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

PubMed

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

2016-01-01

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

Microbial communities associated with uranium in-situ recovery mining process are related to acid mine drainage assemblages.

PubMed

Coral, Thomas; Descostes, Michaël; De Boissezon, Hélène; Bernier-Latmani, Rizlan; de Alencastro, Luiz Felippe; Rossi, Pierre

2018-07-01

A large fraction (47%) of the world's uranium is mined by a technique called "In Situ Recovery" (ISR). This mining technique involves the injection of a leaching fluid (acidic or alkaline) into a uranium-bearing aquifer and the pumping of the resulting solution through cation exchange columns for the recovery of dissolved uranium. The present study reports the in-depth alterations brought to autochthonous microbial communities during acidic ISR activities. Water samples were collected from a uranium roll-front deposit that is part of an ISR mine in operation (Tortkuduk, Kazakhstan). Water samples were obtained at a depth of ca 500 m below ground level from several zones of the Uyuk aquifer following the natural redox zonation inherited from the roll front deposit, including the native mineralized orebody and both upstream and downstream adjacent locations. Samples were collected equally from both the entrance and the exit of the uranium concentration plant. Next-generation sequencing data showed that the redox gradient shaped the community structures, within the anaerobic, reduced, and oligotrophic habitats of the native aquifer zones. Acid injection induced drastic changes in the structures of these communities, with a large decrease in both cell numbers and diversity. Communities present in the acidified (pH values < 2) mining areas exhibited similarities to those present in acid mine drainage, with the dominance of Sulfobacillus sp., Leptospirillum sp. and Acidithiobacillus sp., as well as the archaean Ferroplasma sp. Communities located up- and downstream of the mineralized zone under ISR and affected by acidic fluids were blended with additional facultative anaerobic and acidophilic microorganisms. These mixed biomes may be suitable communities for the natural attenuation of ISR mining-affected subsurface through the reduction of metals and sulfate. Assessing the effect of acidification on the microbial community is critical to evaluating the potential for natural attenuation or active bioremediation strategies. Copyright © 2018 Elsevier B.V. All rights reserved.

Integration of Audit Data Analysis and Mining Techniques into Aide

DTIC Science & Technology

2006-07-01

results produced by the anomaly-detection subsystem. A successor system to NIDES, called EMERALD [35], currently under development at SRI, extends...to represent attack scenarios in a networked environment. eBayes of SRI’s Emerald uses Bayes net technology to analyze bursts of traffic [40...snmpget2, we have to resort to the TCP raw data (packets) to see what operations these connections performed. (E.g., long login trails in the PSSWD attack

CALL FOR ABSTRACTS FOR WORKSHOP ON MINING IMPACTED NATIVE AMERICAN LANDS 2003

EPA Science Inventory

This is a Call for Abstracts for a workshop 9/9-11/2003 in Reno, NV, to unite Tribal members and representatives, and other government officials to examine technical and policy issues related to historic, current, and future mining impacts on Native American Lands.

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.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

A New Heuristic Anonymization Technique for Privacy Preserved Datasets Publication on Cloud Computing

NASA Astrophysics Data System (ADS)

Aldeen Yousra, S.; Mazleena, Salleh

2018-05-01

Recent advancement in Information and Communication Technologies (ICT) demanded much of cloud services to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data information involves medical, census, voter registration, social network, and customer services. Primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfill the confidentiality concerns is Privacy Preserving Data Mining (PPDM). The PPDM service in Cloud Computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to accomplish the privacy requirements. However, the well-known privacy preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, I propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing on cloud. The advantages of K-anonymity, L-diversity and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results revealed the superiority and outperformance of the developed technique than K-anonymity, L-diversity, and (α, k)-anonymity measure.

Toward edge minability for role mining in bipartite networks

NASA Astrophysics Data System (ADS)

Dong, Lijun; Wang, Yi; Liu, Ran; Pi, Benjie; Wu, Liuyi

2016-11-01

Bipartite network models have been extensively used in information security to automatically generate role-based access control (RBAC) from dataset. This process is called role mining. However, not all the topologies of bipartite networks are suitable for role mining; some edges may even reduce the quality of role mining. This causes unnecessary time consumption as role mining is NP-hard. Therefore, to promote the quality of role mining results, the capability that an edge composes roles with other edges, called the minability of edge, needs to be identified. We tackle the problem from an angle of edge importance in complex networks; that is an edge easily covered by roles is considered to be more important. Based on this idea, the k-shell decomposition of complex networks is extended to reveal the different minability of edges. By this way, a bipartite network can be quickly purified by excluding the low-minability edges from role mining, and thus the quality of role mining can be effectively improved. Extensive experiments via the real-world datasets are conducted to confirm the above claims.

HARDROCK MINING 2002 CALL FOR ABSTRACTS

EPA Science Inventory

This flyer will announcement the Hardrock Mining 2002 Conference on May 7-9/2002 in Westminster, CO. This conference will provide participants with an opportunity to examine and discuss current and future environmental issues shaping the hardrock mining industry with emphasis on ...

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

Code of Federal Regulations, 2011 CFR

2011-10-01

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

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

Code of Federal Regulations, 2013 CFR

2013-10-01

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

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

Code of Federal Regulations, 2014 CFR

2014-10-01

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

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

Code of Federal Regulations, 2012 CFR

2012-10-01

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

Mining Land Subsidence Monitoring Using SENTINEL-1 SAR Data

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Prehospital Emergencies in Illegal Gold Mining Sites in French Guiana.

PubMed

Egmann, Gérald; Tattevin, Pierre; Palancade, Renaud; Nacher, Matthieu

2018-03-01

Illegal gold mining is flourishing in French Guiana, existing outside the law due to both the high cost of gold mining permits and the challenges of law enforcement within the Amazon forest. We report the characteristics of, and the medical responses to, medical emergencies in illegal gold mining sites. We performed a retrospective study of all medical emergencies reported from illegal gold mining sites to the centralized call office of SAMU 973 from 1998 through 2000 and from 2008 through 2010. According to the national health care system, any medical emergency within the territory is handled by the prehospital emergency medical service (SAMU 973), irrespective of the patients' legal status. Data were extracted from the SAMU 973 notebook registry (1998-2000) or the SAMU 973 computerized database (2008-2010) and werre collected using a standardized questionnaire. Of 71,932 calls for medical emergencies in French Guiana during the study periods, 340 (0.5%) originated from illegal gold mining sites. Of these, 196 (58%) led to medical evacuation by helicopter, whereas the overall rate of evacuation by helicopter after placing a call to SAMU 973 was only 4% (3020/71,932; P<0.0001 for comparison with illegal gold mining sites). Medical emergencies were classified as illness (48%, mostly infectious), trauma (44%, mostly weapon wounds), and miscellaneous (8%). Medical emergencies at illegal gold mining sites in the Amazon forest mostly include infectious diseases, followed by trauma, and often require medical evacuation by helicopter. Our study suggests that implementation of preventive medicine within gold mining sites, irrespective of their legal status, could be cost-effective and reduce morbidity. Copyright © 2017 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

Petascale Kinetic Simulations in Space Sciences: New Simulations and Data Discovery Techniques and Physics Results

NASA Astrophysics Data System (ADS)

Karimabadi, Homa

2012-03-01

Recent advances in simulation technology and hardware are enabling breakthrough science where many longstanding problems can now be addressed for the first time. In this talk, we focus on kinetic simulations of the Earth's magnetosphere and magnetic reconnection process which is the key mechanism that breaks the protective shield of the Earth's dipole field, allowing the solar wind to enter the Earth's magnetosphere. This leads to the so-called space weather where storms on the Sun can affect space-borne and ground-based technological systems on Earth. The talk will consist of three parts: (a) overview of a new multi-scale simulation technique where each computational grid is updated based on its own unique timestep, (b) Presentation of a new approach to data analysis that we refer to as Physics Mining which entails combining data mining and computer vision algorithms with scientific visualization to extract physics from the resulting massive data sets. (c) Presentation of several recent discoveries in studies of space plasmas including the role of vortex formation and resulting turbulence in magnetized plasmas.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

PubMed Central

Castañón–Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-01-01

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information. PMID:26633417

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

PubMed

Castañón-Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-12-02

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Mining Induced Displacement and Mental Health: A Call for Action

ERIC Educational Resources Information Center

Goessling, Kristen P.

2010-01-01

India is a country of unparalleled diversity within both the cultural and ecological spheres of life. This paper examines the author's experience exploring and inquiring into the mental health implications of mining and mining induced displacement within several Adivasi (tribal) communities in Andhra Pradesh, India. Through collaboration with…

Anonymizing 1:M microdata with high utility

PubMed Central

Gong, Qiyuan; Luo, Junzhou; Yang, Ming; Ni, Weiwei; Li, Xiao-Bai

2016-01-01

Preserving privacy and utility during data publishing and data mining is essential for individuals, data providers and researchers. However, studies in this area typically assume that one individual has only one record in a dataset, which is unrealistic in many applications. Having multiple records for an individual leads to new privacy leakages. We call such a dataset a 1:M dataset. In this paper, we propose a novel privacy model called (k, l)-diversity that addresses disclosure risks in 1:M data publishing. Based on this model, we develop an efficient algorithm named 1:M-Generalization to preserve privacy and data utility, and compare it with alternative approaches. Extensive experiments on real-world data show that our approach outperforms the state-of-the-art technique, in terms of data utility and computational cost. PMID:28603388

iADRs: towards online adverse drug reaction analysis.

PubMed

Lin, Wen-Yang; Li, He-Yi; Du, Jhih-Wei; Feng, Wen-Yu; Lo, Chiao-Feng; Soo, Von-Wun

2012-12-01

Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited pre-marketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

Web mining for topics defined by complex and precise predicates

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Sampathkumar, Sushma

2004-04-01

The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently for any given topic. Several new techniques have been proposed in the recent years for this kind of topic specific web-mining, and among them a key new technique called focused crawling which is able to crawl topic-specific portions of the web without having to explore all pages. Most existing research on focused crawling considers a simple topic definition that typically consists of one or more keywords connected by an OR operator. However this kind of simple topic definition may result in too many irrelevant pages in which the same keyword appears in a wrong context. In this research we explore new strategies for crawling topic specific portions of the web using complex and precise predicates. A complex predicate will allow the user to precisely specify a topic using Boolean operators such as "AND", "OR" and "NOT". Our work will concentrate on defining a format to specify this kind of a complex topic definition and secondly on devising a crawl strategy to crawl the topic specific portions of the web defined by the complex predicate, efficiently and with minimal overhead. Our new crawl strategy will improve the performance of topic-specific web crawling by reducing the number of irrelevant pages crawled. In order to demonstrate the effectiveness of the above approach, we have built a complete focused crawler called "Eureka" with complex predicate support, and a search engine that indexes and supports end-user searches on the crawled pages.

A primer to frequent itemset mining for bioinformatics

PubMed Central

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

2015-01-01

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

Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis

PubMed Central

Bilgrami, Irma; Bain, Christopher; Webb, Geoffrey I.; Orosz, Judit; Pilcher, David

2017-01-01

Introduction Hospitals have seen a rise in Medical Emergency Team (MET) reviews. We hypothesised that the commonest MET calls result in similar treatments. Our aim was to design a pre-emptive management algorithm that allowed direct institution of treatment to patients without having to wait for attendance of the MET team and to model its potential impact on MET call incidence and patient outcomes. Methods Data was extracted for all MET calls from the hospital database. Association rule data mining techniques were used to identify the most common combinations of MET call causes, outcomes and therapies. Results There were 13,656 MET calls during the 34-month study period in 7936 patients. The most common MET call was for hypotension [31%, (2459/7936)]. These MET calls were strongly associated with the immediate administration of intra-venous fluid (70% [1714/2459] v 13% [739/5477] p<0.001), unless the patient was located on a respiratory ward (adjusted OR 0.41 [95%CI 0.25–0.67] p<0.001), had a cardiac cause for admission (adjusted OR 0.61 [95%CI 0.50–0.75] p<0.001) or was under the care of the heart failure team (adjusted OR 0.29 [95%CI 0.19–0.42] p<0.001). Modelling the effect of a pre-emptive management algorithm for immediate fluid administration without MET activation on data from a test period of 24 months following the study period, suggested it would lead to a 68.7% (2541/3697) reduction in MET calls for hypotension and a 19.6% (2541/12938) reduction in total METs without adverse effects on patients. Conclusion Routinely collected data and analytic techniques can be used to develop a pre-emptive management algorithm to administer intravenous fluid therapy to a specific group of hypotensive patients without the need to initiate a MET call. This could both lead to earlier treatment for the patient and less total MET calls. PMID:29281665

APPLYING DATA MINING APPROACHES TO FURTHER ...

EPA Pesticide Factsheets

This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space. This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space.

Understanding Teacher Users of a Digital Library Service: A Clustering Approach

ERIC Educational Resources Information Center

Xu, Beijie; Recker, Mimi

2011-01-01

This article describes the Knowledge Discovery and Data Mining (KDD) process and its application in the field of educational data mining (EDM) in the context of a digital library service called the Instructional Architect (IA.usu.edu). In particular, the study reported in this article investigated a certain type of data mining problem, clustering,…

A systematic mapping study of process mining

NASA Astrophysics Data System (ADS)

Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo

2018-05-01

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

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

Comparative analysis of data mining techniques for business data

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

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

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

Tree-based approach for exploring marine spatial patterns with raster datasets.

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

Data Mining and Knowledge Management: A System Analysis for Establishing a Tiered Knowledge Management Model.

ERIC Educational Resources Information Center

Luan, Jing; Willett, Terrence

This paper discusses data mining--an end-to-end (ETE) data analysis tool that is used by researchers in higher education. It also relates data mining and other software programs to a brand new concept called "Knowledge Management." The paper culminates in the Tier Knowledge Management Model (TKMM), which seeks to provide a stable…

Use of Sodium Dithionite as Part of a More Efficient Groundwater Restoration Method Following In-situ Recovery of Uranium at the Smith-Ranch Highland Site in Wyoming

NASA Astrophysics Data System (ADS)

Harris, R.; Reimus, P. W.; Ware, D.; Williams, K.; Chu, D.; Perkins, G.; Migdissov, A. A.; Bonwell, C.

2017-12-01

Uranium is primarily mined for nuclear power production using an aqueous extraction technique called in-situ recovery (ISR). ISR can pollute groundwater with residual uranium and other heavy metals. Reverse osmosis and groundwater sweep are currently used to restore groundwater after ISR mining, but are not permanent solutions. Sodium dithionite is being tested as part of a method to more permanently restore groundwater after ISR mining at the Smith-Ranch Highland site in Wyoming. Sodium dithionite is a chemical reductant that can reduce sediments that were oxidized during ISR. The reduced sediments can reduce soluble uranium (VI) in the groundwater to insoluble uranium (IV). Laboratory studies that use sodium dithionite to treat sediments and waters from the site may help predict how it will behave during a field deployment. An aqueous batch experiment showed that sodium dithionite reduced uranium in post-mined untreated groundwater from 38 ppm to less than 1 ppm after 1 day. A sediment reduction batch experiment showed that sodium dithionite-treated sediments were capable of reducing uranium in post-mined untreated groundwater from 38 ppm to 2 ppm after 7 days. One column experiment is showing post-mined sodium dithionite-treated sediments are capable of reducing uranium in post-mined groundwater for over 30 pore volumes past the initial injection. While these results are promising for field deployments of sodium dithionite, another column experiment with sodium dithionite-treated sediments containing uranium rich organic matter is showing net production of uranium instead of uranium uptake. Sodium dithionite appears to liberate uranium from the organic matter. Another sediment reduction experiment is being conducted to further investigate this hypothesis. These experiments are helping guide plans for field deployments of sodium dithionite at uranium ISR mining sites.

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

PubMed

Rubio-Escudero, Cristina; Valverde-Fernández, Justo; Nepomuceno-Chamorro, Isabel; Pontes-Balanza, Beatriz; Hernández-Mendoza, Yoedusvany; Rodríguez-Herrera, Alfonso

2017-01-01

Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

Influence of Acid Mine Drainage (AMD) on recent phyto- and zooplankton in "the Anthropogenic Lake District" in south-west Poland

NASA Astrophysics Data System (ADS)

Sienkiewicz, Elwira; Gasiorowski, Michal

2015-04-01

In south-west Poland (central Europe) many the post-mining lakes formed so-called "the Anthropogenic Lake District". Areas, where water comes in contact with lignite beds characterized by high concentration of sulfide minerals are called Acid Mine Drainage (AMD). Pyrite oxidation and other sulfide compounds caused release sulfuric acids and heavy metal ions. These processes caused decline of water pH, sometimes to extremely low pH < 2.8. Presently, pit lakes located in south-west Poland have water pH ranged between 2.7 and 8.9. Differences of water reaction in the mine lakes depend on many factors, such as bedrock buffer capacity, geological structure of carboniferous area, exploitation technique of lignite, methods of filling and water supply of reservoirs and their age. During the evolution of lakes' ecosystems, sulfate-iron-calcium type of waters occurring in acid lakes will transform in alkaline hydrogen-carbonate-calcium type of waters. Due to the different time of the completion of lignite exploitation, lakes' age varied between forty and over one hundred years. Studies showed that younger lakes are more acidic in compare to older. To estimate impact of AMD we analyzed recent diversity of diatoms and Cladocera remains and water chemistry from extremely acidic, relatively young lakes and from alkaline, older water bodies. As we expected, flora and fauna from acidic lakes have shown very low diversity and species richness. Among diatoms, Eunotia exigua (Bréb. ex Kütz.) Rabenhorst and/or E. paludosa Grunow were dominated taxa, while fauna Cladocera did not occurred in lakes with water pH < 3. On this area, exploitation of lignite continued up to 1973. Older lakes were formed in the region where the mine started work in 1880 and lignite mining stopped in 1926. Measurements of pH value in situ point to neutral or alkaline water, but because of the possibility of hysteresis phenomenon, the studies of phyto- and zooplankton have shown if there has already been a widespread neutralization of lake ecosystems, what encompassing both recovery of water chemistry and rebuilding of biota communities. Studies have confirmed, that phyto- and zooplankton living nowadays in lakes located on this area, where exploitation of lignite ended at the beginning of 20th century, indicate completely recovery from acidification caused by coal mine activities. Presently, the lakes were dominated by planktonic diatoms and Cladocera taxa, such as Discostella pseudostelligera (Hust.) Houk & Klee and Bosmina longirostris, respectively.

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

NASA Astrophysics Data System (ADS)

Sharma, Anuj; Kumar Panigrahi, Prabin

2012-02-01

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.

Evaluating bump control techniques through convergence monitoring

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

Campoli, A.A.

1987-07-01

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

Mine Waste at The Kherzet Youcef Mine : Environmental Characterization

NASA Astrophysics Data System (ADS)

Issaad, Mouloud; Boutaleb, Abdelhak; Kolli, Omar

2017-04-01

Mining activity in Algeria has existed since antiquity. But it was very important since the 20th century. This activity has virtually ceased since the beginning of the 1990s, leaving many mine sites abandoned (so-called orphan mines). The abandonment of mining today poses many environmental problems (soil pollution, contamination of surface water, mining collapses...). The mining wastes often occupy large volumes that can be hazardous to the environment and human health, often neglected in the past: Faulting geotechnical implementation, acid mine drainage (AMD), alkalinity, presence of pollutants and toxic substances (heavy metals, cyanide...). The study started already six years ago and it covers all mines located in NE Algeria, almost are stopped for more than thirty years. So the most important is to have an overview of all the study area. After the inventory job of the abandoned mines, the rock drainage prediction will help us to classify sites according to their acid generating potential.

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

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

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

1999-07-01

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

Red-shouldered Hawk (Buteo lineatus) abundance and habitat in a reclaimed mine landscape

USGS Publications Warehouse

Balcerzak, M.J.; Wood, P.B.

2003-01-01

Fragmentation of the landscape by large-scale mining may affect Red-shouldered Hawk (Buteo lineatus) populations by reducing the amount of forested habitat available in a landscape and by creating fragmented forest parches surrounded by reclaimed mine lands. We examined habitat characteristics and relative abundance of Red-shouldered Hawks in reclaimed mine landscapes within four treatments: early-successional grassland habitat, mid-successional shrub/pole habitat, late-successional fragmented forest habitat, and late-successional intact forest habitat. We quantified microhabitat characteristics within an 11.3-m-radius plot centered on 156 vegetation plots throughout the four treatments. We surveyed 48 stations on and adjacent to three mines for Red-shouldered Hawks using standardized broadcast call techniques during February 2000-January 2001 and measured landscape characteristics within 1000-m buffer zones centered on each station from digitized aerial photographs. Mean abundance of Red-shouldered Hawks was significantly higher in the intact forest (x?? = 0.07 detections/ point, SE = 0.03) than the grassland (x?? = 0.01, SE = 0.01) treatment, but did not differ from the fragmented forest (x?? = 0.03, SE = 0.01) or shrub/pole (x?? = 0.03, SE = 0.01) treatments. Most microhabitat characteristics in both fragmented and intact forest differed from shrub/pole and grasslands. Amount of wetland was the most important characteristic determining presence of Red-shouldered Hawks in a forest-dominated landscape. More wetlands in the landscape may provide abundant amphibians and reptiles, which are important in the diet of Red-shouldered Hawks. ?? 2003 The Raptor Research Foundation, Inc.

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.

Digression and Value Concatenation to Enable Privacy-Preserving Regression.

PubMed

Li, Xiao-Bai; Sarkar, Sumit

2014-09-01

Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals' sensitive data. This problem, which we call a "regression attack," has not been addressed in the data privacy literature, and existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach introduces a novel measure, called digression , which assesses the sensitive value disclosure risk in the process of building a regression tree model. Specifically, we develop an algorithm that uses the measure for pruning the tree to limit disclosure of sensitive data. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than a user-defined generalization scheme commonly used in existing approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted using real-world financial, economic and healthcare data. The results of the experiments demonstrate that the proposed approach is very effective in protecting data privacy while preserving data quality for research and analysis.

Cyanide

USGS Publications Warehouse

Creekmore, Lynn H.

1999-01-01

Cyanide poisoning of birds is caused by exposure to cyanide in two forms: inorganic salts and hydrogen cyanide gas (HCN). Two sources of cyanide have been associated with bird mortalities: gold and silver mines that use cyanide in the extraction process and a predator control device called the M-44 sodium cyanide ejector, which uses cyanide as the toxic agent.Most of the cyanide mortality documented in birds is a result of exposure to cyanide used in heap leach and carbonin-pulp mill gold or silver mining processes. At these mines, the animals are exposed when they ingest water that contains cyanide salts used in mining processes or, possibly, when they inhale HCN gas. In heap leach mining operations, the ore is placed on an impermeable pad over which a cyanide solution is sprayed or dripped. The cyanide solution dissolves and attaches to or “leaches out” the gold. The cyanide and gold solution is then drained to a plastic-lined pond, which is commonly called the pregnant pond. The gold is extracted, and the remaining solution is moved into another lined pond, which is commonly called the barren pond. The cyanide concentration in this pond is increased so that the solution is again suitable for use in the leaching process, and the solution is used again on the ore heap (Fig. 46.1). Bird use of the HCN-contaminated water in the ponds (Fig. 46.2) or contaminated water on or at the base of the heap leach pads (Fig. 46.3) can result in mortality.

A strategy for selecting data mining techniques in metabolomics.

PubMed

Banimustafa, Ahmed Hmaidan; Hardy, Nigel W

2012-01-01

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

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

PubMed

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

2012-08-01

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

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

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

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

Technical Challenges of the U.S. Army’s Ground Combat Vehicle Program

DTIC Science & Technology

2012-11-01

for mine protection and a distinctive armored extension on the top, called the doghouse. Those features optimize it for counterinsurgency operations...vehicles. Less complex approaches have also evolved, such as mines designed to attack the weaker bottoms of vehicles or improvised explosive devices...Improvised Explosive Devices, Suicide Bombers, Unexploded Ordnance, and Mines ,” section I-G-10, “Countermeasures.” See also Clay Wilson, Improvised

Helmet-Cam: tool for assessing miners’ respirable dust exposure

PubMed Central

Cecala, A.B.; Reed, W.R.; Joy, G.J.; Westmoreland, S.C.; O’Brien, A.D.

2015-01-01

Video technology coupled with datalogging exposure monitors have been used to evaluate worker exposure to different types of contaminants. However, previous application of this technology used a stationary video camera to record the worker’s activity while the worker wore some type of contaminant monitor. These techniques are not applicable to mobile workers in the mining industry because of their need to move around the operation while performing their duties. The Helmet-Cam is a recently developed exposure assessment tool that integrates a person-wearable video recorder with a datalogging dust monitor. These are worn by the miner in a backpack, safety belt or safety vest to identify areas or job tasks of elevated exposure. After a miner performs his or her job while wearing the unit, the video and dust exposure data files are downloaded to a computer and then merged together through a NIOSH-developed computer software program called Enhanced Video Analysis of Dust Exposure (EVADE). By providing synchronized playback of the merged video footage and dust exposure data, the EVADE software allows for the assessment and identification of key work areas and processes, as well as work tasks that significantly impact a worker’s personal respirable dust exposure. The Helmet-Cam technology has been tested at a number of metal/nonmetal mining operations and has proven to be a valuable assessment tool. Mining companies wishing to use this technique can purchase a commercially available video camera and an instantaneous dust monitor to obtain the necessary data, and the NIOSH-developed EVADE software will be available for download at no cost on the NIOSH website. PMID:26380529

Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

NASA Astrophysics Data System (ADS)

Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

2018-05-01

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

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

NASA Astrophysics Data System (ADS)

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

2012-07-01

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

Investigating MOOCs through Blog Mining

ERIC Educational Resources Information Center

Chen, Yong

2014-01-01

MOOCs (massive open online course) is a disruptive innovation and a current buzzword in higher education. However, the discussion of MOOCs is disparate, fragmented, and distributed among different outlets. Systematic, extensively published research on MOOCs is unavailable. This paper adopts a novel method called blog mining to analyze MOOCs. The…

78 FR 77024 - Telemarketing Sales Rule; Notice of Termination of Caller ID Rulemaking

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-20

..., data mining and anomaly detection, and call-blocking technology). \\19\\ AT&T Servs., Inc., No. 00040, at... technically feasible, by looking at the signaling data . . . to distinguish between a CPN [calling party...

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

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

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

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

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2007-08-01

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

GyneScan

PubMed Central

Acharya, U. Rajendra; Sree, S. Vinitha; Kulshreshtha, Sanjeev; Molinari, Filippo; Koh, Joel En Wei; Saba, Luca; Suri, Jasjit S.

2014-01-01

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. PMID:24325128

Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

PubMed Central

Zhang, Lei; Li, Yin; Guo, Xinfeng; May, Brian H.; Xue, Charlie C. L.; Yang, Lihong; Liu, Xusheng

2014-01-01

Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1) identification of candidate ancient terms; (2) systemic search and assessment of medical records written in classical Chinese; (3) preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction) was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae. PMID:24744808

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

NASA Astrophysics Data System (ADS)

Khoshahval, S.; Farnaghi, M.; Taleai, M.

2017-09-01

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

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

Discovering Knowledge from Noisy Databases Using Genetic Programming.

ERIC Educational Resources Information Center

Wong, Man Leung; Leung, Kwong Sak; Cheng, Jack C. Y.

2000-01-01

Presents a framework that combines Genetic Programming and Inductive Logic Programming, two approaches in data mining, to induce knowledge from noisy databases. The framework is based on a formalism of logic grammars and is implemented as a data mining system called LOGENPRO (Logic Grammar-based Genetic Programming System). (Contains 34…

Engaging Business Students with Data Mining

ERIC Educational Resources Information Center

Brandon, Dan

2016-01-01

The Economist calls it "a golden vein", and many business experts now say it is the new science of winning. Business and technologists have many names for this new science, "business intelligence" (BI), " data analytics," and "data mining" are among the most common. The job market for people skilled in this…

Mining and Indexing Graph Databases

ERIC Educational Resources Information Center

Yuan, Dayu

2013-01-01

Graphs are widely used to model structures and relationships of objects in various scientific and commercial fields. Chemical molecules, proteins, malware system-call dependencies and three-dimensional mechanical parts are all modeled as graphs. In this dissertation, we propose to mine and index those graph data to enable fast and scalable search.…

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.

Mine Improvement and New Emergency Response Act of 2006. Public Law 109-236, S2803

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

NONE

2006-06-15

This Act may be cited as the 'Mine Improvement and New Emergency Response Act of 2006' or the 'MINER Act'. It amends the Federal Mine Safety and Health Act of 1977 to improve the safety of mines and mining. The Act requires operators of underground coal mines to improve accident preparedness. The legislation requires mining companies to develop an emergency response plan specific to each mine they operate, and requires that every mine has at least two rescue teams located within one hour. S. 2803 also limits the legal liability of rescue team members and the companies that employ them.more » The act increases both civil and criminal penalties for violations of federal mining safety standards and gives the Mine Safety and Health Administration (MSHA) the ability to temporarily close a mine that fails to pay the penalties or fines. In addition, the act calls for several studies into ways to enhance mine safety, as well as the establishment of a new office within the National Institute for Occupational Safety and Health devoted to improving mine safety. Finally, the legislation establishes new scholarship and grant programs devoted to training individuals with respect to mine safety.« less

Characterization of Uranium Ore Concentrate Chemical Composition via Raman Spectroscopy

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

Su, Yin-Fong; Tonkyn, Russell G.; Sweet, Lucas E.

Uranium Ore Concentrate (UOC, often called yellowcake) is a generic term that describes the initial product resulting from the mining and subsequent milling of uranium ores en route to production of the U-compounds used in the fuel cycle. Depending on the mine, the ore, the chemical process, and the treatment parameters, UOC composition can vary greatly. With the recent advent of handheld spectrometers, we have chosen to investigate whether either commercial off-the-shelf (COTS) handheld devices or laboratory-grade Raman instruments might be able to i) identify UOC materials, and ii) differentiate UOC samples based on chemical composition and thus suggest themore » mining or milling process. Twenty-eight UOC samples were analyzed via FT-Raman spectroscopy using both 1064 nm and 785 nm excitation wavelengths. These data were also compared with results from a newly developed handheld COTS Raman spectrometer using a technique that lowers background fluorescence signal. Initial chemometric analysis was able to differentiate UOC samples based on mine location. Additional compositional information was obtained from the samples by performing XRD analysis on a subset of samples. The compositional information was integrated with chemometric analysis of the spectroscopic dataset allowing confirmation that class identification is possible based on compositional differences between the UOC samples, typically involving species such as U3O8, α-UO2(OH)2, UO4•2H2O (metastudtite), K(UO2)2O3, etc. While there are clearly excitation λ sensitivities, especially for dark samples, Raman analysis coupled with chemometric data treatment can nicely differentiate UOC samples based on composition and even mine origin.« less

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

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

1982-01-01

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

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

Chironis, N.P.

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

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

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

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

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

2000-01-01

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

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

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.

Ten million tragedies, one step at a time

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

Wurst, J.

Lacking the drama of nuclear weapons, ballistic missiles, or handguns, land mines have generally escaped professional and public attention. As land mines begin to be perceived as obstacles to post-war peacebuilding, some humanitarian groups and a few governments are preparing to fight the production and distribution of these weapons. Last year, a coalition of humanitarian groups including Human Rights Watch, Handicap International, Physicians for Human Rights, and the Vietman Veterans of America Foundation (VVAF) launched an international campaign to ban the production and sale of mines; the United Nations began exploring its options, and some governments called for strengthening anmore » existing convention that was designed to control mines. This article describes the types of land mines and ways in which they are used, estimates some numbers of people injured by land mines in various conflicts, and discusses the market for land mines. Measures for exposing the land mine business and for clearing existing mines are discussed.« less

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

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

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

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

Finite Element Analysis of M15 and M19 Mines Under Wheeled Vehicle Load

DTIC Science & Technology

2008-03-01

the plate statically. An implicit finite element option in a code called LSDYNA was used to model the pressure generated in the explosive by the...figure 4 for the M19 mines. Maximum pressure in the explosive for each mine calculated by LSDYNA code shown for a variety of plate sizes and weights...Director U.S. Army TRADOC Analysis Center-WSMR ATTN: ATRC-WSS-R White Sands Missile Range, NM 88002 Chemical Propulsion Information Agency ATTN

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

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

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

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

Visualising Conversation Structure across Time: Insights into Effective Doctor-Patient Consultations

PubMed Central

Angus, Daniel; Watson, Bernadette; Smith, Andrew; Gallois, Cindy; Wiles, Janet

2012-01-01

Effective communication between healthcare professionals and patients is critical to patients’ health outcomes. The doctor/patient dialogue has been extensively researched from different perspectives, with findings emphasising a range of behaviours that lead to effective communication. Much research involves self-reports, however, so that behavioural engagement cannot be disentangled from patients’ ratings of effectiveness. In this study we used a highly efficient and time economic automated computer visualisation measurement technique called Discursis to analyse conversational behaviour in consultations. Discursis automatically builds an internal language model from a transcript, mines the transcript for its conceptual content, and generates an interactive visual account of the discourse. The resultant visual account of the whole consultation can be analysed for patterns of engagement between interactants. The findings from this study show that Discursis is effective at highlighting a range of consultation techniques, including communication accommodation, engagement and repetition. PMID:22693629

CARIBIAM: constrained Association Rules using Interactive Biological IncrementAl Mining.

PubMed

Rahal, Imad; Rahhal, Riad; Wang, Baoying; Perrizo, William

2008-01-01

This paper analyses annotated genome data by applying a very central data-mining technique known as Association Rule Mining (ARM) with the aim of discovering rules and hypotheses capable of yielding deeper insights into this type of data. In the literature, ARM has been noted for producing an overwhelming number of rules. This work proposes a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to investigators in an incremental and interactive manner.

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

USGS Publications Warehouse

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

1986-01-01

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

Machine learning and medicine: book review and commentary.

PubMed

Koprowski, Robert; Foster, Kenneth R

2018-02-01

This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

PubMed

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

2018-03-07

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

Data Mining: Going beyond Traditional Statistics

ERIC Educational Resources Information Center

Zhao, Chun-Mei; Luan, Jing

2006-01-01

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

Mine Water Treatment in Hongai Coal Mines

NASA Astrophysics Data System (ADS)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

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

Bats, mines, and citizen science in the Rockies: Volunteers make a difference in Colorado

USGS Publications Warehouse

Hayes, Mark A.

2012-01-01

Biologists at the Colorado Division of Wildlife faced a big problem back in 1990. They wanted to protect important bat roosts in the state’s abandoned mines, but first they had to find the bats. Colorado’s rich mining history had left more than 23,000 old mines scattered across the landscape, few of which had ever been surveyed for bat roosts. The magnitude of the task was overwhelming. So the agency put out a call for volunteers, “citizen scientists” willing to donate their time for bat conservation. Two decades later, the results have surpassed their wildest expectations.

Semi-automated based ground-truthing GUI for airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Lydic, Rich; Moore, Tim; Trang, Anh; Agarwal, Sanjeev; Tiwari, Spandan

2005-06-01

Over the past several years, an enormous amount of airborne imagery consisting of various formats has been collected and will continue into the future to support airborne mine/minefield detection processes, improve algorithm development, and aid in imaging sensor development. The ground-truthing of imagery is a very essential part of the algorithm development process to help validate the detection performance of the sensor and improving algorithm techniques. The GUI (Graphical User Interface) called SemiTruth was developed using Matlab software incorporating signal processing, image processing, and statistics toolboxes to aid in ground-truthing imagery. The semi-automated ground-truthing GUI is made possible with the current data collection method, that is including UTM/GPS (Universal Transverse Mercator/Global Positioning System) coordinate measurements for the mine target and fiducial locations on the given minefield layout to support in identification of the targets on the raw imagery. This semi-automated ground-truthing effort has developed by the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division, Airborne Application Branch with some support by the University of Missouri-Rolla.

Estimating Software-Development Costs With Greater Accuracy

NASA Technical Reports Server (NTRS)

Baker, Dan; Hihn, Jairus; Lum, Karen

2008-01-01

COCOMOST is a computer program for use in estimating software development costs. The goal in the development of COCOMOST was to increase estimation accuracy in three ways: (1) develop a set of sensitivity software tools that return not only estimates of costs but also the estimation error; (2) using the sensitivity software tools, precisely define the quantities of data needed to adequately tune cost estimation models; and (3) build a repository of software-cost-estimation information that NASA managers can retrieve to improve the estimates of costs of developing software for their project. COCOMOST implements a methodology, called '2cee', in which a unique combination of well-known pre-existing data-mining and software-development- effort-estimation techniques are used to increase the accuracy of estimates. COCOMOST utilizes multiple models to analyze historical data pertaining to software-development projects and performs an exhaustive data-mining search over the space of model parameters to improve the performances of effort-estimation models. Thus, it is possible to both calibrate and generate estimates at the same time. COCOMOST is written in the C language for execution in the UNIX operating system.

Machine learning for a Toolkit for Image Mining

NASA Technical Reports Server (NTRS)

Delanoy, Richard L.

1995-01-01

A prototype user environment is described that enables a user with very limited computer skills to collaborate with a computer algorithm to develop search tools (agents) that can be used for image analysis, creating metadata for tagging images, searching for images in an image database on the basis of image content, or as a component of computer vision algorithms. Agents are learned in an ongoing, two-way dialogue between the user and the algorithm. The user points to mistakes made in classification. The algorithm, in response, attempts to discover which image attributes are discriminating between objects of interest and clutter. It then builds a candidate agent and applies it to an input image, producing an 'interest' image highlighting features that are consistent with the set of objects and clutter indicated by the user. The dialogue repeats until the user is satisfied. The prototype environment, called the Toolkit for Image Mining (TIM) is currently capable of learning spectral and textural patterns. Learning exhibits rapid convergence to reasonable levels of performance and, when thoroughly trained, Fo appears to be competitive in discrimination accuracy with other classification techniques.

A Feature Mining Based Approach for the Classification of Text Documents into Disjoint Classes.

ERIC Educational Resources Information Center

Nieto Sanchez, Salvador; Triantaphyllou, Evangelos; Kraft, Donald

2002-01-01

Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

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…

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.

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.

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

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

Perry, R L; Roberts, R S

1999-02-01

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

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

Treesearch

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

2017-01-01

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

Using Open Web APIs in Teaching Web Mining

ERIC Educational Resources Information Center

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

2009-01-01

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

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

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

Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity providedmore » by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.« less

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

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,...

A Survey of Educational Data-Mining Research

ERIC Educational Resources Information Center

Huebner, Richard A.

2013-01-01

Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

Quantifying Forest and Coastal Disturbance from Industrial Mining Using Satellite Time Series Analysis Under Very Cloudy Conditions

NASA Astrophysics Data System (ADS)

Alonzo, M.; Van Den Hoek, J.; Ahmed, N.

2015-12-01

The open-pit Grasberg mine, located in the highlands of Western Papua, Indonesia, and operated by PT Freeport Indonesia (PT-FI), is among the world's largest in terms of copper and gold production. Over the last 27 years, PT-FI has used the Ajkwa River to transport an estimated 1.3 billion tons of tailings from the mine into the so-called Ajkwa Deposition Area (ADA). The ADA is the product of aggradation and lateral expansion of the Ajkwa River into the surrounding lowland rainforest and mangroves, which include species important to the livelihoods of indigenous Papuans. Mine tailings that do not settle in the ADA disperse into the Arafura Sea where they increase levels of suspended particulate matter (SPM) and associated concentrations of dissolved copper. Despite the mine's large-scale operations, ecological impact of mine tailings deposition on the forest and estuarial ecosystems have received minimal formal study. While ground-based inquiries are nearly impossible due to access restrictions, assessment via satellite remote sensing is promising but hindered by extreme cloud cover. In this study, we characterize ridgeline-to-coast environmental impacts along the Ajkwa River, from the Grasberg mine to the Arafura Sea between 1987 and 2014. We use "all available" Landsat TM and ETM+ images collected over this time period to both track pixel-level vegetation disturbance and monitor changes in coastal SPM levels. Existing temporal segmentation algorithms are unable to assess both acute and protracted trajectories of vegetation change due to pervasive cloud cover. In response, we employ robust, piecewise linear regression on noisy vegetation index (NDVI) data in a manner that is relatively insensitive to atmospheric contamination. Using this disturbance detection technique we constructed land cover histories for every pixel, based on 199 image dates, to differentiate processes of vegetation decline, disturbance, and regrowth. Using annual reports from PT-FI, we show that the changing extent and spatial patterns of riparian vegetation disturbance directly correlate with yearly tailings production rates. While the rate of vegetation disturbance decreased after 1998, SPM levels along the Arafura coast increased, suggesting the failure of PT-FI to fully confine tailings to the ADA.

A Comparative Study of Data Mining Techniques on Football Match Prediction

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Thallium and Silver binding to dissolved organic matter

NASA Astrophysics Data System (ADS)

Benedetti, M. F.; Martin, L.; Simonucci, C.; Viollier, E.

2017-12-01

Silver (Ag) and thallium (Tl) are potential contaminants at the vicinity of mining sites and are harmful pollutants. Silver can be found in mine but also as released by the dissolution of Silver nanoparticles, a major new emerging contaminant. Tl is both lithophilic and calcophilic elements and found in sulphur ores (associated with lead, zinc, antimony…) or in rocks containing K-feldspar. Speciation of Ag and Tl is poorly known mainly due to their low concentrations in aquatic environments. Review of Ag and Tl geochemistry clearly shows a lack of quantitative information about interactions with natural organic matter. Organic ligands could play an important role in Ag or Tl bioavailability, chemical reactivity (adsorption or photo oxidation inhibition or catalysis) and hence geochemical transfers. Based on equilibrium between two solutions that are separated by a selectively permeable membrane, the so-called "Donnan membrane technique" (DMT) provides a measure of free ion concentrations. Analytes measurements are performed by HR-ICP-MS Element 2 (Thermo Scientific). Experimental setup allows the Donnan equilibrium to be reached after 100 and 120 hours for Tl. Experiments performed with purified natural organic matter allow calculating complexation constants in multiple pH conditions. With this work, we contribute new data and interpretations to an active debate on Ag and Tl geochemical modeling. In conclusion, this work brings a new view on risk assessment for mining activities.

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

PubMed

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

2012-05-01

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

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2017-08-01

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

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

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

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

PubMed Central

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

2005-01-01

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

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

PubMed

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

2017-09-01

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

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

ERIC Educational Resources Information Center

Luan, Jing

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

Development, installation, and testing services for an automatic, point type thermal sensor, fire protection system on a mining dozer. Final report

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

Lease, W.D.

1976-08-01

Lease AFEX, Inc., modified its standard design of an automatic fire protection system used in the past on logging equipment, and long-term, in-mine tested system on a Fiat-Alli's HD-41B dozer at the Lemmons and Company coal mine, Boonville, Ind. The modification of the standard AFEX system involved improving the actuation device. The AFEX system is called a point-type thermal sensor, automatic fire protection system. The in-mine test took place in late 1975, and early 1976. The system was then tested by simulating a fire on the dozer. The system operated successfully after the 4 months of in-mine endurance testing. (Colormore » illustrations reproduced in black and white.)« less

KSC-2011-4145

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4157

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4161

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students give their "thumbs up" after maneuvering their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4150

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4018

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4149

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4162

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4019

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4144

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4002

NASA Image and Video Library

2011-05-25

CAPE CANAVERAL, Fla. -- This tent called a "Lunarena" is a giant "sandbox," with about 60 tons of ultra-fine simulated lunar soil spread on the floor for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Frankie Martin

KSC-2011-4017

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

Field monitoring versus individual miner dosimetry of radon daughter products in mines.

PubMed

Domański, T; Kluszczyński, D; Olszewski, J; Chruscielewski, W

1989-01-01

The paper presents the results realised simultaneously by two different and independent systems of measurement of an assessment of miners' exposure to radon daughter products which naturally occur in the air of mines. The first one, called the Air Sampling System (ASS), was based on the field monitoring of radon progeny in air, the second one, called the Individual Dosimetry System (IDS), was based on the individual dosimeters worn by miners. Experimental comparison of these two systems has been conducted for six years in eleven Polish underground metal-ore mines. This study reveals that no correlation exists between the concentration and annual miners' exposures evaluated by the ASS and IDS. The ratio ASS/IDS for mine population varies from 11.0 to 0.14 in respect of annual concentration means, and in respect to annual exposures, this ratio varies from 4.5 to 0.14. The conclusion to be drawn from six years' observation and comparison of both systems is that correct and true evaluation of miners' exposure to radon progeny can be made only by the use of the Individual Dosimetry System, since the Air Sampling System is too sensitive and too dependent on the Strategy of sampling and its radiation.

Stress monitoring versus microseismic ruptures in an active deep mine

NASA Astrophysics Data System (ADS)

Tonnellier, Alice; Bouffier, Christian; Bigarré, Pascal; Nyström, Anders; Österberg, Anders; Fjellström, Peter

2015-04-01

Nowadays, underground mining industry has developed high-technology mass mining methods to optimise the productivity at deep levels. Such massive extraction induces high-level stress redistribution generating seismic events around the mining works, threatening safety and economics. For this reason mining irregular deep ore bodies calls for steadily enhanced scientific practises and technologies to guarantee the mine environment to be safer and stable for the miners and the infrastructures. INERIS, within the framework of the FP7 European project I2Mine and in partnership with the Swedish mining company Boliden, has developed new methodologies in order to monitor both quasi-static stress changes and ruptures in a seismic prone area. To this purpose, a unique local permanent microseismic and stress monitoring network has been installed into the deep-working Garpenberg mine situated to the north of Uppsala (Sweden). In this mine, ore is extracted using sublevel stoping with paste fill production/distribution system and long-hole drilling method. This monitoring network has been deployed between about 1100 and 1250 meter depth. It consists in six 1-component and five 3-component microseismic probes (14-Hz geophones) deployed in the Lappberget area, in addition to three 3D stress monitoring cells that focus on a very local exploited area. Objective is three-fold: to quantify accurately quasi-static stress changes and freshly-induced stress gradients with drift development in the orebody, to study quantitatively those stress changes versus induced detected and located microseismic ruptures, and possibly to identify quasi-static stress transfer from those seismic ruptures. Geophysical and geotechnical data are acquired continuously and automatically transferred to INERIS datacenter through the web. They are made available on a secured web cloud monitoring infrastructure called e.cenaris and completed with mine data. Such interface enables the visualisation of the monitoring data coming from the mine in quasi-real time and facilitates information exchanges and decision making for experts and stakeholders. On the basis of these data acquisition and sharing, preliminary analysis has been started to highlight whether stress variations and seismic sources behaviour might be directly bound with mine working evolution and could improve the knowledge on the equilibrium states inside the mine. Knowing such parameters indeed will be a potential solution to understand better the response of deep mining activities to the exploitation solicitations and to develop, if possible, methods to prevent from major hazards such as rock bursts and other ground failure phenomena.

Text Mining in Organizational Research

PubMed Central

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

2017-01-01

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

Text Mining in Organizational Research.

PubMed

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

2018-07-01

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

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

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

Kevin McCarthy; Milos Manic

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

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

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

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

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

PubMed

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

2009-11-15

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

Visual analytics of anomaly detection in large data streams

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.; Sharma, Ratnesh K.; Mehta, Abhay

2009-01-01

Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Many analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream. Mining results are quickly presented in graphical representations (i.e., tooltip) for the user to zoom into the problem regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have successfully applied this technique to detect data stream anomalies in large real-world enterprise server performance and data center energy management.

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

Introduction to the mining of clinical data.

PubMed

Harrison, James H

2008-03-01

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

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

PubMed Central

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

2018-01-01

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

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

USGS Publications Warehouse

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

1983-01-01

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

Association-rule-based tuberculosis disease diagnosis

NASA Astrophysics Data System (ADS)

Asha, T.; Natarajan, S.; Murthy, K. N. B.

2010-02-01

Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). This work focuses on finding close association rules, a promising technique in Data Mining, within TB data. The proposed method first normalizes of raw data from medical records which includes categorical, nominal and continuous attributes and then determines Association Rules from the normalized data with different support and confidence. Association rules are applied on a real data set containing medical records of patients with TB obtained from a state hospital. The rules determined describes close association between one symptom to another; as an example, likelihood that an occurrence of sputum is closely associated with blood cough and HIV.

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

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

Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% truemore » positive rates at both.« less

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.

30 CFR 282.28 - Environmental protection measures.

Code of Federal Regulations, 2010 CFR

2010-07-01

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

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

ERIC Educational Resources Information Center

Mardikyan, Sona; Badur, Bertain

2011-01-01

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected…

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).

15 CFR 970.600 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

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

15 CFR 970.600 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

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

15 CFR 970.600 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

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

15 CFR 971.500 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

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

15 CFR 971.500 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

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

15 CFR 971.500 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

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

15 CFR 971.500 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

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

15 CFR 971.500 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

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

15 CFR 970.600 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

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

15 CFR 970.600 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

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

China and Proliferation of Weapons of Mass Destruction and Missiles: Policy Issues

DTIC Science & Technology

2010-08-16

nuclear weapons facilities, while experts from China worked at a uranium mine at Saghand and a centrifuge facility (for uranium enrichment) near...brief interruptions.”85 84 Barbara Opall -Rome and Vago Muradian, “Bush Privately Lauds...confiscated a rare metal used to produce alloy steel (called vanadium) being smuggled to North Korea. In the same month, China’s NHI Shenyang Mining

Text mining and its potential applications in systems biology.

PubMed

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

2006-12-01

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

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

NASA Technical Reports Server (NTRS)

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

1981-01-01

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

Real-time estimation of wildfire perimeters from curated crowdsourcing

NASA Astrophysics Data System (ADS)

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-04-01

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available “curated” crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires.

Real-time estimation of wildfire perimeters from curated crowdsourcing

PubMed Central

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-01-01

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available “curated” crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires. PMID:27063569

Real-time estimation of wildfire perimeters from curated crowdsourcing.

PubMed

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-04-11

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available "curated" crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires.

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…

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

EPA Science Inventory

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

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.

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

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

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

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

DOT National Transportation Integrated Search

2003-06-01

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

Surface Mining and Reclamation Effects on Flood Response of Watersheds in the Central Appalachian Plateau Region

NASA Technical Reports Server (NTRS)

Ferrari, J. R.; Lookingbill, T. R.; McCormick, B.; Townsend, P. A.; Eshleman, K. N.

2009-01-01

Surface mining of coal and subsequent reclamation represent the dominant land use change in the central Appalachian Plateau (CAP) region of the United States. Hydrologic impacts of surface mining have been studied at the plot scale, but effects at broader scales have not been explored adequately. Broad-scale classification of reclaimed sites is difficult because standing vegetation makes them nearly indistinguishable from alternate land uses. We used a land cover data set that accurately maps surface mines for a 187-km2 watershed within the CAP. These land cover data, as well as plot-level data from within the watershed, are used with HSPF (Hydrologic Simulation Program-Fortran) to estimate changes in flood response as a function of increased mining. Results show that the rate at which flood magnitude increases due to increased mining is linear, with greater rates observed for less frequent return intervals. These findings indicate that mine reclamation leaves the landscape in a condition more similar to urban areas rather than does simple deforestation, and call into question the effectiveness of reclamation in terms of returning mined areas to the hydrological state that existed before mining.

Graph mining for next generation sequencing: leveraging the assembly graph for biological insights.

PubMed

Warnke-Sommer, Julia; Ali, Hesham

2016-05-06

The assembly of Next Generation Sequencing (NGS) reads remains a challenging task. This is especially true for the assembly of metagenomics data that originate from environmental samples potentially containing hundreds to thousands of unique species. The principle objective of current assembly tools is to assemble NGS reads into contiguous stretches of sequence called contigs while maximizing for both accuracy and contig length. The end goal of this process is to produce longer contigs with the major focus being on assembly only. Sequence read assembly is an aggregative process, during which read overlap relationship information is lost as reads are merged into longer sequences or contigs. The assembly graph is information rich and capable of capturing the genomic architecture of an input read data set. We have developed a novel hybrid graph in which nodes represent sequence regions at different levels of granularity. This model, utilized in the assembly and analysis pipeline Focus, presents a concise yet feature rich view of a given input data set, allowing for the extraction of biologically relevant graph structures for graph mining purposes. Focus was used to create hybrid graphs to model metagenomics data sets obtained from the gut microbiomes of five individuals with Crohn's disease and eight healthy individuals. Repetitive and mobile genetic elements are found to be associated with hybrid graph structure. Using graph mining techniques, a comparative study of the Crohn's disease and healthy data sets was conducted with focus on antibiotics resistance genes associated with transposase genes. Results demonstrated significant differences in the phylogenetic distribution of categories of antibiotics resistance genes in the healthy and diseased patients. Focus was also evaluated as a pure assembly tool and produced excellent results when compared against the Meta-velvet, Omega, and UD-IDBA assemblers. Mining the hybrid graph can reveal biological phenomena captured by its structure. We demonstrate the advantages of considering assembly graphs as data-mining support in addition to their role as frameworks for assembly.

Analysis of Hospital Processes with Process Mining Techniques.

PubMed

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

2015-01-01

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

Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research.

PubMed

Tang, Qi-Yi; Zhang, Chuan-Xi

2013-04-01

A comprehensive but simple-to-use software package called DPS (Data Processing System) has been developed to execute a range of standard numerical analyses and operations used in experimental design, statistics and data mining. This program runs on standard Windows computers. Many of the functions are specific to entomological and other biological research and are not found in standard statistical software. This paper presents applications of DPS to experimental design, statistical analysis and data mining in entomology. © 2012 The Authors Insect Science © 2012 Institute of Zoology, Chinese Academy of Sciences.

KSC-2011-3939

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3941

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3946

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3944

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3940

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3947

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3943

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3945

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3942

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

KSC-2011-3948

NASA Image and Video Library

2011-05-23

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Cory Huston

Data Mining Techniques for Customer Relationship Management

NASA Astrophysics Data System (ADS)

Guo, Feng; Qin, Huilin

2017-10-01

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

30 CFR 282.23 - Testing Plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

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

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

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

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

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

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

PubMed

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

2017-07-01

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

Using reality mining to improve public health and medicine.

PubMed

Pentland, Alex; Lazer, David; Brewer, Devon; Heibeck, Tracy

2009-01-01

We live our lives in digital networks. We wake up in the morning, check our e-mail, make a quick phone call, commute to work, buy lunch. Many of these transactions leave digital breadcrumbs--tiny records of our daily experiences. Reality mining, which pulls together these crumbs using statistical analysis and machine learning methods, offers an increasingly comprehensive picture of our lives, both individually and collectively, with the potential of transforming our understanding of ourselves, our organizations, and our society in a fashion that was barely conceivable just a few years ago. It is for this reason that reality mining was recently identified by Technology Review as one of "10 emerging technologies that could change the world". Many everyday devices provide the raw database upon which reality mining builds; sensors in mobile phones, cars, security cameras, RFID ('smart card') readers, and others, all allow for the measurement of human physical and social activity. Computational models based on such data have the potential to dramatically transform the arenas of both individual and community health. Reality mining can provide new opportunities with respect to diagnosis, patient and treatment monitoring, health services planning, surveillance of disease and risk factors, and public health investigation and disease control. Currently, the single most important source of reality mining data is the ubiquitous mobile phone. Every time a person uses a mobile phone, a few bits of information are left behind. The phone pings the nearest mobile-phone towers, revealing its location. The mobile phone service provider records the duration of the call and the number dialed. In the near future, mobile phones and other technologies will collect even more information about their users, recording everything from their physical activity to their conversational cadences. While such data pose a potential threat to individual privacy, they also offer great potential value both to individuals and communities. With the aid of data-mining algorithms, these data could shed light on individual patterns of behavior and even on the well-being of communities, creating new ways to improve public health and medicine. To illustrate, consider two examples of how reality mining may benefit individual health care. By taking advantage of special sensors in mobile phones, such as the microphone or the accelerometers built into newer devices such as Apple's iPhone, important diagnostic data can be captured. Clinical pilot data demonstrate that it may be possible to diagnose depression from the way a person talks--a depressed person tends to speak more slowly, a change that speech analysis software on a phone might recognize more readily than friends or family do. Similarly, monitoring a phone's motion sensors can also reveal small changes in gait, which could be an early indicator of ailments such as Parkinson's disease. Within the next few years reality mining will become more common, thanks in part to the proliferation and increasing sophistication of mobile phones. Many handheld devices now have the processing power of low-end desktop computers, and they can also collect more varied data, due to components such as GPS chips that track location. The Chief Technology Officer of EMC, a large digital storage company, estimates that this sort of personal sensor data will balloon from 10% of all stored information to 90% within the next decade. While the promise of reality mining is great, the idea of collecting so much personal information naturally raises many questions about privacy. It is crucial that behavior-logging technology not be forced on anyone. But legal statutes are lagging behind data collection capabilities, making it particularly important to begin discussing how the technology will and should be used. Therefore, an additional focus of this chapter will be the development of a legal and ethical framework concerning the data used by reality mining techniques.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

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

PubMed

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

2016-04-01

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

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

EPA Science Inventory

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

Data Mining in Social Media

NASA Astrophysics Data System (ADS)

Barbier, Geoffrey; Liu, Huan

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

A Fast Projection-Based Algorithm for Clustering Big Data.

PubMed

Wu, Yun; He, Zhiquan; Lin, Hao; Zheng, Yufei; Zhang, Jingfen; Xu, Dong

2018-06-07

With the fast development of various techniques, more and more data have been accumulated with the unique properties of large size (tall) and high dimension (wide). The era of big data is coming. How to understand and discover new knowledge from these data has attracted more and more scholars' attention and has become the most important task in data mining. As one of the most important techniques in data mining, clustering analysis, a kind of unsupervised learning, could group a set data into objectives(clusters) that are meaningful, useful, or both. Thus, the technique has played very important role in knowledge discovery in big data. However, when facing the large-sized and high-dimensional data, most of the current clustering methods exhibited poor computational efficiency and high requirement of computational source, which will prevent us from clarifying the intrinsic properties and discovering the new knowledge behind the data. Based on this consideration, we developed a powerful clustering method, called MUFOLD-CL. The principle of the method is to project the data points to the centroid, and then to measure the similarity between any two points by calculating their projections on the centroid. The proposed method could achieve linear time complexity with respect to the sample size. Comparison with K-Means method on very large data showed that our method could produce better accuracy and require less computational time, demonstrating that the MUFOLD-CL can serve as a valuable tool, at least may play a complementary role to other existing methods, for big data clustering. Further comparisons with state-of-the-art clustering methods on smaller datasets showed that our method was fastest and achieved comparable accuracy. For the convenience of most scholars, a free soft package was constructed.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-05-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

PubMed Central

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-01-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468

The impact of the structural features of the rock mass on seismicity in Polish coal mines

NASA Astrophysics Data System (ADS)

Patyńska, Renata

2017-11-01

The article presents seismic activity induced in the coal mines of the Upper Silesian Coal Basin (GZW) in relation to the locations of the occurrence of rockbursts. The comparison of these measurements with the structural features of the rock mass of coal mines indicates the possibility of estimating the so-called Unitary Energy Expenditure (UEE) in a specific time. The obtained values of UEE were compared with the distribution of seismic activity in GZW mines. The level of seismic activity in the analysed period changed and depended on the intensity of mining works and diverse mining and geological conditions. Five regions, where tremors occurred (Bytom Trough, Main Saddle, Main Trough, Kazimierz Trough, and Jejkowice and Chwałowice Trough) which belong to various structural units of the Upper Silesia were analyzed. It was found out that rock bursts were recorded only in three regions: Main Saddle, Bytom Trough, and Jejkowice and Chwałowice Trough.

Recommendation in Higher Education Using Data Mining Techniques

ERIC Educational Resources Information Center

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

2009-01-01

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

Spatio-temporal Outlier Detection in Precipitation Data

NASA Astrophysics Data System (ADS)

Wu, Elizabeth; Liu, Wei; Chawla, Sanjay

The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-k spatial outliers over several time periods. The top-k spatial outliers are found using the Exact-Grid Top- k and Approx-Grid Top- k algorithms, which are an extension of algorithms developed by Agarwal et al. [1]. Since they use the Kulldorff spatial scan statistic, they are capable of discovering all outliers, unaffected by neighbouring regions that may contain missing values. After generating the outlier sequences, we show one way they can be interpreted, by comparing them to the phases of the El Niño Southern Oscilliation (ENSO) weather phenomenon to provide a meaningful analysis of the results.

Visual mining business service using pixel bar charts

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Casati, Fabio

2004-06-01

Basic bar charts have been commonly available, but they only show highly aggregated data. Finding the valuable information hidden in the data is essential to the success of business. We describe a new visualization technique called pixel bar charts, which are derived from regular bar charts. The basic idea of a pixel bar chart is to present all data values directly instead of aggregating them into a few data values. Pixel bar charts provide data distribution and exceptions besides aggregated data. The approach is to represent each data item (e.g. a business transaction) by a single pixel in the bar chart. The attribute of each data item is encoded into the pixel color and can be accessed and drilled down to the detail information as needed. Different color mappings are used to represent multiple attributes. This technique has been prototyped in three business service applications-Business Operation Analysis, Sales Analysis, and Service Level Agreement Analysis at Hewlett Packard Laboratories. Our applications show the wide applicability and usefulness of this new idea.

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

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

Hypersalinity reduces the risk of cyanide toxicosis to insectivorous bats interacting with wastewater impoundments at gold mines.

PubMed

Griffiths, Stephen R; Donato, David B; Lumsden, Linda F; Coulson, Graeme

2014-01-01

Wildlife and livestock that ingest bioavailable cyanide compounds in gold mining tailings dams are known to experience cyanide toxicosis. Elevated levels of salinity in open impoundments have been shown to prevent wildlife cyanide toxicosis by reducing drinking and foraging. This finding appears to be consistent for diurnal wildlife interacting with open impoundments, however the risks to nocturnal wildlife of cyanide exposure are unknown. We investigated the activity of insectivorous bats in the airspace above both fresh (potable to wildlife) and saline water bodies at two gold mines in the goldfields of Western Australian. During this study, cyanide-bearing solutions stored in open impoundments at both mine sites were hypersaline (range=57,000-295,000 mg/L total dissolved solids (TDS)), well above known physiological tolerance of any terrestrial vertebrate. Bats used the airspace above each water body monitored, but were more active at fresh than saline water bodies. In addition, considerably more terminal echolocation buzz calls were recorded in the airspace above fresh than saline water bodies at both mine sites. However, it was not possible to determine whether these buzz calls corresponded to foraging or drinking bouts. No drinking bouts were observed in 33 h of thermal video footage recorded at one hypersaline tailings dam, suggesting that this water is not used for drinking. There is no information on salinity tolerances of bats, but it could be assumed that bats would not tolerate salinity in drinking water at concentrations greater than those documented as toxic for saline-adapted terrestrial wildlife. Therefore, when managing wastewater impoundments at gold mines to avoid wildlife mortalities, adopting a precautionary principle, bats are unlikely to drink solutions at salinity levels ≥50,000 mg/L TDS. © 2013 Published by Elsevier Inc.

Mind the seafloor

NASA Astrophysics Data System (ADS)

Boetius, Antje; Haeckel, Matthias

2018-01-01

As human use of rare metals has diversified and risen with global development, metal ore deposits from the deep ocean floor are increasingly seen as an attractive future resource. Japan recently completed the first successful test for zinc extraction from the deep seabed, and the number of seafloor exploration licenses filed at the International Seabed Authority (ISA) has tripled in the past 5 years. Seafloor-mining equipment is being tested, and industrial-scale production in national waters could start in a few years. We call for integrated scientific studies of global metal resources, the fluxes and fates of metal uses, and the ecological footprints of mining on land and in the sea, to critically assess the risks of deep-sea mining and the chances for alternative technologies. Given the increasing scientific evidence for long-lasting impacts of mining on the abyssal environment, precautionary regulations for commercial deep-sea mining are essential to protect marine ecosystems and their biodiversity.

Distributed data mining on grids: services, tools, and applications.

PubMed

Cannataro, Mario; Congiusta, Antonio; Pugliese, Andrea; Talia, Domenico; Trunfio, Paolo

2004-12-01

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

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

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques.

PubMed

Chen, Annie T; Zhu, Shu-Hong; Conway, Mike

2015-09-29

The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people's experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people's experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a "hobbyist culture" that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design.

KSC-2011-4154

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- A remote controlled or autonomous excavator, called a lunabot, is on display outside of the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida where university students maneuver their remote controlled lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4148

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- While an event judge looks on, university students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

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

PubMed

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

2016-03-24

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

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

Complex Feeding Tracks of the Sessile Herbivorous Insect Ophiomyia maura as a Function of the Defense against Insect Parasitoids

PubMed Central

Ayabe, Yoshiko; Ueno, Takatoshi

2012-01-01

Because insect herbivores generally suffer from high mortality due to their natural enemies, reducing the risk of being located by natural enemies is of critical importance for them, forcing them to develop a variety of defensive measures. Larvae of leaf-mining insects lead a sedentary life inside a leaf and make conspicuous feeding tracks called mines, exposing themselves to the potential risk of parasitism. We investigated the defense strategy of the linear leafminer Ophiomyia maura Meigen (Diptera: Agromyzidae), by focusing on its mining patterns. We examined whether the leafminer could reduce the risk of being parasitized (1) by making cross structures in the inner area of a leaf to deter parasitoids from tracking the mines due to complex pathways, and (2) by mining along the edge of a leaf to hinder visually searching parasitoids from finding mined leaves due to effective background matching of the mined leaves among intact leaves. We quantified fractal dimension as mine complexity and area of mine in the inner area of the leaf as interior mine density for each sample mine, and analyzed whether these mine traits affected the susceptibility of O. maura to parasitism. Our results have shown that an increase in mine complexity with the development of occupying larvae decreases the probability of being parasitized, while interior mine density has no influence on parasitism. These results suggest that the larval development increases the host defense ability through increasing mine complexity. Thus the feeding pattern of these sessile insects has a defensive function by reducing the risk of parasitism. PMID:22393419

Mining of high utility-probability sequential patterns from uncertain databases

PubMed Central

Zhang, Binbin; Fournier-Viger, Philippe; Li, Ting

2017-01-01

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds. PMID:28742847

Process Mining Online Assessment Data

ERIC Educational Resources Information Center

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

2009-01-01

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

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Detection and Monitoring of Small-Scale Mining Operations in the Eastern Democratic Republic of the Congo (DRC) Using Multi-Temporal, Multi-Sensor Remote Sensing Data

NASA Astrophysics Data System (ADS)

Walther, Christian; Frei, Michaela

2017-04-01

Mining of so-called "conflict minerals" is often related with small-scale mining activities. The here discussed activities are located in forested areas in the eastern DRC, which are often remote, difficult to access and insecure for traditional geological field inspection. In order to accelerate their CTC (Certified Trading Chain)-certification process, remote sensing data are used for detection and monitoring of these small-scale mining operations. This requires a high image acquisition frequency due to mining site relocations and for compensation of year-round high cloud coverage, especially for optical data evaluation. Freely available medium resolution optical data of Sentinel-2 and Landsat-8 as well as SAR data of Sentinel-1 are used for detecting small mining targets with a minimum size of approximately 0.5 km2. The developed method enables a robust multi-temporal detection of mining sites, monitoring of mining site spatio-temporal relocations and environmental changes. Since qualitative and quantitative comparable results are generated, the followed change detection approach is objective and transparent and may push the certification process forward.

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.

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Exploring patterns of epigenetic information with data mining techniques.

PubMed

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

2013-01-01

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

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

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

Israel and Iran: A Dangerous Rivalry

DTIC Science & Technology

2011-01-01

and Knesset view the Islamic Republic as “a bitter ideological enemy that is deter- mined to bring about the physical annihilation of Israel”; only...entirely different set of values. . . . Iran sends children into mine fields. Iran denies the Holocaust. Iran openly calls for Israel’s destruction...compromise on sovereignty by having U.S. troops deployed here.” Quoted in Barbara Opall -Rome, “U.S. to Deploy Radar, Troops In Israel,” Defense News

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

ERIC Educational Resources Information Center

Hung, Jui-Long; Zhang, Ke

2012-01-01

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

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

Treesearch

John A Parrotta; Oliver H. Knowles

1999-01-01

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

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

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

2017-01-01

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

Efficient Kriging Algorithms

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess

2011-01-01

More efficient versions of an interpolation method, called kriging, have been introduced in order to reduce its traditionally high computational cost. Written in C++, these approaches were tested on both synthetic and real data. Kriging is a best unbiased linear estimator and suitable for interpolation of scattered data points. Kriging has long been used in the geostatistic and mining communities, but is now being researched for use in the image fusion of remotely sensed data. This allows a combination of data from various locations to be used to fill in any missing data from any single location. To arrive at the faster algorithms, sparse SYMMLQ iterative solver, covariance tapering, Fast Multipole Methods (FMM), and nearest neighbor searching techniques were used. These implementations were used when the coefficient matrix in the linear system is symmetric, but not necessarily positive-definite.

PRO-Elicere: A Study for Create a New Process of Dependability Analysis of Space Computer Systems

NASA Astrophysics Data System (ADS)

da Silva, Glauco; Netto Lahoz, Carlos Henrique

2013-09-01

This paper presents the new approach to the computer system dependability analysis, called PRO-ELICERE, which introduces data mining concepts and intelligent mechanisms to decision support to analyze the potential hazards and failures of a critical computer system. Also, are presented some techniques and tools that support the traditional dependability analysis and briefly discusses the concept of knowledge discovery and intelligent databases for critical computer systems. After that, introduces the PRO-ELICERE process, an intelligent approach to automate the ELICERE, a process created to extract non-functional requirements for critical computer systems. The PRO-ELICERE can be used in the V&V activities in the projects of Institute of Aeronautics and Space, such as the Brazilian Satellite Launcher (VLS-1).

"A gold mine and a tool for democracy": George Gallup, Elmo Roper, and the business of scientific polling, 1935-1955.

PubMed

Igo, Sarah E

2006-01-01

"Scientific" public opinion polls arrived on the American scene in 1936. Examining the work of opinion surveyors George Gallup and Elmo Roper, this essay tracks the early career of a new social scientific technology, one that powerfully shaped conceptions of "the public." Pollsters described their craft as a democratic one that could accurately represent the U.S. populace. Yet, their assumptions about that same public-and the techniques they employed to measure it-undermined such claims, and even risked calling the polling profession into question. To understand why Gallup and Roper fell short of their stated ambitions, one must turn not only to the state of midcentury sampling methods but also to the corporate sponsors and commercial pressures underlying their enterprise.

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

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

Illustrated surface mining methods

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

Not Available

1979-01-01

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

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

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…

KSC-2011-4005

NASA Image and Video Library

2011-05-25

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Frankie Martin

KSC-2011-3956

NASA Image and Video Library

2011-05-24

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-3955

NASA Image and Video Library

2011-05-24

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4151

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-3954

NASA Image and Video Library

2011-05-24

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4006

NASA Image and Video Library

2011-05-25

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Frankie Martin

KSC-2011-4003

NASA Image and Video Library

2011-05-25

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Frankie Martin

KSC-2011-4016

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-3957

NASA Image and Video Library

2011-05-24

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

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

2003-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

PubMed

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

2013-10-01

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

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Development of Test Rig for Robotization of Mining Technological Processes - Oversized Rock Breaking Process Case

NASA Astrophysics Data System (ADS)

Pawel, Stefaniak; Jacek, Wodecki; Jakubiak, Janusz; Zimroz, Radoslaw

2017-12-01

Production chain (PCh) in underground copper ore mine consists of several subprocesses. From our perspective implementation of so called ZEPA approach (Zero Entry Production Area) might be very interesting [16]. In practice, it leads to automation/robotization of subprocesses in production area. In this paper was investigated a specific part of PCh i.e. a place when cyclic transport by LHDs is replaced with continuous transport by conveying system. Such place is called dumping point. The objective of dumping points with screen is primary classification of the material (into coarse and fine material) and breaking oversized rocks with hydraulic hammer. Current challenges for the underground mining include e.g. safety improvement as well as production optimization related to bottlenecks, stoppages and operational efficiency of the machines. As a first step, remote control of the hydraulic hammer has been introduced, which not only transferred the operator to safe workplace, but also allowed for more comfortable work environment and control over multiple technical objects by a single person. Today literature analysis shows that current mining industry around the world is oriented to automation and robotization of mining processes and reveals technological readiness for 4th industrial revolution. The paper is focused on preliminary analysis of possibilities for the use of the robotic system to rock-breaking process. Prototype test rig has been proposed and experimental works have been carried out. Automatic algorithms for detection of oversized rocks, crushing them as well as sweeping and loosening of material have been formulated. Obviously many simplifications have been assumed. Some near future works have been proposed.

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.

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

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

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

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

Data mining and visualization techniques

DOEpatents

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

[Analysis of syndrome discipline of generalized anxiety disorder using data mining techniques].

PubMed

Tang, Qi-sheng; Sun, Wen-jun; Qu, Miao; Guo, Dong-fang

2012-09-01

To study the use of data mining techniques in analyzing the syndrome discipline of generalized anxiety disorder (GAD). From August 1, 2009 to July 31, 2010, 705 patients with GAD in 10 hospitals of Beijing were investigated over one year. Data mining techniques, such as Bayes net and cluster analysis, were used to analyze the syndrome discipline of GAD. A total of 61 symptoms of GAD were screened out. By using Bayes net, nine syndromes of GAD were abstracted based on the symptoms. Eight syndromes were abstracted by cluster analysis. After screening for duplicate syndromes and combining the experts' experience and traditional Chinese medicine theory, six syndromes of GAD were defined. These included depressed liver qi transforming into fire, phlegm-heat harassing the heart, liver depression and spleen deficiency, heart-kidney non-interaction, dual deficiency of the heart and spleen, and kidney deficiency and liver yang hyperactivity. Based on the results, the draft of Syndrome Diagnostic Criteria for Generalized Anxiety Disorder was developed. Data mining techniques such as Bayes net and cluster analysis have certain future potential for establishing syndrome models and analyzing syndrome discipline, thus they are suitable for the research of syndrome differentiation.

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Data mining in child welfare.

PubMed

Schoech, D; Quinn, A; Rycraft, J R

2000-01-01

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Reforestation of mined land in the northeastern and north-central U.S.

Treesearch

Walter H. Davidson; Russell J. Hutnik; Delbert E. Parr

1984-01-01

This paper reviews the state of the art of surface mine reclamation for forestry in Pennsylvania, Maryland, West Virginia, Ohio, Indiana, and Illinois. Legislative constraints, socioeconomic issues, factors limiting the success of reforestation efforts, post-mining land-use trends, species options, and establishment techniques are discussed. Sources of assistance to...

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Increasing the Reliability of the Work of Artificial Filtering Arrays for the Purification of Quarry Waste Water

NASA Astrophysics Data System (ADS)

Tyulenev, Maxim; Lesin, Yury; Litvin, Oleg; Maliukhina, Elena; Abay, Asmelash

2017-11-01

Features of geological structure of the Kuznetsk coal basin stipulate the application of a low-cost open technique of coal mining, which is more advantageous both from the economic standpoint, and by safety criteria of mining. However, open mining affects significantly the water resources of region. Intensive pollution of reservoirs and water courses, exhaustion of the underground water-bearing layers, violation of a hydrographic network, etc. be-long to the main disadvantages of an open technique of coal mining. Besides, the volume of the water coming into the mining producers exceeds signi-ficantly the needed quantity. According to the data of annual reports of ecology and natural resources department, 348.277 million m3 of water were ta-ken away during production of soft coal, brown coal and lignum fossil from waters of Kemerovo region in 2013 (mostly from underground water objects (96,5%) when draining of mine openings). At the same time, only 87.018 million m3 of water (25%) has been used within a year.

Methods and costs of thin-seam mining. Final report, 25 September 1977-24 January 1979. [Thin seam in association with a thick seam

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

Finch, T.E.; Fidler, E.L.

1981-02-01

This report defines the state of the art (circa 1978) in removing thin coal seams associated with vastly thicker seams found in the surface coal mines of the western United States. New techniques are evaluated and an innovative method and machine is proposed. Western states resource recovery regulations are addressed and representative mining operations are examined. Thin seam recovery is investigated through its effect on (1) overburden removal, (2) conventional seam extraction methods, and (3) innovative techniques. Equations and graphs are used to accommodate the variable stratigraphic positions in the mining sequence on which thin seams occur. Industrial concern andmore » agency regulations provided the impetus for this study of total resource recovery. The results are a compendium of thin seam removal methods and costs. The work explains how the mining industry recovers thin coal seams in western surface mines where extremely thick seams naturally hold the most attention. It explains what new developments imply and where to look for new improvements and their probable adaptability.« less

Field test of an alternative longwall gate road design

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

Cox, R.M.; Vandergrift, T.L.; McDonnell, J.P.

1994-01-01

The US Bureau of Mines (USBM) MULSIM/ML modeling technique has been used to analyze anticipated stress distributions for a proposed alternative longwall gate road design for a western Colorado coal mine. The model analyses indicated that the alternative gate road design would reduce stresses in the headgate entry. To test the validity of the alternative gate road design under actual mining conditions, a test section of the alternative system was incorporated into a subsequent set of gate roads developed at the mine. The alternative gate road test section was instrumented with borehole pressure cells, as part of an ongoing USBMmore » research project to monitor ground pressure changes as longwall mining progressed. During the excavation of the adjacent longwall panels, the behavior of the alternative gate road system was monitored continuously using the USBM computer-assisted Ground Control Management System. During these field tests, the alternative gate road system was first monitored and evaluated as a headgate, and later monitored and evaluated as a tailgate. The results of the field tests confirmed the validity of using the MULSIM/NL modeling technique to evaluate mine designs.« less

All-Optical Fibre Networks For Coal Mines

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1987-09-01

A topic of the paper is fiber-optic integrated network (FOIN) suited to the most hostile environments existing in coal mines. The use of optical fibres for transmission of mine instrumentation data offers the prospects of improved safety and immunity to electromagnetic interference (EMI). The feasibility of optically powered sensors has opened up new opportunities for research into optical signal processing architectures. This article discusses a new fibre-optic sensor network involving a time domain multiplexing(TDM)scheme and optical signal processing techniques. The pros and cons of different FOIN topologies with respect to coal mine applications are considered. The emphasis has been placed on a recently developed all-optical fibre network using spread spectrum code division multiple access (COMA) techniques. The all-optical networks have applications in explosive environments where electrical isolation is required.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

Bats, cyanide, and gold mining

USGS Publications Warehouse

Clark, Donald R.

1991-01-01

Although the boom days of prospectors and gold nuggets are long gone, modern technology enables gold to continue to be extracted from ore. Unfortunately, the extraction method has often been disastrous for bats and other wildlife, an issue I first became aware of in early 1989. Phone calls from Drs. Merlin Tuttle and Elizabeth Pierson, a BCI member and bat researcher from Berkeley, California, alerted me that bats were dying from apparent cyanide poisoning at gold mines in the western United States.

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

The contamination of Lake Superior with amphibole gangue minerals.

PubMed

Langer, A M; Maggiore, C M; Nicholson, W J; Rohl, A N; Rubin, I B; Selikoff, I J

1979-01-01

Iron ore called taconite is mined in the Biwabik Iron Formation in the Eastern Mesabi region of the Mesabi Range, in eastern Minnesota. After mining, ore is shipped to Silver Bay, Minnnesota for processing and wet magnetic extraction. Tailings from the process are dumped, as a slurry, into a man-made containment delta constructed in Lake Superior. Submicroscopic amphibole fibers and/or cleavage fragments, a component of the gangue, apparently escape from the delta at Silver Bay, and enter Lake Superior. These particles contaiminate the potable water supplies of municipalities drawing directly from the lake. One of the gangue minerals is the amphibole grunerite, whose asbestiform variety is called amosite. Major emphasis of this study was directed at identification of submicroscopic particle pollutants, based on morphology, structure and chemical composition. Quantitative determination of fibrous amphibole phases, present in a range of water samples, was undertaken. Transmission electron microscopy, selected area electron diffraction, and an electron microprobe technique was used for identification and enumeration and this information was compared with data sets determined from standards. Grunerite fiber and/or acicular cleavage fragments, in some instances indistinguishable from asbestiform grunerite, are present in the tailings, lake water and drinking water of a number of municipalities, a result of contamination of the lake at the Silver Bay milling operation. This amphibole is found in drinking water in concentrations which range from 0.6 to 2.8 X 10(6) fiber/liter. The risk to health, associated with direct ingestion of grunerite fiber is unknown and is extrapolated from the asbestiform grunerite (amosite) data base. The biological activity of other fibrous amphiboles observed, unrelated to any asbestiform silicate variety, is presently unknown and warrants investigation.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

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

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH CONVEYORS. JIM WALTER RESOURCES INC. MINING DIVISION OPERATES FOUR UNDERGROUND COAL MINES IN THE BLUE CREEK COAL FIELD OF BIRMINGHAM DISTRICT, THREE IN TUSCALOOSA COUNTY AND ONE IN JEFFERSON COUNTY. TOTAL ANNUAL PRODUCTION IS 8,000,000 TONS. AT 2,300 DEEP, JIM WALTER'S BROOKWOOD MINES ARE THE DEEPEST UNDERGROUND COAL MINES IN NORTH AMERICA. THEY PRODUCE A HIGH-GRADE MEDIUM VOLATILE LOW SULPHUR METALLURGICAL COAL. THE BROOKWOOD NO. 5 MINE (PICTURED IN THIS PHOTOGRAPH) EMPLOYS THE LONGWALL MINING TECHNIQUES WITH BELTS CONVEYING COAL FROM UNDERGROUND OPERATIONS TO THE SURFACE. - JIm Walter Resources, Incorporated, Brookwood No. 5 Mine, 12972 Lock 17 Road, Brookwood, Tuscaloosa County, AL

Solutions Network Formulation Report. Landsat Data Continuity Mission Simulated Data Products for Bureau of Land Management and Environmental Protection Agency Abandoned Mine Lands Decision Support

NASA Technical Reports Server (NTRS)

Estep, Leland

2007-01-01

Presently, the BLM (Bureau of Land Management) has identified a multitude of abandoned mine sites in primarily Western states for cleanup. These sites are prioritized and appropriate cleanup has been called in to reclaim the sites. The task is great in needing considerable amounts of agency resources. For instance, in Colorado alone there exists an estimated 23,000 abandoned mines. The problem is not limited to Colorado or to the United States. Cooperation for reclamation is sought at local, state, and federal agency level to aid in identification, inventory, and cleanup efforts. Dangers posed by abandoned mines are recognized widely and will tend to increase with time because some of these areas are increasingly used for recreation and, in some cases, have been or are in the process of development. In some cases, mines are often vandalized once they are closed. The perpetrators leave them open, so others can then access the mines without realizing the danger posed. Abandoned mine workings often fill with water or oxygen-deficient air and dangerous gases following mining. If the workings are accidentally entered into, water or bad air can prove fatal to those underground. Moreover, mine residue drainage negatively impacts the local watershed ecology. Some of the major hazards that might be monitored by higher-resolution satellites include acid mine drainage, clogged streams, impoundments, slides, piles, embankments, hazardous equipment or facilities, surface burning, smoke from underground fires, and mine openings.

Use of an automatic earth resistivity system for detection of abandoned mine workings

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

Peters, W.R.; Burdick, R.

1982-04-01

Under the sponsorship of the US Bureau of Mines, a surface-operated automatic high resolution earth resistivity system and associated computer data processing techniques have been designed and constructed for use as a potential means of detecting abandoned coal mine workings. The hardware and software aspects of the new system are described together with applications of the method to the survey and mapping of abandoned mine workings.

Australia unlocks her uranium reserves. [Will develop deposits in Northern Territories

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

Scott, W.E.

1977-11-01

The economic implications of Australia's move to permit the development of uranium mining and to resume exporting uranium have led to forecasts that range from pessimism over unseen factors to an optimistic estimate of $A20 billion and 500,000 jobs. Direct benefits will go to those involved in road construction, mining equipment, and construction camps. The goverment plan calls for mining operations and yellowcake exports from four major uranium mines by 1985. An overview is given of the development plan, which emphasizes an orderly procedure rather than exploitation and excessive competition. The uranium industry is viewed as a stable long-term suppliermore » for international trade. Customers will be required to submit to international Atomic Energy Agency inspection and must guarantee to limit their uranium use to peaceful projects. (DCK)« less

Coal companies hope to receive carbon credits for methane reductions

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

NONE

2007-09-30

Each year, underground coal mining in the USA liberates 2.4 million tonnes of coal mine methane (CMM), of which less than 30% is recovered and used. One barrier to CMM recovery is cost. Drainage, collection, and utilization systems are complex and expensive to install. Two coal mines have improved the cost equation, however, by signing on to earn money for CMM emissions they are keeping out of the atmosphere. Jim Walter Resources and PinnOak Resources have joined a voluntary greenhouse gas reduction trading program called the Chicago Climate Exchange (CCX) to turn their avoided emissions into carbon credits. The examplemore » they set may encourage other coal mining companies to follow suit, and may bring new projects on the line that would otherwise have not gone forward. 2 refs., 1 fig.« less

Development of ergonomics audits for bagging, haul truck and maintenance and repair operations in mining.

PubMed

Dempsey, Patrick G; Pollard, Jonisha; Porter, William L; Mayton, Alan; Heberger, John R; Gallagher, Sean; Reardon, Leanna; Drury, Colin G

2017-12-01

The development and testing of ergonomics and safety audits for small and bulk bag filling, haul truck and maintenance and repair operations in coal preparation and mineral processing plants found at surface mine sites is described. The content for the audits was derived from diverse sources of information on ergonomics and safety deficiencies including: analysis of injury, illness and fatality data and reports; task analysis; empirical laboratory studies of particular tasks; field studies and observations at mine sites; and maintenance records. These diverse sources of information were utilised to establish construct validity of the modular audits that were developed for use by mine safety personnel. User and interrater reliability testing was carried out prior to finalising the audits. The audits can be implemented using downloadable paper versions or with a free mobile NIOSH-developed Android application called ErgoMine. Practitioner Summary: The methodology used to develop ergonomics audits for three types of mining operations is described. Various sources of audit content are compared and contrasted to serve as a guide for developing ergonomics audits for other occupational contexts.

The Process of People Gold Mining in Paningkaban Village Banyumas Indonesia

NASA Astrophysics Data System (ADS)

Muslihudin; Bambang, Azis Nur; Hendarto, Eko; Putranto, Thomas Triadi

2018-02-01

Gold mining in Paningkaban Banyumas conducted by the community is called the People gold mining. At the beginning, many miners from outside the region have involved and transferred of method, technic and knowledge about gold mining to local people. The aim of the study is to identify the existing process of public gold mining. The method of the study is qualitative by using observation and interview. The result showed that the mining process are: 1. Determining the location of mining well; in this determination there are two references; rational and intuition 2. Mining; at this stage, a deep well is drawn about 50-100 meters that leads vertically and horizontally. It is the most high-risk stage because of work accidents that occurred and potentially environment destruction. 3. Pulverization; this stage is classified as the lowest level of difficulty and risk, therefore in this work many woman included. 4. Rolling; in this stage involves enough technology, electrical mechanic and energy with the dynamo and using mercury that potentially contaminate environment. 5. Filtering; this stage is a quite risky because the workers contact directly with mercury. 6. Burning; is the shortest process to separate mercury with gold grains. 7. Sales to local buyer guided by the international gold market in every Thursday.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Naica Mine, Chihuahua, Mexico

NASA Image and Video Library

2007-10-02

The Naica mine in Chihuahua, Mexico, with its enormous gypsum crystals, may well be called the "Queen of the Giant Crystals localities." Though the Naica mine is no show mine, but still a working lead-zinc mine hosted in layered limestones, the first of several crystal caves was discovered in 1910. This "Cave of the Swords" contained extraordinary large sword-like selenite (gypsum) crystals up to 2 m long. In 2000 another crystal cave system was discovered at 300 m depth, even more spectacular than the original cave. Inside were free growing gypsum crystals up to 12 m long and 2 m in diameter. The ASTER image uses SWIR bands 4, 6, and 8 in RGB. Limestone is displayed in yellow-green colors, vegetation is red. The image was acquired February 16, 2004, covers an area of 26 x 23.5 km, and is located near 27.8 degrees north latitude, 105.5 degrees west longitude. The photo of crystals was taken from: http://www.thatcrystalsite.com/. http://photojournal.jpl.nasa.gov/catalog/PIA10615

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

Neural Networks In Mining Sciences - General Overview And Some Representative Examples

NASA Astrophysics Data System (ADS)

Tadeusiewicz, Ryszard

2015-12-01

The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article - which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for. Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice. The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication in the journal "Archives of Mining Sciences". Only studying these two articles will provide sufficient knowledge for initial guidance in the area of issues under consideration here.

KSC-2011-4155

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4152

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. hirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4141

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition held at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4160

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4004

NASA Image and Video Library

2011-05-25

CAPE CANAVERAL, Fla. -- University students prepare for NASA's second annual Lunabotics Mining Competition inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Frankie Martin

KSC-2011-4158

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4159

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4153

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4171

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, participants applaud the winning team of the competition during the NASA's second annual Lunabotics Mining Competition award ceremony. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4164

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, university students take part in an award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4142

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition held at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4013

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- University students gather for the opening ceremony of NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4169

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, Kennedy Center Director Bob Cabana speaks to university students at the award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4146

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition held at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4143

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition held at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4163

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, university students take part in an award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4140

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students make final preparations for NASA's second annual Lunabotics Mining Competition held at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS

USGS Publications Warehouse

Chirico, Peter G.; Dewitt, Jessica D.

2017-01-01

This study evaluates the use of a small unmanned aerial system (UAS) to collect imagery over artisanal mining sites in West Africa. The purpose of this study is to consider how very high-resolution imagery and digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetric techniques from a small UAS can fill the gap in geospatial data collection between satellite imagery and data gathered during field work to map and monitor informal mining sites in tropical environments. The study compares both wide-angle and narrow field of view camera systems in the collection and analysis of high-resolution orthoimages and DSMs of artisanal mining pits. The results of the study indicate that UAS imagery and SfM photogrammetric techniques permit DSMs to be produced with a high degree of precision and relative accuracy, but highlight the challenges of mapping small artisanal mining pits in remote and data sparse terrain.

An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences.

PubMed

Ye, Kai; Kosters, Walter A; Ijzerman, Adriaan P

2007-03-15

Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In contrast, two algorithms, PRATT2 (http//www.ebi.ac.uk/pratt/) and TEIRESIAS (http://cbcsrv.watson.ibm.com/) are used to directly identify frequent patterns from unaligned biological sequences without an attempt to align them. Here we propose a new algorithm with more efficiency and more functionality than both PRATT2 and TEIRESIAS, and discuss some of its applications to G protein-coupled receptors, a protein family of important drug targets. In this study, we designed and implemented six algorithms to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets. The source code for pattern growth algorithms and their pseudo-code are available at http://www.liacs.nl/home/kosters/pg/.

Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes.

PubMed

Izad Shenas, Seyed Abdolmotalleb; Raahemi, Bijan; Hossein Tekieh, Mohammad; Kuziemsky, Craig

2014-10-01

In this paper, we use data mining techniques, namely neural networks and decision trees, to build predictive models to identify very high-cost patients in the top 5 percentile among the general population. A large empirical dataset from the Medical Expenditure Panel Survey with 98,175 records was used in our study. After pre-processing, partitioning and balancing the data, the refined dataset of 31,704 records was modeled by Decision Trees (including C5.0 and CHAID), and Neural Networks. The performances of the models are analyzed using various measures including accuracy, G-mean, and Area under ROC curve. We concluded that the CHAID classifier returns the best G-mean and AUC measures for top performing predictive models ranging from 76% to 85%, and 0.812 to 0.942 units, respectively. We also identify a small set of 5 non-trivial attributes among a primary set of 66 attributes to identify the top 5% of the high cost population. The attributes are the individual׳s overall health perception, age, history of blood cholesterol check, history of physical/sensory/mental limitations, and history of colonic prevention measures. The small set of attributes are what we call non-trivial and does not include visits to care providers, doctors or hospitals, which are highly correlated with expenditures and does not offer new insight to the data. The results of this study can be used by healthcare data analysts, policy makers, insurer, and healthcare planners to improve the delivery of health services. Copyright © 2014 Elsevier Ltd. All rights reserved.

Treatment of iron(II)-rich acid mine water with limestone and oxygen.

PubMed

Mohajane, G B; Maree, J P; Panichev, N

2014-01-01

The main components of acid mine water are free acid, sulphate, and Fe²⁺. Limestone is the most cost-effective alkali that can be used for neutralization. The purpose of this investigation was to identify conditions where Fe²⁺ is removed with limestone and simultaneously oxidized with oxygen to Fe³⁺, in a polyvinyl chloride pipe under pressure. Gypsum scaling is prevented by passing rubber balls through the pipe of the so-called Oxygen-Pipe-Neutralization (OPeN) process pilot plant. Two synthetic waters were treated: (A) acid mine water containing 123 mg L⁻¹ Fe²⁺ representing gold mine water, and (B) acid mine water containing 6,032 mg L⁻¹ Fe²⁺ representing coal mine water. Batch studies were carried out in a pipe reactor and showed that the rate of Fe²⁺ oxidation depended on the Fe²⁺ concentration, oxygen pressure, amount of recycled sludge, limestone dosage and the mixing rate. Continuous studies in an OPeN process pilot plant resulted in 100% removal of total acidity from synthetic coal mine water and a 98% removal from synthetic gold mine water. Fe²⁺ was removed completely as precipitated Fe(OH)₃ from both synthetic coal and gold mine water at around pH 7 at 200 and 100 kPa oxygen pressure, respectively.

Computer-aided visual assessment in mine planning and design

Treesearch

Michael Hatfield; A. J. LeRoy Balzer; Roger E. Nelson

1979-01-01

A computer modeling technique is described for evaluating the visual impact of a proposed surface mine located within the viewshed of a national park. A computer algorithm analyzes digitized USGS baseline topography and identifies areas subject to surface disturbance visible from the park. Preliminary mine and reclamation plan information is used to describe how the...

A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success

ERIC Educational Resources Information Center

Huebner, Richard A.

2017-01-01

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years

ERIC Educational Resources Information Center

Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald

2018-01-01

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…

Assessing the evolution of sustainability reporting in the mining sector.

PubMed

Perez, Fabiana; Sanchez, Luis E

2009-06-01

Since the 1990s several large companies have been publishing nonfinancial performance reports. Focusing initially on the physical environment, these reports evolved to consider social relations, as well as data on the firm's economic performance. A few mining companies pioneered this trend, and in the last years some of them incorporated the three dimensions of sustainable development, publishing so-called sustainability reports. This article reviews 31 reports published between 2001 and 2006 by four major mining companies. A set of 62 assessment items organized in six categories (namely context and commitment, management, environmental, social and economic performance, and accessibility and assurance) were selected to guide the review. The items were derived from international literature and recommended best practices, including the Global Reporting Initiative G3 framework. A content analysis was performed using the report as a sampling unit, and using phrases, graphics, or tables containing certain information as data collection units. A basic rating scale (0 or 1) was used for noting the presence or absence of information and a final percentage score was obtained for each report. Results show that there is a clear evolution in report's comprehensiveness and depth. Categories "accessibility and assurance" and "economic performance" featured the lowest scores and do not present a clear evolution trend in the period, whereas categories "context and commitment" and "social performance" presented the best results and regular improvement; the category "environmental performance," despite it not reaching the biggest scores, also featured constant evolution. Description of data measurement techniques, besides more comprehensive third-party verification are the items most in need of improvement.

Landscape management in an area affected by surface brown coal mining

NASA Astrophysics Data System (ADS)

Vráblíková, J.; Wildová, E.; Vráblík, P.; Blažková, M.

2017-10-01

The contribution summarizes results of a project concentrated on landscape management of an area affected by brown coal mining located in northern Bohemia (The Most basin) focusing on restoration and reclamation processes. It describes in particular the shares of individual types of reclamations in the area of interest. A strategic document that also supports landscape restoration in anthropogenically burdened regions was written within the project called “Restart” and the second part of the contribution is focused on its chapters which address this issue.

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Application of Modern Tools and Techniques for Mine Safety & Disaster Management

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj

2016-04-01

The implementation of novel systems and adoption of improvised equipment in mines help mining companies in two important ways: enhanced mine productivity and improved worker safety. There is a substantial need for adoption of state-of-the-art automation technologies in the mines to ensure the safety and to protect health of mine workers. With the advent of new autonomous equipment used in the mine, the inefficiencies are reduced by limiting human inconsistencies and error. The desired increase in productivity at a mine can sometimes be achieved by changing only a few simple variables. Significant developments have been made in the areas of surface and underground communication, robotics, smart sensors, tracking systems, mine gas monitoring systems and ground movements etc. Advancement in information technology in the form of internet, GIS, remote sensing, satellite communication, etc. have proved to be important tools for hazard reduction and disaster management. This paper is mainly focused on issues pertaining to mine safety and disaster management and some of the recent innovations in the mine automations that could be deployed in mines for safe mining operations and for avoiding any unforeseen mine disaster.

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

PubMed

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges

PubMed Central

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area. PMID:28401016

Enhanced Product Generation at NASA Data Centers Through Grid Technology

NASA Technical Reports Server (NTRS)

Barkstrom, Bruce R.; Hinke, Thomas H.; Gavali, Shradha; Seufzer, William J.

2003-01-01

This paper describes how grid technology can support the ability of NASA data centers to provide customized data products. A combination of grid technology and commodity processors are proposed to provide the bandwidth necessary to perform customized processing of data, with customized data subsetting providing the initial example. This customized subsetting engine can be used to support a new type of subsetting, called phenomena-based subsetting, where data is subsetted based on its association with some phenomena, such as mesoscale convective systems or hurricanes. This concept is expanded to allow the phenomena to be detected in one type of data, with the subsetting requirements transmitted to the subsetting engine to subset a different type of data. The subsetting requirements are generated by a data mining system and transmitted to the subsetter in the form of an XML feature index that describes the spatial and temporal extent of the phenomena. For this work, a grid-based mining system called the Grid Miner is used to identify the phenomena and generate the feature index. This paper discusses the value of grid technology in facilitating the development of a high performance customized product processing and the coupling of a grid mining system to support phenomena-based subsetting.

Application of the Deformation Information System for automated analysis and mapping of mining terrain deformations - case study from SW Poland

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Grzempowski, Piotr; Milczarek, Wojciech; Nowacka, Anna

2015-04-01

Monitoring, mapping and modelling of mining induced terrain deformations are important tasks for quantifying and minimising threats that arise from underground extraction of useful minerals and affect surface infrastructure, human safety, the environment and security of the mining operation itself. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and expanding with the progress in geographical information technologies. These include for example: terrestrial geodetic measurements, Global Navigation Satellite Systems, remote sensing, GIS based modelling and spatial statistics, finite element method modelling, geological modelling, empirical modelling using e.g. the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The presentation shows the results of numerical modelling and mapping of mining terrain deformations for two cases of underground mining sites in SW Poland, hard coal one (abandoned) and copper ore (active) using the functionalities of the Deformation Information System (DIS) (Blachowski et al, 2014 @ http://meetingorganizer.copernicus.org/EGU2014/EGU2014-7949.pdf). The functionalities of the spatial data modelling module of DIS have been presented and its applications in modelling, mapping and visualising mining terrain deformations based on processing of measurement data (geodetic and GNSS) for these two cases have been characterised and compared. These include, self-developed and implemented in DIS, automation procedures for calculating mining terrain subsidence with different interpolation techniques, calculation of other mining deformation parameters (i.e. tilt, horizontal displacement, horizontal strain and curvature), as well as mapping mining terrain categories based on classification of the values of these parameters as used in Poland. Acknowledgments. This work has been financed from the National Science Centre Project "Development of a numerical method of mining ground deformation modelling in complex geological and mining conditions" UMO-2012/07/B/ST10/04297 executed at the Faculty of Geoengineering, Mining and Geology of the Wroclaw University of Technology (Poland).

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

USGS Publications Warehouse

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

2010-01-01

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

Unmanned Mine of the 21st Centuries

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

High levels of activity of bats at gold mining water bodies: implications for compliance with the International Cyanide Management Code.

PubMed

Griffiths, Stephen R; Donato, David B; Coulson, Graeme; Lumsden, Linda F

2014-06-01

Wildlife and livestock are known to visit and interact with tailings dam and other wastewater impoundments at gold mines. When cyanide concentrations within these water bodies exceed a critical toxicity threshold, significant cyanide-related mortality events can occur in wildlife. Highly mobile taxa such as birds are particularly susceptible to cyanide toxicosis. Nocturnally active bats have similar access to uncovered wastewater impoundments as birds; however, cyanide toxicosis risks to bats remain ambiguous. This study investigated activity of bats in the airspace above two water bodies at an Australian gold mine, to assess the extent to which bats use these water bodies and hence are at potential risk of exposure to cyanide. Bat activity was present on most nights sampled during the 16-month survey period, although it was highly variable across nights and months. Therefore, despite the artificial nature of wastewater impoundments at gold mines, these structures present attractive habitats to bats. As tailings slurry and supernatant pooling within the tailings dam were consistently well below the industry protective concentration limit of 50 mg/L weak acid dissociable (WAD) cyanide, wastewater solutions stored within the tailings dam posed a minimal risk of cyanide toxicosis for wildlife, including bats. This study showed that passively recorded bat echolocation call data provides evidence of the presence and relative activity of bats above water bodies at mine sites. Furthermore, echolocation buzz calls recorded in the airspace directly above water provide indirect evidence of foraging and/or drinking. Both echolocation monitoring and systematic sampling of cyanide concentration in open wastewater impoundments can be incorporated into a gold mine risk-assessment model in order to evaluate the risk of bat exposure to cyanide. In relation to risk minimisation management practices, the most effective mechanism for preventing cyanide toxicosis to wildlife, including bats, is capping the concentration of cyanide in tailings discharged to open impoundments at 50 mg/L WAD.

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

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

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

Identifying the Source of Gem Diamonds: Requirements for a Certification System

NASA Astrophysics Data System (ADS)

Shigley, J. E.

2002-05-01

Recent civil conflicts in several countries, in which profits from the sales of gem diamonds have supported the rival factions, have forced the jewelry industry to confront the need to certify the geographic sources of gem diamonds. The goals of this program are to prohibit the sale of so-called "conflict diamonds", and to prevent the loss of consumer confidence. Efforts to identify unique characteristics of gem diamonds have been hampered so far by the absence of chemical or physical features that are diagnostic of particular sources, and the lack of a representative collection of diamonds from major producing areas that would be required for a rigorous scientific study. The jewelry industry has therefore adopted plans to track gem diamonds from the mine through the manufacturing process to the consumer. Practical requirements for implementation of such a certification system will be summarized. Any proposed solutions for determining the sources of gem diamonds by some analytical technique, or for following diamonds from the mine, must take into account the annual production of several tens of millions of carats of rough diamonds, which are transformed during manufacturing into several hundreds of millions of polished gemstones (with an average weight of only about 0.03 carat, or 0.006 gram).

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

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

Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques

PubMed Central

Zhu, Shu-Hong; Conway, Mike

2015-01-01

Background The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a “hobbyist culture” that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design. PMID:26420469

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

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

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

Use of an automatic resistivity system for detecting abandoned mine workings

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

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

1983-01-01

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

Preventing spontaneous combustion after mine closing

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

Lewicki, G.

1987-11-01

The author explains how the Northern Coal Company and a Houston-based firefighting firm developed an innovative technique to reduce the risk of spontaneous combustion after mine closing in its Rienau number2 Mine. The ''Light Water TM'' ATC series of firefighting foam concentrates were designed for extinguishing flammable liquid fires. By slightly altering the chemicals, the concentrates could be used to seal the coal ribs, floor, and roof, reducing the risk of combustion. Subsequent monitoring of the mine has identified no signs of heating.

Artisanal and Small-Scale Gold Mining Without Mercury

EPA Pesticide Factsheets

Mercury-free techniques are safer for miners, their families and local communities. They can also help miners qualify for certification under fair-mined standards, potentially allowing them to market their gold at higher prices.

The Pollution Detectives: Part II. Lead and Zinc Mining.

ERIC Educational Resources Information Center

Sanderson, P. L.

1988-01-01

Describes a field trip taken to an old mining area to study water pollution. Discussed are methods for silt analysis, reagent preparation, color charts, techniques, fieldwork, field results, and a laboratory study. (CW)

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.

Abundance of arbuscular mychorrizal fungi in rehabilitation area of nickel post-mining land of Sorowako, South Sulawesi

NASA Astrophysics Data System (ADS)

Akib, M. A.; Mustari, K.; Kuswinanti, T.; Syaiful, S. A.

2018-05-01

Acceleration management of land rehabilitation in nickel post-mining in Sorowako has been main attention of Vale Indonesia. This acceleration can be done by utilizing of natural resources, especially indigenous endomycorrhiza. Endomycorrhiza also called arbuscular mycorrhizal has got a lot of attention for its ability to form a mutualistic symbiosis with 80% - 96% of plant species. This study aims to determine the dominance of indigenous endomycorrhiza spores and its potential to accelerate the management of land rehabilitation post-mining of nickel, which is carried out in three stages; sampling rhizosphere, trapping spores, isolation and identification of the arbuscular mycorrhizal spores types. The results showed that the dominance of indigenous endomycorrhiza were Acalauspora sp (75.1%), Gigaspora sp (19.4%) and Glomus sp (5.6%). Research on the effectiveness of indigenous endomycorrhiza using Acalauspora sp in land rehabilitation of nickel post-mining is still ongoing.

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

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schubert, J.

1974-01-01

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

An integrated environment monitoring system for underground coal mines--Wireless Sensor Network subsystem with multi-parameter monitoring.

PubMed

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-07-21

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

Optimization of fertirrigation efficiency in strawberry crops by application of fuzzy logic techniques.

PubMed

de la Torre, M L; Grande, J A; Aroba, J; Andujar, J M

2005-11-01

A high level of price support has favoured intensive agriculture and an increasing use of fertilisers and pesticides. This has resulted in the pollution of water and soils and damage to certain eco-systems. The target relationship that must be established between agriculture and environment can be called "sustainable agriculture". In this work we aim at relating strawberry total yield with nitrate concentration in water at different soil depths. To achieve this objective, we have used the Predictive Fuzzy Rules Generator (PreFuRGe) tool, based on fuzzy logic and data mining, by means of which the dose that allows a balance between yield and environmental damage minimization can be determined. This determination is quite simple and is done directly from the obtained charts. This technique can be used in other types of crops permitting one to determine in a precise way at which depth the appropriate dose of nitrate fertilizer must be correctly applied, on the one hand providing the maximum yield but, on the other hand, with the minimum loss of nitrates that leachate through the saturated zone polluting aquifers.

Chaos as an intermittently forced linear system.

PubMed

Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kaiser, Eurika; Kutz, J Nathan

2017-05-30

Understanding the interplay of order and disorder in chaos is a central challenge in modern quantitative science. Approximate linear representations of nonlinear dynamics have long been sought, driving considerable interest in Koopman theory. We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. This work combines delay embedding and Koopman theory to decompose chaotic dynamics into a linear model in the leading delay coordinates with forcing by low-energy delay coordinates; this is called the Hankel alternative view of Koopman (HAVOK) analysis. This analysis is applied to the Lorenz system and real-world examples including Earth's magnetic field reversal and measles outbreaks. In each case, forcing statistics are non-Gaussian, with long tails corresponding to rare intermittent forcing that precedes switching and bursting phenomena. The forcing activity demarcates coherent phase space regions where the dynamics are approximately linear from those that are strongly nonlinear.The huge amount of data generated in fields like neuroscience or finance calls for effective strategies that mine data to reveal underlying dynamics. Here Brunton et al.develop a data-driven technique to analyze chaotic systems and predict their dynamics in terms of a forced linear model.

The design and implementation of web mining in web sites security

NASA Astrophysics Data System (ADS)

Li, Jian; Zhang, Guo-Yin; Gu, Guo-Chang; Li, Jian-Li

2003-06-01

The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information, so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density-Based Clustering technique is used to reduce resource cost and obtain better efficiency.

SparkText: Biomedical Text Mining on Big Data Framework.

PubMed

Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

Pongamia pinnata inoculated with Bradyrhizobium liaoningense PZHK1 shows potential for phytoremediation of mine tailings.

PubMed

Yu, Xiumei; Li, Yangxin; Li, Yanmei; Xu, Chaohua; Cui, Yongliang; Xiang, Quanju; Gu, Yunfu; Zhao, Ke; Zhang, Xiaoping; Penttinen, Petri; Chen, Qiang

2017-02-01

Mine tailings contain high concentrations of metal contaminants and only little nutrients, making the tailings barren for decades after the mining has been terminated. Effective phytoremediation of mine tailings calls for deep-rooted, metal accumulating, and soil fertility increasing plants with tolerance against harsh environmental conditions. We assessed the potential of the biofuel leguminous tree Pongamia pinnata inoculated with plant growth promoting rhizobia to remediate iron-vanadium-titanium oxide (V-Ti magnetite) mine tailing soil by pot experiment and in situ remediation test. A metal tolerant rhizobia strain PZHK1 was isolated from the tailing soil and identified as Bradyrhizobium liaoningense by phylogenetic analysis. Inoculation with PZHK1 increased the growth of P. pinnata both in V-Ti magnetite mine tailings and in Ni-contaminated soil. Furthermore, inoculation increased the metal accumulation capacity and superoxide dismutase activity of P. pinnata. The concentrations of Ni accumulated by inoculated plants were higher than the hyperaccumulator threshold. Inoculated P. pinnata accumulated high concentration of Fe, far exceeding the upper limit (1000 mg kg -1 ) of Fe in plant tissue. In summary, P. pinnata-B. liaoningense PZHK1 symbiosis showed potential to be applied as an effective phytoremediation technology for mine tailings and to produce biofuel feedstock on the marginal land.

SparkText: Biomedical Text Mining on Big Data Framework

PubMed Central

He, Karen Y.; Wang, Kai

2016-01-01

Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652

Reducing Mercury Pollution from Artisanal and Small-Scale Gold Mining

EPA Pesticide Factsheets

To reduce airborne mercury emissions from these Gold Shops, EPA and the Argonne National Laboratory (ANL) have partnered to design a low cost, easily constructible technology called the Gold Shop Mercury Capture System (MCS).

KSC-2011-4166

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, Eric Reiners, manager with the Product Development and Global Technology Division of Caterpillar Inc., speaks to university students at the award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4156

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students wait their turn to compete in NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida taking place near the complex's Rocket Garden. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4014

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Pat Simpkins, Kennedy Space Center engineering director talks to university students gathered for the opening ceremony of NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4147

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- Outside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, NASA astronaut John McBride (center) discusses the day's events with event leaders during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4170

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, the Laurentian University Team from Ontario, accepts a check for its "lunabot," which came in first place at NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4015

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Tana Utley, Caterpillar Company vice president and chief technology officer talks to university students gathered for the opening ceremony of NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4168

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, Bill Moore, Visitor Complex chief operating officer speaks to university students at the award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4165

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, Rob Mueller Kennedy's chief of the Surface Systems Office speaks to university students at the award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

QuadBase2: web server for multiplexed guanine quadruplex mining and visualization

PubMed Central

Dhapola, Parashar; Chowdhury, Shantanu

2016-01-01

DNA guanine quadruplexes or G4s are non-canonical DNA secondary structures which affect genomic processes like replication, transcription and recombination. G4s are computationally identified by specific nucleotide motifs which are also called putative G4 (PG4) motifs. Despite the general relevance of these structures, there is currently no tool available that can allow batch queries and genome-wide analysis of these motifs in a user-friendly interface. QuadBase2 (quadbase.igib.res.in) presents a completely reinvented web server version of previously published QuadBase database. QuadBase2 enables users to mine PG4 motifs in up to 178 eukaryotes through the EuQuad module. This module interfaces with Ensembl Compara database, to allow users mine PG4 motifs in the orthologues of genes of interest across eukaryotes. PG4 motifs can be mined across genes and their promoter sequences in 1719 prokaryotes through ProQuad module. This module includes a feature that allows genome-wide mining of PG4 motifs and their visualization as circular histograms. TetraplexFinder, the module for mining PG4 motifs in user-provided sequences is now capable of handling up to 20 MB of data. QuadBase2 is a comprehensive PG4 motif mining tool that further expands the configurations and algorithms for mining PG4 motifs in a user-friendly way. PMID:27185890

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry.

PubMed

Anguera, A; Barreiro, J M; Lara, J A; Lizcano, D

2016-01-01

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

Growth and Heavy Metal Accumulation of Koelreuteria Paniculata Seedlings and Their Potential for Restoring Manganese Mine Wastelands in Hunan, China

PubMed Central

Huang, Zhihong; Xiang, Wenhua; Ma, Yu’e; Lei, Pifeng; Tian, Dalun; Deng, Xiangwen; Yan, Wende; Fang, Xi

2015-01-01

The planting of trees on mine wastelands is an effective, long-term technique for phytoremediation of heavy metal-contaminated wastes. In this study, a pot experiment with seedlings of Koelreuteria paniculata under six treatments of local mine wastes was designed to determine the major constraints on tree establishment and to evaluate the feasibility of planting K. paniculata on manganese mine wastelands. Results showed that K. paniculata grew well in mine tailings, and also under a regime of equal amounts of mine tailings and soil provided in adjacent halves of pots. In contrast, mine sludge did not favor survival and growth because its clay texture limited fine root development. The bio-concentration factor and the translocation factor were mostly less than 1, indicating a low phytoextraction potential for K. paniculata. K. paniculata is suited to restore manganese mine sludge by mixing the mine sludge with local mine tailings or soil. PMID:25654773

The status of the Callio Lab Underground Laboratory in the Pyhäsalmi mine

NASA Astrophysics Data System (ADS)

Joutsenvaara, Jari; Enqvist, Timo; Isoherranen, Ville; Jalas, Panu; Kutuniva, Johanna; Kuusiniemi, Pasi

2017-04-01

We present the structure and the latest technical characteristics of the Callio Lab, the new underground laboratory managing the scientific and other non-mining related operations in the Pyhäsalmi mine in Pyhäjärvi, Finland. The very deep laboratory hall space, called Lab 2 of Callio Lab, was finished in spring 2016 at the depth of 1 430 metres (4 100 m.w.e.). Callio Lab has also other easily accessible (by car or truck) halls for laboratory use, for example at the depths of 440 m, 600 m and 990 m. We also review the current and planned activities related to particle physics, applied sciences, industrial R&D and production.

Monitoring of the mercury mining site Almadén implementing remote sensing technologies.

PubMed

Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio

2013-08-01

The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from several sources at different scales greatly improves the regional characterization and monitoring of an area dominated by mercury mining activities. Copyright © 2013 Elsevier Inc. All rights reserved.

The risk of collapse in abandoned mine sites: the issue of data uncertainty

NASA Astrophysics Data System (ADS)

Longoni, Laura; Papini, Monica; Brambilla, Davide; Arosio, Diego; Zanzi, Luigi

2016-04-01

Ground collapses over abandoned underground mines constitute a new environmental risk in the world. The high risk associated with subsurface voids, together with lack of knowledge of the geometric and geomechanical features of mining areas, makes abandoned underground mines one of the current challenges for countries with a long mining history. In this study, a stability analysis of Montevecchia marl mine is performed in order to validate a general approach that takes into account the poor local information and the variability of the input data. The collapse risk was evaluated through a numerical approach that, starting with some simplifying assumptions, is able to provide an overview of the collapse probability. The final results is an easy-accessible-transparent summary graph that shows the collapse probability. This approach may be useful for public administrators called upon to manage this environmental risk. The approach tries to simplify this complex problem in order to achieve a roughly risk assessment, but, since it relies on just a small amount of information, any final user should be aware that a comprehensive and detailed risk scenario can be generated only through more exhaustive investigations.

An Expertise Recommender using Web Mining

NASA Technical Reports Server (NTRS)

Joshi, Anupam; Chandrasekaran, Purnima; ShuYang, Michelle; Ramakrishnan, Ramya

2001-01-01

This report explored techniques to mine web pages of scientists to extract information regarding their expertise, build expertise chains and referral webs, and semi automatically combine this information with directory information services to create a recommender system that permits query by expertise. The approach included experimenting with existing techniques that have been reported in research literature in recent past , and adapted them as needed. In addition, software tools were developed to capture and use this information.

Assessing the Ability of Hyperspectral Data to Detect Lyngbya SPP.: A Potential Biological Indicator for Presence of Metal Objects in the Littoral Environment

DTIC Science & Technology

2006-12-01

environment. This concept would have potential benefits and applications in mine detection and countermeasure techniques. Using a USB2000 field...be distinguished by a different phycocyanin absorption, at 615-632 nm. 15. NUMBER OF PAGES 261 14. SUBJECT TERMS Hyperspectral... applications in mine detection and countermeasure techniques. Using a USB2000 field spectroradiometer, a spectral library was developed for the

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

PubMed

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

2013-01-01

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

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

PubMed Central

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

2013-01-01

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

On the classification techniques in data mining for microarray data classification

NASA Astrophysics Data System (ADS)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Minehunting sonar system research and development

NASA Astrophysics Data System (ADS)

Ferguson, Brian

2002-05-01

Sea mines have the potential to threaten the freedom of the seas by disrupting maritime trade and restricting the freedom of maneuver of navies. The acoustic detection, localization, and classification of sea mines involves a sequence of operations starting with the transmission of a sonar pulse and ending with an operator interpreting the information on a sonar display. A recent improvement to the process stems from the application of neural networks to the computed aided detection of sea mines. The advent of ultrawideband sonar transducers together with pulse compression techniques offers a thousandfold increase in the bandwidth-time product of conventional minehunting sonar transmissions enabling stealth mines to be detected at longer ranges. These wideband signals also enable mines to be imaged at safe standoff distances by applying tomographic image reconstruction techniques. The coupling of wideband transducer technology with synthetic aperture processing enhances the resolution of side scan sonars in both the cross-track and along-track directions. The principles on which conventional and advanced minehunting sonars are based are reviewed and the results of applying novel sonar signal processing algorithms to high-frequency sonar data collected in Australian waters are presented.

Cooperative organic mine avoidance path planning

NASA Astrophysics Data System (ADS)

McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David

2005-06-01

The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.

Slope stability radar for monitoring mine walls

NASA Astrophysics Data System (ADS)

Reeves, Bryan; Noon, David A.; Stickley, Glen F.; Longstaff, Dennis

2001-11-01

Determining slope stability in a mining operation is an important task. This is especially true when the mine workings are close to a potentially unstable slope. A common technique to determine slope stability is to monitor the small precursory movements, which occur prior to collapse. The slope stability radar has been developed to remotely scan a rock slope to continuously monitor the spatial deformation of the face. Using differential radar interferometry, the system can detect deformation movements of a rough wall with sub-millimeter accuracy, and with high spatial and temporal resolution. The effects of atmospheric variations and spurious signals can be reduced via signal processing means. The advantage of radar over other monitoring techniques is that it provides full area coverage without the need for mounted reflectors or equipment on the wall. In addition, the radar waves adequately penetrate through rain, dust and smoke to give reliable measurements, twenty-four hours a day. The system has been trialed at three open-cut coal mines in Australia, which demonstrated the potential for real-time monitoring of slope stability during active mining operations.

Fluid placement of fixated scrubber sludge to reduce surface subsidence and to abate acid mine drainage in abandoned underground coal mines

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

Meiers, R.J.; Golden, D.; Gray, R.

1995-12-31

Indianapolis Power and Light Company (IPL) began researching the use of fluid placement techniques of the fixated scrubber sludge (FSS) to reduce surface subsidence from underground coal mines to develop an economic alternative to low strength concrete grout. Abandoned underground coal mines surround property adjacent to IPL`s coal combustion by-product (CCBP) landfill at the Petersburg Generating Station. Landfill expansion into these areas is in question because of the high potential for sinkhole subsidence to develop. Sinkholes manifesting at the surface would put the integrity of a liner or runoff pond containment structure for a CCBP disposal facility at risk. Themore » fluid placement techniques of the FSS as a subsidence abatement technology was demonstrated during an eight week period in September, October, and November 1994 at the Petersburg Generating Station. The success of this technology will be determined by the percentage of the mine void filled, strength of the FSS placed, and the overall effects on the hydrogeologic environment. The complete report for this project will be finalized in early 1996.« less

Horizontal hydraulic conductivity estimates for intact coal barriers between closed underground mines

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

Mccoy, K.J.; Donovan, J.J.; Leavitt, B.R.

2006-08-15

Unmined blocks of coal, called barriers, separate and restrict horizontal leakage between adjacent bituminous coal mines. Understanding the leakage rate across such barriers is important in planning mine closure and strongly affects recharge calculations for postmining flooding. This study presents upper-limit estimates for hydraulic conductivity (K) of intact barriers in two closed mines at moderate depth (75-300 m) in the Pittsburgh coal basin. The estimates are based on pumping rates from these mines for the years ranging from 1992 to 2000. The two mines do not approach the outcrop and are sufficiently deep that vertical infiltration is thought to bemore » negligible. Similarly, there are no saturated zones on the pumped mines' side of shared barriers with other mines, and therefore pumping is the only outflow. Virtually all of the pumping is attributed to leakage across or over the top of barriers shared with upgradient flooded mines. The length of shared barriers totals 24 km for the two mines, and the barriers range in thickness from 15 to 50 m. K values calculated independently for each of the 9 years of the pumping record ranged from 0.037 m/d to 0.18 m/d using an isotropic model of barrier flow. Using an anisotropic model for differential K in the face cleat (K{sub f}) and butt cleat (K{sub b}) directions, results range from 0.074 to 0.34 m/d for K{sub f} and from 0.022 to 0.099 m/d for K{sub b}.« less

Industrial application of semantic process mining

NASA Astrophysics Data System (ADS)

Espen Ingvaldsen, Jon; Atle Gulla, Jon

2012-05-01

Process mining relates to the extraction of non-trivial and useful information from information system event logs. It is a new research discipline that has evolved significantly since the early work on idealistic process logs. Over the last years, process mining prototypes have incorporated elements from semantics and data mining and targeted visualisation techniques that are more user-friendly to business experts and process owners. In this article, we present a framework for evaluating different aspects of enterprise process flows and address practical challenges of state-of-the-art industrial process mining. We also explore the inherent strengths of the technology for more efficient process optimisation.

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%.

Open-source tools for data mining.

PubMed

Zupan, Blaz; Demsar, Janez

2008-03-01

With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

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

Blasting Rocks and Blasting Cars Applied Engineering

ScienceCinema

LBNL

2017-12-09

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

Environmental Education in Brazil: Preventive Measures to Avoid Contamination with U and Th

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

Silva Pastura, Valeria Fonseca da; Wieland, Patricia

2008-08-07

Aiming at increasing awareness of radiation health effects, environmental issues and preventive measures, the Nuclear Energy National Commission (CNEN) launched in 2004 an education and public outreach programme for mine workers, students, teachers, governmental leaders, labor representatives and members of communities nearby small mining sites at the North and Northeast regions. Many Brazilian conventional mines present a significant risk of exposure to radiation due to Uranium and Thorium. CNEN inspects the mines but there are several small mining sites dedicated to open pit short term mineral extraction, called 'garimpagem', that are of difficult control. Therefore, information at large about preventivemore » measures to avoid contamination during exploration, transportation and storage is necessary. CNEN developed an educational campaign which includes a series of open seminars, talks, folders, booklets and posters. The objective of this paper is to present the Brazilian educational campaign to avoid contamination risks at those small mineral exploration sites and its results. This campaign is a joint task that receives collaboration of other organizations such as federal police, schools and universities.« less

Environmental Education in Brazil: Preventive Measures to Avoid Contamination with U and Th

NASA Astrophysics Data System (ADS)

da Silva Pastura, Valéria Fonseca; Wieland, Patricia

2008-08-01

Aiming at increasing awareness of radiation health effects, environmental issues and preventive measures, the Nuclear Energy National Commission (CNEN) launched in 2004 an education and public outreach programme for mine workers, students, teachers, governmental leaders, labor representatives and members of communities nearby small mining sites at the North and Northeast regions. Many Brazilian conventional mines present a significant risk of exposure to radiation due to Uranium and Thorium. CNEN inspects the mines but there are several small mining sites dedicated to open pit short term mineral extraction, called "garimpagem", that are of difficult control. Therefore, information at large about preventive measures to avoid contamination during exploration, transportation and storage is necessary. CNEN developed an educational campaign which includes a series of open seminars, talks, folders, booklets and posters. The objective of this paper is to present the Brazilian educational campaign to avoid contamination risks at those small mineral exploration sites and its results. This campaign is a joint task that receives collaboration of other organizations such as federal police, schools and universities.

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

PubMed

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

2008-01-01

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

Astroinformatics, data mining and the future of astronomical research

NASA Astrophysics Data System (ADS)

Brescia, Massimo; Longo, Giuseppe

2013-08-01

Astronomy, as many other scientific disciplines, is facing a true data deluge which is bound to change both the praxis and the methodology of every day research work. The emerging field of astroinformatics, while on the one end appears crucial to face the technological challenges, on the other is opening new exciting perspectives for new astronomical discoveries through the implementation of advanced data mining procedures. The complexity of astronomical data and the variety of scientific problems, however, call for innovative algorithms and methods as well as for an extreme usage of ICT technologies.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

The application of satellite data in monitoring strip mines

NASA Technical Reports Server (NTRS)

Sharber, L. A.; Shahrokhi, F.

1977-01-01

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

Back analysis of fault-slip in burst prone environment

NASA Astrophysics Data System (ADS)

Sainoki, Atsushi; Mitri, Hani S.

2016-11-01

In deep underground mines, stress re-distribution induced by mining activities could cause fault-slip. Seismic waves arising from fault-slip occasionally induce rock ejection when hitting the boundary of mine openings, and as a result, severe damage could be inflicted. In general, it is difficult to estimate fault-slip-induced ground motion in the vicinity of mine openings because of the complexity of the dynamic response of faults and the presence of geological structures. In this paper, a case study is conducted for a Canadian underground mine, herein called "Mine-A", which is known for its seismic activities. Using a microseismic database collected from the mine, a back analysis of fault-slip is carried out with mine-wide 3-dimensional numerical modeling. A back analysis is conducted to estimate the physical and mechanical properties of the causative fracture or shear zones. One large seismic event has been selected for the back analysis to detect a fault-slip related seismic event. In the back analysis, the shear zone properties are estimated with respect to moment magnitude of the seismic event and peak particle velocity (PPV) recorded by a strong ground motion sensor. The estimated properties are then validated through comparison with peak ground acceleration recorded by accelerometers. Lastly, ground motion in active mining areas is estimated by conducting dynamic analysis with the estimated values. The present study implies that it would be possible to estimate the magnitude of seismic events that might occur in the near future by applying the estimated properties to the numerical model. Although the case study is conducted for a specific mine, the developed methodology can be equally applied to other mines suffering from fault-slip related seismic events.

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.

Data mining of air traffic control operational errors

DOT National Transportation Integrated Search

2006-01-01

In this paper we present the results of : applying data mining techniques to identify patterns and : anomalies in air traffic control operational errors (OEs). : Reducing the OE rate is of high importance and remains a : challenge in the aviation saf...

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring.

PubMed

Asner, Gregory P; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-11-12

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

Application of a Novel Liquid Nitrogen Control Technique for Heat Stress and Fire Prevention in Underground Mines.

PubMed

Shi, Bobo; Ma, Lingjun; Dong, Wei; Zhou, Fubao

2015-01-01

With the continually increasing mining depths, heat stress and spontaneous combustion hazards in high-temperature mines are becoming increasingly severe. Mining production risks from natural hazards and exposures to hot and humid environments can cause occupational diseases and other work-related injuries. Liquid nitrogen injection, an engineering control developed to reduce heat stress and spontaneous combustion hazards in mines, was successfully utilized for environmental cooling and combustion prevention in an underground mining site named "Y120205 Working Face" (Y120205 mine) of Yangchangwan colliery. Both localized humidities and temperatures within the Y120205 mine decreased significantly with liquid nitrogen injection. The maximum percentage drop in temperature and humidity of the Y120205 mine were 21.9% and 10.8%, respectively. The liquid nitrogen injection system has the advantages of economical price, process simplicity, energy savings and emission reduction. The optimized heat exchanger used in the liquid nitrogen injection process achieved superior air-cooling results, resulting in considerable economic benefits.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring

PubMed Central

Asner, Gregory P.; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-01-01

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests. PMID:24167281

[Hygienic and ergonomic analysis of the technology for sinking main and subsidiary mine shafts].

PubMed

Meniaĭlo, N I; Tyshlek, E G; Gritsenko, V S; Shemiakin, G M

1989-01-01

The labour conditions in mine shafts do not correspond to the existing ergonomic and hygienic norms. Drilling and blasting techniques are most hazardous as to the gravity and duration of the factors involved. Working conditions normalization should be based on the elaboration of specifically innovative technologies which should envisage the workers' periodic staying in the mine shaft area during the work shift.

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

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

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

Detection of buried objects by fusing dual-band infrared images

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

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-11-01

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete. This paper focuses on the fusion of two-band infrared images. We use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infraredmore » images and evaluation of the techniques using two real data sets.« less

Hydraulic hoisting and backfilling

NASA Astrophysics Data System (ADS)

Sauermann, H. B.

In a country such as South Africa, with its large deep level mining industry, improvements in mining and hoisting techniques could result in substantial savings. Hoisting techniques, for example, may be improved by the introduction of hydraulic hoisting. The following are some of the advantages of hydraulic hoisting as against conventional skip hoisting: (1) smaller shafts are required because the pipes to hoist the same quantity of ore hydraulically require less space in the shaft than does skip hoisting equipment; (2) the hoisting capacity of a mine can easily be increased without the necessity of sinking new shafts. Large savings in capital costs can thus be made; (3) fully automatic control is possible with hydraulic hoisting and therefore less manpower is required; and (4) health and safety conditions will be improved.

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

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

Settling Efficiency of Urban Particulate Matter Transported by Stormwater Runoff.

PubMed

Carbone, Marco; Penna, Nadia; Piro, Patrizia

2015-09-01

The main purpose of control measures in urban areas is to retain particulate matter washed out by stormwater over impermeable surfaces. In stormwater control measures, particulate matter removal typically occurs via sedimentation. Settling column tests were performed to examine the settling efficiency of such units using monodisperse and heterodisperse particulate matter (for which the particle size distributions were measured and modelled by the cumulative gamma distribution). To investigate the dependence of settling efficiency from the particulate matter, a variant of the evolutionary polynomial regression (EPR), a Microsoft Excel function based on multi-objective EPR technique (EPR-MOGA), called EPR MOGA XL, was used as a data-mining strategy. The results from this study have shown that settling efficiency is a function of the initial total suspended solids (TSS) concentration and of the median diameter (d50 index), obtained from the particle size distributions (PSDs) of the samples.

Instances selection algorithm by ensemble margin

NASA Astrophysics Data System (ADS)

Saidi, Meryem; Bechar, Mohammed El Amine; Settouti, Nesma; Chikh, Mohamed Amine

2018-05-01

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.

Prospecting for new bacterial metabolites: a glossary of approaches for inducing, activating and upregulating the biosynthesis of bacterial cryptic or silent natural products.

PubMed

Zarins-Tutt, Joseph Scott; Barberi, Tania Triscari; Gao, Hong; Mearns-Spragg, Andrew; Zhang, Lixin; Newman, David J; Goss, Rebecca Jane Miriam

2016-01-01

Covering: up to 2015. Over the centuries, microbial secondary metabolites have played a central role in the treatment of human diseases and have revolutionised the pharmaceutical industry. With the increasing number of sequenced microbial genomes revealing a plethora of novel biosynthetic genes, natural product drug discovery is entering an exciting second golden age. Here, we provide a concise overview as an introductory guide to the main methods employed to unlock or up-regulate these so called 'cryptic', 'silent' and 'orphan' gene clusters, and increase the production of the encoded natural product. With a predominant focus on bacterial natural products we will discuss the importance of the bioinformatics approach for genome mining, the use of first different and simple culturing techniques and then the application of genetic engineering to unlock the microbial treasure trove.

The development of enhanced ripple-fire identification methods using high frequency data from Pinedale

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

Carr, D.; Garbin, H.D.

1996-01-01

A technique called ripple fire used in quarry blasts produces modulations in the spectra of these events. The Deployable Seismic Verification System (DSVS) was installed at the Pinedale Seismic Research Facility in Wyoming, an area with a lot of mining activity. DSVS records at frequencies up to 50 Hz and these data provides us with a unique opportunity to determine how well we can discriminate quarry blasts and if there are operational benefits from using high frequency (>20 Hz) data. We have collected a database of 646 events consisting of known earthquakes, known quarry blasts and unknown signals. We havemore » started to calculate preliminary spectrograms if we get the time-independent banding from the quarry blasts, and at what frequencies the banning occurs. We also detail what we hope to accomplish in FY 1996.« less

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

PubMed

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

2016-08-01

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

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Sustainable rehabilitation of mining waste and acid mine drainage using geochemistry, mine type, mineralogy, texture, ore extraction and climate knowledge.

PubMed

Anawar, Hossain Md

2015-08-01

The oxidative dissolution of sulfidic minerals releases the extremely acidic leachate, sulfate and potentially toxic elements e.g., As, Ag, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Th, U, Zn, etc. from different mine tailings and waste dumps. For the sustainable rehabilitation and disposal of mining waste, the sources and mechanisms of contaminant generation, fate and transport of contaminants should be clearly understood. Therefore, this study has provided a critical review on (1) recent insights in mechanisms of oxidation of sulfidic minerals, (2) environmental contamination by mining waste, and (3) remediation and rehabilitation techniques, and (4) then developed the GEMTEC conceptual model/guide [(bio)-geochemistry-mine type-mineralogy- geological texture-ore extraction process-climatic knowledge)] to provide the new scientific approach and knowledge for remediation of mining wastes and acid mine drainage. This study has suggested the pre-mining geological, geochemical, mineralogical and microtextural characterization of different mineral deposits, and post-mining studies of ore extraction processes, physical, geochemical, mineralogical and microbial reactions, natural attenuation and effect of climate change for sustainable rehabilitation of mining waste. All components of this model should be considered for effective and integrated management of mining waste and acid mine drainage. Copyright © 2015 Elsevier Ltd. All rights reserved.

75 FR 48366 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-10

...: OMB Desk Officer for the Department of Labor--Mine Safety and Health Administration (MSHA), Office of..., electronic, mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration...

Biomedical hypothesis generation by text mining and gene prioritization.

PubMed

Petric, Ingrid; Ligeti, Balazs; Gyorffy, Balazs; Pongor, Sandor

2014-01-01

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.

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

NASA Technical Reports Server (NTRS)

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

1973-01-01

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

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.

Data Mining and Complex Problems: Case Study in Composite Materials

NASA Technical Reports Server (NTRS)

Rabelo, Luis; Marin, Mario

2009-01-01

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Application of geostatistics to coal-resource characterization and mine planning. Final report

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

Kauffman, P.W.; Walton, D.R.; Martuneac, L.

1981-12-01

Geostatistics is a proven method of ore reserve estimation in many non-coal mining areas but little has been published concerning its application to coal resources. This report presents the case for using geostatistics for coal mining applications and describes how a coal mining concern can best utilize geostatistical techniques for coal resource characterization and mine planning. An overview of the theory of geostatistics is also presented. Many of the applications discussed are documented in case studies that are a part of the report. The results of an exhaustive literature search are presented and recommendations are made for needed future researchmore » and demonstration projects.« less

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

PubMed Central

Mande, Sharmila S.

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm. PMID:27124399

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques.

PubMed

Tandon, Disha; Haque, Mohammed Monzoorul; Mande, Sharmila S

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

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

Assessing the Environmental and Socio-Economic Impacts of Artisanal Gold Mining on the Livelihoods of Communities in the Tarkwa Nsuaem Municipality in Ghana.

PubMed

Obiri, Samuel; Mattah, Precious A D; Mattah, Memuna M; Armah, Frederick A; Osae, Shiloh; Adu-kumi, Sam; Yeboah, Philip O

2016-01-26

Gold mining has played an important role in Ghana's economy, however the negative environmental and socio-economic effects on the host communities associated with gold mining have overshadowed these economic gains. It is within this context that this paper assessed in an integrated manner the environmental and socio-economic impacts of artisanal gold mining in the Tarkwa Nsuaem Municipality from a natural and social science perspective. The natural science group collected 200 random samples on bi-weekly basis between January to October 2013 from water bodies in the study area for analysis in line with methods outlined by the American Water Works Association, while the social science team interviewed 250 residents randomly selected for interviews on socio-economic issues associated with mining. Data from the socio-economic survey was analyzed using logistic regression with SPSS version 17. The results of the natural science investigation revealed that the levels of heavy metals in water samples from the study area in most cases exceeded GS 175-1/WHO permissible guideline values, which are in tandem with the results of inhabitants' perceptions of water quality survey (as 83% of the respondents are of the view that water bodies in the study area are polluted). This calls for cost-benefits analysis of mining before new mining leases are granted by the relevant authorities.

Assessing the Environmental and Socio-Economic Impacts of Artisanal Gold Mining on the Livelihoods of Communities in the Tarkwa Nsuaem Municipality in Ghana

PubMed Central

Obiri, Samuel; Mattah, Precious A. D.; Mattah, Memuna M.; Armah, Frederick A.; Osae, Shiloh; Adu-kumi, Sam; Yeboah, Philip O.

2016-01-01

Gold mining has played an important role in Ghana’s economy, however the negative environmental and socio-economic effects on the host communities associated with gold mining have overshadowed these economic gains. It is within this context that this paper assessed in an integrated manner the environmental and socio-economic impacts of artisanal gold mining in the Tarkwa Nsuaem Municipality from a natural and social science perspective. The natural science group collected 200 random samples on bi-weekly basis between January to October 2013 from water bodies in the study area for analysis in line with methods outlined by the American Water Works Association, while the social science team interviewed 250 residents randomly selected for interviews on socio-economic issues associated with mining. Data from the socio-economic survey was analyzed using logistic regression with SPSS version 17. The results of the natural science investigation revealed that the levels of heavy metals in water samples from the study area in most cases exceeded GS 175-1/WHO permissible guideline values, which are in tandem with the results of inhabitants’ perceptions of water quality survey (as 83% of the respondents are of the view that water bodies in the study area are polluted). This calls for cost-benefits analysis of mining before new mining leases are granted by the relevant authorities. PMID:26821039

A prototype system based on visual interactive SDM called VGC

NASA Astrophysics Data System (ADS)

Jia, Zelu; Liu, Yaolin; Liu, Yanfang

2009-10-01

In many application domains, data is collected and referenced by its geo-spatial location. Spatial data mining, or the discovery of interesting patterns in such databases, is an important capability in the development of database systems. Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. For spatial data mining of large data sets to be effective, it is also important to include humans in the data exploration process and combine their flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of today's computers. Visual spatial data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the information and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper a visual interactive spatial data mining prototype system (visual geo-classify) based on VC++6.0 and MapObject2.0 are designed and developed, the basic algorithms of the spatial data mining is used decision tree and Bayesian networks, and data classify are used training and learning and the integration of the two to realize. The result indicates it's a practical and extensible visual interactive spatial data mining tool.

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

Greenwalt, R J; Magnoli, D

The purpose of this study is to determine battlefield effectiveness of the self-healing minefield (''Frogs'') concept system compared to basecases of the standard AP/AT (anti-personnel/anti-tank) mixed minefield, the AT (anti-tank) pure minefield, and no minefields. This involves tactical modeling where a basecase with and without mines is compared to the concept system. However, it is first necessary to establish system characteristics and behavior of the Frog mine and minefield in order to do the tactical modeling. This initial report provides emerging insights into various minefield parameters in order to allow better program definition early in the conceptual development. In themore » following sections of this report, we investigate the self-healing minefield's ground pattern and several concepts for movement (''jump'') of a mine. Basic enemy breaching techniques are compared for the different mine movement concepts. These results are then used in the (Joint Conflict and Tactical Simulation) JCATS tactical model to evaluate minefield effects in a combat situation. The three basecases and the Frogs concept are used against a North Korean mechanized rifle battalion and outcomes are compared. Preliminary results indicate: (1) Possible breaching techniques for the self-healing minefield were proposed and compared through simulation modeling. Of these, the best breaching counter to the self-healing minefield is the ''wide-lane'' breach technique. (2) Several methods for mine movement are tested and the optimal method from this group was selected for use in the modeling. However, continued work is needed on jump criteria; a more sophisticated model may reduce the advantage of the breach counter. (3) The battle scenario used in this study is a very difficult defense for Blue. In the three baseline cases (no mines, AT mines only, and mixed AT/AP minefield), Blue loses. Only in the Frog case does Blue win, and it is a high casualty win.« less

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

75 FR 53345 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... the Department of Labor--Mine Safety and Health Administration (MSHA), Office of Management and Budget..., mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration. Type of Review...

ASSESSING AND MANAGING MERCURY FROM HISTORIC AND CURRENT MINING ACTIVITIES

EPA Science Inventory

In order for ORD to address uncertainties resulting from past or historical mining practices a technology transfer workshop was conducted in November, 2000 in San Francisco, CA. Two primary objectives for this workshop were: 1) identify state-of-the-science practices and techniqu...

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

PubMed

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

2013-11-01

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

Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors

PubMed Central

Chhadé, Hiba Haj; Abdallah, Fahed; Mougharbel, Imad; Gning, Amadou; Julier, Simon; Mihaylova, Lyudmila

2014-01-01

We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach. PMID:25384008

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Blasting Rocks and Blasting Cars Applied Engineering

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

LBNL

2008-07-02

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

Development of a tree classifier for discrimination of surface mine activity from Landsat digital data

NASA Technical Reports Server (NTRS)

Solomon, J. L.; Miller, W. F.; Quattrochi, D. A.

1979-01-01

In a cooperative project with the Geological Survey of Alabama, the Mississippi State Remote Sensing Applications Program has developed a single purpose, decision-tree classifier using band-ratioing techniques to discriminate various stages of surface mining activity. The tree classifier has four levels and employs only two channels in classification at each level. An accurate computation of the amount of disturbed land resulting from the mining activity can be made as a product of the classification output. The utilization of Landsat data provides a cost-efficient, rapid, and accurate means of monitoring surface mining activities.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

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.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Monitoring of environmental effects of coal strip mining from satellite imagery

NASA Technical Reports Server (NTRS)

Brooks, R. L.; Parra, C. G.

1976-01-01

This paper evaluates satellite imagery as a means of monitoring coal strip mines and their environmental effects. The satellite imagery employed is Skylab EREP S-190A and S-190B from SL-2, SL-3 and SL-4 missions; a large variety of camera/film/filter combinations has been reviewed. The investigation includes determining the applicability of satellite imagery for detection of disturbed acreage in areas of coal surface mining as well as the much more detailed monitoring of specific surface-mining operations, including: active mines, inactive mines, highwalls, ramp roads, pits, water impoundments and their associated acidity, graded areas and types of grading, and reclamed areas. Techniques have been developed to enable mining personnel to utilize this imagery in a practical and economic manner, requiring no previous photo-interpretation background and no purchases of expensive viewing or data-analysis equipment. To corroborate the photo-interpretation results, on-site observations were made in the very active mining area near Madisonville, Kentucky.

An Integrated Environment Monitoring System for Underground Coal Mines—Wireless Sensor Network Subsystem with Multi-Parameter Monitoring

PubMed Central

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-01-01

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected. PMID:25051037

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain

PubMed Central

2013-01-01

Background Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. Results A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. Conclusions The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework. PMID:23763826

KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain.

PubMed

Holzinger, Andreas; Zupan, Mario

2013-06-13

Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Establishment of trees and shrubs on lands disturbed by mining in the West

Treesearch

Ardell J. Bjugstad

1984-01-01

Increased research and development of cultural practices and species has assured success of establishment of trees and shrubs on lands disturbed by surface mining. Trickle irrigation and water harvesting techniques have increased survival of planted stock by 250 percent for some species.

Using Text Mining to Characterize Online Discussion Facilitation

ERIC Educational Resources Information Center

Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

Revegetation for aesthetics

Treesearch

Bernard M. Slick

1980-01-01

Surface mining is changing the landscape character of forests in the East. Aesthetic visual aspects of the landscape are considered in the analysis, planning, and design of revegetation strategies. Application of landscape architectural design techniques in the revegetation of surface-mined lands, as well as knowledge of biological characteristics, will enhance the...

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

Applying Data Mining Principles to Library Data Collection.

ERIC Educational Resources Information Center

Guenther, Kim

2000-01-01

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

Arguments for the need of mining education continuity and development in Romania

NASA Astrophysics Data System (ADS)

Bud, I.; Duma, S.; Pasca, I.; Gusat, D.

2018-01-01

Mining is considered the oldest conscious man activity. In the beginning, man searched for hard rocks in the outskirts area and used it to make weapons and ornaments. Subsequently, civilizations evolved through the development of infrastructure, buildings and monuments and finally, weapons. For all these it was necessary to have mineral raw materials obtained under increasingly difficult conditions, through increasingly evolved techniques. In this way, the art of mining and metallurgy was born, which led to the formation of scientific bases. The mining activity was equally art and science. The art and the science have been learned and taught in schools since ancient times and continue today in large universities with mining engineering, metallurgy, mining topography, mining environmental protection, and geology. Lately, in Romania, the mining high school has reached a deadlock and the middle and professional school has collapsed. The development of infrastructure, construction, etc. requires the exploitation and valorisation of mineral resources based on specialists. The paper warns against the danger of losing tradition and skills in mining engineers formation and militate for the re-establishment of professional and technical schools.

Mining problems caused by tectonic stress in Illinois basin

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

Nelson, W.J.

1991-08-01

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

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

Development of Multiscale Biological Image Data Analysis: Review of 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics, Santa Barbara, USA (BII06)

PubMed Central

Auer, Manfred; Peng, Hanchuan; Singh, Ambuj

2007-01-01

The 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics was held at Santa Barbara, on Sept 7–8, 2006. Based on the presentations at the workshop, we selected and compiled this collection of research articles related to novel algorithms and enabling techniques for bio- and biomedical image analysis, mining, visualization, and biology applications. PMID:17634090

Black Thunder Coal Mine and Los Alamos National Laboratory experimental study of seismic energy generated by large scale mine blasting

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

Martin, R.L.; Gross, D.; Pearson, D.C.

In an attempt to better understand the impact that large mining shots will have on verifying compliance with the international, worldwide, Comprehensive Test Ban Treaty (CTBT, no nuclear explosion tests), a series of seismic and videographic experiments has been conducted during the past two years at the Black Thunder Coal Mine. Personnel from the mine and Los Alamos National Laboratory have cooperated closely to design and perform experiments to produce results with mutual benefit to both organizations. This paper summarizes the activities, highlighting the unique results of each. Topics which were covered in these experiments include: (1) synthesis of seismic,more » videographic, acoustic, and computer modeling data to improve understanding of shot performance and phenomenology; (2) development of computer generated visualizations of observed blasting techniques; (3) documentation of azimuthal variations in radiation of seismic energy from overburden casting shots; (4) identification of, as yet unexplained, out of sequence, simultaneous detonation in some shots using seismic and videographic techniques; (5) comparison of local (0.1 to 15 kilometer range) and regional (100 to 2,000 kilometer range) seismic measurements leading to determine of the relationship between local and regional seismic amplitude to explosive yield for overburden cast, coal bulking and single fired explosions; and (6) determination of the types of mining shots triggering the prototype International Monitoring System for the CTBT.« less

Application of underground microseismic monitoring for ground failure and secure longwall coal mining operation: A case study in an Indian mine

NASA Astrophysics Data System (ADS)

Ghosh, G. K.; Sivakumar, C.

2018-03-01

Longwall mining technique has been widely used around the globe due to its safe mining process. However, mining operations are suspended when various problems arise like collapse of roof falls, cracks and fractures propagation in the roof and complexity in roof strata behaviors. To overcome these colossal problems, an underground real time microseismic monitoring technique has been implemented in the working panel-P2 in the Rajendra longwall underground coal mine at South Eastern Coalfields Limited (SECL), India. The target coal seams appears at the panel P-2 within a depth of 70 m to 76 m. In this process, 10 to 15 uniaxial geophones were placed inside a borehole at depth range of 40 m to 60 m located over the working panel-P2 with high rock quality designation value for better seismic signal. Various microseismic events were recorded with magnitude ranging from -5 to 2 in the Richter scale. The time-series processing was carried out to get various seismic parameters like activity rate, potential energy, viscosity rate, seismic moment, energy index, apparent volume and potential energy with respect to time. The used of these parameters helped tracing the events, understanding crack and fractures propagation and locating both high and low stress distribution zones prior to roof fall occurrence. In most of the cases, the events were divided into three stage processes: initial or preliminary, middle or building, and final or falling. The results of this study reveal that underground microseismic monitoring provides sufficient prior information of underground weighting events. The information gathered during the study was conveyed to the mining personnel in advance prior to roof fall event. This permits to take appropriate action for safer mining operations and risk reduction during longwall operation.

toxoMine: an integrated omics data warehouse for Toxoplasma gondii systems biology research

PubMed Central

Rhee, David B.; Croken, Matthew McKnight; Shieh, Kevin R.; Sullivan, Julie; Micklem, Gos; Kim, Kami; Golden, Aaron

2015-01-01

Toxoplasma gondii (T. gondii) is an obligate intracellular parasite that must monitor for changes in the host environment and respond accordingly; however, it is still not fully known which genetic or epigenetic factors are involved in regulating virulence traits of T. gondii. There are on-going efforts to elucidate the mechanisms regulating the stage transition process via the application of high-throughput epigenomics, genomics and proteomics techniques. Given the range of experimental conditions and the typical yield from such high-throughput techniques, a new challenge arises: how to effectively collect, organize and disseminate the generated data for subsequent data analysis. Here, we describe toxoMine, which provides a powerful interface to support sophisticated integrative exploration of high-throughput experimental data and metadata, providing researchers with a more tractable means toward understanding how genetic and/or epigenetic factors play a coordinated role in determining pathogenicity of T. gondii. As a data warehouse, toxoMine allows integration of high-throughput data sets with public T. gondii data. toxoMine is also able to execute complex queries involving multiple data sets with straightforward user interaction. Furthermore, toxoMine allows users to define their own parameters during the search process that gives users near-limitless search and query capabilities. The interoperability feature also allows users to query and examine data available in other InterMine systems, which would effectively augment the search scope beyond what is available to toxoMine. toxoMine complements the major community database ToxoDB by providing a data warehouse that enables more extensive integrative studies for T. gondii. Given all these factors, we believe it will become an indispensable resource to the greater infectious disease research community. Database URL: http://toxomine.org PMID:26130662

Coal mine burns drainage gas to generate power for profit

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

Scholes, W.A.

A recently commissioned gas turbine power plant that uses methane gas recovered from a coal mine is described. The power plant uses the ASEA Stal GT35B series gas turbines with a base load rating on gas of 12.9 MW at 29.3% efficiency. The plant was installed at a cost of $4 million, as part of an extensive system for removing the methane from the coal mine, enabling higher ratio of coal production to be achieved in safety with modern longwall mining techniques. The plant will save the mine up to $250,000 per month on its electricity bill plus generate profitmore » from the sale of surplus power to the local activity.« less

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

78 FR 39012 - Public Land Order No. 7816; Partial Revocation of the Executive Order dated April 17, 1926; Idaho

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-28

... for non-metaliferous minerals under the United States mining laws, for protection of springs and... device for the deaf (TDD) may call the Federal Information Relay Service (FIRS) at 1-800- 877-8339 to...

KSC-2011-4167

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Apollo/Saturn V Center, Jerry Hartman, Education Lead with the Exploration Systems Mission Directorate at NASA Headquarters and Susan Sawyer, Lunabotics Project Coordinator with ReDe/Critique, display the trophy the winning team will receive at the award ceremony for NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India participated in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Forecasting the ocean optical environment in support of Navy mine warfare operations

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Jolliff, J.; Casey, B.; Matulewski, K.

2012-06-01

A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders, surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and sensor tow height predictions that are based on visual detection and identification metrics using actual mine target images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system performance and is proving important for the MIW community as both a tactical decision aid and for use in operational planning, improving timeliness and efficiency in clearance operations.

Knowledge discovery through games and game theory

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D.

2001-03-01

A fuzzy logic based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. The initial version of the algorithm was optimized using a genetic algorithm employing fitness functions constructed based on expertise. A new approach is being explored that involves embedding the resource manager in a electronic game environment. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the database as required. The game allows easy evaluation of the information mined in the second step. The measure of effectiveness (MOE) for re-optimization is discussed. The mined information is extremely valuable as shown through demanding scenarios.

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.

The MINE project: Minority Involvement in NASA Engineering

NASA Technical Reports Server (NTRS)

Allen, H., Jr.

1977-01-01

The Mine Project developed by Lewis Research Center (LRC) along with Tennessee State University and Tuskegee Institute, is described. The project calls for LRC to assemble on-going NASA university affairs programs aimed at benefiting the school, its faculty, and its student body. The schools receive grants to pursue research and technology projects that are relevant to NASA's missions. Upon request from the universities, LRC furnishes instructors and lecturers. The schools have use of surplus government equipment and access to NASA research facilities for certain projects. Both the faculty and students of the universities are eligible for summer employment at LRC through special programs. The MINE Project is designed to establish a continuing active relationship of 3 to 5 years between NASA and the universities, and will afford LRC with an opportunity to increase its recruitment of minority and women employees.

Mining Longitudinal Web Queries: Trends and Patterns.

ERIC Educational Resources Information Center

Wang, Peiling; Berry, Michael W.; Yang, Yiheng

2003-01-01

Analyzed user queries submitted to an academic Web site during a four-year period, using a relational database, to examine users' query behavior, to identify problems they encounter, and to develop techniques for optimizing query analysis and mining. Linguistic analyses focus on query structures, lexicon, and word associations using statistical…

Traffic Flow Management: Data Mining Update

NASA Technical Reports Server (NTRS)

Grabbe, Shon R.

2012-01-01

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

Restoring tropical forests on bauxite mined lands: lessons from the Brazilian Amazon

Treesearch

John A. Parrotta; Oliver H. Knowles

2001-01-01

Restoring self-sustaining tropical forest ecosystems on surface mined sites is a formidable challenge that requires the integration of proven reclamation techniques and reforestation strategies appropriate to specific site conditions, including landscape biodiversity patterns. Restorationists working in most tropical settings are usually hampered by lack of basic...

76 FR 9376 - Proposed Extension of Existing Information Collection;

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-17

... helps to assure that requested data can be provided in the desired format, reporting burden (time and...) that are removed during mining operations or separated from mined coal and deposited on the surface... collection techniques or other forms of information technology, e.g., permitting electronic submissions of...

76 FR 65180 - Proposed Information Collection; Comment Request; Deep Seabed Mining Exploration Licenses

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-20

... Collection; Comment Request; Deep Seabed Mining Exploration Licenses AGENCY: National Oceanic and Atmospheric... documentation electronically when feasible. III. Data OMB Control Number: 0648-0145. Form Number: None. Type of... information on respondents, including through the use of automated collection techniques or other forms of...

Spatial database of mining-related features in 2001 at selected phosphate mines, Bannock, Bear Lake, Bingham, and Caribou Counties, Idaho

USGS Publications Warehouse

Moyle, Phillip R.; Kayser, Helen Z.

2006-01-01

This report describes the spatial database, PHOSMINE01, and the processes used to delineate mining-related features (active and inactive/historical) in the core of the southeastern Idaho phosphate resource area. The spatial data have varying degrees of accuracy and attribution detail. Classification of areas by type of mining-related activity at active mines is generally detailed; however, for many of the closed or inactive mines the spatial coverage does not differentiate mining-related surface disturbance features. Nineteen phosphate mine sites are included in the study, three active phosphate mines - Enoch Valley (nearing closure), Rasmussen Ridge, and Smoky Canyon - and 16 inactive (or historical) phosphate mines - Ballard, Champ, Conda, Diamond Gulch, Dry Valley, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake, Waterloo, and Wooley Valley. Approximately 6,000 hc (15,000 ac), or 60 km2 (23 mi2) of phosphate mining-related surface disturbance are documented in the spatial coverage. Spatial data for the inactive mines is current because no major changes have occurred; however, the spatial data for active mines were derived from digital maps prepared in early 2001 and therefore recent activity is not included. The inactive Gay Mine has the largest total area of disturbance, 1,900 hc (4,700 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda Mine with 610 hc (1,500 ac), and it is nearly four times the area of the Smoky Canyon Mine, the largest of the active mines with about 550 hc (1,400 ac). The wide range of phosphate mining-related surface disturbance features (141) from various industry maps were reduced to 15 types or features based on a generic classification system used for this study: mine pit; backfilled mine pit; waste rock dump; adit and waste rock dump; ore stockpile; topsoil stockpile; tailings or tailings pond; sediment catchment; facilities; road; railroad; water reservoir; disturbed land, undifferentiated; and undisturbed land. In summary, the spatial coverage includes polygons totaling about 1,100 hc (2,800 ac) of mine pits, 440 hc (1100 ac) of backfilled mine pits, 1,600 hc (3,800 ac) of waste rock dumps, 31 hc (75 ac) of ore stockpiles, and 44 hc (110 ac) of tailings or tailings ponds. Areas of undifferentiated phosphate mining-related land disturbances, called 'disturbed land, undifferentiated,' total about 2,200 hc (5,500 ac) or nearly 22 km2 (8.6 mi2). No determination has been made as to status of reclamation on any of the lands. Subsequent site-specific studies to delineate distinct mine features will allow additional revisions to this spatial database.

Efficient frequent pattern mining algorithm based on node sets in cloud computing environment

NASA Astrophysics Data System (ADS)

Billa, V. N. Vinay Kumar; Lakshmanna, K.; Rajesh, K.; Reddy, M. Praveen Kumar; Nagaraja, G.; Sudheer, K.

2017-11-01

The ultimate goal of Data Mining is to determine the hidden information which is useful in making decisions using the large databases collected by an organization. This Data Mining involves many tasks that are to be performed during the process. Mining frequent itemsets is the one of the most important tasks in case of transactional databases. These transactional databases contain the data in very large scale where the mining of these databases involves the consumption of physical memory and time in proportion to the size of the database. A frequent pattern mining algorithm is said to be efficient only if it consumes less memory and time to mine the frequent itemsets from the given large database. Having these points in mind in this thesis we proposed a system which mines frequent itemsets in an optimized way in terms of memory and time by using cloud computing as an important factor to make the process parallel and the application is provided as a service. A complete framework which uses a proven efficient algorithm called FIN algorithm. FIN algorithm works on Nodesets and POC (pre-order coding) tree. In order to evaluate the performance of the system we conduct the experiments to compare the efficiency of the same algorithm applied in a standalone manner and in cloud computing environment on a real time data set which is traffic accidents data set. The results show that the memory consumption and execution time taken for the process in the proposed system is much lesser than those of standalone system.

Managing biological networks by using text mining and computer-aided curation

NASA Astrophysics Data System (ADS)

Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

2015-11-01

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

ALCF Data Science Program: Productive Data-centric Supercomputing

NASA Astrophysics Data System (ADS)

Romero, Nichols; Vishwanath, Venkatram

The ALCF Data Science Program (ADSP) is targeted at big data science problems that require leadership computing resources. The goal of the program is to explore and improve a variety of computational methods that will enable data-driven discoveries across all scientific disciplines. The projects will focus on data science techniques covering a wide area of discovery including but not limited to uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows. Project teams will be among the first to access Theta, ALCFs forthcoming 8.5 petaflops Intel/Cray system. The program will transition to the 200 petaflop/s Aurora supercomputing system when it becomes available. In 2016, four projects have been selected to kick off the ADSP. The selected projects span experimental and computational sciences and range from modeling the brain to discovering new materials for solar-powered windows to simulating collision events at the Large Hadron Collider (LHC). The program will have a regular call for proposals with the next call expected in Spring 2017.http://www.alcf.anl.gov/alcf-data-science-program This research used resources of the ALCF, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

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

NASA Astrophysics Data System (ADS)

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

2008-11-01

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

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

NASA Astrophysics Data System (ADS)

Baertlein, Brian A.; Liao, Wen-Jiao

2000-08-01

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

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

PubMed Central

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

2015-01-01

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

Western energy related overhead monitoring project. Phase 2: Summary. [Campbell County, Wyoming and coal strip mines in Montana and New Mexico

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1979-01-01

Assistance by NASA to EPA in the establishment and maintenance of a fully operational energy-related monitoring system included: (1) regional analysis applications based on LANDSAT and auxiliary data; (2) development of techniques for using aircraft MSS data to rapidly monitor site specific surface coal mine activities; and (3) registration of aircraft MSS data to a map base. The coal strip mines used in the site specific task were in Campbell County, Wyoming; Big Horn County, Montana; and the Navajo mine in San Juan County, New Mexico. The procedures and software used to accomplish these tasks are described.

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

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

Corley, Courtney D.; Cook, Diane; Mikler, Armin R.

Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

Speciation and distribution of copper in a mining soil using multiple synchrotron-based bulk and microscopic techniques.

PubMed

Yang, Jianjun; Liu, Jin; Dynes, James J; Peak, Derek; Regier, Tom; Wang, Jian; Zhu, Shenhai; Shi, Jiyan; Tse, John S

2014-02-01

Molecular-level understanding of soil Cu speciation and distribution assists in management of Cu contamination in mining sites. In this study, one soil sample, collected from a mining site contaminated since 1950s, was characterized complementarily by multiple synchrotron-based bulk and spatially resolved techniques for the speciation and distribution of Cu as well as other related elements (Fe, Ca, Mn, K, Al, and Si). Bulk X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy revealed that soil Cu was predominantly associated with Fe oxides instead of soil organic matter. This agreed with the closest association of Cu to Fe by microscopic X-ray fluorescence (U-XRF) and scanning transmission X-ray microscopy (STXM) nanoanalysis, along with the non-occurrence of photoreduction of soil Cu(II) by quick Cu L3,2-edge XANES spectroscopy (Q-XANES) which often occurs when Cu organic complexes are present. Furthermore, bulk-EXAFS and STXM-coupled Fe L3,2-edge nano-XANES analysis revealed soil Cu adsorbed primarily to Fe(III) oxides by inner-sphere complexation. Additionally, Cu K-edge μ-XANES, L3,2-edge bulk-XANES, and successive Q-XANES results identified the presence of Cu2S rather than radiation-damage artifacts dominant in certain microsites of the mining soil. This study demonstrates the great benefits in use of multiple combined synchrotron-based techniques for comprehensive understanding of Cu speciation in heterogeneous soil matrix, which facilitates our prediction of Cu reactivity and environmental fate in the mining site.

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

PubMed

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

2018-06-24

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

Digital database of mining-related features at selected historic and active phosphate mines, Bannock, Bear Lake, Bingham, and Caribou counties, Idaho

USGS Publications Warehouse

Causey, J. Douglas; Moyle, Phillip R.

2001-01-01

This report provides a description of data and processes used to produce a spatial database that delineates mining-related features in areas of historic and active phosphate mining in the core of the southeastern Idaho phosphate resource area. The data have varying degrees of accuracy and attribution detail. Classification of areas by type of mining-related activity at active mines is generally detailed; however, the spatial coverage does not differentiate mining-related surface disturbance features at many of the closed or inactive mines. Nineteen phosphate mine sites are included in the study. A total of 5,728 hc (14,154 ac), or more than 57 km2 (22 mi2), of phosphate mining-related surface disturbance are documented in the spatial coverage of the core of the southeast Idaho phosphate resource area. The study includes 4 active phosphate mines—Dry Valley, Enoch Valley, Rasmussen Ridge, and Smoky Canyon—and 15 historic phosphate mines—Ballard, Champ, Conda, Diamond Gulch, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake Canyon, Waterloo, and Wooley Valley. Spatial data on the inactive historic mines is relatively up-to-date; however, spatially described areas for active mines are based on digital maps prepared in early 1999. The inactive Gay mine has the largest total area of disturbance: 1,917 hc (4,736 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda mine with 607 hc (1,504 ac), and it is nearly four times the area of the Smoky Canyon mine, the largest of the active mines with 497 hc (1,228 ac). The wide range of phosphate mining-related surface disturbance features (approximately 80) were reduced to 13 types or features used in this study—adit and pit, backfilled mine pit, facilities, mine pit, ore stockpile, railroad, road, sediment catchment, tailings or tailings pond, topsoil stockpile, water reservoir, and disturbed land (undifferentiated). In summary, the spatial coverage includes polygons totaling 1,114 hc (2,753 ac) of mine pits, 272 hc (671 ac) of backfilled mine pits, 1,570 hc (3,880 ac) of waste dumps, 26 hc (64 ac) of ore stockpiles, and 44 hc (110 ac) of tailings or tailings ponds. Areas of undifferentiated phosphate mining-related land disturbances, called “disturbed land,” total 2,176 (5,377 ac) or nearly 21.8 km2 (8.4 mi2). No determination has been made as to status of reclamation on these lands. Subsequent site-specific studies to delineate distinct mine features will allow modification of this preliminary spatial database.

Use of geospatial data to predict downstream influence of coal mining in Appalachia

EPA Science Inventory

A 2001 Supreme Court decision first called into question whether some headwater streams could be considered jurisdictional under the Clean Water Act. A subsequent decision then required that non-navigable waters must be "relatively permanent" or "possess a significant nexus" to ...

Measurement and Instrumentation

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

Kirkham, Harold

This is a chapter for a book called the Standard Handbook for Electrical Engineering. Though it is not obvious from the title, the book deals mainly with power engineering. The first chapter (not mine) is about the fundamental quantities used in measurement. This chapter is about the process and the instrumentation.

Roadway backfill method to prevent geohazards induced by room and pillar mining: a case study in Changxing coal mine, China

NASA Astrophysics Data System (ADS)

Zhou, Nan; Li, Meng; Zhang, Jixiong; Gao, Rui

2016-11-01

Coal mines in the western areas of China experience low mining rates and induce many geohazards when using the room and pillar mining method. In this research, we proposed a roadway backfill method during longwall mining to target these problems. We tested the mechanical properties of the backfill materials to determine a reasonable ratio of backfill materials for the driving roadway during longwall mining. We also introduced the roadway layout and the backfill mining technique required for this method. Based on the effects of the abutment stress from a single roadway driving task, we designed the distance between roadways and a driving and filling sequence for multiple-roadway driving. By doing so, we found the movement characteristics of the strata with quadratic stabilization for backfill mining during roadway driving. Based on this research, the driving and filling sequence of the 3101 working face in Changxing coal mine was optimized to avoid the superimposed influence of mining-induced stress. According to the analysis of the surface monitoring data, the accumulated maximum subsidence is 15 mm and the maximum horizontal deformation is 0.8 mm m-1, which indicated that the ground basically had no obvious deformation after the implementation of the roadway backfill method at 3101 working face.

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.

The Perceived Consequences of Gold Mining in Postwar El Salvador: A Qualitative Study.

PubMed

Zakrison, Tanya L; Cabezas, Pedro; Valle, Evan; Kornfeld, Julie; Muntaner, Carles; Soklaridis, Sophie

2015-11-01

We investigated themes related to the health and environmental impacts of gold mining in El Salvador. Over a 1-month period in 2013, we conducted focus groups (n = 32 participants in total) and individual semistructured interviews (n = 11) with community leaders until we achieved thematic saturation. Data collection took place in 4 departments throughout the country. We used a combination of criterion-purposive and snowballing sampling techniques to identify participants. Multiple themes emerged: (1) the fallacy of economic development; (2) critique of mining activities; (3) the creation of mining-related violence, with parallels to El Salvador's civil war; and (4) solutions and alternatives to mining activity. Solutions involved the creation of cooperative microenterprises for sustainable economic growth, political empowerment within communities, and development of local participatory democracies. Gold mining in El Salvador is perceived as a significant environmental and public health threat. Local solutions may be applicable broadly.

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

Sequential Extraction Results and Mineralogy of Mine Waste and Stream Sediments Associated With Metal Mines in Vermont, Maine, and New Zealand

USGS Publications Warehouse

Piatak, N.M.; Seal, R.R.; Sanzolone, R.F.; Lamothe, P.J.; Brown, Z.A.; Adams, M.

2007-01-01

We report results from sequential extraction experiments and the quantitative mineralogy for samples of stream sediments and mine wastes collected from metal mines. Samples were from the Elizabeth, Ely Copper, and Pike Hill Copper mines in Vermont, the Callahan Mine in Maine, and the Martha Mine in New Zealand. The extraction technique targeted the following operationally defined fractions and solid-phase forms: (1) soluble, adsorbed, and exchangeable fractions; (2) carbonates; (3) organic material; (4) amorphous iron- and aluminum-hydroxides and crystalline manganese-oxides; (5) crystalline iron-oxides; (6) sulfides and selenides; and (7) residual material. For most elements, the sum of an element from all extractions steps correlated well with the original unleached concentration. Also, the quantitative mineralogy of the original material compared to that of the residues from two extraction steps gave insight into the effectiveness of reagents at dissolving targeted phases. The data are presented here with minimal interpretation or discussion and further analyses and interpretation will be presented elsewhere.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Coal rib response during bench mining: A case study

PubMed Central

Sears, Morgan M.; Rusnak, John; Van Dyke, Mark; Rashed, Gamal; Mohamed, Khaled; Sloan, Michael

2018-01-01

In 2016, room-and-pillar mining provided nearly 40% of underground coal production in the United States. Over the past decade, rib falls have resulted in 12 fatalities, representing 28% of the ground fall fatalities in U.S. underground coal mines. Nine of these 12 fatalities (75%) have occurred in room-and-pillar mines. The objective of this research is to study the geomechanics of bench room-and-pillar mining and the associated response of high pillar ribs at overburden depths greater than 300 m. This paper provides a definition of the bench technique, the pillar response due to loading, observational data for a case history, a calibrated numerical model of the observed rib response, and application of this calibrated model to a second site. PMID:29862125

The Application of LANDSAT Multi-Temporal Thermal Infrared Data to Identify Coal Fire in the Khanh Hoa Coal Mine, Thai Nguyen province, Vietnam

NASA Astrophysics Data System (ADS)

Trinh, Le Hung; Zablotskii, V. R.

2017-12-01

The Khanh Hoa coal mine is a surface coal mine in the Thai Nguyen province, which is one of the largest deposits of coal in the Vietnam. Numerous reasons such as improper mining techniques and policy, as well as unauthorized mining caused surface and subsurface coal fire in this area. Coal fire is a dangerous phenomenon which affects the environment seriously by releasing toxic fumes which causes forest fires, and subsidence of infrastructure surface. This article presents study on the application of LANDSAT multi-temporal thermal infrared images, which help to detect coal fire. The results obtained in this study can be used to monitor fire zones so as to give warnings and solutions to prevent coal fire.

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology

PubMed Central

Zhang, Guang Lan; Riemer, Angelika B.; Keskin, Derin B.; Chitkushev, Lou; Reinherz, Ellis L.; Brusic, Vladimir

2014-01-01

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/ PMID:24705205

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology.

PubMed

Zhang, Guang Lan; Riemer, Angelika B; Keskin, Derin B; Chitkushev, Lou; Reinherz, Ellis L; Brusic, Vladimir

2014-01-01

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.

New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

NASA Astrophysics Data System (ADS)

Brescia, Massimo

2012-11-01

Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced.

Ultrawideband radar echoes of land mine targets measured at oblique incidence using a 250-kW impulse radar system

NASA Astrophysics Data System (ADS)

Chant, Ian J.; Staines, Geoff

1997-07-01

United Nations Peacekeeping forces around the world need to transport food, personnel and medical supplies through disputed regions were land mines are in active use as road blocks and terror weapons. A method of fast, effective land mine detection is needed to combat this threat to road transport. The technique must operate from a vehicle travelling at a reasonable velocity and give warning far enough ahead for the vehicle to stop in time to avoid the land mine. There is particular interest in detecting low- metallic content land mines. One possible solutionis the use of ultra-wide-band (UWB) radar. The Australian Defence Department is investigating the feasibility of using UWB radar for land mine detection from a vehicle. A 3 GHz UWB system has been used to collect target response from a series of inert land mines and mine-like objects placed on the ground and buried in the ground. The targets measured were a subset of those in the target set described in Wong et al. with the addition of inert land mines corresponding to some of the surrogate targets in this set. The results are encouraging for the detection of metallic land mines and the larger non-metallic land mines. Smaller low-metallic- content anti-personnel land mines are less likely to be detected.

e-Research and Learning Theory: What Do Sequence and Process Mining Methods Contribute?

ERIC Educational Resources Information Center

Reimann, Peter; Markauskaite, Lina; Bannert, Maria

2014-01-01

This paper discusses the fundamental question of how data-intensive e-research methods could contribute to the development of learning theories. Using methodological developments in research on self-regulated learning as an example, it argues that current applications of data-driven analytical techniques, such as educational data mining and its…

Mining and Analyzing Circulation and ILL Data for Informed Collection Development

ERIC Educational Resources Information Center

Link, Forrest E.; Tosaka, Yuji; Weng, Cathy

2015-01-01

The authors investigated quantitative methods of collection use analysis employing library data that are available in ILS and ILL systems to better understand library collection use and user needs. For the purpose of the study, the authors extracted circulation and ILL records from the library's systems using data-mining techniques. By comparing…

A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.

ERIC Educational Resources Information Center

Chen, Ruey-Shun; Hu, Yi-Chung

2003-01-01

Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)

Expanding the Role of Institutional Research at Small Private Universities: A Case Study in Enrollment Management Using Data Mining

ERIC Educational Resources Information Center

Antons, Christopher M.; Maltz, Elliot N.

2006-01-01

This case study documents a successful application of data-mining techniques in enrollment management through a partnership between the admissions office, a business administration master's-degree program, and the institutional research office at Willamette University (Salem, Oregon). (Contains 1 table and 3 figures.)

Binary Coded Web Access Pattern Tree in Education Domain

ERIC Educational Resources Information Center

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

2008-01-01

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

Data Mining in Earth System Science (DMESS 2011)

Treesearch

Forrest M. Hoffman; J. Walter Larson; Richard Tran Mills; Bhorn-Gustaf Brooks; Auroop R. Ganguly; William Hargrove; et al

2011-01-01

From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques—such as cluster analysis, singular value decomposition, block entropy, Fourier and...

Characterization and speciation of mercury-bearing mine wastes using X-ray absorption spectroscopy

USGS Publications Warehouse

Kim, C.S.; Brown, Gordon E.; Rytuba, J.J.

2000-01-01

Mining of mercury deposits located in the California Coast Range has resulted in the release of mercury to the local environment and water supplies. The solubility, transport, and potential bioavailability of mercury are controlled by its chemical speciation, which can be directly determined for samples with total mercury concentrations greater than 100 mg kg-1 (ppm) using X-ray absorption spectroscopy (XAS). This technique has the additional benefits of being non-destructive to the sample, element-specific, relatively sensitive at low concentrations, and requiring minimal sample preparation. In this study, Hg L(III)-edge extended X-ray absorption fine structure (EXAFS) spectra were collected for several mercury mine tailings (calcines) in the California Coast Range. Total mercury concentrations of samples analyzed ranged from 230 to 1060 ppm. Speciation data (mercury phases present and relative abundances) were obtained by comparing the spectra from heterogeneous, roasted (calcined) mine tailings samples with a spectral database of mercury minerals and sorbed mercury complexes. Speciation analyses were also conducted on known mixtures of pure mercury minerals in order to assess the quantitative accuracy of the technique. While some calcine samples were found to consist exclusively of mercuric sulfide, others contain additional, more soluble mercury phases, indicating a greater potential for the release of mercury into solution. Also, a correlation was observed between samples from hot-spring mercury deposits, in which chloride levels are elevated, and the presence of mercury-chloride species as detected by the speciation analysis. The speciation results demonstrate the ability of XAS to identify multiple mercury phases in a heterogeneous sample, with a quantitative accuracy of ??25% for the mercury-containing phases considered. Use of this technique, in conjunction with standard microanalytical techniques such as X-ray diffraction and electron probe microanalysis, is beneficial in the prioritization and remediation of mercury-contaminated mine sites. (C) 2000 Elsevier Science B.V.

Building Searchable Collections of Enterprise Speech Data.

ERIC Educational Resources Information Center

Cooper, James W.; Viswanathan, Mahesh; Byron, Donna; Chan, Margaret

The study has applied speech recognition and text-mining technologies to a set of recorded outbound marketing calls and analyzed the results. Since speaker-independent speech recognition technology results in a significantly lower recognition rate than that found when the recognizer is trained for a particular speaker, a number of post-processing…

Ball clay

USGS Publications Warehouse

Virta, Robert L.

2010-01-01

The article reports on the global market performance of ball clay in 2009 and presents an outlook for its 2010 performance. Several companies mined ball call in the country including Old Hickey Clay Co., Kentucky-Tennessee Clay Co., and H.C. Spinks Clay Co. Information on the decline in ball clay imports and exports is also presented.

Visual data mining for quantized spatial data

NASA Technical Reports Server (NTRS)

Braverman, Amy; Kahn, Brian

2004-01-01

In previous papers we've shown how a well known data compression algorithm called Entropy-constrained Vector Quantization ( can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we descuss how to visualize and understand the content of such reduced data sets.