–2006 mine change map focusing on areas with surface mine expansion. In 2001, 8,324 km2 of surface mines were mapped. The footprint increased to 9,181 km2 in 2006, representing a 10·3% increase over 5 years. These methods exhibit merit as a timely approach to generate wall-to-wall, spatially explicit maps representing the recent extent of a wide range of surface mining activities across the country. 

30 CFR 75.703-1 - Approved method of grounding.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approved method of grounding. 75.703-1 Section 75.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Grounding § 75.703-1 Approved method...

30 CFR 75.703-1 - Approved method of grounding.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Approved method of grounding. 75.703-1 Section 75.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Grounding § 75.703-1 Approved method...

30 CFR 75.703-1 - Approved method of grounding.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Approved method of grounding. 75.703-1 Section 75.703-1 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Grounding § 75.703-1 Approved method...

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)

Channel dynamics and geomorphic resilience in an ephemeral Mediterranean river affected by gravel mining

NASA Astrophysics Data System (ADS)

Calle, Mikel; Alho, Petteri; Benito, Gerardo

2017-05-01

Gravel mining has been a widespread activity in ephemeral rivers worldwide whose long-lasting hydrogeomorphological impacts preclude effective implementation of water and environmental policies. This paper presents a GIS-based method for temporal assessment of morphosedimentary changes in relation to in-channel gravel mining in a typical ephemeral Mediterranean stream, namely the Rambla de la Viuda (eastern Spain). The aims of this work were to identify morphosedimentary changes and responses to human activities and floods, quantify river degradations and analyze factors favoring fluvial recovery for further applications in other rivers. Aerial photographs and LiDAR topography data were studied to analyze geomorphic evolution over the past 70 years along a 7.5-km reach of an ephemeral gravel stream that has been mined intensively since the 1970s. To evaluate changes in the riverbed, we mapped comparable units applying morphological, hydraulic, and stability (based on vegetation density and elevation) criteria to 13 sets of aerial photographs taken from 1946 to 2012. A detailed spatiotemporal analysis of comparable units revealed a 50% reduction in the active section and a 20% increase in stable areas, compared to the conditions observed prior to gravel mining. Instream mining was first observed in 1976 aerial photograph covering already up to 50% of the 1956 riverbed area. River degradation since then was quantified by means of a LiDAR DTM and RTK-GPS measurements, which revealed a 3.5-m incision that had started simultaneously with gravel mining. Climate and land use changes were present but the effects were completely masked by changes produced by instream gravel mining. Therefore, river incision/degradation was triggered by scarcity of sediment and lack of longitudinal sedimentary connection, creating an unbalanced river system that is still adjusting to the present hydrosedimentary conditions.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

Efficient mining of association rules for the early diagnosis of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Chaves, R.; Górriz, J. M.; Ramírez, J.; Illán, I. A.; Salas-Gonzalez, D.; Gómez-Río, M.

2011-09-01

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make use of them for the early diagnosis of Alzheimer's disease (AD). Firstly, voxel-as-feature-based activation estimation methods are used to find the tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs serve as input to secondly mine ARs with a minimum support and confidence among activation blocks by using a set of controls. In this context, support and confidence measures are related to the proportion of functional areas which are singularly and mutually activated across the brain. Finally, we perform image classification by comparing the number of ARs verified by each subject under test to a given threshold that depends on the number of previously mined rules. Several classification experiments were carried out in order to evaluate the proposed methods using a SPECT database that consists of 41 controls (NOR) and 56 AD patients labeled by trained physicians. The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD.

Explosion and/or fire risk assessment methodology: a common approach, structured for underground coalmine environments / Metoda szacowania ryzyka wybuchu i pożarów: podejście ogólne, dostosowane do środowiska kopalni podziemnej

NASA Astrophysics Data System (ADS)

Cioca, Ionel-Lucian; Moraru, Roland Iosif

2012-10-01

In order to meet statutory requirements concerning the workers health and safety, it is necessary for mine managers within Valea Jiului coal basin in Romania to address the potential for underground fires and explosions and their impact on the workforce and the mine ventilation systems. Highlighting the need for a unified and systematic approach of the specific risks, the authors are developing a general framework for fire/explosion risk assessment in gassy mines, based on the quantification of the likelihood of occurrence and gravity of the consequences of such undesired events and employing Root-Cause analysis method. It is emphasized that even a small fire should be regarded as being a major hazard from the point of view of explosion initiation, should a combustible atmosphere arise. The developed methodology, for the assessment of underground fire and explosion risks, is based on the known underground explosion hazards, fire engineering principles and fire test criteria for potentially combustible materials employed in mines.

Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

An efficient and practical approach to obtain a better optimum solution for structural optimization

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu; Huang, Jyun-Hao

2013-08-01

For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.

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

Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

PubMed Central

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization. PMID:24371834

Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.

PubMed

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

Appraisement of environment remote sensing method in mining area

NASA Astrophysics Data System (ADS)

Yang, Fengjie; Zhen, Han; Jiang, Tao; Lei, Liqing; Gong, Cailan

1998-08-01

Coal mining is attached great importance by society as a key profession of environmental pollution. The monitor and protection of coal-mine environment is a developing profession in China. The sulfur dioxide, carbon dioxide, carbon monoxide and other waste gases, which are put out by the spontaneous combustion or weathering of gangue are an important pollution resource of atmosphere. The stack of gangue held down many farmlands. Smoke, coal dust and powder coal ash pollute the environment of mining area and surroundings though the affection of monsoon. The pH value of water which coal mine drained off is low, and the drinking, farming and animal husbandry water where it flowed are affected. The surface subsidence which mining caused is a typical destruction of ground environment. The people pay attention to remote sensing as a method of rapidly, cheaply regional environment investigation. The paper tires making an appraisement of mining area environment monitor by many kind methods of remote sensing from the characteristic of mining area environment.

Implicit prosody mining based on the human eye image capture technology

NASA Astrophysics Data System (ADS)

Gao, Pei-pei; Liu, Feng

2013-08-01

The technology of eye tracker has become the main methods of analyzing the recognition issues in human-computer interaction. Human eye image capture is the key problem of the eye tracking. Based on further research, a new human-computer interaction method introduced to enrich the form of speech synthetic. We propose a method of Implicit Prosody mining based on the human eye image capture technology to extract the parameters from the image of human eyes when reading, control and drive prosody generation in speech synthesis, and establish prosodic model with high simulation accuracy. Duration model is key issues for prosody generation. For the duration model, this paper put forward a new idea for obtaining gaze duration of eyes when reading based on the eye image capture technology, and synchronous controlling this duration and pronunciation duration in speech synthesis. The movement of human eyes during reading is a comprehensive multi-factor interactive process, such as gaze, twitching and backsight. Therefore, how to extract the appropriate information from the image of human eyes need to be considered and the gaze regularity of eyes need to be obtained as references of modeling. Based on the analysis of current three kinds of eye movement control model and the characteristics of the Implicit Prosody reading, relative independence between speech processing system of text and eye movement control system was discussed. It was proved that under the same text familiarity condition, gaze duration of eyes when reading and internal voice pronunciation duration are synchronous. The eye gaze duration model based on the Chinese language level prosodic structure was presented to change previous methods of machine learning and probability forecasting, obtain readers' real internal reading rhythm and to synthesize voice with personalized rhythm. This research will enrich human-computer interactive form, and will be practical significance and application prospect in terms of disabled assisted speech interaction. Experiments show that Implicit Prosody mining based on the human eye image capture technology makes the synthesized speech has more flexible expressions.

Prospect Theory and Interval-Valued Hesitant Set for Safety Evacuation Model

NASA Astrophysics Data System (ADS)

Kou, Meng; Lu, Na

2018-01-01

The study applies the research results of prospect theory and multi attribute decision making theory, combined with the complexity, uncertainty and multifactor influence of the underground mine fire system and takes the decision makers’ psychological behavior of emotion and intuition into full account to establish the intuitionistic fuzzy multiple attribute decision making method that is based on the prospect theory. The model established by this method can explain the decision maker’s safety evacuation decision behavior in the complex system of underground mine fire due to the uncertainty of the environment, imperfection of the information and human psychological behavior and other factors.

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

Removal of phosphorus from agricultural wastewaters using adsorption media prepared from acid mine drainage sludge

USGS Publications Warehouse

Sibrell, Philip L.; Montgomery, Gary A.; Ritenour, Kelsey L.; Tucker, Travis W.

2009-01-01

Excess phosphorus in wastewaters promotes eutrophication in receiving waterways. A??cost-effective method for the removal of phosphorus from water would significantly reduce the impact of such wastewaters on the environment. Acid mine drainage sludge is a waste product produced by the neutralization of acid mine drainage, and consists mainly of the same metal hydroxides used in traditional wastewater treatment for the removal of phosphorus. In this paper, we describe a method for the drying and pelletization of acid mine drainage sludge that results in a particulate media, which we have termed Ferroxysorb, for the removal of phosphorus from wastewater in an efficient packed bed contactor. Adsorption capacities are high, and kinetics rapid, such that a contact time of less than 5 min is sufficient for removal of 60-90% of the phosphorus, depending on the feed concentration and time in service. In addition, the adsorption capacity of the Ferroxysorb media was increased dramatically by using two columns in an alternating sequence so that each sludge bed receives alternating rest and adsorption cycles. A stripping procedure based on treatment with dilute sodium hydroxide was also developed that allows for recovery of the P from the media, with the possibility of generating a marketable fertilizer product. These results indicate that acid mine drainage sludges - hitherto thought of as undesirable wastes - can be used to remove phosphorus from wastewater, thus offsetting a portion of acid mine drainage treatment costs while at the same time improving water quality in sensitive watersheds.

Luobei graphite mines surrounding ecological environment monitoring based on high-resolution satellite data

NASA Astrophysics Data System (ADS)

Zhang, Lifeng; Liu, Xiaosha; Wan, Huawei; Liu, Xiaoman

2014-11-01

Graphite is one of the important industrial mineral raw materials, but the high content of heavy metals in tailings may cause soil pollution and other regional ecological environmental problems. Luobei has already become the largest production base of graphite. To find out the ecological situation in the region, further ecological risk analysis has been carried out. Luobei graphite mine which is located in Yabdanhe basin has been selected as the study area, SVM classifiers method with the support of GF-1 Satellite remote sensing data has been used, which is the first high-resolution earth observation satellite in China. The surrounding ecological environment was monitored and its potential impact on the ecological environment was analyzed by GIS platform. The results showed that the Luobei graphite mine located Yadanhe basin covers an area of 499.65 km2, the main types of forest ecosystems ( 44.05% of the total basin area ), followed by agricultural area( 35.14% ), grass area( 15.52% ), residential area ( 4.34% ), mining area ( 0.64% ) and water area( 0.30% ). By confirming the classification results, the total accuracy is 91.61%, the Kappa coefficient is 0.8991. Overall, GF-1 Satellite data can obtain regional ecosystems quickly, and provide a better data support for regional ecological resource protection zone. For Luobei graphite mines area, farmland and residential areas within its watershed are most vulnerable to mining, the higher proportion of farmland in duck river basin. The regulatory tailings need to be strengthened in the process of graphite mining processing.

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

Atmospheric Mining in the Outer Solar System: Aerial Vehicle Mission and Design Issues

NASA Technical Reports Server (NTRS)

Palaszewski, Bryan

2015-01-01

Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and deuterium can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and deuterium were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. With these two additional gases, the potential for fueling small and large fleets of additional exploration and exploitation vehicles exists. The mining aerospacecraft (ASC) could fly through the outer planet atmospheres, for global weather observations, localized storm or other disturbance investigations, wind speed measurements, polar observations, etc. Analyses of orbital transfer vehicles (OTVs), landers, and in-situ resource utilization (ISRU) mining factories are included. Preliminary observations are presented on near-optimal selections of moon base orbital locations, OTV power levels, and OTV and lander rendezvous points.

Acidity and alkalinity in mine drainage: Theoretical considerations

USGS Publications Warehouse

Kirby, Carl S.; Cravotta,, Charles A.

2004-01-01

Acidity, net acidity, and net alkalinity are widely used parameters for the characterization of mine drainage, but these terms are not well defined and are often misunderstood. Incorrect interpretation of acidity, alkalinity, and derivative terms can lead to inadequate treatment design or poor regulatory decisions. We briefly explain derivations of theoretical expressions of three types of alkalinities (caustic, phenolphthalein, and total) and acidities (mineral, CO2, and total). Theoretically defined total alkalinity is closely analogous to measured alkalinity and presents few practical interpretation problems. Theoretically defined “CO2- acidity” is closely related to most standard titration methods used for mine drainage with an endpoint pH of 8.3, but it presents numerous interpretation problems, and it is unfortunately named because CO2 is intentionally driven off during titration of mine-drainage samples. Using the proton condition/massaction approach and employing graphs for visualization, we explore the concept of principal components and how to assign acidity contributions to solution species, including aqueous complexes, commonly found in mine drainage. We define a comprehensive theoretical definition of acidity in mine drainage on the basis of aqueous speciation at the sample pH and the capacity of these species to undergo hydrolysis to pH 8.3. This definition indicates the computed acidity in milligrams per liter (mg L-1 ) as CaCO3 (based on pH and analytical concentrations of dissolved FeIII , FeII , Mn, and Al in mg L-1 ): Aciditycomputed = 50. (10(3-pH) + 3.CFeIII/55.8 + 2.CFeII/55.8 + 2.CMn/54.9 + 3.CAl/27.0) underestimates contributions from HSO4 - and H+ , but overestimates the acidity due to Fe3+. These errors tend to approximately cancel each other. We demonstrate that “net alkalinity” is a valid mathematical construction based on theoretical definitions of alkalinity and acidity. We demonstrate that, for most mine-drainage solutions, a useful net alkalinity value can be derived from: 1) alkalinity and acidity values based on aqueous speciation, 2) measured alkalinity - computed acidity, or 3) taking the negative of the value obtained in a standard method “hot peroxide” acidity titration, provided that labs report negative values. We recommend the third approach; i.e., Net alkalinity = - Hot Acidity.

Various Approaches for Targeting Quasar Candidates

NASA Astrophysics Data System (ADS)

Zhang, Y.; Zhao, Y.

2015-09-01

With the establishment and development of space-based and ground-based observational facilities, the improvement of scientific output of high-cost facilities is still a hot issue for astronomers. The discovery of new and rare quasars attracts much attention. Different methods to select quasar candidates are in bloom. Among them, some are based on color cuts, some are from multiwavelength data, some rely on variability of quasars, some are based on data mining, and some depend on ensemble methods.

Trees for strip-mined lands

Treesearch

George Hart; William R. Byrnes

1960-01-01

Open-pit or strip mining has become an important method of mining bituminous coal in Pennsylvania. In 1958 some 19.5 million tons of soft coal - 29 percent of the total bituminous production in the State - were produced by this method.

Predicting future discoveries from current scientific literature.

PubMed

Petrič, Ingrid; Cestnik, Bojan

2014-01-01

Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

Advantages and difficulties of implementation of the international GNA standards in sustainable mining development. (Invited)

NASA Astrophysics Data System (ADS)

Masaitis, A.

2013-12-01

Conflicts in the development of mining projects are now common between the mining proponents, NGO's and communities. These conflicts can sometimes be alleviated by early development of modes of communication, and a formal discussion format that allows airing of concerns and potential resolution of problems. One of the methods that can formalize this process is to establish a Good Neighbor Agreement (GNA), which deals specifically with challenges in relationships between mining operations and the local communities. It is a new practice related to mining operations that are oriented toward social needs and concerns of local communities that arise during the normal life of a mine, which can achieve sustainable mining practices in both developing and developed countries. The GNA project being currently developed at the University of Nevada, Reno in cooperation with the Newmont Mining Corporation has a goal to create an open company/community dialog that is based on the international standards and that will help identify and address sociological and environmental concerns associated with mining, as well as find methods for communication and conflict resolution. GNA standards should be based on trust doctrine, open information access, and community involvement in the decision making process. It should include the following components: emergency response and community communications; environmental issues, including air and water quality standards; reclamation and recultivation; socio-economic issues: transportation, safety, training, and local hiring; and financial issues, particularly related to mitigation offsets and community needs. The GNA standards help identify and evaluate conflict criteria in mining/community relationships; determine the status of concerns; focus on the local political and government systems; separate the acute and the chronic concerns; determine the role and responsibilities of stakeholders; analyze problem resolution feasibility; maintain the community involvement and support through economic benefits and environmental safeguards; develop options for the concerns resolution; develop and manage short and long-term plans. Difficulties in establishing the GNA standards include identification of the full list of stakeholders, lack of responsible environmental protection practices, dependence on the government and political system, lack of will to disclose full information to the public. It is further complicated by the lack of insurance/bonding policies, and by the lack of audit and monitoring that could determine the level of exposure of the local community and the environment to the contaminants released at the mine sites. Since many problems of mines can occur during closure and post-closure, GNA's should address those issues also. Determined the process for the GNA implementation as a conflict prevention/resolution tool, analyzed conflict/concerns criteria associated with mining operations, determined the role of the stakeholders, worked out the process of stakeholders monitoring, carried out the sociological survey of the stakeholders and the community. Frequent conflicts between mining companies and surrounding communities that lead to work disruptions or even mine closures show the necessity of a less confrontational approach to environmental and social justice. Establishment of GNA standards for use in both developed and developing nations can decrease these conflicts.

Drug safety data mining with a tree-based scan statistic.

PubMed

Kulldorff, Martin; Dashevsky, Inna; Avery, Taliser R; Chan, Arnold K; Davis, Robert L; Graham, David; Platt, Richard; Andrade, Susan E; Boudreau, Denise; Gunter, Margaret J; Herrinton, Lisa J; Pawloski, Pamala A; Raebel, Marsha A; Roblin, Douglas; Brown, Jeffrey S

2013-05-01

In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright © 2013 John Wiley & Sons, Ltd.

Pattern mining of user interaction logs for a post-deployment usability evaluation of a radiology PACS client.

PubMed

Jorritsma, Wiard; Cnossen, Fokie; Dierckx, Rudi A; Oudkerk, Matthijs; van Ooijen, Peter M A

2016-01-01

To perform a post-deployment usability evaluation of a radiology Picture Archiving and Communication System (PACS) client based on pattern mining of user interaction log data, and to assess the usefulness of this approach compared to a field study. All user actions performed on the PACS client were logged for four months. A data mining technique called closed sequential pattern mining was used to automatically extract frequently occurring interaction patterns from the log data. These patterns were used to identify usability issues with the PACS. The results of this evaluation were compared to the results of a field study based usability evaluation of the same PACS client. The interaction patterns revealed four usability issues: (1) the display protocols do not function properly, (2) the line measurement tool stays active until another tool is selected, rather than being deactivated after one use, (3) the PACS's built-in 3D functionality does not allow users to effectively perform certain 3D-related tasks, (4) users underuse the PACS's customization possibilities. All usability issues identified based on the log data were also found in the field study, which identified 48 issues in total. Post-deployment usability evaluation based on pattern mining of user interaction log data provides useful insights into the way users interact with the radiology PACS client. However, it reveals few usability issues compared to a field study and should therefore not be used as the sole method of usability evaluation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Neural network analysis of electrodynamic activity of yeast cells around 1 kHz

NASA Astrophysics Data System (ADS)

Janca, R.

2011-12-01

This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non-synchronized yeasts not only in total power, but also discrete frequencies.

Studies of short and long memory in mining-induced seismic processes

NASA Astrophysics Data System (ADS)

Węglarczyk, Stanisław; Lasocki, Stanisław

2009-09-01

Memory of a stochastic process implies its predictability, understood as a possibility to gain information on the future above the random guess level. Here we search for memory in the mining-induced seismic process (MIS), that is, a process induced or triggered by mining operations. Long memory is investigated by means of the Hurst rescaled range analysis, and the autocorrelation function estimate is used to test for short memory. Both methods are complemented with result uncertainty analyses based on different resampling techniques. The analyzed data comprise event series from Rudna copper mine in Poland. The studies show that the interevent time and interevent distance processes have both long and short memory. MIS occurrences and locations are internally interrelated. Internal relations among the sizes of MIS events are apparently weaker than those of other two studied parameterizations and are limited to long term interactions.

From protein sequence to dynamics and disorder with DynaMine.

PubMed

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

2013-01-01

Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine--a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov.

PubMed

Su, Eric Wen; Sanger, Todd M

2017-01-01

Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts "Serious Adverse Events" (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).

Spectral signature verification using statistical analysis and text mining

NASA Astrophysics Data System (ADS)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective.

Enriching semantic knowledge bases for opinion mining in big data applications.

PubMed

Weichselbraun, A; Gindl, S; Scharl, A

2014-10-01

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

PubMed Central

Király, András; Abonyi, János

2014-01-01

During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers. PMID:24616651

Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

PubMed Central

Yang, Zhaowan; Fang, Ming; Zhang, Libin; Zhou, Yanhong

2014-01-01

Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification. PMID:24895499

A Novel Method of Case Representation and Retrieval in CBR for E-Learning

ERIC Educational Resources Information Center

Khamparia, Aditya; Pandey, Babita

2017-01-01

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the…

Evaluation of airborne thermal infrared imagery for locating mine drainage sites in the Lower Kettle Creek and Cooks Run Basins, Pennsylvania, USA

USGS Publications Warehouse

Sams, James I.; Veloski, Garret

2003-01-01

High-resolution airborne thermal infrared (TIR) imagery data were collected over 90.6 km2 (35 mi2) of remote and rugged terrain in the Kettle Creek and Cooks Run Basins, tributaries of the West Branch of the Susquehanna River in north-central Pennsylvania. The purpose of this investigation was to evaluate the effectiveness of TIR for identifying sources of acid mine drainage (AMD) associated with abandoned coal mines. Coal mining from the late 1800s resulted in many AMD sources from abandoned mines in the area. However, very little detailed mine information was available, particularly on the source locations of AMD sites. Potential AMD sources were extracted from airborne TIR data employing custom image processing algorithms and GIS data analysis. Based on field reconnaissance of 103 TIR anomalies, 53 sites (51%) were classified as AMD. The AMD sources had low pH (<4) and elevated concentrations of iron and aluminum. Of the 53 sites, approximately 26 sites could be correlated with sites previously documented as AMD. The other 27 mine discharges identified in the TIR data were previously undocumented. This paper presents a summary of the procedures used to process the TIR data and extract potential mine drainage sites, methods used for field reconnaissance and verification of TIR data, and a brief summary of water-quality data.

Data Mining Web Services for Science Data Repositories

NASA Astrophysics Data System (ADS)

Graves, S.; Ramachandran, R.; Keiser, K.; Maskey, M.; Lynnes, C.; Pham, L.

2006-12-01

The maturation of web services standards and technologies sets the stage for a distributed "Service-Oriented Architecture" (SOA) for NASA's next generation science data processing. This architecture will allow members of the scientific community to create and combine persistent distributed data processing services and make them available to other users over the Internet. NASA has initiated a project to create a suite of specialized data mining web services designed specifically for science data. The project leverages the Algorithm Development and Mining (ADaM) toolkit as its basis. The ADaM toolkit is a robust, mature and freely available science data mining toolkit that is being used by several research organizations and educational institutions worldwide. These mining services will give the scientific community a powerful and versatile data mining capability that can be used to create higher order products such as thematic maps from current and future NASA satellite data records with methods that are not currently available. The package of mining and related services are being developed using Web Services standards so that community-based measurement processing systems can access and interoperate with them. These standards-based services allow users different options for utilizing them, from direct remote invocation by a client application to deployment of a Business Process Execution Language (BPEL) solutions package where a complex data mining workflow is exposed to others as a single service. The ability to deploy and operate these services at a data archive allows the data mining algorithms to be run where the data are stored, a more efficient scenario than moving large amounts of data over the network. This will be demonstrated in a scenario in which a user uses a remote Web-Service-enabled clustering algorithm to create cloud masks from satellite imagery at the Goddard Earth Sciences Data and Information Services Center (GES DISC).

Trace metal content in inhalable particulate matter (PM2.5-10 and PM2.5) collected from historical mine waste deposits using a laboratory-based approach.

PubMed

Martin, Rachael; Dowling, Kim; Pearce, Dora C; Florentine, Singarayer; McKnight, Stafford; Stelcer, Eduard; Cohen, David D; Stopic, Attila; Bennett, John W

2017-06-01

Mine wastes and tailings are considered hazardous to human health because of their potential to generate large quantities of highly toxic emissions of particulate matter (PM). Human exposure to As and other trace metals in PM may occur via inhalation of airborne particulates or through ingestion of contaminated dust. This study describes a laboratory-based method for extracting PM 2.5-10 (coarse) and PM 2.5 (fine) particles from As-rich mine waste samples collected from an historical gold mining region in regional, Victoria, Australia. We also report on the trace metal and metalloid content of the coarse and fine fraction, with an emphasis on As as an element of potential concern. Laser diffraction analysis showed that the proportions of coarse and fine particles in the bulk samples ranged between 3.4-26.6 and 0.6-7.6 %, respectively. Arsenic concentrations were greater in the fine fraction (1680-26,100 mg kg -1 ) compared with the coarse fraction (1210-22,000 mg kg -1 ), and Co, Fe, Mn, Ni, Sb and Zn were found to be present in the fine fraction at levels around twice those occurring in the coarse. These results are of particular concern given that fine particles can accumulate in the human respiratory system. Our study demonstrates that mine wastes may be an important source of metal-enriched PM for mining communities.

Environmental impacts of coal mine and thermal power plant to the surroundings of Barapukuria, Dinajpur, Bangladesh.

PubMed

Hossain, Md Nazir; Paul, Shitangsu Kumar; Hasan, Md Muyeed

2015-04-01

The study was carried out to analyse the environmental impacts of coal mine and coal-based thermal power plant to the surrounding environment of Barapukuria, Dinajpur. The analyses of coal, water, soil and fly ash were carried out using standard sample testing methods. This study found that coal mining industry and coal-based thermal power plant have brought some environmental and socio-economic challenges to the adjacent areas such as soil, water and air pollution, subsidence of agricultural land and livelihood insecurity of inhabitants. The pH values, heavy metal, organic carbon and exchangeable cations of coal water treated in the farmland soil suggest that coal mining deteriorated the surrounding water and soil quality. The SO4(2-) concentration in water samples was beyond the range of World Health Organisation standard. Some physico-chemical properties such as pH, conductivity, moisture content, bulk density, unburned carbon content, specific gravity, water holding capacity, liquid and plastic limit were investigated on coal fly ash of Barapukuria thermal power plant. Air quality data provided by the Barapukuria Coal Mining Company Limited were contradictory with the result of interview with the miners and local inhabitants. However, coal potentially contributes to the development of economy of Bangladesh but coal mining deteriorates the environment by polluting air, water and soil. In general, this study includes comprehensive baseline data for decision makers to evaluate the feasibility of coal power industry at Barapukuria and the coalmine itself.

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.

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

NASA Astrophysics Data System (ADS)

Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

2016-05-01

Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

A Comparative Analysis of the Influence of Surface Mining on Hydrological and Geochemical Response of Selected Headwater Streams in the Elk Valley, British Columbia, Canada.

NASA Astrophysics Data System (ADS)

Carey, S. K.; Shatilla, N. J.; Szmudrowska, B.; Rastelli, J.; Wellen, C.

2014-12-01

Surface mining is a common method of accessing coal. Blasting of overburden rock allows access to mineable ore. In high-elevation environments, the removed overburden rock is deposited in adjacent valleys as waste rock spoils. As part of a multi-year R&D program examining the influence of surface mining on watershed hydrological and water quality responses in the Elk Valley, British Columbia, this study reports on how surface mining affects streamflow hydrological and geochemical response at four reference and four mine-influenced catchments. The hydrology of this environment is dominated by snowmelt and steep topographic gradients. Flows were attenuated in mine-influenced catchments, with spring freshet delayed and more muted responses to precipitation events observed. Dissolved ions were an order of magnitude greater in mine-influenced streams, with more dilution-based responses to flows compared with chemostatic behavior observed in reference streams. Stable isotope signatures in stream water suggested that in both mine-influenced and reference watersheds, stream water was derived from well mixed groundwater as annual variability of stream isotope signatures was dampened compared with precipitation signatures. However, deflection of stream isotopes in response to precipitation were more apparent in reference watersheds. As a group, mine influenced catchments had a heavier isotope signature than reference watersheds, suggesting an enhanced influence of rainfall on recharge. Transit time distributions indicate existing waste rock spoils increase the average time water takes to move through the catchment.

Detecting Plastic PFM-1 Butterfly Mines Using Thermal Infrared Sensing

NASA Astrophysics Data System (ADS)

Baur, J.; de Smet, T.; Nikulin, A.

2017-12-01

Remnant plastic-composite landmines, such as the mass-produced PFM-1, represent an ongoing humanitarian threat aggravated by high costs associated with traditional demining efforts. These particular unexploded ordnance (UXO) devices pose a challenge to conventional geophysical detection methods, due their plastic-body design and small size. Additionally, the PFM-1s represent a particularly heinous UXO, due to their low mass ( 25 lb) trigger limit and "butterfly" wing design, earning them the reputation of a "toy mine" - disproportionally impacting children across post-conflict areas. We developed a detection algorithm based on data acquired by a thermal infrared camera mounted to a commercial UAV to detect time-variable temperature difference between the PFM-1 and the surrounding environment. We present results of a field study focused on thermal detection and identification of the PFM-1 anti-personnel landmines from a remotely operated unmanned aerial vehicle (UAV). We conducted a series of field detection experiments meant to simulate the mountainous terrains where PFM-1 mines were historically deployed and remain in place. In our tests, 18 inert PFM-1 mines along with the aluminum KSF-1 casing were randomly dispersed to mimic an ellipsoidal minefield of 8-10 x 18-20 m dimensions in a de-vegetated rubble yard at Chenango Valley State Park (New York State). We collected multiple thermal infrared imagery datasets focused on these model minefields with the FLIR Vue Pro R attached to the 3DR Solo UAV flying at approximately at 2 m. We identified different environmental variables to constrain the optimal time of day and daily temperature variations to reveal presence of these plastic UXOs. We show that in the early-morning hours when thermal inertia is greatest, the PFM-1 mines can be detected based on their differential thermal inertia. Because the mines have statistically different temperatures than background and a characteristic shape, we were able to train a supervised learning algorithm to automate detection of the mines over large areas. We anticipate that following further development, this remote sensing method can aid in significantly reducing the cost and time associated with landmine remediation in post-conflict nations.

Wetland-based passive treatment systems for gold ore processing effluents containing residual cyanide, metals and nitrogen species.

PubMed

Alvarez, R; Ordóñez, A; Loredo, J; Younger, P L

2013-10-01

Gold extraction operations generate a variety of wastes requiring responsible disposal in compliance with current environmental regulations. During recent decades, increased emphasis has been placed on effluent control and treatment, in order to avoid the threat to the environment posed by toxic constituents. In many modern gold mining and ore processing operations, cyanide species are of most immediate concern. Given that natural degradation processes are known to reduce the toxicity of cyanide over time, trials have been made at laboratory and field scales into the feasibility of using wetland-based passive systems as low-cost and environmentally friendly methods for long-term treatment of leachates from closed gold mine tailing disposal facilities. Laboratory experiments on discrete aerobic and anaerobic treatment units supported the development of design parameters for the construction of a field-scale passive system at a gold mine site in northern Spain. An in situ pilot-scale wetland treatment system was designed, constructed and monitored over a nine-month period. Overall, the results suggest that compost-based constructed wetlands are capable of detoxifying cyanidation effluents, removing about 21.6% of dissolved cyanide and 98% of Cu, as well as nitrite and nitrate. Wetland-based passive systems can therefore be considered as a viable technology for removal of residual concentrations of cyanide from leachates emanating from closed gold mine tailing disposal facilities.

Evaluation of selected static methods used to estimate element mobility, acid-generating and acid-neutralizing potentials associated with geologically diverse mining wastes

USGS Publications Warehouse

Hageman, Philip L.; Seal, Robert R.; Diehl, Sharon F.; Piatak, Nadine M.; Lowers, Heather

2015-01-01

A comparison study of selected static leaching and acid–base accounting (ABA) methods using a mineralogically diverse set of 12 modern-style, metal mine waste samples was undertaken to understand the relative performance of the various tests. To complement this study, in-depth mineralogical studies were conducted in order to elucidate the relationships between sample mineralogy, weathering features, and leachate and ABA characteristics. In part one of the study, splits of the samples were leached using six commonly used leaching tests including paste pH, the U.S. Geological Survey (USGS) Field Leach Test (FLT) (both 5-min and 18-h agitation), the U.S. Environmental Protection Agency (USEPA) Method 1312 SPLP (both leachate pH 4.2 and leachate pH 5.0), and the USEPA Method 1311 TCLP (leachate pH 4.9). Leachate geochemical trends were compared in order to assess differences, if any, produced by the various leaching procedures. Results showed that the FLT (5-min agitation) was just as effective as the 18-h leaching tests in revealing the leachate geochemical characteristics of the samples. Leaching results also showed that the TCLP leaching test produces inconsistent results when compared to results produced from the other leaching tests. In part two of the study, the ABA was determined on splits of the samples using both well-established traditional static testing methods and a relatively quick, simplified net acid–base accounting (NABA) procedure. Results showed that the traditional methods, while time consuming, provide the most in-depth data on both the acid generating, and acid neutralizing tendencies of the samples. However, the simplified NABA method provided a relatively fast, effective estimation of the net acid–base account of the samples. Overall, this study showed that while most of the well-established methods are useful and effective, the use of a simplified leaching test and the NABA acid–base accounting method provide investigators fast, quantitative tools that can be used to provide rapid, reliable information about the leachability of metals and other constituents of concern, and the acid-generating potential of metal mining waste.

MINING METHODS AND COSTS, CALYX NOS. 3 AND 8 URANIUM MINES, TEMPLE MOUNTAIN DISTRICT, EMERY COUNTY, UTAH

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

Dare, W.L.

1957-04-01

Descriptions are given of the Calyx No. 3 mine operated by American Reduc Uranium Corp. and Calyx No. 8 operated by Cline Co. The deposits are composed of numerous small, irregular bodies and are worked through 36 inch Calyx drill holes. The U--V ores are concentrated chiefly in the lower 30 feet of the Moss Back sandstone. In general it follows the strnta. The mine is worked by open stoping with random pillar support. The operations and mining practices of these two mines are very similar and typify the mining methods and practioes used by many small U producers onmore » the Colorado Plateau. (R.V.J.)« less

An evaluation of problems arising from acid mine drainage in the vicinity of Shasta Lake, Shasta County, California

USGS Publications Warehouse

Fuller, Richard H.; Shay, J.M.; Ferreira, R.F.; Hoffman, R.J.

1978-01-01

Streams draining the mined areas of massive sulfide ore deposits in the Shasta Mining Districts of northern California are generally acidic and contain large concentrations of dissolved metals, including iron, copper, and zinc. The streams, including Flat, Little Backbone, Spring, West Squaw, Horse, and Zinc Creeks, discharge into Shasta Reservoir and the Sacramento River and have caused numerous fish kills. The sources of pollution are discharge from underground mines, streams that flow into open pits, and streams that flow through pyritic mine dumps where the oxidation of pyrite and other sulfide minerals results in the production of acid and the mobilization of metals. Suggested methods of treatment include the use of air and hydraulic seals in the mines, lime neutralization of mine effluent, channeling of runoff and mine effluent away from mine and tailing areas, and the grading and sealing of mine dumps. A comprehensive preabatement and postabatement program is recommended to evaluate the effects of any treatment method used. (Woodard-USGS)

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.

PubMed

Raimondi, Daniele; Orlando, Gabriele; Pancsa, Rita; Khan, Taushif; Vranken, Wim F

2017-08-18

Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.

Research on PM2.5 time series characteristics based on data mining technology

NASA Astrophysics Data System (ADS)

Zhao, Lifang; Jia, Jin

2018-02-01

With the development of data mining technology and the establishment of environmental air quality database, it is necessary to discover the potential correlations and rules by digging the massive environmental air quality information and analyzing the air pollution process. In this paper, we have presented a sequential pattern mining method based on the air quality data and pattern association technology to analyze the PM2.5 time series characteristics. Utilizing the real-time monitoring data of urban air quality in China, the time series rule and variation properties of PM2.5 under different pollution levels are extracted and analyzed. The analysis results show that the time sequence features of the PM2.5 concentration is directly affected by the alteration of the pollution degree. The longest time that PM2.5 remained stable is about 24 hours. As the pollution degree gets severer, the instability time and step ascending time gradually changes from 12-24 hours to 3 hours. The presented method is helpful for the controlling and forecasting of the air quality while saving the measuring costs, which is of great significance for the government regulation and public prevention of the air pollution.

Causes Of Low Efficiency Of Combined Ventilation System In Coal Mines In Resolving The Problem Of Air Leaks (Inflows) Between Levels And Surface

NASA Astrophysics Data System (ADS)

Popov, Valeriy; Filatov, Yuriy; Lee, Hee; Golik, Anatoliy

2017-11-01

The paper discusses the problem of the underground mining safety control. The long-term air intake to coal accumulations is reviewed as one of the reasons of endogenous fires during mining. The methods of combating air leaks (inflows) in order to prevent endogenous fires are analyzed. The calculations showing the discrepancy between the design calculations for the mine ventilation, disregarding a number of mining-andgeological and mining-engineering factors, and the actual conditions of mining are given. It is proved that the conversion of operating mines to combined (pressure and exhaust) ventilation system in order to reduce the endogenous fire hazard of underground mining is unreasonable due to impossibility of providing an optimal distribution of aerodynamic pressure in mines. The conversion does not exclude the entry of air into potentially hazardous zones of endogenous fires. The essence of the combined application of positive and negative control methods for the distribution of air pressure is revealed. It consists of air doors installation in easily ventilated airways and installation of pressure equalization chambers equipped with auxiliary fans near the stoppings, working sections and in parallel airways.The effectiveness of the combined application of negative and positive control methods for the air pressure distribution in order to reduce endogenous fire hazard of mining operations is proved.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Magnetic sensor technology for detecting mines, UXO, and other concealed security threats

NASA Astrophysics Data System (ADS)

Czipott, Peter V.; Iwanowski, Mark D.

1997-01-01

Magnetic sensors have been the sensor of choice in the detection and classification of buried mines and unexploded ordnance (UXO), both on land and underwater, Quantum Magnetics (QM), together with its research partner IBM, have developed a variety of advanced, very high sensitivity superconducting and room temperature magnetic sensors to meet military needs. This work has led to the development and utilization of a three-sensor gradiometer (TSG) patented by IBM, which cannot only detect, but also localize mines and ordnance. QM is also working with IBM and the U.S. Navy to develop an advanced superconducting gradiometer for buried underwater mine detection. The ability to both detect and classify buried non-metallic mines is virtually impossible with existing magnetic sensors. To solve this problem, Quantum Magnetics, building on work of the Naval Research Laboratory (NRL), is pioneering work in the development of quadrupole resonance (QR) methods which can be used to detect the explosive material directly. Based on recent laboratory work done at QM and previous work done in the U.S., Russia and the United Kingdom, we are confident that QR can be effectively applied to the non-metallic mine identification problem.

Treatment of antimony mine drainage: challenges and opportunities with special emphasis on mineral adsorption and sulfate reducing bacteria.

PubMed

Li, Yongchao; Hu, Xiaoxian; Ren, Bozhi

2016-01-01

The present article summarizes antimony mine distribution, antimony mine drainage generation and environmental impacts, and critically analyses the remediation approach with special emphasis on iron oxidizing bacteria and sulfate reducing bacteria. Most recent research focuses on readily available low-cost adsorbents, such as minerals, wastes, and biosorbents. It is found that iron oxides prepared by chemical methods present superior adsorption ability for Sb(III) and Sb(V). However, this process is more costly and iron oxide activity can be inhibited by plenty of sulfate in antimony mine drainage. In the presence of sulfate reducing bacteria, sulfate can be reduced to sulfide and form Sb(2)S(3) precipitates. However, dissolved oxygen and lack of nutrient source in antimony mine drainage inhibit sulfate reducing bacteria activity. Biogenetic iron oxide minerals from iron corrosion by iron-oxidizing bacteria may prove promising for antimony adsorption, while the micro-environment generated from iron corrosion by iron oxidizing bacteria may provide better growth conditions for symbiotic sulfate reducing bacteria. Finally, based on biogenetic iron oxide adsorption and sulfate reducing bacteria followed by precipitation, the paper suggests an alternative treatment for antimony mine drainage that deserves exploration.

Long-term predictions of minewater geothermal systems heat resources

NASA Astrophysics Data System (ADS)

Harcout-Menou, Virginie; de ridder, fjo; laenen, ben; ferket, helga

2014-05-01

Abandoned underground mines usually flood due to the natural rise of the water table. In most cases the process is relatively slow giving the mine water time to equilibrate thermally with the the surrounding rock massif. Typical mine water temperature is too low to be used for direct heating, but is well suited to be combined with heat pumps. For example, heat extracted from the mine can be used during winter for space heating, while the process could be reversed during summer to provide space cooling. Altough not yet widely spread, the use of low temperature geothermal energy from abandoned mines has already been implemented in the Netherlands, Spain, USA, Germany and the UK. Reliable reservoir modelling is crucial to predict how geothermal minewater systems will react to predefined exploitation schemes and to define the energy potential and development strategy of a large-scale geothermal - cold/heat storage mine water systems. However, most numerical reservoir modelling software are developed for typical environments, such as porous media (a.o. many codes developed for petroleum reservoirs or groundwater formations) and cannot be applied to mine systems. Indeed, mines are atypical environments that encompass different types of flow, namely porous media flow, fracture flow and open pipe flow usually described with different modelling codes. Ideally, 3D models accounting for the subsurface geometry, geology, hydrogeology, thermal aspects and flooding history of the mine as well as long-term effects of heat extraction should be used. A new modelling approach is proposed here to predict the long-term behaviour of Minewater geothermal systems in a reactive and reliable manner. The simulation method integrates concepts for heat and mass transport through various media (e.g., back-filled areas, fractured rock, fault zones). As a base, the standard software EPANET2 (Rossman 1999; 2000) was used. Additional equations for describing heat flow through the mine (both through open pipes and from the rock massif) have been implemented. Among others, parametric methods are used to bypass some shortcomings in the physical models used for the subsurface. The advantage is that the complete geometry of the mine workings can be integrated and that computing is fast enough to allow implementing and testing several scenarios (e.g. contributions from fault zones, different assumptions about the actual status of shafts, drifts and mined out areas) in an efficient way (Ferket et al., 2011). EPANET allows to incorporate the full complexity of the subsurface mine structure. As a result, the flooded mine is considered as a network of pipes, each with a custom-defined diameter, length and roughness.

Features of the Asynchronous Correlation between the China Coal Price Index and Coal Mining Accidental Deaths.

PubMed

Huang, Yuecheng; Cheng, Wuyi; Luo, Sida; Luo, Yun; Ma, Chengchen; He, Tailin

2016-01-01

The features of the asynchronous correlation between accident indices and the factors that influence accidents can provide an effective reference for warnings of coal mining accidents. However, what are the features of this correlation? To answer this question, data from the China coal price index and the number of deaths from coal mining accidents were selected as the sample data. The fluctuation modes of the asynchronous correlation between the two data sets were defined according to the asynchronous correlation coefficients, symbolization, and sliding windows. We then built several directed and weighted network models, within which the fluctuation modes and the transformations between modes were represented by nodes and edges. Then, the features of the asynchronous correlation between these two variables could be studied from a perspective of network topology. We found that the correlation between the price index and the accidental deaths was asynchronous and fluctuating. Certain aspects, such as the key fluctuation modes, the subgroups characteristics, the transmission medium, the periodicity and transmission path length in the network, were analyzed by using complex network theory, analytical methods and spectral analysis method. These results provide a scientific reference for generating warnings for coal mining accidents based on economic indices.

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.

Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques.

PubMed

Ghaibeh, A Ammar; Kasem, Asem; Ng, Xun Jin; Nair, Hema Latha Krishna; Hirose, Jun; Thiruchelvam, Vinesh

2018-01-01

The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.

Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

NASA Technical Reports Server (NTRS)

Port, Dan; Nikora, Allen; Hihn, Jairus; Huang, LiGuo

2011-01-01

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

Monitoring diesel particulate matter and calculating diesel particulate densities using Grimm model 1.109 real-time aerosol monitors in underground mines.

PubMed

Kimbal, Kyle C; Pahler, Leon; Larson, Rodney; VanDerslice, Jim

2012-01-01

Currently, there is no Mine Safety and Health Administration (MSHA)-approved sampling method that provides real-time results for ambient concentrations of diesel particulates. This study investigated whether a commercially available aerosol spectrometer, the Grimm Portable Aerosol Spectrometer Model 1.109, could be used during underground mine operations to provide accurate real-time diesel particulate data relative to MSHA-approved cassette-based sampling methods. A subset was to estimate size-specific diesel particle densities to potentially improve the diesel particulate concentration estimates using the aerosol monitor. Concurrent sampling was conducted during underground metal mine operations using six duplicate diesel particulate cassettes, according to the MSHA-approved method, and two identical Grimm Model 1.109 instruments. Linear regression was used to develop adjustment factors relating the Grimm results to the average of the cassette results. Statistical models using the Grimm data produced predicted diesel particulate concentrations that highly correlated with the time-weighted average cassette results (R(2) = 0.86, 0.88). Size-specific diesel particulate densities were not constant over the range of particle diameters observed. The variance of the calculated diesel particulate densities by particle diameter size supports the current understanding that diesel emissions are a mixture of particulate aerosols and a complex host of gases and vapors not limited to elemental and organic carbon. Finally, diesel particulate concentrations measured by the Grimm Model 1.109 can be adjusted to provide sufficiently accurate real-time air monitoring data for an underground mining environment.

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.

Efficacy Evaluation of Current and Future Naval Mine Warfare Neutralization Method

DTIC Science & Technology

2016-12-01

Distribution is unlimited. EFFICACY EVALUATION OF CURRENT AND FUTURE NAVAL MINE WARFARE NEUTRALIZATION METHOD by Team MIW Cohort SE311-152O...EFFICACY EVALUATION OF CURRENT AND FUTURE NAVAL MINE WARFARE NEUTRALIZATION METHOD 5. FUNDING NUMBERS 6. AUTHOR (S) Team MIW, Systems Engineering...NEUTRALIZATION METHOD Team MIW, Systems Engineering Cohort SE311-152O Submitted in partial fulfillment of the requirements for the degrees of

Improving the Repair Planning System for Mining Equipment on the Basis of Non-destructive Evaluation Data

NASA Astrophysics Data System (ADS)

Drygin, Michael; Kuryshkin, Nicholas

2017-11-01

The article tells about forming a new concept of scheduled preventive repair system of the equipment at coal mining enterprises, based on the use of modem non-destructive evaluation methods. The approach to the solution for this task is based on the system-oriented analysis of the regulatory documentation, non-destructive evaluation methods and means, experimental studies with compilation of statistics and subsequent grapho-analytical analysis. The main result of the work is a feasible explanation of using non-destructive evaluation methods within the current scheduled preventive repair system, their high efficiency and the potential of gradual transition to condition-based maintenance. In practice wide use of nondestructive evaluation means w;ill allow to reduce significantly the number of equipment failures and to repair only the nodes in pre-accident condition. Considering the import phase-out policy, the solution for this task will allow to adapt the SPR system to Russian market economy conditions and give the opportunity of commercial move by reducing the expenses for maintenance of Russian-made and imported equipment.

Effects of abandoned arsenic mine on water resources pollution in north west of iran.

PubMed

Hajalilou, Behzad; Mosaferi, Mohammad; Khaleghi, Fazel; Jadidi, Sakineh; Vosugh, Bahram; Fatehifar, Esmail

2011-01-01

Pollution due to mining activities could have an important role in health and welfare of people who are living in mining area. When mining operation finishes, environ-ment of mining area can be influenced by related pollution e.g. heavy metals emission to wa-ter resources. The present study was aimed to evaluate Valiloo abandoned arsenic mine ef-fects on drinking water resources quality and possible health effects on the residents of min-ing area in the North West of Iran. Water samples and some limited composite wheat samples in downstream of min-ing area were collected. Water samples were analyzed for chemical parameters according to standard methods. For determination of arsenic in water samples, Graphite Furnace Atomic Absorption Spectrometric Method (GFAAS) and for wheat samples X - Ray Fluorescence (XRF) and Inductively Coupled Plasma Method (ICP) were used. Information about possible health effects due to exposure to arsenic was collected through interviews in studied villages and health center of Herris City. The highest concentrations of arsenic were measured near the mine (as high as 2000 µg/L in Valiloo mine opening water). With increasing distance from the mine, concentration was decreased. Arsenic was not detectable in any of wheat samples. Fortunately, no health effects had been reported between residents of studied area due to exposure to arsenic. Valiloo abandoned arsenic mine has caused release of arsenic to the around en-vironment of the mine, so arsenic concentration has been increased in the groundwater and also downstream river that requires proper measures to mitigate spread of arsenic.

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.

Determining the area of influence of depression cone in the vicinity of lignite mine by means of triangle method and LANDSAT TM/ETM+ satellite images.

PubMed

Zawadzki, Jarosław; Przeździecki, Karol; Miatkowski, Zygmunt

2016-01-15

Problems with lowering of water table are common all over the world. Intensive pumping of water from aquifers for consumption, irrigation, industrial or mining purposes often causes groundwater depletion and results in the formation of cone of depression. This can severely decrease water pressure, even over vast areas, and can create severe problems such as degradation of agriculture or natural environment sometimes depriving people and animals of water supply. In this paper, the authors present a method for determining the area of influence of a groundwater depression cone resulting from prolonged drainage, by means of satellite images in optical, near infrared and thermal infrared bands from TM sensor (Thematic Mapper) and ETM+ sensor (Enhanced Thematic Mapper +) placed on Landsat 5 and Landsat 7 satellites. The research area was Szczercowska Valley (Pol. Kotlina Szczercowska), Central Poland, located within a range of influence of a groundwater drainage system of the lignite coal mine in Belchatow. It is the biggest lignite coal mine in Poland and one of the largest in Europe exerting an enormous impact on the environment. The main method of satellite data analysis for determining soil moisture, was the so-called triangle method. This method, based on TVDI (Temperature Vegetation Dryness Index) was supported by additional spatial analysis including ordinary kriging used in order to combine fragmentary information obtained from areas covered by meadows. The results obtained are encouraging and confirm the usefulness of the triangle method not only for soil moisture determination but also for assessment of the temporal and spatial changes in the area influenced by the groundwater depression cone. The range of impact of the groundwater depression cone determined by means of above-described remote sensing analysis shows good agreement with that determined by ground measurements. The developed satellite method is much faster and cheaper than in-situ measurements, and allows for systematic monitoring of the vast area in the vicinity of Belchatow lignite mine. Besides, this method could be useful as a helper in in-situ measurement allowing a significant reduction of the number of in-situ measurements by performing them only within problematic areas. Hence, the triangle method can be used as an effective supplement to field measurements. Although the research area is located in Poland, in the vicinity of lignite mine, the method of observation of depression cones provided in this study is universal and effective, and therefore could also be useful to an international audience. Copyright © 2015 Elsevier Ltd. All rights reserved.

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2003-09-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. This paper describes a method for training several multivariate Gaussian classifiers such that their And-ing dramatically reduces false alarms while maintaining a high probability of classification. This training approach is referred to as the Focused- Training method. This work extends our 2001-2002 work where the Focused-Training method was used with three other types of classifiers: the Attractor-based K-Nearest Neighbor Neural Network (a type of radial-basis, probabilistic neural network), the Optimal Discrimination Filter Classifier (based linear discrimination theory), and the Quadratic Penalty Function Support Vector Machine (QPFSVM). Although our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to a wide range of pattern recognition and automatic target recognition (ATR) problems.

Stratified Sampling Design Based on Data Mining

PubMed Central

Kim, Yeonkook J.; Oh, Yoonhwan; Park, Sunghoon; Cho, Sungzoon

2013-01-01

Objectives To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. Methods We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers' profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. Results Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. Conclusions This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea. PMID:24175117

Competency-Based Teaching of Shakespeare: How to Master "King Lear"

ERIC Educational Resources Information Center

Ribes, Purificación

2011-01-01

Shakespeare's hypotext has invited so many hypertextual transformations over the last four hundred years that twenty-first century students deserve the chance of digging into this rich mine of information and dramatic possibilities. The practical approach of a competency-based teaching method offers great advantages over traditional practices in…

78 FR 72025 - Security Zones; Naval Base Point Loma; Naval Mine Anti Submarine Warfare Command; San Diego Bay...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-02

...-AA87 Security Zones; Naval Base Point Loma; Naval Mine Anti Submarine Warfare Command; San Diego Bay... establishing a new security zone at the Naval Mine and Anti-Submarine Warfare Command to protect the relocated... Commander of Naval Base Point Loma, the Commander of the Naval Mine Anti Submarine Warfare Command, and the...

Mining the protein data bank with CReF to predict approximate 3-D structures of polypeptides.

PubMed

Dorn, Márcio; de Souza, Osmar Norberto

2010-01-01

n this paper we describe CReF, a Central Residue Fragment-based method to predict approximate 3-D structures of polypeptides by mining the Protein Data Bank (PDB). The approximate predicted structures are good enough to be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF is very fast and we illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted structures adopt the expected fold, similar to the experimental ones.

Condition-Based Conveyor Belt Replacement Strategy in Lignite Mines with Random Belt Deterioration

NASA Astrophysics Data System (ADS)

Blazej, Ryszard; Jurdziak, Leszek

2017-12-01

In Polish lignite surface mines, condition-based belt replacement strategies are applied in order to assure profitable refurbishment of worn out belts performed by external firms specializing in belt maintenance. In two of three lignite mines, staff asses belt condition subjectively during visual inspections. Only one mine applies specialized diagnostic device (HRDS) allowing objective magnetic evaluation of belt core condition in order to choose the most profitable moment for the dismantling of worn out belt segments from conveyors and sending them to the maintenance firm which provides their refurbishment. This article describes the advantages of a new diagnostic device called DiagBelt. It was developed at the Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology. Economic gains from its application are calculated for the lignite mine and for the belt maintenance firm, taking into account random life (durability) of new and reconditioned belts (after the 1st and the 2nd refurbishment). Recursive calculations for following years allow the estimation of the length and costs of replaced, reconditioned and purchased belts on an annual basis, while the use of the Monte Carlo method allows the estimation of their variability caused by random deterioration of belts. Savings are obtained due to better selection of moments (times) for the replacement of belt segments and die to the possibility to qualify worn out belts for refurbishment without the need to remove their covers. In effect, increased belt durability and lowered share of waste belts (which were not qualified for reconditioning) create savings which can quickly cover expenditures on new diagnostic tools and regular belt inspections in the mine.

Publications - SR 68 | Alaska Division of Geological & Geophysical Surveys

Science.gov Websites

Mining District; Base Metals; Bethel Mining District; Bismuth; Black Mining District; Bluff (Place ; Livengood Mining District; Lode; Marshall Mining District; Massive Sulfide Deposit; Massive Sulfide Occurrence; Massive Sulfide Prospect; Massive Sulfides; McGrath Mining District; Melozitna Mining District

Environmental impact assessment of european non-ferro mining industries through life-cycle assessment

NASA Astrophysics Data System (ADS)

Hisan Farjana, Shahjadi; Huda, Nazmul; Parvez Mahmud, M. A.

2018-05-01

European mining industries are the vast industrial sector which contributes largely on their economy which constitutes of ferro and non-ferro metals and minerals industries. The non-ferro metals extraction and processing industries require focus of attention due to sustainability concerns as their manufacturing processes are highly energy intensive and impacts globally on environment. This paper analyses major environmental effects caused by European metal industries based on the life-cycle impact analysis technologies. This research work is the first work in considering the comparative environmental impact analysis of European non-ferro metal industries which will reveal their technological similarities and dissimilarities to assess their environmental loads. The life-cycle inventory datasets are collected from the EcoInvent database while the analysis is done using the CML baseline and ReCipe endpoint method using SimaPro software version 8.4. The CML and ReCipe method are chosen because they are specialized impact assessment methods for European continent. The impact categories outlined for discussion here are human health, global warming and ecotoxicity. The analysis results reveal that the gold industry is vulnerable for the environment due to waste emission and similar result retained by silver mines a little bit. But copper, lead, manganese and zinc mining processes and industries are environment friendly in terms of metal extraction technologies and waste emissions.

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.

Swamp Works: A New Approach to Develop Space Mining and Resource Extraction Technologies at the National Aeronautics Space Administration (NASA) Kennedy Space Center (KSC)

NASA Technical Reports Server (NTRS)

Mueller, R. P.; Sibille, L.; Leucht, K.; Smith, J. D.; Townsend, I. I.; Nick, A. J.; Schuler, J. M.

2015-01-01

The first steps for In Situ Resource Utilization (ISRU) on target bodies such as the Moon, Mars and Near Earth Asteroids (NEA), and even comets, involve the same sequence of steps as in the terrestrial mining of resources. First exploration including prospecting must occur, and then the resource must be acquired through excavation methods if it is of value. Subsequently a load, haul and dump sequence of events occurs, followed by processing of the resource in an ISRU plant, to produce useful commodities. While these technologies and related supporting operations are mature in terrestrial applications, they will be different in space since the environment and indigenous materials are different than on Earth. In addition, the equipment must be highly automated, since for the majority of the production cycle time, there will be no humans present to assist or intervene. This space mining equipment must withstand a harsh environment which includes vacuum, radical temperature swing cycles, highly abrasive lofted dust, electrostatic effects, van der Waals forces effects, galactic cosmic radiation, solar particle events, high thermal gradients when spanning sunlight terminators, steep slopes into craters / lava tubes and cryogenic temperatures as low as 40 K in permanently shadowed regions. In addition the equipment must be tele-operated from Earth or a local base where the crew is sheltered. If the tele-operation occurs from Earth then significant communications latency effects mandate the use of autonomous control systems in the mining equipment. While this is an extremely challenging engineering design scenario, it is also an opportunity, since the technologies developed in this endeavor could be used in the next generations of terrestrial mining equipment, in order to mine deeper, safer, more economical and with a higher degree of flexibility. New space technologies could precipitate new mining solutions here on Earth. The NASA KSC Swamp Works is an innovation environment and methodology, with associated laboratories that uses lean development methods and creativity-enhancing processes to invent and develop new solutions for space exploration. This paper will discuss the Swamp Works approach to developing space mining and resource extraction systems and the vision of space development it serves. The ultimate goal of the Swamp Works is to expand human civilization into the solar system via the use of local resources utilization. By mining and using the local resources in situ, it is conceivable that one day the logistics supply train from Earth can be eliminated and Earth independence of a space-based community will be enabled.

A comprehensive review on privacy preserving data mining.

PubMed

Aldeen, Yousra Abdul Alsahib S; Salleh, Mazleena; Razzaque, Mohammad Abdur

2015-01-01

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

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

Determinants of Interest Rates on Corporate Bonds of Mining Enterprises

NASA Astrophysics Data System (ADS)

Ranosz, Robert

2017-09-01

This article is devoted to the determinants of interest rates on corporate bonds of mining enterprises. The study includes a comparison between the cost of foreign capital as resulting from the issue of debt instruments in different sectors of the economy in relation to the mining industry. The article also depicts the correlation between the rating scores published by the three largest rating agencies: S&P, Moody's, and Fitch. The test was based on simple statistical methods. The analysis performed indicated that there is a dependency between the factors listed and the amount of interest rates on corporate bonds of global mining enterprises. Most significant factors include the rating level and the period for which the given series of bonds was issued. Additionally, it is not without significance whether the given bond has additional options. Pursuant to the obtained results, is should be recognized that in order to reduce the interest rate on bonds, mining enterprises should pay particular attention to the rating and attempt to include additional options in issued bonds. Such additional options may comprise, for example, an ability to exchange bonds to shares or raw materials.

Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

PubMed

Sahadevan, S; Hofmann-Apitius, M; Schellander, K; Tesfaye, D; Fluck, J; Friedrich, C M

2012-10-01

In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from cattle and pigs. For this purpose, the rather noncomprehensive resources of pig and cattle gene and protein terminologies were enriched with orthologue synonyms, integrated in the NER platform, ProMiner, which is successfully used in human genomics domain. Based on the performance tests done, the present system achieved a fair performance with precision 0.64, recall 0.74, and F(1) measure of 0.69 in a test scenario based on cattle literature.

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.

Reduced-order model for underwater target identification using proper orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ramesh, Sai Sudha; Lim, Kian Meng

2017-03-01

Research on underwater acoustics has seen major development over the past decade due to its widespread applications in domains such as underwater communication/navigation (SONAR), seismic exploration and oceanography. In particular, acoustic signatures from partially or fully buried targets can be used in the identification of buried mines for mine counter measures (MCM). Although there exist several techniques to identify target properties based on SONAR images and acoustic signatures, these methods first employ a feature extraction method to represent the dominant characteristics of a data set, followed by the use of an appropriate classifier based on neural networks or the relevance vector machine. The aim of the present study is to demonstrate the applications of proper orthogonal decomposition (POD) technique in capturing dominant features of a set of scattered pressure signals, and subsequent use of the POD modes and coefficients in the identification of partially buried underwater target parameters such as its location, size and material density. Several numerical examples are presented to demonstrate the performance of the system identification method based on POD. Although the present study is based on 2D acoustic model, the method can be easily extended to 3D models and thereby enables cost-effective representations of large-scale data.

North American Bats and Mines Project: A cooperative approach for integrating bat conservation and mine-land reclamation

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

Ducummon, S.L.

Inactive underground mines now provide essential habitat for more than half of North America`s 44 bat species, including some of the largest remaining populations. Thousands of abandoned mines have already been closed or are slated for safety closures, and many are destroyed during renewed mining in historic districts. The available evidence suggests that millions of bats have already been lost due to these closures. Bats are primary predators of night-flying insects that cost American farmers and foresters billions of dollars annually, therefore, threats to bat survival are cause for serious concern. Fortunately, mine closure methods exist that protect both batsmore » and humans. Bat Conservation International (BCI) and the USDI-Bureau of Land Management founded the North American Bats and Mines Project to provide national leadership and coordination to minimize the loss of mine-roosting bats. This partnership has involved federal and state mine-land and wildlife managers and the mining industry. BCI has trained hundreds of mine-land and wildlife managers nationwide in mine assessment techniques for bats and bat-compatible closure methods, published technical information on bats and mine-land management, presented papers on bats and mines at national mining and wildlife conferences, and collaborated with numerous federal, state, and private partners to protect some of the most important mine-roosting bat populations. Our new mining industry initiative, Mining for Habitat, is designed to develop bat habitat conservation and enhancement plans for active mining operations. It includes the creation of cost-effective artificial underground bat roosts using surplus mining materials such as old mine-truck tires and culverts buried beneath waste rock.« less

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.

PubMed

Chen, Xiaoyi; Faviez, Carole; Schuck, Stéphane; Lillo-Le-Louët, Agnès; Texier, Nathalie; Dahamna, Badisse; Huot, Charles; Foulquié, Pierre; Pereira, Suzanne; Leroux, Vincent; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Katsahian, Sandrine; Bousquet, Cédric; Burgun, Anita

2018-01-01

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation , but also Side effects . Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Method for Assessing the Integrated Risk of Soil Pollution in Industrial and Mining Gathering Areas

PubMed Central

Guan, Yang; Shao, Chaofeng; Gu, Qingbao; Ju, Meiting; Zhang, Qian

2015-01-01

Industrial and mining activities are recognized as major sources of soil pollution. This study proposes an index system for evaluating the inherent risk level of polluting factories and introduces an integrated risk assessment method based on human health risk. As a case study, the health risk, polluting factories and integrated risks were analyzed in a typical industrial and mining gathering area in China, namely, Binhai New Area. The spatial distribution of the risk level was determined using a Geographic Information System. The results confirmed the following: (1) Human health risk in the study area is moderate to extreme, with heavy metals posing the greatest threat; (2) Polluting factories pose a moderate to extreme inherent risk in the study area. Such factories are concentrated in industrial and urban areas, but are irregularly distributed and also occupy agricultural land, showing a lack of proper planning and management; (3) The integrated risks of soil are moderate to high in the study area. PMID:26580644

Relative extraction ratio (RER) for arsenic and heavy metals in soils and tailings from various metal mines, Korea.

PubMed

Son, Hye Ok; Jung, Myung Chae

2011-01-01

This study focused on the evaluation of leaching behaviours for arsenic and heavy metals (Cd, Cu, Ni, Pb and Zn) in soils and tailings contaminated by mining activities. Ten representative mine soils were taken at four representative metal mines in Korea. To evaluate the leaching characteristics of the samples, eight extraction methods were adapted namely 0.1 M HCl, 0.5 M HCl, 1.0 M HCl, 3.0 M HCl, Korean Standard Leaching Procedure for waste materials (KSLP), Synthetic Precipitation Leaching Procedure (SPLP), Toxicity Characteristic Leaching Procedure (TCLP) and aqua regia extraction (AR) methods. In order to compare element concentrations as extraction methods, relative extraction ratios (RERs, %), defined as element concentration extracted by the individual leaching method divided by that extracted by aqua regia based on USEPA method 3050B, were calculated. Although the RER values can vary upon sample types and elements, they increase with increasing ionic strength of each extracting solution. Thus, the RER for arsenic and heavy metals in the samples increased in the order of KSLP < SPLP < TCLP < 0.1 M HCl < 0.5 M HCl < 1.0 M HCl < 3.0 M HCl. In the same extraction method, the RER values for Cd and Zn were relatively higher than those for As, Cu, Ni and Pb. This may be due to differences in geochemical behaviour of each element, namely high solubility of Cd and Zn and low solubility of As, Cu, Ni and Pb in surface environment. Thus, the extraction results can give important information on the degree and extent of arsenic and heavy metal dispersion in the surface environment.

Cost estimates and economic evaluations for conceptual LLRW disposal facility designs

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

Baird, R.D.; Chau, N.; Breeds, C.D.

1995-12-31

Total life-cycle costs were estimated in support of the New York LLRW Siting Commission`s project to select a disposal method from four near-surface LLRW disposal methods (namely, uncovered above-grade vaults, covered above-grade vaults, below-grade vaults, and augered holes) and two mined methods (namely, vertical shaft mines and drift mines). Conceptual designs for the disposal methods were prepared and used as the basis for the cost estimates. Typical economic performance of each disposal method was assessed. Life-cycle costs expressed in 1994 dollars ranged from $ 1,100 million (for below-grade vaults and both mined disposal methods) to $2,000 million (for augered holes).more » Present values ranged from $620 million (for below-grade vaults) to $ 1,100 million (for augered holes).« less

Combined mine tremors source location and error evaluation in the Lubin Copper Mine (Poland)

NASA Astrophysics Data System (ADS)

Leśniak, Andrzej; Pszczoła, Grzegorz

2008-08-01

A modified method of mine tremors location used in Lubin Copper Mine is presented in the paper. In mines where an intensive exploration is carried out a high accuracy source location technique is usually required. The effect of the flatness of the geophones array, complex geological structure of the rock mass and intense exploitation make the location results ambiguous in such mines. In the present paper an effective method of source location and location's error evaluations are presented, combining data from two different arrays of geophones. The first consists of uniaxial geophones spaced in the whole mine area. The second is installed in one of the mining panels and consists of triaxial geophones. The usage of the data obtained from triaxial geophones allows to increase the hypocenter vertical coordinate precision. The presented two-step location procedure combines standard location methods: P-waves directions and P-waves arrival times. Using computer simulations the efficiency of the created algorithm was tested. The designed algorithm is fully non-linear and was tested on the multilayered rock mass model of the Lubin Copper Mine, showing a computational better efficiency than the traditional P-wave arrival times location algorithm. In this paper we present the complete procedure that effectively solves the non-linear location problems, i.e. the mine tremor location and measurement of the error propagation.

Lunar surface mine feasibility study

NASA Astrophysics Data System (ADS)

Blair, Brad R.

This paper describes a lunar surface mine, and demonstrates the economic feasibility of mining oxygen from the moon. The mine will be at the Apollo 16 landing site. Mine design issues include pit size and shape, excavation equipment, muck transport, and processing requirements. The final mine design will be driven by production requirements, and constrained by the lunar environment. This mining scenario assumes the presence of an operating lunar base. Lunar base personnel will set-up a and run the mine. The goal of producing lunar oxygen is to reduce dependence on fuel shipped from Earth. Thus, the lunar base is the customer for the finished product. The perspective of this paper is that of a mining contractor who must produce a specific product at a remote location, pay local labor, and sell the product to an onsite captive market. To make a profit, it must be less costly to build and ship specialized equipment to the site, and pay high labor and operating costs, than to export the product directly to the site.

Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis

USDA-ARS?s Scientific Manuscript database

Objective: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. Methods: The conditional inference tree analysis, a data mining approach, was used to con...

Enriching semantic knowledge bases for opinion mining in big data applications

PubMed Central

Weichselbraun, A.; Gindl, S.; Scharl, A.

2014-01-01

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process. PMID:25431524

Pessimistic Determination of Mechanical Conditions and Micro/macroeconomic Evaluation of Mine Pillar Replacement

NASA Astrophysics Data System (ADS)

Chen, Qingfa; Zhao, Fuyu

2017-12-01

Numerous pillars are left after mining of underground mineral resources using the open stope method or after the first step of the partial filling method. The mineral recovery rate can, however, be improved by replacement recovery of pillars. In the present study, the relationships among the pillar type, minimum pillar width, and micro/macroeconomic factors were investigated from two perspectives, namely mechanical stability and micro/macroeconomic benefit. Based on the mechanical stability formulas for ore and artificial pillars, the minimum width for a specific pillar type was determined using a pessimistic criterion. The microeconomic benefit c of setting an ore pillar, the microeconomic benefit w of artificial pillar replacement, and the economic net present value (ENPV) of the replacement process were calculated. The values of c and w were compared with respect to ENPV, based on which the appropriate pillar type and economical benefit were determined.

30 CFR 75.215 - Longwall mining systems.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Longwall mining systems. 75.215 Section 75.215... MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.215 Longwall mining systems. For each longwall mining section, the roof control plan shall specify— (a) The methods that will be used to maintain...

30 CFR 75.215 - Longwall mining systems.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Longwall mining systems. 75.215 Section 75.215... MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.215 Longwall mining systems. For each longwall mining section, the roof control plan shall specify— (a) The methods that will be used to maintain...

Identification of ex-sand mining area using optical and SAR imagery

NASA Astrophysics Data System (ADS)

Indriasari, Novie; Kusratmoko, Eko; Indra, Tito Latif; Julzarika, Atriyon

2018-05-01

Open mining activities in Sumedang Regency has been operated since 1984 impacted to degradation of environment due to large area of ex-mining. Therefore, identification of ex-mining area which generally been used for sand mining is crucial and important to detect and monitor recent environmental degradation impacted from the ex-mining activities. In this research, identification ex-sand mining area using optical and SAR data in Sumedang Regency will be discussed. We use Landsat 5 TM acquisition date August 01, 2009 and Landsat 8 OLI acquired on June 24, 2016 to identify location of sand mining area, processed using Tasselled Cap Trasformation (TCT), while the landform deformation approached using ALOS PALSAR in 2009 and ALOS PALSAR 2 in 2016 processed using SAR interferometry (InSAR) method. The results show that TCT and InSAR method can can be used to identify the areas of ex-sand mining clearly. In 2016 the total area of ex-mining were 352.92 Ha. The land deformation show that during 7 years period since 2009 has impacted to the deformation at 7 meters.

40 CFR 440.50 - Applicability; description of the titanium ore subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) mills beneficiating titanium ores by electrostatic methods, magnetic and physical methods, or flotation methods; and (c) mines engaged in the dredge mining of placer deposits of sands containing rutile... methods in conjunction with electrostatic or magnetic methods). ...

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.

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

Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

NASA Astrophysics Data System (ADS)

Cavuoti, Stefano; Brescia, Massimo; Longo, Giuseppe

2014-05-01

In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

PubMed

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more detailed information on flow and chemical behaviour (dissolved sulphate concentrations, remaining mass of solid sulphate) in the network. Nevertheless, both modelling methods require hydrological and chemical parameters (recharge flow rate, outflows, volume of mine voids, mass of solids, kinetic constants of the dissolution-precipitation reactions), which are commonly not available for a mine and therefore call for calibration data.

Experience of creating a multifunctional safety system at the coal mining enterprise

NASA Astrophysics Data System (ADS)

Reshetnikov, V. V.; Davkaev, K. S.; Korolkov, M. V.; Lyakhovets, M. V.

2018-05-01

The principles of creating multifunctional safety systems (MFSS) based on mathematical models with Markov properties are considered. The applicability of such models for the analysis of the safety of the created systems and their effectiveness is substantiated. The method of this analysis and the results of its testing are discussed. The variant of IFSB implementation in the conditions of the operating coal-mining enterprise is given. The functional scheme, data scheme and operating modes of the MFSS are given. The automated workplace of the industrial safety controller is described.

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets

PubMed Central

Aubry, Marc; Monnier, Annabelle; Chicault, Celine; de Tayrac, Marie; Galibert, Marie-Dominique; Burgun, Anita; Mosser, Jean

2006-01-01

Background Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). Results We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). Conclusion The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions. PMID:16674810

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.

Data mining applications in the context of casemix.

PubMed

Koh, H C; Leong, S K

2001-07-01

In October 1999, the Singapore Government introduced casemix-based funding to public hospitals. The casemix approach to health care funding is expected to yield significant benefits, including equity and rationality in financing health care, the use of comparative casemix data for quality improvement activities, and the provision of information that enables hospitals to understand their cost behaviour and reinforces the drive for more cost-efficient services. However, there is some concern about the "quicker and sicker" syndrome (that is, the rapid discharge of patients with little regard for the quality of outcome). As it is likely that consequences of premature discharges will be reflected in the readmission data, an analysis of possible systematic patterns in readmission data can provide useful insight into the "quicker and sicker" syndrome. This paper explores potential data mining applications in the context of casemix by using readmission data as an illustration. In particular, it illustrates how data mining can be used to better understand readmission data and to detect systematic patterns, if any. From a technical perspective, data mining (which is capable of analysing complex non-linear and interaction relationships) supplements and complements traditional statistical methods in data analysis. From an applications perspective, data mining provides the technology and methodology to analyse mass volume of data to detect hidden patterns in data. Using readmission data as an illustrative data mining application, this paper explores potential data mining applications in the general casemix context.

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

PubMed

Rollins, Derrick K; Teh, Ailing

2010-12-17

Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (PM) effectively identified critical genes in these studies based on comparison with the current method (CM). The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

A software tool for determination of breast cancer treatment methods using data mining approach.

PubMed

Cakır, Abdülkadir; Demirel, Burçin

2011-12-01

In this work, breast cancer treatment methods are determined using data mining. For this purpose, software is developed to help to oncology doctor for the suggestion of application of the treatment methods about breast cancer patients. 462 breast cancer patient data, obtained from Ankara Oncology Hospital, are used to determine treatment methods for new patients. This dataset is processed with Weka data mining tool. Classification algorithms are applied one by one for this dataset and results are compared to find proper treatment method. Developed software program called as "Treatment Assistant" uses different algorithms (IB1, Multilayer Perception and Decision Table) to find out which one is giving better result for each attribute to predict and by using Java Net beans interface. Treatment methods are determined for the post surgical operation of breast cancer patients using this developed software tool. At modeling step of data mining process, different Weka algorithms are used for output attributes. For hormonotherapy output IB1, for tamoxifen and radiotherapy outputs Multilayer Perceptron and for the chemotherapy output decision table algorithm shows best accuracy performance compare to each other. In conclusion, this work shows that data mining approach can be a useful tool for medical applications particularly at the treatment decision step. Data mining helps to the doctor to decide in a short time.

A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks

ERIC Educational Resources Information Center

Anaya, Antonio R.; Boticario, Jesus G.

2009-01-01

Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…

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

Large-Scale Constraint-Based Pattern Mining

ERIC Educational Resources Information Center

Zhu, Feida

2009-01-01

We studied the problem of constraint-based pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these patterns. For item-set data, we observed robustness of…

Preliminary study of detection of buried landmines using a programmable hyperspectral imager

NASA Astrophysics Data System (ADS)

McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.

1996-05-01

Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.

Fission-track dating applied to mineral exploration

USGS Publications Warehouse

Naeser, C.W.

1984-01-01

The partial to total resetting of fission-track ages of minerals in country rock near a mineralized area can be used to (1) locate a thermal anomaly, and (2) date the mineralizing event. Two mining districts in Colorado have been studied - Rico and Gilman. Rico is a precious- and base-metal mining district. Initial fission-track dating of a sill located about 6 km from the center of the district gave ages of 20 Myr and 65 Myr for apatite and zircon, respectively. The Eagle Mine in the Gilman District is the largest producer of zinc in the state of Colorado. Fission-track dating of zircon from a 70 Myr-old sill shows partial resetting of the zircon (45 Myr). The thermal anomaly identified by fission-track dating is seen in both districts far outside the area affected by obvious alteration. Based on the results of these two pilot studies, fission-track dating can be a useful exploration method for thermal anomalies associated with buried or otherwise poorly expressed mineral deposits.

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.

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.

Analytical Fingerprint of Wolframite Ore Concentrates.

PubMed

Gäbler, Hans-Eike; Schink, Wilhelm; Goldmann, Simon; Bahr, Andreas; Gawronski, Timo

2017-07-01

Ongoing violent conflicts in Central Africa are fueled by illegal mining and trading of tantalum, tin, and tungsten ores. The credibility of document-based traceability systems can be improved by an analytical fingerprint applied as an independent method to confirm or doubt the documented origin of ore minerals. Wolframite (Fe,Mn)WO 4 is the most important ore mineral for tungsten and is subject to artisanal mining in Central Africa. Element concentrations of wolframite grains analyzed by laser ablation-inductively coupled plasma-mass spectrometry are used to establish the analytical fingerprint. The data from ore concentrate samples are multivariate, not normal or log-normal distributed. The samples cannot be regarded as representative aliquots of a population. Based on the Kolmogorov-Smirnov distance, a measure of similarity between a sample in question and reference samples from a database is determined. A decision criterion is deduced to recognize samples which do not originate from the declared mine site. © 2017 American Academy of Forensic Sciences.

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

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches.

PubMed

Zounemat-Kermani, Mohammad; Ramezani-Charmahineh, Abdollah; Adamowski, Jan; Kisi, Ozgur

2018-06-13

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

Parametric analysis of the biomechanical response of head subjected to the primary blast loading--a data mining approach.

PubMed

Zhu, Feng; Kalra, Anil; Saif, Tal; Yang, Zaihan; Yang, King H; King, Albert I

2016-01-01

Traumatic brain injury due to primary blast loading has become a signature injury in recent military conflicts and terrorist activities. Extensive experimental and computational investigations have been conducted to study the interrelationships between intracranial pressure response and intrinsic or 'input' parameters such as the head geometry and loading conditions. However, these relationships are very complicated and are usually implicit and 'hidden' in a large amount of simulation/test data. In this study, a data mining method is proposed to explore such underlying information from the numerical simulation results. The heads of different species are described as a highly simplified two-part (skull and brain) finite element model with varying geometric parameters. The parameters considered include peak incident pressure, skull thickness, brain radius and snout length. Their interrelationship and coupling effect are discovered by developing a decision tree based on the large simulation data-set. The results show that the proposed data-driven method is superior to the conventional linear regression method and is comparable to the nonlinear regression method. Considering its capability of exploring implicit information and the relatively simple relationships between response and input variables, the data mining method is considered to be a good tool for an in-depth understanding of the mechanisms of blast-induced brain injury. As a general method, this approach can also be applied to other nonlinear complex biomechanical systems.

Determination of Particular Endogenous Fires Hazard Zones in Goaf with Caving of Longwall

NASA Astrophysics Data System (ADS)

Tutak, Magdalena; Brodny, Jaroslaw

2017-12-01

Hazard of endogenous fires is one of the basic and common presented occupational safety hazards in coal mine in Poland and in the world. This hazard means possibility of coal self-ignition as the result of its self-heating process in mining heading or its surrounding. In underground coal-mining during ventilating of operating longwalls takes place migration of parts of airflow to goaf with caving. In a case when in these goaf a coal susceptible to selfignition occurs, then the airflow through these goaf may influence on formation of favourable conditions for coal oxidation and subsequently to its self-heating and self-ignition. Endogenous fire formed in such conditions can pose a serious hazard for the crew and for continuity of operation of mining plant. From the practical point of view, a very significant meaning has determination of the zone in the goaf with caving, in which necessary conditions for occurrence of endogenous fire are fulfilled. In the real conditions determination of such a zone is practically impossible. Therefore, authors of paper developed a methodology of determination of this zone basing on the results of modelling tests. This methodology includes a development of model of tested area, determination of boundary conditions and carrying out the simulation calculations. Based on the obtained results particular hazardous zone of endogenous fire is determined. A base for development of model of investigated region and selection of boundary conditions are the results of real tests. In the paper fundamental assumption of developed methodology, particularly in a range of assumed hazard criterion and sealing coefficient of goaf with caving were discussed. Also a mathematical model of gas flow through the porous media was characterized. Example of determination of a zone particularly endangered by endogenous fire for real system of mining heading in one of the hard coal mine was presented. Longwall ventilated in the „Y” system was subjected to the tests. For determined mining-geological conditions, the critical value of velocity of airflow and oxygen concentration in goaf, conditioning initiation of coal oxidation process were determined. For calculations ANSYS Fluent software based on finite volume method, which enable very precisely to determine the physical and chemical air and parameters at any point of tested mining heading and goaf with caving was used. Such precisely determination of these parameters on the base of the test in real conditions is practically impossible. Obtained results allowed to take early proper actions in order to limit the occurrence of endogenous fire. One can conclude, that presented methodology creates great possibilities of practical application of modelling tests for improvement of the occupational safety state in mine.

International SUSMIN-project aims at sustainable gold mining in EU

NASA Astrophysics Data System (ADS)

Backnäs, Soile; Neitola, Raisa; Turunen, Kaisa; Lima, Alexandre; Fiúza, António; Szlachta, Malgorzata; Wójtowicz, Patryk; Maftei, Raluca; Munteanu, Marian; Alakangas, Lena; Baciu, Calin; Fernández, Dámaris

2015-04-01

Although the gold demand has been constantly increasing in past years, the commodity findings have been decreasing and the extraction of gold has complicated due to increasing complexity and decreasing grade of the ores. Additionally, even gold mining could increase economical development, it has also challenges in eco-efficiency and extraction methods (e.g. cyanide). Thus, the novel energy and resource-efficient methods and technologies for mineral processing should be developed to concentrate selectively different gold bearing minerals. Furthermore, technologies for efficient treatment of mine waters, sustainable management of wastes, and methods to diminish environmental and social impacts of mining are needed. These problems will be addressed by the three year long project SUSMIN. The SUSMIN-project identifies and evaluates environmental impacts and economical challenges of gold mining within EU. The objective of the project is to increase the transnational cooperation and to support environmentally, socially and economically sustainable viable gold production. The focus is to develop and test geophysical techniques for gold exploration, eco-efficient ore beneficiation methods and alternatives for cyanide leaching. Additionally, the research will improve treatment methods for mine waters by the development and testing of advanced adsorbents. The research on socio-economic issues pursues to develop tools for enhancing the mechanisms of the corporate social responsibility as well as community engagement and management of the relations with the stakeholders. Moreover, with the environmental risk assessment and better knowledge of the geochemistry and long-term transformation of the contaminants in mining wastes and mine waters, the mining companies are able to predict and prevent the impacts to the surrounding environment, resulting in an improved environmental management solution. The SUSMIN consortium led by Geological Survey of Finland (GTK) includes seven research partners from six EU member states Finland, Sweden, Portugal, Romania, Poland and Ireland. Additionally eight globally on mining industry working industry partners will contribute in the SUSMIN consortium, so implementation of results from the project will translate into direct and significant economic benefits.

Analysis, Mining and Visualization Service at NCSA

NASA Astrophysics Data System (ADS)

Wilhelmson, R.; Cox, D.; Welge, M.

2004-12-01

NCSA's goal is to create a balanced system that fully supports high-end computing as well as: 1) high-end data management and analysis; 2) visualization of massive, highly complex data collections; 3) large databases; 4) geographically distributed Grid computing; and 5) collaboratories, all based on a secure computational environment and driven with workflow-based services. To this end NCSA has defined a new technology path that includes the integration and provision of cyberservices in support of data analysis, mining, and visualization. NCSA has begun to develop and apply a data mining system-NCSA Data-to-Knowledge (D2K)-in conjunction with both the application and research communities. NCSA D2K will enable the formation of model-based application workflows and visual programming interfaces for rapid data analysis. The Java-based D2K framework, which integrates analytical data mining methods with data management, data transformation, and information visualization tools, will be configurable from the cyberservices (web and grid services, tools, ..) viewpoint to solve a wide range of important data mining problems. This effort will use modules, such as a new classification methods for the detection of high-risk geoscience events, and existing D2K data management, machine learning, and information visualization modules. A D2K cyberservices interface will be developed to seamlessly connect client applications with remote back-end D2K servers, providing computational resources for data mining and integration with local or remote data stores. This work is being coordinated with SDSC's data and services efforts. The new NCSA Visualization embedded workflow environment (NVIEW) will be integrated with D2K functionality to tightly couple informatics and scientific visualization with the data analysis and management services. Visualization services will access and filter disparate data sources, simplifying tasks such as fusing related data from distinct sources into a coherent visual representation. This approach enables collaboration among geographically dispersed researchers via portals and front-end clients, and the coupling with data management services enables recording associations among datasets and building annotation systems into visualization tools and portals, giving scientists a persistent, shareable, virtual lab notebook. To facilitate provision of these cyberservices to the national community, NCSA will be providing a computational environment for large-scale data assimilation, analysis, mining, and visualization. This will be initially implemented on the new 512 processor shared memory SGI's recently purchased by NCSA. In addition to standard batch capabilities, NCSA will provide on-demand capabilities for those projects requiring rapid response (e.g., development of severe weather, earthquake events) for decision makers. It will also be used for non-sequential interactive analysis of data sets where it is important have access to large data volumes over space and time.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Method for Location of An External Dump in Surface Mining Using the A-Star Algorithm

NASA Astrophysics Data System (ADS)

Zajączkowski, Maciej; Kasztelewicz, Zbigniew; Sikora, Mateusz

2014-10-01

The construction of a surface mine always involves the necessity of accessing deposits through the removal of the residual overburden above. In the beginning phase of exploitation, the masses of overburden are located outside the perimeters of the excavation site, on the external dump, until the moment of internal dumping. In the case of lignite surface mines, these dumps can cover a ground surface of several dozen to a few thousand hectares. This results from a high concentration of lignite extraction, counted in millions of Mg per year, and the relatively large depth of its residual deposits. Determining the best place for the location of an external dump requires a detailed analysis of existing options, followed by a choice of the most favorable one. This article, using the case study of an open-cast lignite mine, presents the selection method for an external dump location based on graph theory and the A-star algorithm. This algorithm, based on the spatial distribution of individual intersections on the graph, seeks specified graph states, continually expanding them with additional elementary fields until the required surface area for the external dump - defined by the lowest value of the occupied site - is achieved. To do this, it is necessary to accurately identify the factors affecting the choice of dump location. On such a basis, it is then possible to specify the target function, which reflects the individual costs of dump construction on a given site. This is discussed further in chapter 3. The area of potential dump location has been divided into elementary fields, each represented by a corresponding geometrical locus. Ascribed to this locus, in addition to its geodesic coordinates, are the appropriate attributes reflecting the degree of development of its elementary field. These tasks can be carried out automatically thanks to the integration of the method with the system of geospatial data management for the given area. The collection of loci, together with geodesic coordinates, constitutes the points on the graph used during exploration. This is done using the A-star algorithm, which uses a heuristic function, allowing it to identify the optimal solution; therefore, the collection of elementary fields, which occupy the potential construction area of a dump, characterized by the lowest value representing the cost of occupation and dumping of overburden in the area. The precision of the boundary, generated by the algorithm, is dependent on the established size of the elementary field, and should be refined each time by the designer of the surface mine. This article presents the application of the above method of dump location using the example of "Tomisławice," a lignite surface mine owned by PAK KWB Konin S. A. The method made it possible to identify the most favorable dump location on the northeast side of the initial pit, within 2 kilometers of its surrounding area (discussed further in chapter 3). This method is universal in nature and, after certain modifications, can be implemented for other surface mines as well.

Cell-based Metabolomics for Assessing Chemical Exposure and Toxicity of Environmental Surface Waters

EPA Science Inventory

Waste water treatment plants (WWTPs), concentrated animal feeding operations (CAFOs), mining activities, and agricultural operations release contaminants that negatively affect surface water quality. Traditional methods using live animals/fish to monitor/assess contaminant exposu...

CoBOP: Electro-Optic Identification Laser Line Sean Sensors

DTIC Science & Technology

1998-01-01

Electro - Optic Identification Sensors Project[1] is to develop and demonstrate high resolution underwater electro - optic (EO) imaging sensors, and associated image processing/analysis methods, for rapid visual identification of mines and mine-like contacts (MLCs). Identification of MLCs is a pressing Fleet need. During MCM operations, sonar contacts are classified as mine-like if they are sufficiently similar to signatures of mines. Each contact classified as mine-like must be identified as a mine or not a mine. During MCM operations in littoral areas,

Model for detection and assessment of abiotic stress caused by uranium mining in European Black Pine landscapes

NASA Astrophysics Data System (ADS)

Filchev, Lachezar; Roumenina, Eugenia

2013-10-01

The article presents the results obtained from a study for detection and assessment of abiotic stress through pollution with heavy metals, metalloids, and natural radionuclides in European Black Pine (Pinus nigra L.) forests caused by uranium mining using ground-based biogeochemical, biophysical, and field spectrometry data. The forests are located on a territory subject to underground and open uranium mining. An operational model of the study is proposed. The areas subject to technogeochemical load are outlined based on the aggregate pollution index Zc. Laboratory and field spectrometry data were used to detect the signals of abiotic stress at pixel level. The methods used for determination of stressed and unstressed black pine forests are: four vegetation indices (TCARI, MCARI, MTVI 2, and PRI 1) for stress detection, and the position, depth, asymmetry, and shift of the red-edge. Based on the "blue shift" and the depth and position of the red-edge, registered by the laboratory analysis and field spectral reflectance, it is established that coniferous forests subject to abiotic stress show an increase in total chlorophyll content and carotene. It has been found that the vegetation indices MTVI 2 and PRI 1, as well as the combination of vegetation indices and pigments may be used as a direct indicator of abiotic stress in coniferous forests caused by uranium mining.

Geomechanical Assessments of Simultaneous Operation in the Case of Transition from Open Pit to Underground Mine in Vietnam

NASA Astrophysics Data System (ADS)

Niedbalski, Zbigniew; Nguyen, Phu Minh Vuong; Widzyk-Capehart, Eleonora

2018-03-01

Nowadays, for a number of reasons, many open pit mines are considering a transition from Open Pit (OP) to Underground (UG) to remain competitive. In OP-UG transition, UG operation is operated simultaneously with the OP operation for a certain period of time. Guidelines for the simultaneous operation of OP and UG are very difficult to establish, as there are very few case studies available. Yet, because of the OP-UG interactions; the operation has a higher safety, technical and management requirements than the OP or UG methods when considered separately. In Vietnam, Cao Son is one of many OP mines, which decided to change the operational system from OP to UG. Simultaneous operation started in 2015 and will be conducted until 2030 when the OP mine Cao Son ends its mining activities. In this paper, selected geomechanical considerations of the simultaneous operation are presented. A number of numerical modelling calculations using finitedifference software with code FLAC were carried out for calibration process, slope stability analysis and the OP-UG interaction analysis for the Cao Son - Khe Cham II-IV mine. Based on the results obtained from numerical modelling, the geomechanical assessments of simultaneous operation Cao Son - Khe Cham II-IV are discussed in this paper.

Using association rule mining to identify risk factors for early childhood caries.

PubMed

Ivančević, Vladimir; Tušek, Ivan; Tušek, Jasmina; Knežević, Marko; Elheshk, Salaheddin; Luković, Ivan

2015-11-01

Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Bačka area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An IPSO-SVM algorithm for security state prediction of mine production logistics system

NASA Astrophysics Data System (ADS)

Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

2017-06-01

A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

LANDSAT inventory of surface-mined areas using extendible digital techniques

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schultz, D. T.; Buchman, N.

1975-01-01

Multispectral LANDSAT imagery was analyzed to provide a rapid and accurate means of identification, classification, and measurement of strip-mined surfaces in Western Maryland. Four band analysis allows distinction of a variety of strip-mine associated classes, but has limited extendibility. A method for surface area measurements of strip mines, which is both geographically and temporally extendible, has been developed using band-ratioed LANDSAT reflectance data. The accuracy of area measurement by this method, averaged over three LANDSAT scenes taken between September 1972 and July 1974, is greater than 93%. Total affected acreage of large (50 hectare/124 acre) mines can be measured to within 1.0%.

Common Criteria related security design patterns--validation on the intelligent sensor example designed for mine environment.

PubMed

Bialas, Andrzej

2010-01-01

The paper discusses the security issues of intelligent sensors that are able to measure and process data and communicate with other information technology (IT) devices or systems. Such sensors are often used in high risk applications. To improve their robustness, the sensor systems should be developed in a restricted way to provide them with assurance. One of assurance creation methodologies is Common Criteria (ISO/IEC 15408), used for IT products and systems. The contribution of the paper is a Common Criteria compliant and pattern-based method for the intelligent sensors security development. The paper concisely presents this method and its evaluation for the sensor detecting methane in a mine, focusing on the security problem of the intelligent sensor definition and solution. The aim of the validation is to evaluate and improve the introduced method.

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.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.

PubMed

Akay, Altug; Dragomir, Andrei; Erlandsson, Björn-Erik

2015-01-01

A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.

Automated Analysis of Renewable Energy Datasets ('EE/RE Data Mining')

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

Bush, Brian; Elmore, Ryan; Getman, Dan

This poster illustrates methods to substantially improve the understanding of renewable energy data sets and the depth and efficiency of their analysis through the application of statistical learning methods ('data mining') in the intelligent processing of these often large and messy information sources. The six examples apply methods for anomaly detection, data cleansing, and pattern mining to time-series data (measurements from metering points in buildings) and spatiotemporal data (renewable energy resource datasets).

Numerical linear algebra in data mining

NASA Astrophysics Data System (ADS)

Eldén, Lars

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

PubMed Central

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2015-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods. PMID:25938136

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

PubMed

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems

ERIC Educational Resources Information Center

Romero, Cristobal; Ventura, Sebastian; Zafra, Amelia; de Bra, Paul

2009-01-01

Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in…

Features of the Asynchronous Correlation between the China Coal Price Index and Coal Mining Accidental Deaths

PubMed Central

Huang, Yuecheng; Cheng, Wuyi; Luo, Sida; Luo, Yun; Ma, Chengchen; He, Tailin

2016-01-01

The features of the asynchronous correlation between accident indices and the factors that influence accidents can provide an effective reference for warnings of coal mining accidents. However, what are the features of this correlation? To answer this question, data from the China coal price index and the number of deaths from coal mining accidents were selected as the sample data. The fluctuation modes of the asynchronous correlation between the two data sets were defined according to the asynchronous correlation coefficients, symbolization, and sliding windows. We then built several directed and weighted network models, within which the fluctuation modes and the transformations between modes were represented by nodes and edges. Then, the features of the asynchronous correlation between these two variables could be studied from a perspective of network topology. We found that the correlation between the price index and the accidental deaths was asynchronous and fluctuating. Certain aspects, such as the key fluctuation modes, the subgroups characteristics, the transmission medium, the periodicity and transmission path length in the network, were analyzed by using complex network theory, analytical methods and spectral analysis method. These results provide a scientific reference for generating warnings for coal mining accidents based on economic indices. PMID:27902748

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Mines Systems Safety Improvement Using an Integrated Event Tree and Fault Tree Analysis

NASA Astrophysics Data System (ADS)

Kumar, Ranjan; Ghosh, Achyuta Krishna

2017-04-01

Mines systems such as ventilation system, strata support system, flame proof safety equipment, are exposed to dynamic operational conditions such as stress, humidity, dust, temperature, etc., and safety improvement of such systems can be done preferably during planning and design stage. However, the existing safety analysis methods do not handle the accident initiation and progression of mine systems explicitly. To bridge this gap, this paper presents an integrated Event Tree (ET) and Fault Tree (FT) approach for safety analysis and improvement of mine systems design. This approach includes ET and FT modeling coupled with redundancy allocation technique. In this method, a concept of top hazard probability is introduced for identifying system failure probability and redundancy is allocated to the system either at component or system level. A case study on mine methane explosion safety with two initiating events is performed. The results demonstrate that the presented method can reveal the accident scenarios and improve the safety of complex mine systems simultaneously.

StemTextSearch: Stem cell gene database with evidence from abstracts.

PubMed

Chen, Chou-Cheng; Ho, Chung-Liang

2017-05-01

Previous studies have used many methods to find biomarkers in stem cells, including text mining, experimental data and image storage. However, no text-mining methods have yet been developed which can identify whether a gene plays a positive or negative role in stem cells. StemTextSearch identifies the role of a gene in stem cells by using a text-mining method to find combinations of gene regulation, stem-cell regulation and cell processes in the same sentences of biomedical abstracts. The dataset includes 5797 genes, with 1534 genes having positive roles in stem cells, 1335 genes having negative roles, 1654 genes with both positive and negative roles, and 1274 with an uncertain role. The precision of gene role in StemTextSearch is 0.66, and the recall is 0.78. StemTextSearch is a web-based engine with queries that specify (i) gene, (ii) category of stem cell, (iii) gene role, (iv) gene regulation, (v) cell process, (vi) stem-cell regulation, and (vii) species. StemTextSearch is available through http://bio.yungyun.com.tw/StemTextSearch.aspx. Copyright © 2017. Published by Elsevier Inc.

Novel methods for detecting buried explosive devices

NASA Astrophysics Data System (ADS)

Kercel, Stephen W.; Burlage, Robert S.; Patek, David R.; Smith, Cyrus M.; Hibbs, Andrew D.; Rayner, Timothy J.

1997-07-01

Oak Ridge National Laboratory and Quantum Magnetics, Inc. are exploring novel landmine detection technologies. Technologies considered here include bioreporter bacteria, swept acoustic resonance, nuclear quadrupole resonance (NQR), and semiotic data fusion. Bioreporter bacteria look promising for third-world humanitarian applications; they are inexpensive, and deployment does not require high-tech methods. Swept acoustic resonance may be a useful adjunct to magnetometers in humanitarian demining. For military demining, NQR is a promising method for detecting explosive substances; of 50,000 substances that have been tested, one has an NQR signature that can be mistaken for RDX or TNT. For both military and commercial demining, sensor fusion entails two daunting tasks, identifying fusible features in both present-day and emerging technologies, and devising a fusion algorithm that runs in real-time on cheap hardware. Preliminary research in these areas is encouraging. A bioreporter bacterium for TNT detection is under development. Investigation has just started in swept acoustic resonance as an approach to a cheap mine detector for humanitarian use. Real-time wavelet processing appears to be a key to extending NQR bomb detection into mine detection, including TNT-based mines. Recent discoveries in semiotics may be the breakthrough that will lead to a robust fused detection scheme.

Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity

PubMed Central

Jia, Yi; Huan, Jun; Buhr, Vincent; Zhang, Jintao; Carayannopoulos, Leonidas N

2009-01-01

Background Automatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the "twilight-" or "midnight-" zones where pair-wise sequence identities to known sequences fall below 25% and sequence-based functional annotations often fail. Results Here we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification. The biologic motivation of our study is to recognize common structure patterns in "immunoevasins", proteins mediating virus evasion of host immune defense. Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method. Conclusion We present a theoretic framework, offer a practical software implementation for incorporating prior domain knowledge, such as substitution matrices as studied here, and devise an efficient algorithm to identify approximate matched frequent subgraphs. By doing so, we significantly expanded the analytical power of sophisticated data mining algorithms in dealing with large volume of complicated and noisy protein structure data. And without loss of generality, choice of appropriate compatibility matrices allows our method to be easily employed in domains where subgraph labels have some uncertainty. PMID:19208148

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Validation of an association rule mining-based method to infer associations between medications and problems.

PubMed

Wright, A; McCoy, A; Henkin, S; Flaherty, M; Sittig, D

2013-01-01

In a prior study, we developed methods for automatically identifying associations between medications and problems using association rule mining on a large clinical data warehouse and validated these methods at a single site which used a self-developed electronic health record. To demonstrate the generalizability of these methods by validating them at an external site. We received data on medications and problems for 263,597 patients from the University of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice that uses the Allscripts Enterprise commercial electronic health record product. We then conducted association rule mining to identify associated pairs of medications and problems and characterized these associations with five measures of interestingness: support, confidence, chi-square, interest and conviction and compared the top-ranked pairs to a gold standard. 25,088 medication-problem pairs were identified that exceeded our confidence and support thresholds. An analysis of the top 500 pairs according to each measure of interestingness showed a high degree of accuracy for highly-ranked pairs. The same technique was successfully employed at the University of Texas and accuracy was comparable to our previous results. Top associations included many medications that are highly specific for a particular problem as well as a large number of common, accurate medication-problem pairs that reflect practice patterns.

Stratified sampling design based on data mining.

PubMed

Kim, Yeonkook J; Oh, Yoonhwan; Park, Sunghoon; Cho, Sungzoon; Park, Hayoung

2013-09-01

To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers' profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea.

Synthesis of engineering designs of drilling facilities

NASA Astrophysics Data System (ADS)

Porozhsky, K.

2018-03-01

The article sets forth key principles of engineering of drilling equipment based on successive analysis of the goals of the production method, technologies of its implementation and conditions of mineral mining using a new approach to systematization of drilling methods. Potential advancement in the technologies and equipment of drilling is illustrated in terms of oil-well drilling.

A Method of Measuring the Costs and Benefits of Applied Research.

ERIC Educational Resources Information Center

Sprague, John W.

The Bureau of Mines studied the application of the concepts and methods of cost-benefit analysis to the problem of ranking alternative applied research projects. Procedures for measuring the different classes of project costs and benefits, both private and public, are outlined, and cost-benefit calculations are presented, based on the criteria of…

76 FR 20940 - Troy Mine, Incorporated, Troy Mine Revised Reclamation Plan, Kootenai National Forest, Lincoln...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-14

... mine. The ore is mined using the ``room-and-pillar method.'' The mine permit area covers 2,782 acres of.... Production stopped in 1993 and reinitiated in 2005 and is projected to continue for 3-5 years until the... evaluations necessary to complete design of reclamation elements that include a short-term water management...

Mercury contamination from historical gold mining in California

USGS Publications Warehouse

Alpers, Charles N.; Hunerlach, Michael P.; May, Jason T.; Hothem, Roger L.

2005-01-01

Mercury contamination from historical gold mines represents a potential risk to human health and the environment. This fact sheet provides background information on the use of mercury in historical gold mining and processing operations in California, with emphasis on historical hydraulic mining areas. It also describes results of recent USGS projects that address the potential risks associated with mercury contamination. Miners used mercury (quicksilver) to recover gold throughout the western United States. Gold deposits were either hardrock (lode, gold-quartz veins) or placer (alluvial, unconsolidated gravels). Underground methods (adits and shafts) were used to mine hardrock gold deposits. Hydraulic, drift, or dredging methods were used to mine the placer gold deposits. Mercury was used to enhance gold recovery in all the various types of mining operations; historical records indicate that more mercury was used and lost at hydraulic mines than at other types of mines. On the basis of USGS studies and other recent work, a better understanding is emerging of mercury distribution, ongoing transport, transformation processes, and the extent of biological uptake in areas affected by historical gold mining. This information has been used extensively by federal, state, and local agencies responsible for resource management and public health in California.

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.

Mine wastes and human health

USGS Publications Warehouse

Plumlee, Geoffrey S.; Morman, Suzette A.

2011-01-01

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

30 CFR 77.1000 - Highwalls, pits and spoil banks; plans.

Code of Federal Regulations, 2010 CFR

2010-07-01

... safe working conditions. The mining methods employed by the operator shall be selected to insure... Section 77.1000 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND...

43 CFR 3930.12 - Performance standards for underground mining.

Code of Federal Regulations, 2014 CFR

2014-10-01

... reserves. (c) Operators/lessees must adopt measures consistent with known technology to prevent or, where the mining method used requires subsidence, control subsidence, maximize mine stability, and maintain... temporarily abandon a mine or portions thereof. (e) The operator/lessee must have the BLM's prior approval to...

43 CFR 3930.12 - Performance standards for underground mining.

Code of Federal Regulations, 2013 CFR

2013-10-01

... reserves. (c) Operators/lessees must adopt measures consistent with known technology to prevent or, where the mining method used requires subsidence, control subsidence, maximize mine stability, and maintain... temporarily abandon a mine or portions thereof. (e) The operator/lessee must have the BLM's prior approval to...

43 CFR 3930.12 - Performance standards for underground mining.

Code of Federal Regulations, 2012 CFR

2012-10-01

... reserves. (c) Operators/lessees must adopt measures consistent with known technology to prevent or, where the mining method used requires subsidence, control subsidence, maximize mine stability, and maintain... temporarily abandon a mine or portions thereof. (e) The operator/lessee must have the BLM's prior approval to...

43 CFR 3930.12 - Performance standards for underground mining.

Code of Federal Regulations, 2011 CFR

2011-10-01

... reserves. (c) Operators/lessees must adopt measures consistent with known technology to prevent or, where the mining method used requires subsidence, control subsidence, maximize mine stability, and maintain... temporarily abandon a mine or portions thereof. (e) The operator/lessee must have the BLM's prior approval to...

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,...

Do post-mining constructed channels replace functional characteristics of headwater streams?

EPA Science Inventory

Mountaintop mining and valley fill (MTMVF) is a method of coal mining common in eastern Kentucky and southern West Virginia. Over 1200 miles of stream channel have been buried by MTMVF. Permits for surface coal mining have recognized constructed drainage ditches associated with ...

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation.

PubMed

Arjunan, Satya Nanda Vel; Tomita, Masaru

2010-03-01

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium Escherichia coli, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the in vivo MinDE localization dynamics by accounting for the previously reported properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally. The online version of this article (doi:10.1007/s11693-009-9047-2) contains supplementary material, which is available to authorized users.

Monitoring and inversion on land subsidence over mining area with InSAR technique

USGS Publications Warehouse

Wang, Y.; Zhang, Q.; Zhao, C.; Lu, Z.; Ding, X.

2011-01-01

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism. ?? 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Cell-based metabolomics for assessing chemical exposure and toxicity of environmental surface waters (presentation)

EPA Science Inventory

Introduction: Waste water treatment plants (WWTPs), concentrated animal feeding operations (CAFOs), mining activities, and agricultural operations release contaminants that negatively affect surface water quality. Traditional methods using live animals (e.g. fish) to monitor/as...

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.

Source Pulse Estimation of Mine Shock by Blind Deconvolution

NASA Astrophysics Data System (ADS)

Makowski, R.

The objective of seismic signal deconvolution is to extract from the signal information concerning the rockmass or the signal in the source of the shock. In the case of blind deconvolution, we have to extract information regarding both quantities. Many methods of deconvolution made use of in prospective seismology were found to be of minor utility when applied to shock-induced signals recorded in the mines of the Lubin Copper District. The lack of effectiveness should be attributed to the inadequacy of the model on which the methods are based, with respect to the propagation conditions for that type of signal. Each of the blind deconvolution methods involves a number of assumptions; hence, only if these assumptions are fulfilled, we may expect reliable results.Consequently, we had to formulate a different model for the signals recorded in the copper mines of the Lubin District. The model is based on the following assumptions: (1) The signal emitted by the sh ock source is a short-term signal. (2) The signal transmitting system (rockmass) constitutes a parallel connection of elementary systems. (3) The elementary systems are of resonant type. Such a model seems to be justified by the geological structure as well as by the positions of the shock foci and seismometers. The results of time-frequency transformation also support the dominance of resonant-type propagation.Making use of the model, a new method for the blind deconvolution of seismic signals has been proposed. The adequacy of the new model, as well as the efficiency of the proposed method, has been confirmed by the results of blind deconvolution. The slight approximation errors obtained with a small number of approximating elements additionally corroborate the adequacy of the model.

Calculation of parameters of technological equipment for deep-sea mining

NASA Astrophysics Data System (ADS)

Yungmeister, D. A.; Ivanov, S. E.; Isaev, A. I.

2018-03-01

The actual problem of extracting minerals from the bottom of the world ocean is considered. On the ocean floor, three types of minerals are of interest: iron-manganese concretions (IMC), cobalt-manganese crusts (CMC) and sulphides. The analysis of known designs of machines and complexes for the extraction of IMC is performed. These machines are based on the principle of excavating the bottom surface; however such methods do not always correspond to “gentle” methods of mining. The ecological purity of such mining methods does not meet the necessary requirements. Such machines require the transmission of high electric power through the water column, which in some cases is a significant challenge. The authors analyzed the options of transportation of the extracted mineral from the bottom. The paper describes the design of machines that collect IMC by the method of vacuum suction. In this method, the gripping plates or drums are provided with cavities in which a vacuum is created and individual IMC are attracted to the devices by a pressure drop. The work of such machines can be called “gentle” processing technology of the bottom areas. Their environmental impact is significantly lower than mechanical devices that carry out the raking of IMC. The parameters of the device for lifting the IMC collected on the bottom are calculated. With the use of Kevlar ropes of serial production up to 0.06 meters in diameter, with a cycle time of up to 2 hours and a lifting speed of up to 3 meters per second, a productivity of about 400,000 tons per year can be realized for IMC. The development of machines based on the calculated parameters and approbation of their designs will create a unique complex for the extraction of minerals at oceanic deposits.

Design pattern mining using distributed learning automata and DNA sequence alignment.

PubMed

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

3D Modeling of Landslide in Open-pit Mining on Basis of Ground-based LIDAR Data

NASA Astrophysics Data System (ADS)

Hu, H.; Fernandez-Steeger, T. M.; Azzam, R.; Arnhardt, C.

2009-04-01

Slope stability is not only an important problem which is related to production and safety in open-pit mining, but also very complex task. There are three main reasons which affect the slope stability as follows: geotechnical factors: Geological structure, lithologic characteristics, water, cohesion, friction, etc.; climate factors: Rainfall and temperature; and external factors: Open-pit mining process, explosion vibration, dynamic load, etc.. The 3rd reason, as a specially one in open-pit mining, not only causes some dynamic problems but also induces the fast geometry changing which must be considered in the following research using numerical simulation and stability analysis. Recently, LIDAR technology has been applied in many fields and places in the world wide. Ground-based LIDAR technology with high accuracy up to 3mm increasingly accommodates to monitoring landslides and detecting changing. LIDAR data collection and preprocessing research have been carried out by Department of Engineering Geology and Hydrogeology at RWTH Aachen University. LIDAR data, so-called a point-cloud of mass data in high density can be obtained in short time for the sensitive open-pit mining area by using ground-based LIDAR. To obtain a consistent surface model, it is necessary to set up multiple scans with the ground-based LIDAR. The framework of data preprocessing which can be implemented by Poly-Works is introduced as follows: gross error detection and elimination, integration of reference frame, model fusion of different scans (re-sampled in overlap region), data reduction without removing the useful information which is a challenge and research front in LIDAR data processing. After data preprocessing, 3D surface model can be directly generated in Poly-Works or generated in other software by building the triangular meshes. The 3D surface landslide model can be applied to further researches such as: real time landslide geometry monitoring due to the fast data collection and processing; change detecting by means of overlying different periods of topographic or geometric data; FEM (Finite Element Method) numerical simulation on basis of combining with the geotechnical properties and parameters to analyze slope stability and predict future movements for designing and rectifying the open-pit mining process; using the reverse engineering thought for developing constitutive models. An improved 3D surface model (HRDEM) which is based on fast data collection and precise data processing on basis of ground-based LIDAR technology is important contribution for further researches of slope stability in open-pit mining area.

Hydro-economic modelling in mining catchments

NASA Astrophysics Data System (ADS)

Ossa Moreno, J. S.; McIntyre, N.; Rivera, D.; Smart, J. C. R.

2017-12-01

Hydro-economic models are gaining momentum because of their capacity to model both the physical processes related to water supply, and socio-economic factors determining water demand. This is particularly valuable in the midst of the large uncertainty upon future climate conditions and social trends. Agriculture, urban uses and environmental flows have received a lot of attention from researchers, as these tend to be the main consumers of water in most catchments. Mine water demand, although very important in several small and medium-sized catchments worldwide, has received less attention and only few models have attempted to reproduce its dynamics with other users. This paper describes an on-going project that addresses this gap, by developing a hydro-economic model in the upper Aconcagua River in Chile. This is a mountain catchment with large scale mining and hydro-power users at high altitudes, and irrigation areas in a downstream valley. Relevant obstacles to the model included the lack of input climate data, which is a common feature in several mining areas, the complex hydrological processes in the area and the difficulty of quantifying the value of water used by mines. A semi-distributed model developed within the Water Evaluation and Planning System (WEAP), was calibrated to reproduce water supply, and this was complemented with an analysis of the value of water for mining based on two methods; water markets and an analysis of its production processes. Agriculture and other users were included through methods commonly used in similar models. The outputs help understanding the value of water in the catchment, and its sensitivity to changes in climate variables, market prices, environmental regulations and changes in the production of minerals, crops and energy. The results of the project highlight the importance of merging hydrology and socio-economic calculations in mining regions, in order to better understand trade-offs and cost of opportunity of using water for an economic activity with high revenues, averse to water risks and with potentially large catchment impacts.

Meta Data Mining in Earth Remote Sensing Data Archives

NASA Astrophysics Data System (ADS)

Davis, B.; Steinwand, D.

2014-12-01

Modern search and discovery tools for satellite based remote sensing data are often catalog based and rely on query systems which use scene- (or granule-) based meta data for those queries. While these traditional catalog systems are often robust, very little has been done in the way of meta data mining to aid in the search and discovery process. The recently coined term "Big Data" can be applied in the remote sensing world's efforts to derive information from the vast data holdings of satellite based land remote sensing data. Large catalog-based search and discovery systems such as the United States Geological Survey's Earth Explorer system and the NASA Earth Observing System Data and Information System's Reverb-ECHO system provide comprehensive access to these data holdings, but do little to expose the underlying scene-based meta data. These catalog-based systems are extremely flexible, but are manually intensive and often require a high level of user expertise. Exposing scene-based meta data to external, web-based services can enable machine-driven queries to aid in the search and discovery process. Furthermore, services which expose additional scene-based content data (such as product quality information) are now available and can provide a "deeper look" into remote sensing data archives too large for efficient manual search methods. This presentation shows examples of the mining of Landsat and Aster scene-based meta data, and an experimental service using OPeNDAP to extract information from quality band from multiple granules in the MODIS archive.

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

PubMed

Bekhuis, Tanja

2006-04-03

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.

Commercial Online Social Network Data and Statin Side-Effect Surveillance: A Pilot Observational Study of Aggregate Mentions on Facebook.

PubMed

Huesch, Marco D

2017-12-01

Surveillance of the safety of prescribed drugs after marketing approval has been secured remains fraught with complications. Formal ascertainment by providers and reporting to adverse-event registries, formal surveys by manufacturers, and mining of electronic medical records are all well-known approaches with varying degrees of difficulty, cost, and success. Novel approaches may be a useful adjunct, especially approaches that mine or sample internet-based methods such as online social networks. A novel commercial software-as-a-service data-mining product supplied by Sysomos from Datasift/Facebook was used to mine all mentions on Facebook of statins and stain-related side effects in the US in the 1-month period 9 January 2017 through 8 February 2017. A total of 4.3% of all 25,700 mentions of statins also mentioned typical stain-related side effects. Multiple methodological weaknesses stymie interpretation of this percentage, which is however not inconsistent with estimates that 5-20% of patients taking statins will experience typical side effects at some time. Future work on pharmacovigilance may be informed by this novel commercial tool, but the inability to mine the full text of a posting poses serious challenges to content categorization.

Toward Accountable Discrimination-Aware Data Mining: The Importance of Keeping the Human in the Loop-and Under the Looking Glass.

PubMed

Berendt, Bettina; Preibusch, Sören

2017-06-01

"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. We describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. We derive data protection by design strategies for making decision-making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors.

Integration of MOOCs in Advanced Mining Training Programmes

NASA Astrophysics Data System (ADS)

Saveleva, Irina; Greenwald, Oksana; Kolomiets, Svetlana; Medvedeva, Elena

2017-11-01

The paper covers the concept of innovative approaches in education based on incorporating MOOCs options into traditional classroom. It takes a look at the ways higher education instructors working with the mining engineers enrolled in advanced training programmes can brighten, upgrade and facilitate the learning process. The shift of higher education from in-class to online format has changed the learning environment and the methods of teaching including professional retraining courses. In addition, the need of mining companies for managers of a new kind obligates high school retraining centres rapidly move towards the 21st century skill framework. One of widely recognized innovations in the sphere of e-learning is MOOCs (Massive Open Online Courses) that can be used as an effective teaching tool for organizing professional training of managing staff of mining companies within the walls of a university. The authors share their instructional experience and show the benefits of introducing MOOCs options at the courses designed for retraining mining engineers and senior managers of coal enterprises. Though in recent researches the pedagogical value of MOOCs is highly questioned and even negated this invention of the 21st century can become an essential and truly helpful instrument in the hands of educators.

[Analysis of on medication rules for Qi-deficiency and blood-stasis syndrome of chronic heart failure based on data mining technology].

PubMed

Wang, Qian; Yao, Geng-Zhen; Pan, Guang-Ming; Huang, Jing-Yi; An, Yi-Pei; Zou, Xu

2017-01-01

To analyze the medication features and the regularity of prescriptions of traditional Chinese medicine in treating patients with Qi-deficiency and blood-stasis syndrome of chronic heart failure based on modern literature. In this article, CNKI Chinese academic journal database, Wanfang Chinese academic journal database and VIP Chinese periodical database were all searched from January 2000 to December 2015 for the relevant literature on traditional Chinese medicine treatment for Qi-deficiency and blood-stasis syndrome of chronic heart failure. Then a normalized database was established for further data mining and analysis. Subsequently, the medication features and the regularity of prescriptions were mined by using traditional Chinese medicine inheritance support system(V2.5), association rules, improved mutual information algorithm, complex system entropy clustering and other mining methods. Finally, a total of 171 articles were included, involving 171 prescriptions, 140 kinds of herbs, with a total frequency of 1 772 for the herbs. As a result, 19 core prescriptions and 7 new prescriptions were mined. The most frequently used herbs included Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma), Fuling(Poria), Renshen(Ginseng Radix et Rhizoma), Tinglizi(Semen Lepidii), Baizhu(Atractylodis Macrocephalae Rhizoma), and Guizhi(Cinnamomum Ramulus). The core prescriptions were composed of Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma) and Fuling(Poria), etc. The high frequent herbs and core prescriptions not only highlight the medication features of Qi-invigorating and blood-circulating therapy, but also reflect the regularity of prescriptions of blood-circulating, Yang-warming, and urination-promoting therapy based on syndrome differentiation. Moreover, the mining of the new prescriptions provide new reference and inspiration for clinical treatment of various accompanying symptoms of chronic heart failure. In conclusion, this article provides new reference for traditional Chinese medicine in the treatment of chronic heart failure. Copyright© by the Chinese Pharmaceutical Association.

Guidelines for geophysical investigations of mines under highways mine research project-GUE 70-14.10, PID no. 18459.

DOT National Transportation Integrated Search

2003-06-01

It is estimated that approximately 8,500 abandoned underground mines are present in Ohio and mine-related : subsidence has been a problem dating back to the 1920's. Many investigative methods have been utilized with : varying degrees of success in an...

Proceedings: Fourth Workshop on Mining Scientific Datasets

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

Kamath, C

Commercial applications of data mining in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in the JCDD community. However, there is a significant amount of innovative data mining work taking place in the context of scientific and engineering applications that is not well represented in the mainstream KDD conferences. For example, scientific data mining techniques are being developed and applied to diverse fields such as remote sensing, physics, chemistry, biology, astronomy, structural mechanics, computational fluid dynamics etc. In these areas, data mining frequently complements and enhances existing analysis methods based on statistics, exploratorymore » data analysis, and domain-specific approaches. On the surface, it may appear that data from one scientific field, say genomics, is very different from another field, such as physics. However, despite their diversity, there is much that is common across the mining of scientific and engineering data. For example, techniques used to identify objects in images are very similar, regardless of whether the images came from a remote sensing application, a physics experiment, an astronomy observation, or a medical study. Further, with data mining being applied to new types of data, such as mesh data from scientific simulations, there is the opportunity to apply and extend data mining to new scientific domains. This one-day workshop brings together data miners analyzing science data and scientists from diverse fields to share their experiences, learn how techniques developed in one field can be applied in another, and better understand some of the newer techniques being developed in the KDD community. This is the fourth workshop on the topic of Mining Scientific Data sets; for information on earlier workshops, see http://www.ahpcrc.org/conferences/. This workshop continues the tradition of addressing challenging problems in a field where the diversity of applications is matched only by the opportunities that await a practitioner.« less

Method for Determining the Coalbed Methane Content with Determination the Uncertainty of Measurements

NASA Astrophysics Data System (ADS)

Szlązak, Nikodem; Korzec, Marek

2016-06-01

Methane has a bad influence on safety in underground mines as it is emitted to the air during mining works. Appropriate identification of methane hazard is essential to determining methane hazard prevention methods, ventilation systems and methane drainage systems. Methane hazard is identified while roadways are driven and boreholes are drilled. Coalbed methane content is one of the parameters which is used to assess this threat. This is a requirement according to the Decree of the Minister of Economy dated 28 June 2002 on work safety and hygiene, operation and special firefighting protection in underground mines. For this purpose a new method for determining coalbed methane content in underground coal mines has been developed. This method consists of two stages - collecting samples in a mine and testing the sample in the laboratory. The stage of determining methane content in a coal sample in a laboratory is essential. This article presents the estimation of measurement uncertainty of determining methane content in a coal sample according to this methodology.

Use of fly-ash slurry in backfill grouting in coal mines.

PubMed

Jiang, Ning; Zhao, Jinhai; Sun, Xizhen; Bai, Liyang; Wang, Changxiang

2017-11-01

Cave backfill grouting implies grouting of the caving rock mass prior to it being compacted. The filling materials strengthen the caving rock and support the overlying strata to achieve the purpose of slowing down the surface subsidence. The broken roof will fail and collapse during mining operations performed without appropriate supporting measures being taken. It is difficult to perform continuous backfill mining on the working face of such roofs using the existing mining technology. In order to solve the above problems, fly ash and mine water are considered as filling materials, and flow characteristics of fly-ash slurry are investigated through laboratory experiments and theoretical analyses. Laws governing the diffusion of fly-ash slurry in the void of caving rock masses and in the void between a caving rock mass and a basic roof are obtained and verified. Based on the results obtained from the above analyses and actual conditions at the Zhaoguan coal mine, Shandong Province, China, a cave backfill grouting system of the hauling pipeline is developed and successfully tested at the 1703 working face in the Zhaoguan coal mine. The results demonstrate that a filling rate of 43.46% is achieved, and the surface subsidence coefficient of the grouting process is found to be 0.475. Compared to the total caving method, the proposed system is found to achieve a reduction rate of 40.63%. This effectively helps in lowering the value of the surface subsidence coefficient. Fly ash and mine water, considered as primary materials in this study, also play a significant role in improving the air quality and water environment.

Research on parallel algorithm for sequential pattern mining

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Qin, Bai; Wang, Yu; Hao, Zhongxiao

2008-03-01

Sequential pattern mining is the mining of frequent sequences related to time or other orders from the sequence database. Its initial motivation is to discover the laws of customer purchasing in a time section by finding the frequent sequences. In recent years, sequential pattern mining has become an important direction of data mining, and its application field has not been confined to the business database and has extended to new data sources such as Web and advanced science fields such as DNA analysis. The data of sequential pattern mining has characteristics as follows: mass data amount and distributed storage. Most existing sequential pattern mining algorithms haven't considered the above-mentioned characteristics synthetically. According to the traits mentioned above and combining the parallel theory, this paper puts forward a new distributed parallel algorithm SPP(Sequential Pattern Parallel). The algorithm abides by the principal of pattern reduction and utilizes the divide-and-conquer strategy for parallelization. The first parallel task is to construct frequent item sets applying frequent concept and search space partition theory and the second task is to structure frequent sequences using the depth-first search method at each processor. The algorithm only needs to access the database twice and doesn't generate the candidated sequences, which abates the access time and improves the mining efficiency. Based on the random data generation procedure and different information structure designed, this paper simulated the SPP algorithm in a concrete parallel environment and implemented the AprioriAll algorithm. The experiments demonstrate that compared with AprioriAll, the SPP algorithm had excellent speedup factor and efficiency.

Development and application of the Safe Performance Index as a risk-based methodology for identifying major hazard-related safety issues in underground coal mines

NASA Astrophysics Data System (ADS)

Kinilakodi, Harisha

The underground coal mining industry has been under constant watch due to the high risk involved in its activities, and scrutiny increased because of the disasters that occurred in 2006-07. In the aftermath of the incidents, the U.S. Congress passed the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which strengthened the existing regulations and mandated new laws to address the various issues related to a safe working environment in the mines. Risk analysis in any form should be done on a regular basis to tackle the possibility of unwanted major hazard-related events such as explosions, outbursts, airbursts, inundations, spontaneous combustion, and roof fall instabilities. One of the responses by the Mine Safety and Health Administration (MSHA) in 2007 involved a new pattern of violations (POV) process to target mines with a poor safety performance, specifically to improve their safety. However, the 2010 disaster (worst in 40 years) gave an impression that the collective effort of the industry, federal/state agencies, and researchers to achieve the goal of zero fatalities and serious injuries has gone awry. The Safe Performance Index (SPI) methodology developed in this research is a straight-forward, effective, transparent, and reproducible approach that can help in identifying and addressing some of the existing issues while targeting (poor safety performance) mines which need help. It combines three injury and three citation measures that are scaled to have an equal mean (5.0) in a balanced way with proportionate weighting factors (0.05, 0.15, 0.30) and overall normalizing factor (15) into a mine safety performance evaluation tool. It can be used to assess the relative safety-related risk of mines, including by mine-size category. Using 2008 and 2009 data, comparisons were made of SPI-associated, normalized safety performance measures across mine-size categories, with emphasis on small-mine safety performance as compared to large- and medium-sized mines. The accident rates (NDL IR, NFDL IR, SM/100) of very small and small mines in 2008 and 2009 were less than those of medium and large mines. The data indicates a heavy occurrence of very severe injuries in a number of very small and small mines. In another application which is a part of this research, the six normalized safety measures and the SPI are used to evaluate the risk that existed at mines in the two years preceding the occurrence of a fatality. This mine safety performance tracking method could have been helpful to the companies, state agency, or MSHA in recognizing and addressing emerging problems with actions that may have been able to prevent high-risk conditions, the fatality, and/or other serious injuries. The approach would have given scrutiny to the risk of mines that encompassed 74% of the fatalities during 2007-2010. In order to assess the SPI as a comparable risk measurement tool, a traditional risk approach is also developed using data embracing frequency and severity in the final equation to analyze the relative risk for all underground coal mines for the years 2007--2010. Then, the SPI is compared with this traditional risk analysis method to demonstrate that the results attained by either method provide the relative safety-related risk of underground coal mines regarding injuries and citations for violations of regulations. The comparison reveals that the SPI does emulate a traditional approach to risk analysis. A correlation coefficient of --0.89 or more was observed between the results of these two methodologies and either can be used to assist companies, the Mine Safety and Health Administration (MSHA), or state agencies in target-ing mines with high risk for serious injuries and elevated citations for remediation of their injury and/or violation experience. The SPI, however, provides a more understandable approach for mine operators to apply using measures compatible with MSHA's enforcement tools. These methodologies form an all-encompassing approach that can be used to assist companies, the MSHA, or state agencies in targeting mines with high risk for serious injuries and elevated citations. Once targeted as high risk, mines can then pursue appropriate intervention to remediate their violation and/or injury experience. This research may help in plugging the gap in the safety system and better pursue the goal of zero fatalities and serious injuries in the underground coal mines.

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems.

PubMed

Mao, Yingchi; Zhong, Haishi; Xiao, Xianjian; Li, Xiaofang

2017-03-06

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment-based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

NASA Astrophysics Data System (ADS)

Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

2017-11-01

Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

Integration of Text- and Data-Mining Technologies for Use in Banking Applications

NASA Astrophysics Data System (ADS)

Maslankowski, Jacek

Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it's not always easy to find, access, analyze or use (Robb 2004). That is why it is important to use solutions based on text and data mining. This solution is known as duo mining. This leads to improve management based on knowledge owned in organization. The results are interesting. Data mining provides to lead with structuralized data, usually powered from data warehouses. Text mining, sometimes called web mining, looks for patterns in unstructured data — memos, document and www. Integrating text-based information with structured data enriches predictive modeling capabilities and provides new stores of insightful and valuable information for driving business and research initiatives forward.

Using text mining for study identification in systematic reviews: a systematic review of current approaches.

PubMed

O'Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

2015-01-14

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities. Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings. The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall). Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.

Mercury Pollution from Small-Scale Gold Mining Can Be Stopped by Implementing the Gravity-Borax Method--A Two-Year Follow-Up Study from Two Mining Communities in the Philippines.

PubMed

Køster-Rasmussen, Rasmus; Westergaard, Maria L; Brasholt, Marie; Gutierrez, Richard; Jørs, Erik; Thomsen, Jane F

2016-02-01

Mercury is used globally to extract gold in artisanal and small-scale gold mining. The mercury-free gravity-borax method for gold extraction was introduced in two mining communities using mercury in the provinces Kalinga and Camarines Norte. This article describes project activities and quantitative changes in mercury consumption and analyzes the implementation with diffusion of innovations theory. Activities included miner-to-miner training; seminars for health-care workers, school teachers, and children; and involvement of community leaders. Baseline (2011) and follow-up (2013) data were gathered on mining practices and knowledge about mercury toxicology. Most miners in Kalinga converted to the gravity-borax method, whereas only a few did so in Camarines Norte. Differences in the nature of the social systems impacted the success of the implementation, and involvement of the tribal organization facilitated the shift in Kalinga. In conclusion, the gravity-borax method is a doable alternative to mercury use in artisanal and small-scale gold mining, but support from the civil society is needed. © The Author(s) 2016.

A simplified economic filter for open-pit mining and heap-leach recovery of copper in the United States

USGS Publications Warehouse

Long, Keith R.; Singer, Donald A.

2001-01-01

Determining the economic viability of mineral deposits of various sizes and grades is a critical task in all phases of mineral supply, from land-use management to mine development. This study evaluates two simple tools for estimating the economic viability of porphyry copper deposits mined by open-pit, heap-leach methods when only limited information on these deposits is available. These two methods are useful for evaluating deposits that either (1) are undiscovered deposits predicted by a mineral resource assessment, or (2) have been discovered but for which little data has been collected or released. The first tool uses ordinary least-squared regression analysis of cost and operating data from selected deposits to estimate a predictive relationship between mining rate, itself estimated from deposit size, and capital and operating costs. The second method uses cost models developed by the U.S. Bureau of Mines (Camm, 1991) updated using appropriate cost indices. We find that the cost model method works best for estimating capital costs and the empirical model works best for estimating operating costs for mines to be developed in the United States.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

THE MARY KATHLEEN URANIUM PROJECT

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

Nelson, A.

1960-02-01

A description is given of uranium mining and milling methods at the Mary Kathleen Mine in the Cloncurry-Mt. Isa district of Queensland, Australia. The discovery of this property and its development are outlined. The deposit cecurs in highly altered meta-sediments in the corella beds of lower proterozoic age. Because of the considerable internal waste in the deposit, it was necessary to devise a selective mining method which would keep dilution to the lowest possible level. The mining, haulage and handling, premilling program, drilling, and blasting are discussed. (M.C.G.)

WHAT INNOVATIVE APPROACHES CAN BE DEVELOPED FOR MINING SITES?

EPA Science Inventory

Mining is essential to maintain our way of life. However, based upon industry's reporting in the most recent Toxic Release Inventory (TRI), the primary sources of heavy metal releases to the environment are mining and mining related activities. The hard rock mining industry rel...

Educational Data Mining and Problem-Based Learning

ERIC Educational Resources Information Center

Walldén, Sari; Mäkinen, Erkki

2014-01-01

This paper considers the use of log data provided by learning management systems when studying whether students obey the problem-based learning (PBL) method. Log analysis turns out to be a valuable tool in measuring the use of the learning material of interest. It gives reliable figures concerning not only the number of use sessions but also the…