Plant succession patterns on residual open-pit gravel mines deposits Bogota

Directory of Open Access Journals (Sweden)

Ricardo A. Mora Goyes

1999-07-01

Full Text Available Based on both: the study of composition and structure of plant communities and the analysis of the physico-chemical characteristics of mining wastes, the initial patterns of primary succession were determined. These patterns were present in three deposits of waste material abandoned during 18, 36 and 120 months respectively. Sue materials were originated in open-pit gravel mines located to the south of Bogota (Colombia. This study pretends to contribute to the knowledge of the meehanlsms of natural restauration of tropical ecosystems subjected to man-borne degradation.

Mining for Social Media: Usage Patterns of Small Businesses

OpenAIRE

Balan, Shilpa; Rege, Janhavi

2017-01-01

Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are...

SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

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S. Khoshahval

2017-09-01

Full Text Available 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.

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

Science.gov (United States)

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/.

LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones

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Kifayat Ullah Khan

2017-11-01

Full Text Available Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI, which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work. These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.

GRAMI: Frequent subgraph and pattern mining in a single large graph

KAUST Repository

Elseidy, M.

2014-01-01

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or proteinprotein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GRAMI, a novel framework for frequent subgraph mining in a single large graph. GRAMI undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GRAMI that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGRAMI, a version supporting structural and semantic constraints, and AGRAMI, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches. 2014 VLDB Endowment.

Monitoring coal mine changes and their impact on landscape patterns in an alpine region: a case study of the Muli coal mine in the Qinghai-Tibet Plateau.

Science.gov (United States)

Qian, Dawen; Yan, Changzhen; Xing, Zanpin; Xiu, Lina

2017-10-14

The Muli coal mine is the largest open-cast coal mine in the Qinghai-Tibet Plateau, and it consists of two independent mining sites named Juhugeng and Jiangcang. It has received much attention due to the ecological problems caused by rapid expansion in recent years. The objective of this paper was to monitor the mining area and its surrounding land cover over the period 1976-2016 utilizing Landsat images, and the network structure of land cover changes was determined to visualize the relationships and pattern of the mining-induced land cover changes. In addition, the responses of the surrounding landscape pattern were analysed by constructing gradient transects. The results show that the mining area was increasing in size, especially after 2000 (increased by 71.68 km 2 ), and this caused shrinkage of the surrounding lands, including alpine meadow wetland (53.44 km 2 ), alpine meadow (6.28 km 2 ) and water (6.24 km 2 ). The network structure of the mining area revealed the changes in lands surrounding the mining area. The impact of mining development on landscape patterns was mainly distributed within a range of 1-6 km. Alpine meadow wetland was most affected in Juhugeng, while alpine meadow was most affected in Jiangcang. The results of this study provide a reference for the ecological assessment and restoration of the Muli coal mine land.

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

Science.gov (United States)

Fournier-Viger, Philippe; Nkambou, Roger; Faghihi, Usef; Nguifo, Engelbert Mephu

We propose two mechanisms for agent learning based on the idea of mining temporal patterns from agent behavior. The first one consists of extracting temporal patterns from the perceived behavior of other agents accomplishing a task, to learn the task. The second learning mechanism consists in extracting temporal patterns from an agent's own behavior. In this case, the agent then reuses patterns that brought self-satisfaction. In both cases, no assumption is made on how the observed agents' behavior is internally generated. A case study with a real application is presented to illustrate each learning mechanism.

A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

Science.gov (United States)

Obulesu, O.; Rama Mohan Reddy, A., Dr; Mahendra, M.

2017-08-01

Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

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Unil Yun

2016-05-01

Full Text Available Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs from databases composed of graph transaction data, which can effectively express complex and large data in the real world. In addition, various applications for graph mining have been suggested. Traditional graph pattern mining methods use a single minimum support threshold factor in order to check whether or not mined patterns are interesting. However, it is not a sufficient factor that can consider valuable characteristics of graphs such as graph sizes and features of graph elements. That is, previous methods cannot consider such important characteristics in their mining operations since they only use a fixed minimum support threshold in the mining process. For this reason, in this paper, we propose a novel graph mining algorithm that can consider various multiple, minimum support constraints according to the types of graph elements and changeable minimum support conditions, depending on lengths of graph patterns. In addition, the proposed algorithm performs in mining operations more efficiently because it can minimize duplicated operations and computational overheads by considering symmetry features of graphs. Experimental results provided in this paper demonstrate that the proposed algorithm outperforms previous mining approaches in terms of pattern generation, runtime and memory usage.

Incremental temporal pattern mining using efficient batch-free stream clustering

NARCIS (Netherlands)

Lu, Y.; Hassani, M.; Seidl, T.

2017-01-01

This paper address the problem of temporal pattern mining from multiple data streams containing temporal events. Temporal events are considered as real world events aligned with comprehensive starting and ending timing information rather than simple integer timestamps. Predefined relations, such as

Pattern recognition algorithms for data mining scalability, knowledge discovery and soft granular computing

CERN Document Server

Pal, Sankar K

2004-01-01

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

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

Science.gov (United States)

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

2016-01-01

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

Exploring the potential of data mining techniques for the analysis of accident patterns

DEFF Research Database (Denmark)

Prato, Carlo Giacomo; Bekhor, Shlomo; Galtzur, Ayelet

2010-01-01

Research in road safety faces major challenges: individuation of the most significant determinants of traffic accidents, recognition of the most recurrent accident patterns, and allocation of resources necessary to address the most relevant issues. This paper intends to comprehend which data mining...... and association rules) data mining techniques are implemented for the analysis of traffic accidents occurred in Israel between 2001 and 2004. Results show that descriptive techniques are useful to classify the large amount of analyzed accidents, even though introduce problems with respect to the clear...... importance of input and intermediate neurons, and the relative importance of hundreds of association rules. Further research should investigate whether limiting the analysis to fatal accidents would simplify the task of data mining techniques in recognizing accident patterns without the “noise” probably...

A New Fast Vertical Method for Mining Frequent Patterns

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Zhihong Deng

2010-12-01

Full Text Available Vertical mining methods are very effective for mining frequent patterns and usually outperform horizontal mining methods. However, the vertical methods become ineffective since the intersection time starts to be costly when the cardinality of tidset (tid-list or diffset is very large or there are a very large number of transactions. In this paper, we propose a novel vertical algorithm called PPV for fast frequent pattern discovery. PPV works based on a data structure called Node-lists, which is obtained from a coding prefix-tree called PPC-tree. The efficiency of PPV is achieved with three techniques. First, the Node-list is much more compact compared with previous proposed vertical structure (such as tid-lists or diffsets since transactions with common prefixes share the same nodes of the PPC-tree. Second, the counting of support is transformed into the intersection of Node-lists and the complexity of intersecting two Node-lists can be reduced to O(m+n by an efficient strategy, where m and n are the cardinalities of the two Node-lists respectively. Third, the ancestor-descendant relationship of two nodes, which is the basic step of intersecting Node-lists, can be very efficiently verified by Pre-Post codes of nodes. We experimentally compare our algorithm with FP-growth, and two prominent vertical algorithms (Eclat and dEclat on a number of databases. The experimental results show that PPV is an efficient algorithm that outperforms FP-growth, Eclat, and dEclat.

Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

Directory of Open Access Journals (Sweden)

Lahiru Iddamalgoda

2016-08-01

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

A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping

Science.gov (United States)

Shen, Bin; Zheng, Qiuhua; Li, Xingsen; Xu, Libo

2015-01-01

With the quick development of RFID technology and the decreasing prices of RFID devices, RFID is becoming widely used in various intelligent services. Especially in the retail application domain, RFID is increasingly adopted to capture the shopping tracks and behavior of in-store customers. To further enhance the potential of this promising application, in this paper, we propose a unified framework for RFID-based path analytics, which uses both in-store shopping paths and RFID-based purchasing data to mine actionable navigation patterns. Four modules of this framework are discussed, which are: (1) mapping from the physical space to the cyber space, (2) data preprocessing, (3) pattern mining and (4) knowledge understanding and utilization. In the data preprocessing module, the critical problem of how to capture the mainstream shopping path sequences while wiping out unnecessary redundant and repeated details is addressed in detail. To solve this problem, two types of redundant patterns, i.e., loop repeat pattern and palindrome-contained pattern are recognized and the corresponding processing algorithms are proposed. The experimental results show that the redundant pattern filtering functions are effective and scalable. Overall, this work builds a bridge between indoor positioning and advanced data mining technologies, and provides a feasible way to study customers’ shopping behaviors via multi-source RFID data. PMID:25751076

Mining for Social Media: Usage Patterns of Small Businesses

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Balan Shilpa

2017-03-01

Full Text Available Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted.

GraMi: Generalized Frequent Pattern Mining in a Single Large Graph

KAUST Repository

Saeedy, Mohammed El

2011-11-01

Mining frequent subgraphs is an important operation on graphs. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs or protein-protein interaction in bioinformatics, are modeled as a single large graph. Interesting interactions in such applications may be transitive (e.g., friend of a friend). Existing methods, however, search for frequent isomorphic (i.e., exact match) subgraphs and cannot discover many useful patterns. In this paper the authors propose GRAMI, a framework that generalizes frequent subgraph mining in a large single graph. GRAMI discovers frequent patterns. A pattern is a graph where edges are generalized to distance-constrained paths. Depending on the definition of the distance function, many instantiations of the framework are possible. Both directed and undirected graphs, as well as multiple labels per vertex, are supported. The authors developed an efficient implementation of the framework that models the frequency resolution phase as a constraint satisfaction problem, in order to avoid the costly enumeration of all instances of each pattern in the graph. The authors also implemented CGRAMI, a version that supports structural and semantic constraints; and AGRAMI, an approximate version that supports very large graphs. The experiments on real data demonstrate that the authors framework is up to 3 orders of magnitude faster and discovers more interesting patterns than existing approaches.

Design Pattern Mining Using Distributed Learning Automata and DNA Sequence Alignment

Science.gov (United States)

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

Context Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. Objective This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. Method The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. Results The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. Conclusion The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns. PMID:25243670

Design pattern mining using distributed learning automata and DNA sequence alignment.

Directory of Open Access Journals (Sweden)

Mansour Esmaeilpour

Full Text Available CONTEXT: Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. OBJECTIVE: This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA and deoxyribonucleic acid (DNA sequences alignment. METHOD: The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. RESULTS: The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. CONCLUSION: The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

Design pattern mining using distributed learning automata and DNA sequence alignment.

Science.gov (United States)

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

Science.gov (United States)

Ibrahim, Heba; Saad, Amr; Abdo, Amany; Sharaf Eldin, A

2016-04-01

Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori". The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns. Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among

Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

Directory of Open Access Journals (Sweden)

Sylvia Jane Annatje Sumarauw

2007-06-01

Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping

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Bin Shen

2015-03-01

Full Text Available With the quick development of RFID technology and the decreasing prices of RFID devices, RFID is becoming widely used in various intelligent services. Especially in the retail application domain, RFID is increasingly adopted to capture the shopping tracks and behavior of in-store customers. To further enhance the potential of this promising application, in this paper, we propose a unified framework for RFID-based path analytics, which uses both in-store shopping paths and RFID-based purchasing data to mine actionable navigation patterns. Four modules of this framework are discussed, which are: (1 mapping from the physical space to the cyber space, (2 data preprocessing, (3 pattern mining and (4 knowledge understanding and utilization. In the data preprocessing module, the critical problem of how to capture the mainstream shopping path sequences while wiping out unnecessary redundant and repeated details is addressed in detail. To solve this problem, two types of redundant patterns, i.e., loop repeat pattern and palindrome-contained pattern are recognized and the corresponding processing algorithms are proposed. The experimental results show that the redundant pattern filtering functions are effective and scalable. Overall, this work builds a bridge between indoor positioning and advanced data mining technologies, and provides a feasible way to study customers’ shopping behaviors via multi-source RFID data.

Site-specific climate analysis elucidates revegetation challenges for post-mining landscapes in eastern Australia

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P. Audet

2013-10-01

Full Text Available In eastern Australia, the availability of water is critical for the successful rehabilitation of post-mining landscapes and climatic characteristics of this diverse geographical region are closely defined by factors such as erratic rainfall and periods of drought and flooding. Despite this, specific metrics of climate patterning are seldom incorporated into the initial design of current post-mining land rehabilitation strategies. Our study proposes that a few common rainfall parameters can be combined and rated using arbitrary rainfall thresholds to characterise bioregional climate sensitivity relevant to the rehabilitation these landscapes. This approach included assessments of annual rainfall depth, average recurrence interval of prolonged low intensity rainfall, average recurrence intervals of short or prolonged high intensity events, median period without rain (or water-deficit and standard deviation for this period in order to address climatic factors such as total water availability, seasonality and intensity – which were selected as potential proxies of both short- and long-term biological sensitivity to climate within the context of post-disturbance ecological development and recovery. Following our survey of available climate data, we derived site "climate sensitivity" indexes and compared the performance of 9 ongoing mine sites: Weipa, Mt. Isa and Cloncurry, Eromanga, Kidston, the Bowen Basin (Curragh, Tarong, North Stradbroke Island, and the Newnes Plateau. The sites were then ranked from most-to-least sensitive and compared with natural bioregional patterns of vegetation density using mean NDVI. It was determined that regular rainfall and relatively short periods of water-deficit were key characteristics of sites having less sensitivity to climate – as found among the relatively more temperate inland mining locations. Whereas, high rainfall variability, frequently occurring high intensity events, and (or prolonged seasonal

Site-specific climate analysis elucidates revegetation challenges for post-mining landscapes in eastern Australia

Science.gov (United States)

Audet, P.; Arnold, S.; Lechner, A. M.; Baumgartl, T.

2013-10-01

In eastern Australia, the availability of water is critical for the successful rehabilitation of post-mining landscapes and climatic characteristics of this diverse geographical region are closely defined by factors such as erratic rainfall and periods of drought and flooding. Despite this, specific metrics of climate patterning are seldom incorporated into the initial design of current post-mining land rehabilitation strategies. Our study proposes that a few common rainfall parameters can be combined and rated using arbitrary rainfall thresholds to characterise bioregional climate sensitivity relevant to the rehabilitation these landscapes. This approach included assessments of annual rainfall depth, average recurrence interval of prolonged low intensity rainfall, average recurrence intervals of short or prolonged high intensity events, median period without rain (or water-deficit) and standard deviation for this period in order to address climatic factors such as total water availability, seasonality and intensity - which were selected as potential proxies of both short- and long-term biological sensitivity to climate within the context of post-disturbance ecological development and recovery. Following our survey of available climate data, we derived site "climate sensitivity" indexes and compared the performance of 9 ongoing mine sites: Weipa, Mt. Isa and Cloncurry, Eromanga, Kidston, the Bowen Basin (Curragh), Tarong, North Stradbroke Island, and the Newnes Plateau. The sites were then ranked from most-to-least sensitive and compared with natural bioregional patterns of vegetation density using mean NDVI. It was determined that regular rainfall and relatively short periods of water-deficit were key characteristics of sites having less sensitivity to climate - as found among the relatively more temperate inland mining locations. Whereas, high rainfall variability, frequently occurring high intensity events, and (or) prolonged seasonal drought were primary

A Partial Join Approach for Mining Co-Location Patterns: A Summary of Results

National Research Council Canada - National Science Library

Yoo, Jin S; Shekhar, Shashi

2005-01-01

.... They propose a novel partial-join approach for mining co-location patterns efficiently. It transactionizes continuous spatial data while keeping track of the spatial information not modeled by transactions...

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

Science.gov (United States)

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

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

Mining Experiential Patterns from Game-Logs of Board Game

Directory of Open Access Journals (Sweden)

Liang Wang

2015-01-01

Full Text Available In board games, game-logs record past game processes, which can be regarded as an accumulation of experience. Similar to a real person, a computer player can gradually increase its skill by learning from game-logs. Therefore, the game becomes more interesting. This paper proposes an extensible approach to mine experiential patterns from increasing game-logs. The computer player improves its strategies by utilizing these growing patterns, just as it acquires experience. To evaluate the effect and performance of the approach, we designed a sample board game as a test platform and elaborated an experiment consisting of a series of tests. Experimental results show that our approach is effective and efficient.

Mining compressing sequential problems

NARCIS (Netherlands)

Hoang, T.L.; Mörchen, F.; Fradkin, D.; Calders, T.G.K.

2012-01-01

Compression based pattern mining has been successfully applied to many data mining tasks. We propose an approach based on the minimum description length principle to extract sequential patterns that compress a database of sequences well. We show that mining compressing patterns is NP-Hard and

Trace metal depositional patterns from an open pit mining activity as revealed by archived avian gizzard contents.

Science.gov (United States)

Bendell, L I

2011-02-15

Archived samples of blue grouse (Dendragapus obscurus) gizzard contents, inclusive of grit, collected yearly between 1959 and 1970 were analyzed for cadmium, lead, zinc, and copper content. Approximately halfway through the 12-year sampling period, an open-pit copper mine began activities, then ceased operations 2 years later. Thus the archived samples provided a unique opportunity to determine if avian gizzard contents, inclusive of grit, could reveal patterns in the anthropogenic deposition of trace metals associated with mining activities. Gizzard concentrations of cadmium and copper strongly coincided with the onset of opening and the closing of the pit mining activity. Gizzard zinc and lead demonstrated significant among year variation; however, maximum concentrations did not correlate to mining activity. The archived gizzard contents did provide a useful tool for documenting trends in metal depositional patterns related to an anthropogenic activity. Further, blue grouse ingesting grit particles during the time of active mining activity would have been exposed to toxicologically significant levels of cadmium. Gizzard lead concentrations were also of toxicological significance but not related to mining activity. This type of "pulse" toxic metal exposure as a consequence of open-pit mining activity would not necessarily have been revealed through a "snap-shot" of soil, plant or avian tissue trace metal analysis post-mining activity. Copyright © 2010 Elsevier B.V. All rights reserved.

Selection and specification criteria for fills for cut-and-fill mining

Energy Technology Data Exchange (ETDEWEB)

Thomas, E. G.

1980-05-15

Because of significant differences in placement and loading conditions, the ideal fill material for a cut-and-fill operation has different characteristics to those for a fill for a filled open stoping operation. The differing requirements of the two mining operations must be understood and accounted for in establishing fill selection and specification criteria. Within the paper, aspects of the particular requirements of cut-and-fill mining are analyzed and related to the specific fill tests and properties required. Emphasis is placed upon the role of fill in ground support, though this cannot be isolated from overall fill performance. Where appropriate, test data are introduced and areas requiring continuing research highlighted.

Frequent pattern mining

CERN Document Server

Aggarwal, Charu C

2014-01-01

Proposes numerous methods to solve some of the most fundamental problems in data mining and machine learning Presents various simplified perspectives, providing a range of information to benefit both students and practitioners Includes surveys on key research content, case studies and future research directions

Service mining framework and application

CERN Document Server

Chang, Wei-Lun

2014-01-01

The shifting focus of service from the 1980s to 2000s has proved that IT not only lowers the cost of service but creates avenues to enhance and increase revenue through service. The new type of service, e-service, is mobile, flexible, interactive, and interchangeable. While service science provides an avenue for future service researches, the specific research areas from the IT perspective still need to be elaborated. This book introduces a novel concept-service mining-to address several research areas from technology, model, management, and application perspectives. Service mining is defined as "a systematical process including service discovery, service experience, service recovery, and service retention to discover unique patterns and exceptional values within the existing services." The goal of service mining is similar to data mining, text mining, or web mining, and aims to "detect something new" from the service pool. The major difference is the feature of service is quite distinct from the mining targe...

Trace metal depositional patterns from an open pit mining activity as revealed by archived avian gizzard contents

Energy Technology Data Exchange (ETDEWEB)

Bendell, L.I., E-mail: bendell@sfu.ca

2011-02-15

Archived samples of blue grouse (Dendragapus obscurus) gizzard contents, inclusive of grit, collected yearly between 1959 and 1970 were analyzed for cadmium, lead, zinc, and copper content. Approximately halfway through the 12-year sampling period, an open-pit copper mine began activities, then ceased operations 2 years later. Thus the archived samples provided a unique opportunity to determine if avian gizzard contents, inclusive of grit, could reveal patterns in the anthropogenic deposition of trace metals associated with mining activities. Gizzard concentrations of cadmium and copper strongly coincided with the onset of opening and the closing of the pit mining activity. Gizzard zinc and lead demonstrated significant among year variation; however, maximum concentrations did not correlate to mining activity. The archived gizzard contents did provide a useful tool for documenting trends in metal depositional patterns related to an anthropogenic activity. Further, blue grouse ingesting grit particles during the time of active mining activity would have been exposed to toxicologically significant levels of cadmium. Gizzard lead concentrations were also of toxicological significance but not related to mining activity. This type of 'pulse' toxic metal exposure as a consequence of open-pit mining activity would not necessarily have been revealed through a 'snap-shot' of soil, plant or avian tissue trace metal analysis post-mining activity. - Research Highlights: {yields} Archived gizzard samples reveals mining history. {yields} Grit ingestion exposes grouse to cadmium and lead. {yields} Grit selection includes particles enriched in cadmium. {yields} Cadmium enriched particles are of toxicological significance.

A Frame Work for Ontological Privacy Preserved Mining

OpenAIRE

Sriman Narayana Iyengar. N.Ch.; Geetha Mary. A

2010-01-01

Data Mining analyses the stocked data and helps in foretelling the future trends. There are different techniques by which data can be mined. These different techniques reveal different types of hiddenknowledge. Using the right procedure of technique result specific patterns emerge.Ontology is a specification of conceptualization. It is a description of concepts and relationships that can exist for an agent or a community of agents. To make software more user-friendly, ontology could be used t...

Mining known attack patterns from security-related events

Directory of Open Access Journals (Sweden)

Nicandro Scarabeo

2015-10-01

Full Text Available Managed Security Services (MSS have become an essential asset for companies to have in order to protect their infrastructure from hacking attempts such as unauthorized behaviour, denial of service (DoS, malware propagation, and anomalies. A proliferation of attacks has determined the need for installing more network probes and collecting more security-related events in order to assure the best coverage, necessary for generating incident responses. The increase in volume of data to analyse has created a demand for specific tools that automatically correlate events and gather them in pre-defined scenarios of attacks. Motivated by Above Security, a specialized company in the sector, and by National Research Council Canada (NRC, we propose a new data mining system that employs text mining techniques to dynamically relate security-related events in order to reduce analysis time, increase the quality of the reports, and automatically build correlated scenarios.

Mining Spatiotemporal Patterns of the Elder's Daily Movement

Science.gov (United States)

Chen, C. R.; Chen, C. F.; Liu, M. E.; Tsai, S. J.; Son, N. T.; Kinh, L. V.

2016-06-01

With rapid developments in wearable device technology, a vast amount of spatiotemporal data, such as people's movement and physical activities, are generated. Information derived from the data reveals important knowledge that can contribute a long-term care and psychological assessment of the elders' living condition especially in long-term care institutions. This study aims to develop a method to investigate the spatial-temporal movement patterns of the elders with their outdoor trajectory information. To achieve the goal, GPS based location data of the elderly subjects from long-term care institutions are collected and analysed with geographic information system (GIS). A GIS statistical model is developed to mine the elderly subjects' spatiotemporal patterns with the location data and represent their daily movement pattern at particular time. The proposed method first finds the meaningful trajectory and extracts the frequent patterns from the time-stamp location data. Then, a density-based clustering method is used to identify the major moving range and the gather/stay hotspot in both spatial and temporal dimensions. The preliminary results indicate that the major moving area of the elderly people encompasses their dorm and has a short moving distance who often stay in the same site. Subjects' outdoor appearance are corresponded to their life routine. The results can be useful for understanding elders' social network construction, risky area identification and medical care monitoring.

Quantifying Surface Coal-Mining Patterns to Promote Regional Sustainability in Ordos, Inner Mongolia

Directory of Open Access Journals (Sweden)

Xiaoji Zeng

2018-04-01

Full Text Available Ordos became the new “coal capital” of China within a few decades since the country’s economic reform in 1978, as large-scale surface coal mining dramatically propelled its per capita GDP from being one of the lowest to one of the highest in China, exceeding Hong Kong in 2009. Surface coal-mining areas (SCMAs have continued to expand in this region during recent decades, resulting in serious environmental and socioeconomic consequences. To understand these impacts and promote regional sustainability, quantifying the spatiotemporal patterns of SCMAs is urgently needed. Thus, the main objectives of this study were to quantify the spatiotemporal patterns of SCMAs in the Ordos region from 1990 to 2015, and to examine some of the major environmental and socioeconomic impacts in the study region. We extracted the SCMAs using remote-sensing data, and then quantified their spatiotemporal patterns using landscape metrics. The loss of natural habitat and several socioeconomic indicators were examined in relation to surface coal mining. Our results show that the area of SCMAs increased from 7.12 km2 to 355.95 km2, an increase of nearly 49 times from 1990 to 2015 in the Ordos region. The number of SCMAs in this region increased from 82 to 651, a nearly seven-fold increase. In particular, Zhungeer banner (an administrative division, Yijinhuoluo banner, Dongsheng District and Dalate banner in the north-eastern part of the Ordos region had higher growth rates of SCMAs. The income gap between urban and rural residents increased along with the growth in SCMAs, undermining social equity in the Ordos region. Moreover, the rapid increase in SCMAs resulted in natural habitat loss (including grasslands, forests, and deserts across this region. Thus, we suggest that regional sustainability in Ordos needs to emphasize effective measures to curb large-scale surface coal mining in order to reduce the urban–rural income gap, and to restore degraded natural

Efficient frequent pattern mining algorithm based on node sets in cloud computing environment

Science.gov (United States)

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.

Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

Directory of Open Access Journals (Sweden)

Md Zahidul Islam

2016-11-01

Full Text Available There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

Property Specification Patterns for intelligence building software

Science.gov (United States)

Chun, Seungsu

2018-03-01

In this paper, through the property specification pattern research for Modal MU(μ) logical aspects present a single framework based on the pattern of intelligence building software. In this study, broken down by state property specification pattern classification of Dwyer (S) and action (A) and was subdivided into it again strong (A) and weaknesses (E). Through these means based on a hierarchical pattern classification of the property specification pattern analysis of logical aspects Mu(μ) was applied to the pattern classification of the examples used in the actual model checker. As a result, not only can a more accurate classification than the existing classification systems were easy to create and understand the attributes specified.

Mining Emerging Patterns for Recognizing Activities of Multiple Users in Pervasive Computing

DEFF Research Database (Denmark)

Gu, Tao; Wu, Zhanqing; Wang, Liang

2009-01-01

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants...... activity models, and propose an Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single-user and multiuser activities. We conduct our empirical studies by collecting real-world activity traces done by two volunteers over a period of two weeks in a smart home environment...... sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution. We exploit Emerging Pattern – a type of knowledge pattern that describes significant changes between classes of data – for constructing our...

Pattern Recognition of Signals for the Fault-Slip Type of Rock Burst in Coal Mines

Directory of Open Access Journals (Sweden)

X. S. Liu

2015-01-01

Full Text Available The fault-slip type of rock burst is a major threat to the safety of coal mining, and effectively recognizing its signals patterns is the foundation for the early warning and prevention. At first, a mechanical model of the fault-slip was established and the mechanism of the rock burst induced by the fault-slip was revealed. Then, the patterns of the electromagnetic radiation, acoustic emission (AE, and microseismic signals in the fault-slip type of rock burst were proposed, in that before the rock burst occurs, the electromagnetic radiation intensity near the sliding surface increases rapidly, the AE energy rises exponentially, and the energy released by microseismic events experiences at least one peak and is close to the next peak. At last, in situ investigations were performed at number 1412 coal face in the Huafeng Mine, China. Results showed that the signals patterns proposed are in good agreement with the process of the fault-slip type of rock burst. The pattern recognition can provide a basis for the early warning and the implementation of relief measures of the fault-slip type of rock burst.

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

Science.gov (United States)

Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

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

Science.gov (United States)

Liu, Shiliang; Li, Wenping; Wang, Qiqing

2018-09-01

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

Analysis of gas migration patterns in fractured coal rocks under actual mining conditions

Directory of Open Access Journals (Sweden)

Gao Mingzhong

2017-01-01

Full Text Available Fracture fields in coal rocks are the main channels for gas seepage, migration, and extraction. The development, evolution, and spatial distribution of fractures in coal rocks directly affect the permeability of the coal rock as well as gas migration and flow. In this work, the Ji-15-14120 mining face at the No. 8 Coal Mine of Pingdingshan Tian’an Coal Mining Co. Ltd., Pingdingshan, China, was selected as the test site to develop a full-parameter fracture observation instrument and a dynamic fracture observation technique. The acquired video information of fractures in the walls of the boreholes was vectorized and converted to planarly expanded images on a computer-aided design platform. Based on the relative spatial distances between the openings of the boreholes, simultaneous planar images of isolated fractures in the walls of the boreholes along the mining direction were obtained from the boreholes located at various distances from the mining face. Using this information, a 3-D fracture network under mining conditions was established. The gas migration pattern was calculated using a COMSOL computation platform. The results showed that between 10 hours and 1 day the fracture network controlled the gas-flow, rather than the coal seam itself. After one day, the migration of gas was completely controlled by the fractures. The presence of fractures in the overlying rock enables the gas in coal seam to migrate more easily to the surrounding rocks or extraction tunnels situated relatively far away from the coal rock. These conclusions provide an important theoretical basis for gas extraction.

Hospitalization patterns associated with Appalachian coal mining.

Science.gov (United States)

Hendryx, Michael; Ahern, Melissa M; Nurkiewicz, Timothy R

2007-12-01

The goal of this study was to test whether the volume of coal mining was related to population hospitalization risk for diseases postulated to be sensitive or insensitive to coal mining by-products. The study was a retrospective analysis of 2001 adult hospitalization data (n = 93,952) for West Virginia, Kentucky, and Pennsylvania, merged with county-level coal production figures. Hospitalization data were obtained from the Health Care Utilization Project National Inpatient Sample. Diagnoses postulated to be sensitive to coal mining by-product exposure were contrasted with diagnoses postulated to be insensitive to exposure. Data were analyzed using hierarchical nonlinear models, controlling for patient age, gender, insurance, comorbidities, hospital teaching status, county poverty, and county social capital. Controlling for covariates, the volume of coal mining was significantly related to hospitalization risk for two conditions postulated to be sensitive to exposure: hypertension and chronic obstructive pulmonary disease (COPD). The odds for a COPD hospitalization increased 1% for each 1462 tons of coal, and the odds for a hypertension hospitalization increased 1% for each 1873 tons of coal. Other conditions were not related to mining volume. Exposure to particulates or other pollutants generated by coal mining activities may be linked to increased risk of COPD and hypertension hospitalizations. Limitations in the data likely result in an underestimate of associations.

Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.; Nijssen, S.; De Raedt, L.

2007-01-01

We propose a relational database model towards the integration of data mining into relational database systems, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules, decision trees and clusterings, can be

WildSpan: mining structured motifs from protein sequences

Directory of Open Access Journals (Sweden)

Chen Chien-Yu

2011-03-01

Full Text Available Abstract Background Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions that incorporates several pruning strategies to largely reduce the mining cost. Results WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode

Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.

2008-01-01

We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules and decision

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

Science.gov (United States)

2014-01-01

Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism

Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

DEFF Research Database (Denmark)

Ozer, Mert; Keles, Ilkcan; Toroslu, Hakki

2016-01-01

In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal...... and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity...... the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets....

Practical graph mining with R

CERN Document Server

Hendrix, William; Jenkins, John; Padmanabhan, Kanchana; Chakraborty, Arpan

2014-01-01

Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. De...

In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

DEFF Research Database (Denmark)

Lartillot, Olivier

2014-01-01

presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity. A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented....... The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis. The system, implemented in Matlab, is made publicly available as part of Mining......Suite, a new open-source framework for audio and music analysis....

A DATA-MINING BASED METHOD FOR THE GAIT PATTERN ANALYSIS

Directory of Open Access Journals (Sweden)

Marcelo Rudek

2015-12-01

Full Text Available The paper presents a method developed for the gait classification based on the analysis of the trajectory of the pressure centres (CoP extracted from the contact points of the feet with the ground during walking. The data acquirement is performed ba means of a walkway with embedded tactile sensors. The proposed method includes capturing procedures, standardization of data, creation of an organized repository (data warehouse, and development of a process mining. A graphical analysis is applied to looking at the footprint signature patterns. The aim is to obtain a visual interpretation of the grouping by situating it into the normal walking patterns or deviations associated with an individual way of walking. The method consists of data classification automation which divides them into healthy and non-healthy subjects in order to assist in rehabilitation treatments for the people with related mobility problems.

Problems and Alternatives of Settlement Lagoons for Mine Water Treatment System

Science.gov (United States)

Lee, Dong-Kil

2015-04-01

A field test and computational flow analysis were conducted to identify the structural problems with existing settlement lagoons and to propose effective alternatives. When it comes to existing settlement lagoons without any specifically designed internal structure, mine water flows along a specific route while other regions remained stagnant. Such a flow pattern along a specific region causes a significant reduction in retention time as well as the ineffective use of the space in a settlement lagoon. When applying the modified settlement lagoon design proposed in this study, the flow distribution of mine drainage became uniform and the time taken for mine drainage to reach the outlet was improved by as much as 360 times and the exchange efficiency was significantly enhanced from 14.5% to 82.7%.

Using data mining and OLAP to discover patterns in a database of patients with Y-chromosome deletions.

Science.gov (United States)

Dzeroski, S; Hristovski, D; Peterlin, B

2000-01-01

The paper presents a database of published Y chromosome deletions and the results of analyzing the database with data mining techniques. The database describes 382 patients for which 177 different markers were tested: 364 of the 382 patients had deletions. Two data mining techniques, clustering and decision tree induction were used. Clustering was used to group patients according to the overall presence/absence of deletions at the tested markers. Decision trees and On-Line-Analytical-Processing (OLAP) were used to inspect the resulting clustering and look for correlations between deletion patterns, populations and the clinical picture of infertility. The results of the analysis indicate that there are correlations between deletion patterns and patient populations, as well as clinical phenotype severity.

Analysis of the electrical disturbances in CERN power distribution network with pattern mining methods

CERN Document Server

Abramenko, Oleksii

2017-01-01

The current research focuses on the perturbations within the electrical network of the LHC and its subsystems by analyzing measurements collected from oscilloscopes installed across different CERN sites, and alarms by electrical equipments. We analyze amplitude and duration of the glitches and, together with other relevant variables, correlate them with beam stopping events. The work also tries to identify assets affected by such perturbations using data mining and, in particular, frequent pattern mining methods. On the practical side we summarize results of our work by putting forward a prototype of a software tool enabling online monitoring of the alarms coming from the electrical network and facilitating glitch detection and analysis by a technical operator.

REGULAR PATTERN MINING (WITH JITTER ON WEIGHTED-DIRECTED DYNAMIC GRAPHS

Directory of Open Access Journals (Sweden)

A. GUPTA

2017-02-01

Full Text Available Real world graphs are mostly dynamic in nature, exhibiting time-varying behaviour in structure of the graph, weight on the edges and direction of the edges. Mining regular patterns in the occurrence of edge parameters gives an insight into the consumer trends over time in ecommerce co-purchasing networks. But such patterns need not necessarily be precise as in the case when some product goes out of stock or a group of customers becomes unavailable for a short period of time. Ignoring them may lead to loss of useful information and thus taking jitter into account becomes vital. To the best of our knowledge, no work has been yet reported to extract regular patterns considering a jitter of length greater than unity. In this article, we propose a novel method to find quasi regular patterns on weight and direction sequences of such graphs. The method involves analysing the dynamic network considering the inconsistencies in the occurrence of edges. It utilizes the relation between the occurrence sequence and the corresponding weight and direction sequences to speed up this process. Further, these patterns are used to determine the most central nodes (such as the most profit yielding products. To accomplish this we introduce the concept of dynamic closeness centrality and dynamic betweenness centrality. Experiments on Enron e-mail dataset and a synthetic dynamic network show that the presented approach is efficient, so it can be used to find patterns in large scale networks consisting of many timestamps.

Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

Science.gov (United States)

Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

Kajian Algoritma Sequential Pattern Mining Dan Market Basket Analysis Dalam Pengenalan Pola Belanja Customer Untuk Layout Toko

Directory of Open Access Journals (Sweden)

Rusito Rusito

2016-01-01

Full Text Available Penelitian ini membahas tentang keterkaitan antar item yang dibeli oleh customer dalam toko ritel. Pengetahuan keterkaitan item yang dibeli dapat digunakan untuk  menentukan tata letak barang dagangan toko ritel. Hal ini penting agar konsumen dapat mudah mendapatkan barang yang dibutuhkan. Sehingga dapat meningkatkan omzet penjualan toko ritel sehingga akhirnya menambah keuntungan bagi pemilik toko ritel. Teknik yang digunakan untuk menyelesaikan penggalian data dan keterkaitan pembelian tersebut menggunakan pendekatan Association rule dan Market Basket Analysis. Sedangkan untuk mencari keterkaitan item tersebut digunakan algoritma Sequential Pattern Mining. Digunakan karena mampu menangani jumlah database yang besar dan sangat baik disisi kecepatan pemrosesan. Berbagai aplikasi telah diidentifikasi, termasuk misalnya, cross-selling, analisis situs Web, pendukung keputusan, evaluasi kredit, acara prediksi kriminal, analisis perilaku pelanggan  dan deteksi penipuan. Dari penelitian yang telah dilakukan diperoleh  pola-pola belanja customer untuk membentuk suatu layout display dalam toko ritel. Penelitian ini juga menyajikan suatu kerja algoritma yang lebih efektif dari algoritma asli karena terdapat pembatasan perulangan. Untuk kombinasi maksimal 5 item dengan waktu eksekusi 421.06 detik untuk 200 nota.   Kata kunci : Data Mining, Algoritma Sequential Pattern Mining, Market Basket Analysis, Apriori, Layout, Toko Ritel

Information mining in remote sensing imagery

Science.gov (United States)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and

World Wide Web Usage Mining Systems and Technologies

Directory of Open Access Journals (Sweden)

Wen-Chen Hu

2003-08-01

Full Text Available Web usage mining is used to discover interesting user navigation patterns and can be applied to many real-world problems, such as improving Web sites/pages, making additional topic or product recommendations, user/customer behavior studies, etc. This article provides a survey and analysis of current Web usage mining systems and technologies. A Web usage mining system performs five major tasks: i data gathering, ii data preparation, iii navigation pattern discovery, iv pattern analysis and visualization, and v pattern applications. Each task is explained in detail and its related technologies are introduced. A list of major research systems and projects concerning Web usage mining is also presented, and a summary of Web usage mining is given in the last section.

Implications of Emerging Data Mining

Science.gov (United States)

Kulathuramaiyer, Narayanan; Maurer, Hermann

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

Science.gov (United States)

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human-viral Infection Patterns.

Science.gov (United States)

Smoly, Ilan; Carmel, Amir; Shemer-Avni, Yonat; Yeger-Lotem, Esti; Ziv-Ukelson, Michal

2016-03-01

Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.

Improve Data Mining and Knowledge Discovery through the use of MatLab

Science.gov (United States)

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

Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population

DEFF Research Database (Denmark)

Eriksson, Robert; Werge, Thomas; Jensen, Lars Juhl

2014-01-01

patient-specific adverse events (AEs) and links these to specific drugs and dosages in a temporal manner, based on integration of text mining results and structured data. The structured data contained precise information on drug identity, dosage and strength.When applying the method to the 3,394 patients...... all indication areas.The aim of this study was to take advantage of techniques for temporal data mining of EPRs in order to detect ADRs in a patient- and dose-specific manner.We used a psychiatric hospital’s EPR system to investigate undesired drug effects. Within one workflow the method identified...

Biosorption of metal and salt tolerant microbial isolates from a former uranium mining area. Their impact on changes in rare earth element patterns in acid mine drainage.

Science.gov (United States)

Haferburg, Götz; Merten, Dirk; Büchel, Georg; Kothe, Erika

2007-12-01

The concentration of metals in microbial habitats influenced by mining operations can reach enormous values. Worldwide, much emphasis is placed on the research of resistance and biosorptive capacities of microorganisms suitable for bioremediation purposes. Using a collection of isolates from a former uranium mining area in Eastern Thuringia, Germany, this study presents three Gram-positive bacterial strains with distinct metal tolerances. These strains were identified as members of the genera Bacillus, Micrococcus and Streptomyces. Acid mine drainage (AMD) originating from the same mining area is characterized by high metal concentrations of a broad range of elements and a very low pH. AMD was analyzed and used as incubation solution. The sorption of rare earth elements (REE), aluminum, cobalt, copper, manganese, nickel, strontium, and uranium through selected strains was studied during a time course of four weeks. Biosorption was investigated after one hour, one week and four weeks by analyzing the concentrations of metals in supernatant and biomass. Additionally, dead biomass was investigated after four weeks of incubation. The maximum of metal removal was reached after one week. Up to 80% of both Al and Cu, and more than 60% of U was shown to be removed from the solution. High concentrations of metals could be bound to the biomass, as for example 2.2 mg/g U. The strains could survive four weeks of incubation. Distinct and different patterns of rare earth elements of the inoculated and non-inoculated AMD water were observed. Changes in REE patterns hint at different binding types of heavy metals regarding incubation time and metabolic activity of the cells. (c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

Science.gov (United States)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

Pattern specification in the insect embryo. [uv radiation, Smittia

Energy Technology Data Exchange (ETDEWEB)

Sander, K

1975-01-01

Specification of developmental pathways by specific determining substances prelocalized in the egg cytoplasm is discussed using the so-called germ cell determinants as an example. Some theoretical considerations speak against the assumption that in insects the various elements of the basic body plan are specified by a prelocalized mosaic of specific determinants. Experimental evidence also points towards a largely epigenetic mode of pattern specification. The process of axial pattern specification can be altered drastically by experiment: in some insects, tail ends may be formed in place of head parts and identical sequences of body segments may be specified two or even three times along the longitudinal egg axis. The experimental results indicate that polarity and regional character of pattern elements formed are specified by one and the same influence, and that this influence can be shifted to or simulated in various other egg regions by transposition or elimination of egg components, or by uv irradiation. Evidence obtained from several types of experiment in the chironomid midge Smittia points towards a key role for local metabolism or energy charge in determination of cell polarity and in pattern spcification. A model for embryonic pattern specification involving differential reaction of cells to a system of longitudinal gradients, which was proposed in 1960, can in principle formally account for all results described. Some striking coincidences of model and experimental results with Wolpert's concept of positional information are noted. Finally it is pointed out that universality of mechanisms for pattern specification is much more likely with respect to formal principles than at the level of their physiological realization.

Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

Science.gov (United States)

Chang, Qingliang; Zhou, Huaqiang; Bai, Jianbiao

2014-01-01

Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application. PMID:25258737

Mining Web-based Educational Systems to Predict Student Learning Achievements

Directory of Open Access Journals (Sweden)

José del Campo-Ávila

2015-03-01

Full Text Available Educational Data Mining (EDM is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases. The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students. It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models.

Data Mining in Education : A Review on the Knowledge Discovery Perspective

OpenAIRE

Pratiyush Guleria; Manu Sood

2014-01-01

Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where data mining is the core of this process. Data minin g can be used to mine understandable meaningful patterns from large databases and these patterns ma y then be converted into knowledge.Data mining is t he process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehou se and...

A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES

OpenAIRE

R. Rajamani*1 & S. Saranya2

2017-01-01

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. T...

Vegetation pattern and heavy metal accumulation at a mine tailing at Gyöngyösoroszi, hungary.

Science.gov (United States)

Tamás, János; Kovács, Alza

2005-01-01

Vegetation at an abandoned heavy metal bearing mine tailing may have multifunctional roles such as modification of water balance, erosion control and landscape rehabilitation. Research on the vegetation of mine tailings can provide useful information on tolerance, accumulation and translocation properties of species potentially applicable at moderately contaminated sites. Analyses of the relationship between heavy metal content (Pb, Zn and Cu) and vegetation in a mine tailing were carried out. These analyses included: (1) spatial analysis of relationship among heavy metal distribution, pH and vegetation patterns, and (2) analysis of heavy metal accumulation and translocation in some plant species. Presence of vegetation was found to be significantly dependent on pH value, which confirms that phytotoxicity is a function of element concentration in solution, which is primarily controlled by pH value in mine tailings. Among the most abundant plant species, dewberry (Rubus caesius), vipersbugloss (Echium vulgare), scarlet pimpernel (Anagallis arvensis) and narrowleaf plantain (Plantago lanceolata) accumulate significant amounts of Pb, Cu and Zn, while in the case of annual bluegrass (Poa annua) only Pb can be measured in elevated contents. Considering the translocation features, scarlet pimpernel, narrowleaf plantain, and dewberry accumulate heavy metals primarily in their roots, while heavy metal concentration in vipersbugloss and annual bluegrass is higher in the shoots.

A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases.

Science.gov (United States)

Pérez, Joaquín; Iturbide, Emmanuel; Olivares, Víctor; Hidalgo, Miguel; Martínez, Alicia; Almanza, Nelva

2015-11-01

It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50% or up to 70% of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.

Assessment of Quantitative Aftershock Productivity Potential in Mining-Induced Seismicity

Science.gov (United States)

Kozłowska, Maria; Orlecka-Sikora, Beata

2017-03-01

Strong mining-induced earthquakes exhibit various aftershock patterns. The aftershock productivity is governed by the geomechanical properties of rock in the seismogenic zone, mining-induced stress and coseismic stress changes related to the main shock's magnitude, source geometry and focal mechanism. In order to assess the quantitative aftershock productivity potential in the mining environment we apply a forecast model based on natural seismicity properties, namely constant tectonic loading and the Gutenberg-Richter frequency-magnitude distribution. Although previous studies proved that mining-induced seismicity does not obey the simple power law, here we apply it as an approximation of seismicity distribution to resolve the number of aftershocks, not considering their magnitudes. The model used forecasts the aftershock productivity based on the background seismicity level estimated from an average seismic moment released per earthquake and static stress changes caused by a main shock. Thus it accounts only for aftershocks directly triggered by coseismic process. In this study we use data from three different mines, Mponeng (South Africa), Rudna and Bobrek (Poland), representing different geology, exploitation methods and aftershock patterns. Each studied case is treated with individual parameterization adjusted to the data specifics. We propose the modification of the original model, i.e. including the non-uniformity of M 0, resulting from spatial correlation of mining-induced seismicity with exploitation. The results show that, even when simplified seismicity distribution parameters are applied, the modified model predicts the number of aftershocks for each analyzed case well and accounts for variations between these values. Such results are thus another example showing that coseismic processes of mining-induced seismicity reflect features of natural seismicity and that similar models can be applied to study the aftershock rate in both the natural and the

Restoring tropical forests on bauxite mined lands: lessons from the Brazilian Amazon

Science.gov (United States)

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...

Software tool for data mining and its applications

Science.gov (United States)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Percolator: Scalable Pattern Discovery in Dynamic Graphs

Energy Technology Data Exchange (ETDEWEB)

Choudhury, Sutanay; Purohit, Sumit; Lin, Peng; Wu, Yinghui; Holder, Lawrence B.; Agarwal, Khushbu

2018-02-06

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walking through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.

Finding occupational accident patterns in the extractive industry using a systematic data mining approach

International Nuclear Information System (INIS)

Silva, Joaquim F.; Jacinto, Celeste

2012-01-01

This paper deals with occupational accident patterns of in the Portuguese Extractive Industry. It constitutes a significant advance with relation to a previous study made in 2008, both in terms of methodology and extended knowledge on the patterns’ details. This work uses more recent data (2005–2007) and this time the identification of the “typical accident” shifts from a bivariate, to a multivariate pattern, for characterising more accurately the accident mechanisms. Instead of crossing only two variables (Deviation x Contact), the new methodology developed here uses data mining techniques to associate nine variables, through their categories, and to quantify the statistical cohesion of each pattern. The results confirmed the “typical accident” of the 2008 study, but went much further: it reveals three statistically significant patterns (the top-3 categories in frequency); moreover, each pattern includes now more variables (4–5 categories) and indicates their statistical cohesion. This approach allowed a more accurate vision of the reality, which is fundamental for risk management. The methodology is best suited for large groups, such as national Authorities, Insurers or Corporate Groups, to assist them planning target-oriented safety strategies. Not least importantly, researchers can apply the same algorithm to other study areas, as it is not restricted to accidents, neither to safety.

Data mining in pharma sector: benefits.

Science.gov (United States)

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.

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care.

Science.gov (United States)

Ismail, Walaa N; Hassan, Mohammad Mehedi

2017-04-26

The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in smart homes. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Directory of Open Access Journals (Sweden)

Walaa N. Ismail

2017-04-01

Full Text Available The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants’ health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in smart homes. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.

Process mining online assessment data

NARCIS (Netherlands)

Pechenizkiy, M.; Trcka, N.; Vasilyeva, E.; Aalst, van der W.M.P.; De Bra, P.M.E.; Barnes, T.; Desmarais, M.; Romero, C.; Ventura, S.

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

Discovering More Accurate Frequent Web Usage Patterns

OpenAIRE

Bayir, Murat Ali; Toroslu, Ismail Hakki; Cosar, Ahmet; Fidan, Guven

2008-01-01

Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web users. As in classical data mining, data preparation and pattern discovery are the main issues in web usage mining. The first phase of web usage mining is the data processing phase, which includes the session reconstruction operation from server logs. Session reconstruction success directly affects the quality of the frequent patterns disc...

Data Mining and Statistics for Decision Making

CERN Document Server

Tufféry, Stéphane

2011-01-01

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized lin

Data mining for dummies

CERN Document Server

Brown, Meta S

2014-01-01

Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business''s entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn''t take a data scientist to gain

Discovering significant evolution patterns from satellite image time series.

Science.gov (United States)

Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

2011-12-01

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

Citizens and the planning of sustainability of mining

DEFF Research Database (Denmark)

Hoffmann, Birgitte

2014-01-01

will have a large impact on the citizen’s everyday life through the ongoing changes of settlement patterns and livelihoods. The key question of this paper is how the citizens may inform and influence the sustainability of planning and implementation of local raw material projects and urban planning. Further...... that the social, economic and environmental sustainability will depend on the degree to which the citizens are engaged in both local developments of specific mining projects, as well as in societal planning where multiple and complex issues are at stake such as urban settlement patterns, cultures, livelihood...

Occupational Malfunctioning and Fatigue Related Work Stress Disorders (FRWSDs): An Emerging Issue in Indian Underground Mine (UGM) Operations

Science.gov (United States)

Dey, Shibaji Ch.; Dey, Netai Chandra; Sharma, Gourab Dhara

2018-04-01

Indian underground mining (UGM) transport system largely deals with different fore and back bearing work processes associated with different occupational disorders and fatigue related work stress disorders (FRWSDs). Therefore, this research study is specifically aimed to determine the fatigue related problems in general and determination of Recovery Heart Rate (Rec HR) pattern and exact cause of FRWSDs in particular. A group of twenty (N = 20) UGM operators are selected for the study. Heart rate profiles and work intensities of selected workforces have been recorded continuously during their regular mine operation and the same workforces are tested on a treadmill on surface with almost same work intensity (%Maximal Heart Rate) which was earlier observed in the mine. Recovery Heart Rate (Rec HR) in both the experiment zones is recorded. It is observed that with almost same work intensity, the recovery patterns of submaximal prolonged work in mine are different as compared to treadmill. This research study indicates that non-biomechanical muscle activity along with environmental stressors may have an influence on recovery pattern and FRWSDs.

Data mining in radiology

International Nuclear Information System (INIS)

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining

Large Scale Frequent Pattern Mining using MPI One-Sided Model

Energy Technology Data Exchange (ETDEWEB)

Vishnu, Abhinav; Agarwal, Khushbu

2015-09-08

In this paper, we propose a work-stealing runtime --- Library for Work Stealing LibWS --- using MPI one-sided model for designing scalable FP-Growth --- {\\em de facto} frequent pattern mining algorithm --- on large scale systems. LibWS provides locality efficient and highly scalable work-stealing techniques for load balancing on a variety of data distributions. We also propose a novel communication algorithm for FP-growth data exchange phase, which reduces the communication complexity from state-of-the-art O(p) to O(f + p/f) for p processes and f frequent attributed-ids. FP-Growth is implemented using LibWS and evaluated on several work distributions and support counts. An experimental evaluation of the FP-Growth on LibWS using 4096 processes on an InfiniBand Cluster demonstrates excellent efficiency for several work distributions (87\\% efficiency for Power-law and 91% for Poisson). The proposed distributed FP-Tree merging algorithm provides 38x communication speedup on 4096 cores.

Windblown Dust Deposition Forecasting and Spread of Contamination around Mine Tailings.

Science.gov (United States)

Stovern, Michael; Guzmán, Héctor; Rine, Kyle P; Felix, Omar; King, Matthew; Ela, Wendell P; Betterton, Eric A; Sáez, Avelino Eduardo

2016-02-01

Wind erosion, transport and deposition of windblown dust from anthropogenic sources, such as mine tailings impoundments, can have significant effects on the surrounding environment. The lack of vegetation and the vertical protrusion of the mine tailings above the neighboring terrain make the tailings susceptible to wind erosion. Modeling the erosion, transport and deposition of particulate matter from mine tailings is a challenge for many reasons, including heterogeneity of the soil surface, vegetative canopy coverage, dynamic meteorological conditions and topographic influences. In this work, a previously developed Deposition Forecasting Model (DFM) that is specifically designed to model the transport of particulate matter from mine tailings impoundments is verified using dust collection and topsoil measurements. The DFM is initialized using data from an operational Weather Research and Forecasting (WRF) model. The forecast deposition patterns are compared to dust collected by inverted-disc samplers and determined through gravimetric, chemical composition and lead isotopic analysis. The DFM is capable of predicting dust deposition patterns from the tailings impoundment to the surrounding area. The methodology and approach employed in this work can be generalized to other contaminated sites from which dust transport to the local environment can be assessed as a potential route for human exposure.

Process Mining Online Assessment Data

Science.gov (United States)

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…

Data Stream Mining

Science.gov (United States)

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

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

Specialized mining GIS system MineGIS SMZ Jelšava

Directory of Open Access Journals (Sweden)

Peter Sasvári

2005-12-01

Full Text Available Following, the real needs for new mining information system requested by SMZ Jelšava, the Department of Mineral Deposits and Applied Geology (KLaAG at the Technical University of Košice (TUKE has prepared a specification for the specialized mining geographic information system called MineGIS SMZ Jelšava. The main roles of the new system have been defined as follows of reserves: the administration, analyse and the visualization of all mining geo-data related to the estimation.

Quantification of Operational Risk Using A Data Mining

Science.gov (United States)

Perera, J. Sebastian

1999-01-01

What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process

Mapping and monitoring coal mine subsidence using LiDAR and InSAR

Energy Technology Data Exchange (ETDEWEB)

Froese, C.R.; Mei, S. [Alberta Geological Survey, Edmonton, AB (Canada). Energy Resources Conservation Board

2008-07-01

In the early 1900s, the abandonment of coal mines in Alberta was not regulated and closure documentation was poor. Although the general locations of mines are known, the locations of the specific adits and shafts are not. As such, there are many cases in southwestern Alberta where infrastructure was built on top of old coal mine workings without any detailed records of the abandoned mine or displacement monitoring. The crowns of these workings have been subject to ongoing strain that is reflected at the surface. The rate at which the strain is progressing prior to collapse is not well understood. Mitigation of collapse events is site specific and reactive. This paper demonstrated that airborne LiDAR and spaceborne InSAR technologies can provide valuable information on the distribution of abandoned underground coal mine workings. Both remote sensing techniques were used on Turtle Mountain in the Crowsnest Pass to obtain quantitative information on landslide mechanics, including the patterns and rate of ground movement and subsidence. These techniques can be used to map the location of surface collapse and delineate the location of the coal mine workings that were not previously documented. It was concluded that these technologies will likely become more readily available in the future and incorporated into geo-engineering practices for use in ground hazard detection, monitoring and management. 8 refs., 6 figs.

Educational Data Mining Application for Estimating Students Performance in Weka Environment

Science.gov (United States)

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

2017-11-01

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

Below a Historic Mercury Mine: Non-linear Patterns of Mercury Bioaccumulation in Aquatic Organisms

Science.gov (United States)

Haas, J.; Ichikawa, G.; Ode, P.; Salsbery, D.; Abel, J.

2001-12-01

Unlike most heavy metals, mercury is capable of bioaccumulating in aquatic food-chains, primarily because it is methylated by bacteria in sediment to the more toxic methylmercury form. Mercury concentrations in a number of riparian systems in California are highly elevated as a result of historic mining activities. These activities included both the mining of cinnabar in the coastal ranges to recover elemental mercury and the use of elemental mercury in the gold fields of the Sierra Nevada Mountains. The most productive mercury mining area was the New Almaden District, now a county park, located in the Guadalupe River drainage of Santa Clara County, where cinnabar was mined and retorted for over 100 years. As a consequence, riparian systems in several subwatersheds of the Guadalupe River drainage are contaminated with total mercury concentrations that exceed state hazardous waste criteria. Mercury concentrations in fish tissue frequently exceed human health guidelines. However, the potential ecological effects of these elevated mercury concentrations have not been thoroughly evaluated. One difficulty is in extrapolating sediment concentrations to fish tissue concentrations without accounting for physical and biological processes that determine bioaccumulation patterns. Many processes, such as methylation and demethylation of mercury by bacteria, assimilation efficiency in invertebrates, and metabolic rates in fish, are nonlinear, a factor that often confounds attempts to evaluate the effects of mercury contamination on aquatic food webs. Sediment, benthic macroinvertebrate, and fish tissue samples were collected in 1998 from the Guadalupe River drainage in Santa Clara County at 13 sites upstream and downstream from the historic mining district. Sediment and macroinvertebrate samples were analyzed for total mercury and methylmercury. Fish samples were analyzed for total mercury as whole bodies, composited by species and size. While linear correlations of sediment

Automated effect-specific mammographic pattern measures

DEFF Research Database (Denmark)

Raundahl, Jakob; Loog, Marco; Pettersen, Paola

2008-01-01

We investigate the possibility to develop methodologies for assessing effect specific structural changes of the breast tissue using a general statistical machine learning framework. We present an approach of obtaining objective mammographic pattern measures quantifying a specific biological effect......, such as hormone replacement therapy (HRT). We compare results using this approach to using standard density measures. We show that the proposed method can quantify both age related effects and effects caused by HRT. Age effects are significantly detected by our method where standard methodologies fail...

Mining-induced seismicity at the Lucky Friday Mine: Seismic events of magnitude >2.5, 1989--1994

Energy Technology Data Exchange (ETDEWEB)

Whyatt, J.K.; Williams, T.J. [USDOE, Spokane, WA (United States). Spokane Research Center; Blake, W. [Blake (W.), Hayden Lake, ID (United States); Sprenke, K. [Idaho Univ., Moscow, ID (United States); Wideman, C. [Montana Tech, Butte, MT (United States)

1996-09-01

An understanding of the types of seismic events that occur in a deep mine provides a foundation for assessing the seismic characteristics of these events and the degree to which initiation of these events can be anticipated or controlled. This study is a first step toward developing such an understanding of seismic events generated by mining in the Coeur d`Alene Mining District of northern Idaho. It is based on information developed in the course of a long-standing rock burst research effort undertaken by the U. S. Bureau of Mines in cooperation with Coeur d`Alene Mining District mines and regional universities. This information was collected for 39 seismic events with local magnitudes greater than 2.5 that occurred between 1989 and 1994. One of these events occurred, on average, every 8 weeks during the study period. Five major types of characteristic events were developed from the data; these five types describe all but two of the 39 events that were studied. The most common types of events occurred, on average, once every 30 weeks. The characteristic mechanisms, first-motion patterns, damage patterns, and relationships to mining and major geologic structures were defined for each type of event. These five types of events need to be studied further to assess their ability to camouflage clandestine nuclear tests as well as the degree to which they can be anticipated and controlled.

Spatiotemporal Data Mining: A Computational Perspective

Directory of Open Access Journals (Sweden)

Shashi Shekhar

2015-10-01

Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Fuzzy Clustering: An Approachfor Mining Usage Profilesfrom Web

OpenAIRE

Ms.Archana N. Boob; Prof. D. M. Dakhane

2012-01-01

Web usage mining is an application of data mining technology to mining the data of the web server log file. It can discover the browsing patterns of user and some kind of correlations between the web pages. Web usage mining provides the support for the web site design, providing personalization server and other business making decision, etc. Web mining applies the data mining, the artificial intelligence and the chart technology and so on to the web data and traces users' visiting characteris...

Mining frequent binary expressions

NARCIS (Netherlands)

Calders, T.; Paredaens, J.; Kambayashi, Y.; Mohania, M.K.; Tjoa, A.M.

2000-01-01

In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type of patterns, binary expressions. Based on the properties of a specified binary test, such as reflexivity, transitivity

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

NARCIS (Netherlands)

Mihaescu, Marian; Tanasie, Alexandru; Dascalu, Mihai; Trausan-Matu, Stefan

2016-01-01

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers,

A Data Mining Approach for Cardiovascular Diagnosis

Directory of Open Access Journals (Sweden)

Pereira Joana

2017-12-01

Full Text Available The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

Knowledge Discovery and Data Mining in Iran's Climatic Researches

Science.gov (United States)

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

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Science.gov (United States)

Gan, Wensheng; Zhang, Binbin

2015-01-01

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns. PMID:25811038

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.

Mining High-Dimensional Data

Science.gov (United States)

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.

Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity

DEFF Research Database (Denmark)

Miller, Yury I; Choi, Soo-Ho; Wiesner, Philipp

2011-01-01

are a major target of innate immunity, recognized by a variety of "pattern recognition receptors" (PRRs). By analogy with microbial "pathogen-associated molecular patterns" (PAMPs), we postulate that host-derived, oxidation-specific epitopes can be considered to represent "danger (or damage......)-associated molecular patterns" (DAMPs). We also argue that oxidation-specific epitopes present on apoptotic cells and their cellular debris provided the primary evolutionary pressure for the selection of such PRRs. Furthermore, because many PAMPs on microbes share molecular identity and/or mimicry with oxidation...

Sparse feature selection identifies H2A.Z as a novel, pattern-specific biomarker for asymmetrically self-renewing distributed stem cells

Directory of Open Access Journals (Sweden)

Yang Hoon Huh

2015-03-01

Full Text Available There is a long-standing unmet clinical need for biomarkers with high specificity for distributed stem cells (DSCs in tissues, or for use in diagnostic and therapeutic cell preparations (e.g., bone marrow. Although DSCs are essential for tissue maintenance and repair, accurate determination of their numbers for medical applications has been problematic. Previous searches for biomarkers expressed specifically in DSCs were hampered by difficulty obtaining pure DSCs and by the challenges in mining complex molecular expression data. To identify such useful and specific DSC biomarkers, we combined a novel sparse feature selection method with combinatorial molecular expression data focused on asymmetric self-renewal, a conspicuous property of DSCs. The analysis identified reduced expression of the histone H2A variant H2A.Z as a superior molecular discriminator for DSC asymmetric self-renewal. Subsequent molecular expression studies showed H2A.Z to be a novel “pattern-specific biomarker” for asymmetrically self-renewing cells, with sufficient specificity to count asymmetrically self-renewing DSCs in vitro and potentially in situ.

On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases

Science.gov (United States)

Yun, Unil

Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.

Country-Specific Dietary Patterns and Associations with Socioeconomic Status in European Children

DEFF Research Database (Denmark)

Fernandez-Alvira, Juan M.; Bammann, Karin; Pala, Valeria

2014-01-01

Background/objectives:Children from lower socioeconomic status (SES) may be at higher risk of unhealthy eating. We described country-specific dietary patterns among children aged 2-9 years from eight European countries participating in the IDEFICS study and assessed the association of dietary...... patterns with an additive SES indicator.Subjects/Methods:Children aged 2-9 years from eight European countries were recruited in 2007-2008. Principal component analysis was applied to identify dietary country-specific patterns. Linear regression analyses were applied to assess their association with SES....... Results:Two to four dietary patterns were identified in the participating regions. The existence of a 'processed' pattern was found in the eight regions. Also, a 'healthy' pattern was identified in seven of the eight regions. In addition, region-specific patterns were identified, reflecting the existing...

Prairie of mine(s) : cultural reclamation of the Estevan/Bienfait Coalfields

Energy Technology Data Exchange (ETDEWEB)

Baxter, S.

2010-07-01

A cultural reclamation project was launched in the Bienfait region of southern Saskatchewan where lignite mining has been ongoing since the 1800s. Evidence of 5 surface mines, 2 power stations and thousands of acres of spoil piles remain at the abandoned site. The region also comprises 140 abandoned underground mines and 4 mined-out townsites. The project introduced cultural reclamation into the role of landscape architecture, specifically in the planning and design of reclaimed mining lands. At the present time, the reclamation of post-extractive sites is limited to focusing almost exclusively on ecological factors, but failing to recognize the people and the industrial processes that actively transformed the landscape can disengage people from their past. The project concludes with a proposed master plan in addition to a few site-specific interventions that interrogate and explore the role of experiential, cultural, and historical elements in the reclamation of a site. In doing so, awareness is created about the ways in which various landscapes are manipulated every day in order for people to live in greater comfort.

Granular-relational data mining how to mine relational data in the paradigm of granular computing ?

CERN Document Server

Hońko, Piotr

2017-01-01

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining r...

An Efficient System Based On Closed Sequential Patterns for Web Recommendations

OpenAIRE

Utpala Niranjan; R.B.V. Subramanyam; V-Khana

2010-01-01

Sequential pattern mining, since its introduction has received considerable attention among the researchers with broad applications. The sequential pattern algorithms generally face problems when mining long sequential patterns or while using very low support threshold. One possible solution of such problems is by mining the closed sequential patterns, which is a condensed representation of sequential patterns. Recently, several researchers have utilized the sequential pattern discovery for d...

DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

OpenAIRE

Pragati Sharma; Dr. Sanjiv Sharma

2018-01-01

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational da...

First Mining workshop of Mining and metallurgical of MERCOSUR

International Nuclear Information System (INIS)

1994-01-01

In the city of Montevideo, capital of the Oriental Republic of Uruguay, at 23 days of September 1994, under the First Meeting of Mercosur Mining Metallurgical, meet representatives of the mining sector in the countries signed the Treaty of Asuncion , attended as observers, authorities of the Republic of Bolivia and Ecuador and representatives of the productive labor, legislative and research. The primary objective is to integrate the mining sectors of those countries, taking into account the specificity of the mining, given by the resource it uses, the need for high-risk investment with slow recoveries of capital and infrastructure problems, taking into account leverage and its remarkable impact on the development of regional economies.

Efficient discovery of risk patterns in medical data.

Science.gov (United States)

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

2009-01-01

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

Data Mining – Innovative Method for Obtaining Information in Marketingand Business Management

Directory of Open Access Journals (Sweden)

Mirela-Cristina Voicu

2011-05-01

Full Text Available The existence of massive amounts of data raised the question of using their reorientation to a retrospective to a prospective operation. Data mining offers the promise of an important aid for discovering hidden patterns in data that can be used to predict the behavior of customers, products and processes. Data mining tools must be guided by users who understand the business, the general nature of the data and analytical methods involved. It discovers information within the data that queries and reports can’t effectively reveal. It is vital to collect data and prepare properly, to face reality models. Choosing the most appropriate product data mining is to find a tool with the capabilities required, an interface that matches the skills of users and can be applied in a specific business problem. In this context, the purpose of this paper is to illustrate some of the problems of company activity problems which can be solved by using data mining techniques.

Study of acid mine drainage management with evaluating climate and rainfall in East Pit 3 West Banko coal mine

Science.gov (United States)

Rochyani, Neny

2017-11-01

Acid mine drainage is a major problem for the mining environment. The main factor that formed acid mine drainage is the volume of rainfall. Therefore, it is important to know clearly the main climate pattern of rainfall and season on the management of acid mine drainage. This study focuses on the effects of rainfall on acid mine water management. Based on daily rainfall data, monthly and seasonal patterns by using Gumbel approach is known the amount of rainfall that occurred in East Pit 3 West Banko area. The data also obtained the highest maximum daily rainfall on 165 mm/day and the lowest at 76.4 mm/day, where it is known that the rainfall conditions during the period 2007 - 2016 is from November to April so the use of lime is also slightly, While the low rainfall is from May to October and the use of lime will be more and more. Based on calculation of lime requirement for each return period, it can be seen the total of lime and financial requirement for treatment of each return period.

In situ solution mining technique

International Nuclear Information System (INIS)

Learmont, R.P.

1978-01-01

A method of in situ solution mining is disclosed in which a primary leaching process employing an array of 5-spot leaching patterns of production and injection wells is converted to a different pattern by converting to injection wells all the production wells in alternate rows

The Effects of Elevated Specific Conductivity on the Chronic Toxicity of Mining Influenced Streams Using Ceriodaphnia dubia.

Science.gov (United States)

Armstead, Mindy Yeager; Bitzer-Creathers, Leah; Wilson, Mandee

2016-01-01

Salinization of freshwater ecosystems as a result of human activities has markedly increased in recent years. Much attention is currently directed at evaluating the effects of increased salinity on freshwater biota. In the Central Appalachian region of the eastern United States, specific conductance from alkaline discharges associated with mountain top mining practices has been implicated in macroinvertebrate community declines in streams receiving coal mining discharges. Whole effluent toxicity testing of receiving stream water was used to test the hypothesis that mine discharges are toxic to laboratory test organisms and further, that toxicity is related to ionic concentrations as indicated by conductivity. Chronic toxicity testing using Ceriodaphnia dubia was conducted by contract laboratories at 72 sites with a total of 129 tests over a 3.5 year period. The database was evaluated to determine the ionic composition of mine effluent dominated streams and whether discharge constituents were related to toxicity in C. dubia. As expected, sulfate was found to be the dominant anion in streams receiving mining discharges with bicarbonate variable and sometimes a substantial component of the dissolved solids. Overall, the temporal variability in conductance was low at each site which would indicate fairly stable water quality conditions. Results of the toxicity tests show no relationship between conductance and survival of C. dubia in the mining influenced streams with the traditional toxicity test endpoints. However, consideration of the entire dataset revealed a significant inverse relationship between conductivity and neonate production. While conductivity explained very little of the high variability in the offspring production (r2 = 0.1304), the average numbers of offspring were consistently less than 20 neonates at the highest conductivities.

The Effects of Elevated Specific Conductivity on the Chronic Toxicity of Mining Influenced Streams Using Ceriodaphnia dubia.

Directory of Open Access Journals (Sweden)

Mindy Yeager Armstead

Full Text Available Salinization of freshwater ecosystems as a result of human activities has markedly increased in recent years. Much attention is currently directed at evaluating the effects of increased salinity on freshwater biota. In the Central Appalachian region of the eastern United States, specific conductance from alkaline discharges associated with mountain top mining practices has been implicated in macroinvertebrate community declines in streams receiving coal mining discharges. Whole effluent toxicity testing of receiving stream water was used to test the hypothesis that mine discharges are toxic to laboratory test organisms and further, that toxicity is related to ionic concentrations as indicated by conductivity. Chronic toxicity testing using Ceriodaphnia dubia was conducted by contract laboratories at 72 sites with a total of 129 tests over a 3.5 year period. The database was evaluated to determine the ionic composition of mine effluent dominated streams and whether discharge constituents were related to toxicity in C. dubia. As expected, sulfate was found to be the dominant anion in streams receiving mining discharges with bicarbonate variable and sometimes a substantial component of the dissolved solids. Overall, the temporal variability in conductance was low at each site which would indicate fairly stable water quality conditions. Results of the toxicity tests show no relationship between conductance and survival of C. dubia in the mining influenced streams with the traditional toxicity test endpoints. However, consideration of the entire dataset revealed a significant inverse relationship between conductivity and neonate production. While conductivity explained very little of the high variability in the offspring production (r2 = 0.1304, the average numbers of offspring were consistently less than 20 neonates at the highest conductivities.

HSM: Heterogeneous Subspace Mining in High Dimensional Data

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Seidl, Thomas

2009-01-01

Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the "curse of dimensionality" pose additional...... challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes. In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant...... for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines...

Modeled atmospheric radon concentrations from uranium mines

Energy Technology Data Exchange (ETDEWEB)

Droppo, J.G.

1985-04-01

Uranium mining and milling operations result in the release of radon from numerous sources of various types and strengths. The US Environmental Protection Agency (EPA) under the Clean Air Act, is assessing the health impact of air emissions of radon from underground uranium mines. In this case, the radon emissions may impact workers and residents in the mine vicinity. To aid in this assessment, the EPA needs to know how mine releases can affect the radon concentrations at populated locations. To obtain this type of information, Pacific Northwest Laboratory used the radon emissions, release characteristics and local meterological conditions for a number of mines to model incremental radon concentrations. Long-term, average, incremental radon concentrations were computed based on the best available information on release rates, plume rise parameters, number and locations of vents, and local dispersion climatology. Calculations are made for a model mine, individual mines, and multiple mines. Our approach was to start with a general case and then consider specific cases for comparison. A model underground uranium mine was used to provide definition of the order of magnitude of typical impacts. Then computations were made for specific mines using the best mine-specific information available for each mine. These case study results are expressed as predicted incremental radon concentration contours plotted on maps with local population data from a previous study. Finally, the effect of possible overlap of radon releases from nearby mines was studied by calculating cumulative radon concentrations for multiple mines in a region with many mines. The dispersion model, modeling assumptions, data sources, computational procedures, and results are documented in this report. 7 refs., 27 figs., 18 tabs.

Modeled atmospheric radon concentrations from uranium mines

International Nuclear Information System (INIS)

Droppo, J.G.

1985-04-01

Uranium mining and milling operations result in the release of radon from numerous sources of various types and strengths. The US Environmental Protection Agency (EPA) under the Clean Air Act, is assessing the health impact of air emissions of radon from underground uranium mines. In this case, the radon emissions may impact workers and residents in the mine vicinity. To aid in this assessment, the EPA needs to know how mine releases can affect the radon concentrations at populated locations. To obtain this type of information, Pacific Northwest Laboratory used the radon emissions, release characteristics and local meterological conditions for a number of mines to model incremental radon concentrations. Long-term, average, incremental radon concentrations were computed based on the best available information on release rates, plume rise parameters, number and locations of vents, and local dispersion climatology. Calculations are made for a model mine, individual mines, and multiple mines. Our approach was to start with a general case and then consider specific cases for comparison. A model underground uranium mine was used to provide definition of the order of magnitude of typical impacts. Then computations were made for specific mines using the best mine-specific information available for each mine. These case study results are expressed as predicted incremental radon concentration contours plotted on maps with local population data from a previous study. Finally, the effect of possible overlap of radon releases from nearby mines was studied by calculating cumulative radon concentrations for multiple mines in a region with many mines. The dispersion model, modeling assumptions, data sources, computational procedures, and results are documented in this report. 7 refs., 27 figs., 18 tabs

ENVIRONMENTAL MANAGEMENT OF MINE WATER, CONSIDERING EUROPEAN WATER LEGISLATION. CASE STUDY OF MEGALOPOLIS MINES

OpenAIRE

Dimitrakopoulos, D.; Vassiliou, E.; Tsangaratos, P.; Ilia, I.

2017-01-01

Mining activities causes many environmental problems to the surrounding areas, as other industrial activities do also. However mine water pollution, is considered a tough task to handle, as it requires specific regulations, quite distinct from those applicable to most other industrial processes. Even though there are several federal laws and regulations in Greece and in the European Union that influences the mining industry and mine water management, still certain factors complicates their im...

Data mining applications in the context of casemix.

Science.gov (United States)

Koh, H C; Leong, S K

2001-07-01

In October 1999, the Singapore Government introduced casemix-based funding to public hospitals. The casemix approach to health care funding is expected to yield significant benefits, including equity and rationality in financing health care, the use of comparative casemix data for quality improvement activities, and the provision of information that enables hospitals to understand their cost behaviour and reinforces the drive for more cost-efficient services. However, there is some concern about the "quicker and sicker" syndrome (that is, the rapid discharge of patients with little regard for the quality of outcome). As it is likely that consequences of premature discharges will be reflected in the readmission data, an analysis of possible systematic patterns in readmission data can provide useful insight into the "quicker and sicker" syndrome. This paper explores potential data mining applications in the context of casemix by using readmission data as an illustration. In particular, it illustrates how data mining can be used to better understand readmission data and to detect systematic patterns, if any. From a technical perspective, data mining (which is capable of analysing complex non-linear and interaction relationships) supplements and complements traditional statistical methods in data analysis. From an applications perspective, data mining provides the technology and methodology to analyse mass volume of data to detect hidden patterns in data. Using readmission data as an illustrative data mining application, this paper explores potential data mining applications in the general casemix context.

OCHRE PRECIPITATES AND ACID MINE DRAINAGE IN A MINE ENVIRONMENT

Directory of Open Access Journals (Sweden)

BRANISLAV MÁŠA

2012-03-01

Full Text Available This paper is focused to characterize the ochre precipitates and the mine water effluents of some old mine adits and settling pits after mining of polymetallic ores in Slovakia. It was shown that the mine water effluents from two different types of deposits (adits; settling pits have similar composition and represent slightly acidic sulphate water (pH in range 5.60-6.05, sulphate concentration from 1160 to 1905 g.dm-3. The ochreous precipitates were characterized by methods of X-ray diffraction analysis (XRD, scanning electron microscopy (SEM and B.E.T. method for measuring the specific surface area and porosity. The dominant phases were ferrihydrite with goethite or goethite with lepidocrocide.

Data analysis and pattern recognition in multiple databases

CERN Document Server

Adhikari, Animesh; Pedrycz, Witold

2014-01-01

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...

SMM-system: A mining tool to identify specific markers in Salmonella enterica.

Science.gov (United States)

Yu, Shuijing; Liu, Weibing; Shi, Chunlei; Wang, Dapeng; Dan, Xianlong; Li, Xiao; Shi, Xianming

2011-03-01

This report presents SMM-system, a software package that implements various personalized pre- and post-BLASTN tasks for mining specific markers of microbial pathogens. The main functionalities of SMM-system are summarized as follows: (i) converting multi-FASTA file, (ii) cutting interesting genomic sequence, (iii) automatic high-throughput BLASTN searches, and (iv) screening target sequences. The utility of SMM-system was demonstrated by using it to identify 214 Salmonella enterica-specific protein-coding sequences (CDSs). Eighteen primer pairs were designed based on eighteen S. enterica-specific CDSs, respectively. Seven of these primer pairs were validated with PCR assay, which showed 100% inclusivity for the 101 S. enterica genomes and 100% exclusivity of 30 non-S. enterica genomes. Three specific primer pairs were chosen to develop a multiplex PCR assay, which generated specific amplicons with a size of 180bp (SC1286), 238bp (SC1598) and 405bp (SC4361), respectively. This study demonstrates that SMM-system is a high-throughput specific marker generation tool that can be used to identify genus-, species-, serogroup- and even serovar-specific DNA sequences of microbial pathogens, which has a potential to be applied in food industries, diagnostics and taxonomic studies. SMM-system is freely available and can be downloaded from http://foodsafety.sjtu.edu.cn/SMM-system.html. Copyright © 2011 Elsevier B.V. All rights reserved.

Measures to restore metallurgical mine wasteland using ecological restoration technologies: A case study at Longnan Rare Earth Mine

Science.gov (United States)

Rao, Yunzhang; Gu, Ruizhi; Guo, Ruikai; Zhang, Xueyan

2017-01-01

Whereas mining activities produce the raw materials that are crucial to economic growth, such activities leave extensive scarring on the land, contributing to the waste of valuable land resources and upsetting the ecological environment. The aim of this study is therefore to investigate various ecological technologies to restore metallurgical mine wastelands. These technologies include measures such as soil amelioration, vegetation restoration, different vegetation planting patterns, and engineering technologies. The Longnan Rare Earth Mine in the Jiangxi Province of China is used as the case study. The ecological restoration process provides a favourable reference for the restoration of a metallurgical mine wasteland.

Gender specific pattern of left ventricular cardiac adaptation to ...

African Journals Online (AJOL)

Background: Cardiac adaptation to hypertension and obesity may be related to many factors such as race, gender and haemodynamic status. Some gender specific associations with left ventricular structure and function have been described among Caucasians. Objectives: To describe the sex specific pattern of left ...

Methane emissions from coal mining

International Nuclear Information System (INIS)

Boyer, C.M.; Kelafant, J.R.; Kuuskraa, V.A.; Manger, K.C.; Kruger, D.

1990-09-01

The report estimates global methane emissions from coal mining on a country specific basis, evaluates the technologies available to degasify coal seams and assesses the economics of recovering methane liberated during mining. 33 to 64 million tonnes were liberated in 1987 from coal mining, 75 per cent of which came from China, the USSR, Poland and the USA. Methane emissions from coal mining are likely to increase. Emission levels vary between surface and underground mines. The methane currently removed from underground mines for safety reasons could be used in a number of ways, which may be economically attractive. 55 refs., 19 figs., 24 tabs

Genet-specific spawning patterns in Acropora palmata

Science.gov (United States)

Miller, M. W.; Williams, D. E.; Fisch, J.

2016-12-01

The broadcast spawning elkhorn coral, Acropora palmata, requires outcrossing among different genets for effective fertilization. Hence, a low density of genets in parts of its range emphasizes the need for precise synchrony among neighboring genets as sperm concentration dilutes rapidly in open-ocean conditions. We documented the genet-specific nightly occurrence of spawning of A. palmata over 8 yr in a depauperate population in the Florida Keys to better understand this potential reproductive hurdle. The observed population failed to spawn within the predicted monthly window (nights 2-6 after the full moon in August) in three of the 8 yr of observation; negligible spawning was observed in a fourth year. Moreover, genet-specific patterns are evident in that (1) certain genets have significantly greater odds of spawning overall and (2) certain genets predictably spawn on the earlier and others on the later lunar nights within the predicted window. Given the already low genet density in this population, this pattern implies a substantial degree of wasted reproductive effort and supports the hypothesis that depensatory factors are impairing recovery in this species.

Radioactivity measurements in Egyptian phosphate mines and their significance in the occupational exposure of mine workers

International Nuclear Information System (INIS)

Bigu, J.; Hussein, Mohamed I.; Hussein, A.Z.

2000-01-01

Radioactivity measurements have been conducted in nine underground phosphate mines in the Egyptian Eastern Desert in order to estimate the occupational radiation exposure of mine workers in those mining sites. Measurements were carried out of airborne radon ( 222 Rn) and its short-lived decay products (progeny) and thoron ( 220 Rn) progeny, as well as γ-radiation from mine walls, ceilings and floors. Comparison of experimental data and theoretical predictions showed partial agreement between these two sets of data. This result is partly attributed to the complex layout of these mines which causes undesirable ventilation conditions, such as recirculation airflow patterns which could not be adequately identified or quantified. The radiation data obtained were used to estimate the Maximum Annual Dose (MAD), and other important occupational radiation exposure variables. These calculations indicate that in eight out of the nine mines surveyed, the MAD exceeded (by a factor of up to 7) the maximum recommended level by ICRP-60. A number of suggestions are made in order to reduce the MAD in the affected mines

Correlating Microbial Diversity Patterns with Geochemistry in an Extreme and Heterogeneous Environment of Mine Tailings

Science.gov (United States)

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

2014-01-01

Recent molecular surveys have advanced our understanding of the forces shaping the large-scale ecological distribution of microbes in Earth's extreme habitats, such as hot springs and acid mine drainage. However, few investigations have attempted dense spatial analyses of specific sites to resolve the local diversity of these extraordinary organisms and how communities are shaped by the harsh environmental conditions found there. We have applied a 16S rRNA gene-targeted 454 pyrosequencing approach to explore the phylogenetic differentiation among 90 microbial communities from a massive copper tailing impoundment generating acidic drainage and coupled these variations in community composition with geochemical parameters to reveal ecological interactions in this extreme environment. Our data showed that the overall microbial diversity estimates and relative abundances of most of the dominant lineages were significantly correlated with pH, with the simplest assemblages occurring under extremely acidic conditions and more diverse assemblages associated with neutral pHs. The consistent shifts in community composition along the pH gradient indicated that different taxa were involved in the different acidification stages of the mine tailings. Moreover, the effect of pH in shaping phylogenetic structure within specific lineages was also clearly evident, although the phylogenetic differentiations within the Alphaproteobacteria, Deltaproteobacteria, and Firmicutes were attributed to variations in ferric and ferrous iron concentrations. Application of the microbial assemblage prediction model further supported pH as the major factor driving community structure and demonstrated that several of the major lineages are readily predictable. Together, these results suggest that pH is primarily responsible for structuring whole communities in the extreme and heterogeneous mine tailings, although the diverse microbial taxa may respond differently to various environmental conditions

URL Mining Using Agglomerative Clustering Algorithm

Directory of Open Access Journals (Sweden)

Chinmay R. Deshmukh

2015-02-01

Full Text Available Abstract The tremendous growth of the web world incorporates application of data mining techniques to the web logs. Data Mining and World Wide Web encompasses an important and active area of research. Web log mining is analysis of web log files with web pages sequences. Web mining is broadly classified as web content mining web usage mining and web structure mining. Web usage mining is a technique to discover usage patterns from Web data in order to understand and better serve the needs of Web-based applications. URL mining refers to a subclass of Web mining that helps us to investigate the details of a Uniform Resource Locator. URL mining can be advantageous in the fields of security and protection. The paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is a clickthrough data each record consist of a users query to a search engine along with the URL which the user selected from among the candidates offered by search engine. By viewing this dataset as a bipartite graph with the vertices on one side corresponding to queries and on the other side to URLs one can apply an agglomerative clustering algorithm to the graphs vertices to identify related queries and URLs.

Reestablishment of woody plants on mine spoils and management of mine water impoundments: an overview of Forest Service research on the northern High Plain

Energy Technology Data Exchange (ETDEWEB)

Bjugstad, A J

1977-01-01

The function of the research unit at Rapid city, S. Dakota, is to provide guidelines for the reestablisment of shrubs and trees on land characteristic of the High Plains, and for the mitigation of possible detrimental effects of surface mining on ground water and surface water. One possible problem posed by surface mining concerns the formation of land drainage patterns that could result in post-mining formations of large salt playas. Surface mining could affect shallow ground water aquifers up to /sup 1///sub 4/ mile from the mine site. Research is being conducted on the reclamation of mine spoils and on the rehabilitation and management of impounded mine water.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

K.M. Hettne (Kristina); J. Boorsma (Jeffrey); D.A.M. van Dartel (Dorien A M); J.J. Goeman (Jelle); E.C. de Jong (Esther); A.H. Piersma (Aldert); R.H. Stierum (Rob); J. Kleinjans (Jos); J.A. Kors (Jan)

2013-01-01

textabstractBackground: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with

Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques

OpenAIRE

Saini, Priyanka

2014-01-01

Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Recently, one of the remarkable facts in higher educational institute is the rapid growth data and this educational data is expanding quickly without any advantage to the educational management. The main aim of the management is to refine the education standard; therefore by applying the various data mining techniques on this data one ca...

A partition enhanced mining algorithm for distributed association rule mining systems

Directory of Open Access Journals (Sweden)

A.O. Ogunde

2015-11-01

Full Text Available The extraction of patterns and rules from large distributed databases through existing Distributed Association Rule Mining (DARM systems is still faced with enormous challenges such as high response times, high communication costs and inability to adapt to the constantly changing databases. In this work, a Partition Enhanced Mining Algorithm (PEMA is presented to address these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process is divided into two stages. In the first stage, the data agents horizontally segment the databases with small average transaction length into relatively smaller partitions based on the number of available sites and the available memory. On the other hand, databases with relatively large average transaction length were vertically partitioned. After this, Mobile Agent-Based Association Rule Mining-Agents, which are the mining agents, carry out the discovery of the local frequent itemsets. At the second stage, the local frequent itemsets were incrementally integrated by the from one data site to another to get the global frequent itemsets. This reduced the response time and communication cost in the system. Results from experiments conducted on real datasets showed that the average response time of PEMA showed an improvement over existing algorithms. Similarly, PEMA incurred lower communication costs with average size of messages exchanged lower when compared with benchmark DARM systems. This result showed that PEMA could be efficiently deployed for efficient discovery of valuable knowledge in distributed databases.

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

Science.gov (United States)

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…

Preparatory mining and geotechnical work for safe closure of the Asse mine

International Nuclear Information System (INIS)

Schmidt, M.W.; Schauermann, V.; Kappei, G.

2002-01-01

After the GSF Research Centre for Environment and Health discontinued its own research into the safe ultimate storage of waste hazardous to the environment in deep geological formations and the work of the Institute for Deep Storage was likewise suspended there is no longer any need to continue operation of the Asse mine for research purposes. Hence the closure of the mine is being prepared on the basis of the Federal Mining Act. The GSF accordingly has to submit a final operating plan, with which a comprehensive safety report containing inter alia proof of the long-time safety should be enclosed. The proof should be furnished according to the specific location and takes into account the geological, hydrogeological, geochemical, geotechnical, mining-related conditions and the radionuclide inventory in the Asse mine. (orig.) [de

Seasonal and spatial patterns of metals at a restored copper mine site. I. Stream copper and zinc

International Nuclear Information System (INIS)

Bambic, Dustin G.; Alpers, Charles N.; Green, Peter G.; Fanelli, Eileen; Silk, Wendy K.

2006-01-01

Seasonal and spatial variations in metal concentrations and pH were found in a stream at a restored copper mine site located near a massive sulfide deposit in the Foothill copper-zinc belt of the Sierra Nevada, California. At the mouth of the stream, copper concentrations increased and pH decreased with increased streamflow after the onset of winter rain and, unexpectedly, reached extreme values 1 or 2 months after peaks in the seasonal hydrographs. In contrast, aqueous zinc and sulfate concentrations were highest during low-flow periods. Spatial variation was assessed in 400 m of reach encompassing an acidic, metal-laden seep. At this seep, pH remained low (2-3) throughout the year, and copper concentrations were highest. In contrast, the zinc concentrations increased with downstream distance. These spatial patterns were caused by immobilization of copper by hydrous ferric oxides in benthic sediments, coupled with increasing downstream supply of zinc from groundwater seepage. - Seasonal hydrology and benthic sediments control copper and zinc concentrations in a stream through a restored mine site

Supporting Solar Physics Research via Data Mining

Science.gov (United States)

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.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

Hettne, K.M.; Boorsma, A.; Dartel, D.A. van; Goeman, J.J.; Jong, E. de; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

2013-01-01

BACKGROUND: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

Hettne, K.M.; Boorsma, A.; Dartel, van D.A.M.; Goeman, J.J.; Jong, de E.; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

2013-01-01

Background: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

Directory of Open Access Journals (Sweden)

Hettne Kristina M

2013-01-01

Full Text Available Abstract Background Availability of chemical response-specific lists of genes (gene sets for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM, and that these can be used with gene set analysis (GSA methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human and 588 (mouse gene sets from the Comparative Toxicogenomics Database (CTD. We tested for significant differential expression (SDE (false discovery rate -corrected p-values Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

Energy Technology Data Exchange (ETDEWEB)

Purohit, Sumit; Choudhury, Sutanay; Holder, Lawrence B.

2017-12-11

Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). We explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.

Trace element patterns in lichens following uranium mine closures

International Nuclear Information System (INIS)

Fahselt, D.; Wu, T.W.; Mott, B.

1995-01-01

Instrumental neutron activation analysis was used to determine trace elements in Cladina mitis (Sandst). Hale ampersand Culb. along transects extending from uranium mines at Elliot Lake and Agnew Lake in central Ontario, Canada. Levels of 11 elements were reported and the presence of uranium (U) was confirmed, although U concentrations were much less than in Cladina rangiferina 10 years earlier. Among the elements identified in lichen thalli was Th, which occurred in higher concentrations than U. All trace elements, including the two radionuclides, were found in deteriorating thallus parts as well as living podetia, and five of these seem to have originated as airborne particulates from minesites. In spite of mine closures, levels of Th and U remained higher near sources of ore dust and there was little relationship between radionuclide concentrations in thallus and substrate. 24 refs., 4 figs., 3 tabs

Freshwater Ecosystem Services in Mining Regions: Modelling Options for Policy Development Support

Directory of Open Access Journals (Sweden)

Daniel Mercado-Garcia

2018-04-01

Full Text Available The ecosystem services (ES approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i methodological complexity (data types, number of parameters, processes and ecosystem–human integration level and (ii potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations. Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground- and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause–effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES.

Mining Long, Sharable Patterns in Trajectories of Moving Objects

DEFF Research Database (Denmark)

Gidofalvi, Gyozo; Pedersen, Torben Bach

2009-01-01

The efficient analysis of spatio-temporal data, generated by moving objects, is an essential requirement for intelligent location-based services. Spatio-temporal rules can be found by constructing spatio-temporal baskets, from which traditional association rule mining methods can discover spatio...

Geomechanics in hard rock mining-Lessons from two case histories

International Nuclear Information System (INIS)

Heuze, F.E.

1982-01-01

This paper summarizes the geomechanics programs conducted in two hard rock underground mining operations in the Western United States, between 1966 and 1981. The two projects were directed towards understanding the behavior of the rock masses, at the scale of the caverns. To this end, the emphasis was put on large scale field measurements, complemented by limited laboratory testing. The results of these observations were used to build realistic finite element models of the underground chambers. In the marble mine, at Crestmore, California, the models were applied to the structural optimization of the room-and-pillar pattern. In the granite mining, at Climax, Nevada Test Site, the models explained some unusual stress changes observed during excavation. Based on the large number of geomechanical techniques employed, specific conclusions and recommendations are offered regarding the quality, applicability, and usefulness of the various methods. The two case histories clearly indicate that numerical models are extremely useful for a detailed understanding of the structural behavior of mine openings. To be realistic, these models must be based first and foremost on large scale field observations. The lessons learned on these two projects also are directly applicable to the design and analysis of nuclear waste repositories in hard rocks such as basalt, granite, and welded tuff

Application of data mining techniques for nuclear data and instrumentation

International Nuclear Information System (INIS)

Toshniwal, Durga

2013-01-01

Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly

ASEAN mining industry`s development

Energy Technology Data Exchange (ETDEWEB)

Simatupang, M [ASEAN Federation of Mining Associations (AFMA), Jakarta (Indonesia)

1994-12-31

A report is presented on the potential and challenges of mining in the ASEAN region. Legal and financial provision, the and business climate for future investment is also discussed. One problem is the small scale of many of the mining operations, so special guidance is needed, especially in environmental matters. Specific discussion is presented of mining in Indonesia, Malaysia, Thailand, the Philippines, Brunei, and Vietnam and Myanmar. 8 refs., 3 figs.

Radon exposure in abandoned metalliferous mines of South America

International Nuclear Information System (INIS)

Silva, A.A.R. da; Umisedo, N.; Yoshimura, E.M.; Anjos, R.M.; Valladares, D.L.; Velasco, H.; Rizzotto, M.

2011-01-01

Since the days of the Spanish and Portuguese conquerors, South America has been closely associated with the metalliferous ore mining. Gold, silver, tin, lead, tungsten, nickel, copper, and palladium ores have been explored over the last centuries. In addition, there has also been the development and promotion of other economic activities related to mining, as the underground mine tourism. A few works have been published on radon levels in the South American mining. In this study, we investigated the radon transport process and its health hazard in two exhausted and abandoned mines in San Luis Province, Argentina. These mines were chosen because they have different physical configurations in their cavities, features which can affect the air flow patterns and radon concentrations. La Carolina gold mine (32 deg 48' 0'' S, 66 deg 60' 0'' W) is currently a blind end system, corresponding to a horizontal excavation into the side of a mountain, with only a main adit. Los Condores wolfram mine (32 deg 33' 25'' S, 65 deg 15' 20'' W) is also a horizontal excavation into the side of a mountain, but has a vertical output (a shaft) at the end of the main gallery. Three different experimental methodologies were used. Radon concentration measurements were performed by CR-39 nuclear track detectors. The distribution of natural radionuclide activities ( 40 K, 232 Th and 238 U) was determined from rock samples collected along their main adits, using in laboratory gamma-ray spectrometry. The external gamma dose rate was evaluated using thermoluminescent dosimeters and a portable survey meter. The values for the 222 Rn concentration ranged from 0.43 ± 0.04 to 1.48 ± 0.12 kBq/m 3 in the Los Condores wolfram mine and from 1.8 ± 0.1 to 6.0±0.5 kBq/m 3 in the La Carolina gold mine, indicating that, in this mine, the radon levels exceed up to four times the action level of 1.5 kBq/m 3 recommended by the ICRP. The patterns of the radon transport process revealed that the La Carolina

Construction over abandoned mine workings

Energy Technology Data Exchange (ETDEWEB)

Healy, P R; Head, J M

1984-01-01

Guidance is given for engineers involved with the planning and development of sites previously undermined for coal and other minerals. Past methods of mining employed in Britain are described, and their short- and long-term effects on surface stability are assessed. Where modern methods of mining are relevant, or where structural design techniques for the surface effects of mining can be applied, these are included for illustration and completeness. Additional objectives over and above those for conventional site investigations are identified, and details are provided for the planning and execution of a mining investigation. Techniques for consolidation of old mine workings and remedial measures for mine shafts are described. Foundation design options are included for cases where expected ground movements can be accommodated. A comprehensive guide to sources of information on previous mining is presented, together with an example of a specification suitable for the consolidation of old shallow mine workings. (50 refs.)

Rehabilitation materials from surface- coal mines in western U.S.A. III. Relations between elements in mine soil and uptake by plants.

Science.gov (United States)

Severson, R.C.; Gough, L.P.

1984-01-01

Plant uptake of Cd, Co, Cu, Fe, Mn, Ni, Pb and Zn from mine soils was assessed using alfalfa Medicago sativa, sainfoin Onobrychis viciaefolia, smooth brome Bromus inermis, crested wheatgrass Agropyron cristatum, slender wheatgrass A. trachycaulum and intermediate wheatgrass A. intermedium; mine soil (cover-soil and spoil material) samples were collected from rehabilitated areas of 11 western US surface-coal mines in North Dakota, Montana, Wyoming and Colorado. Correlations between metals in plants and DTPA-extractable metals from mine soils were generally not statistically significant and showed no consistent patterns for a single metal or for a single plant species. Metal uptake by plants, relative to amounts in DTPA extracts of mine soil, was positively related to mine soil organic matter content or negatively related to mine soil pH. DTPA-extractable metal levels were significantly correlated with mine soil pH and organic-matter content.-from Authors

Investigating Patterns of Errors for Specific Comprehension and Fluency Difficulties

Science.gov (United States)

Koriakin, Taylor A.; Kaufman, Alan S.

2017-01-01

Although word reading has traditionally been viewed as a foundational skill for development of reading fluency and comprehension, some children demonstrate "specific" reading comprehension problems, in the context of intact word reading. The purpose of this study was to identify specific patterns of errors associated with reading…

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

International Nuclear Information System (INIS)

Tahat Amani; Marti Jordi; Khwaldeh Ali; Tahat Kaher

2014-01-01

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)

Mine water treatment in Donbass

Energy Technology Data Exchange (ETDEWEB)

Azarenkov, P A; Anisimov, V M; Krol, V A

1980-10-01

About 2,000,000 m$SUP$3 of mine water are discharged by coal mines yearly to surface waters in the Donbass. Mine water in the region is rich in mineral salts and suspended matter (coal and rock particles). The DonUGI Institute developed a system of mine water treatment which permits the percentage of suspended matter to be reduced to 1.5 mg/l. The treated mine water can be used in fire fighting and in dust suppression systems in coal mines. A scheme of the water treatment system is shown. It consists of the following stages: reservoir of untreated mine water, chamber where mine water is mixed with reagents, primary sedimentation tanks, sand filters, and chlorination. Aluminium sulphate is used as a coagulation agent. To intensify coagulation polyacrylamide is added. Technical specifications of surface structures in which water treatment is carried out are discussed. Standardized mine water treatment systems with capacities of 600 m$SUP$3/h, with 900, 1200, 1500, 1800 and 2100 m$SUP$3/h capacities are used. (In Russian)

An Adaptive Sensor Mining Framework for Pervasive Computing Applications

Science.gov (United States)

Rashidi, Parisa; Cook, Diane J.

Analyzing sensor data in pervasive computing applications brings unique challenges to the KDD community. The challenge is heightened when the underlying data source is dynamic and the patterns change. We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model. In our framework, the frequent and periodic patterns of data are first discovered by the Frequent and Periodic Pattern Miner (FPPM) algorithm; and then any changes in the discovered patterns over the lifetime of the system are discovered by the Pattern Adaptation Miner (PAM) algorithm, in order to adapt to the changing environment. This framework also captures vital context information present in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

Efficient constraint-based Sequential Pattern Mining (SPM algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset

Directory of Open Access Journals (Sweden)

Niti Ashish Kumar Desai

2015-12-01

Full Text Available Business Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user’s interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of six additional constraints is as well to ensure that all patterns are recently active (Recency, active for certain time span (Compactness, profitable and indicative of next timeline for purchase (Length―Item―Gap. The article also attempts to throw light on how proposed Constraint-based Prefix Span algorithm is helpful to understand buying behaviour of customer which is in formative stage.

Data mining in healthcare: decision making and precision

Directory of Open Access Journals (Sweden)

Ionuţ ŢĂRANU

2016-05-01

Full Text Available The trend of application of data mining in healthcare today is increased because the health sector is rich with information and data mining has become a necessity. Healthcare organizations generate and collect large volumes of information to a daily basis. Use of information technology enables automation of data mining and knowledge that help bring some interesting patterns which means eliminating manual tasks and easy data extraction directly from electronic records, electronic transfer system that will secure medical records, save lives and reduce the cost of medical services as well as enabling early detection of infectious diseases on the basis of advanced data collection. Data mining can enable healthcare organizations to anticipate trends in the patient's medical condition and behaviour proved by analysis of prospects different and by making connections between seemingly unrelated information. The raw data from healthcare organizations are voluminous and heterogeneous. It needs to be collected and stored in organized form and their integration allows the formation unite medical information system. Data mining in health offers unlimited possibilities for analyzing different data models less visible or hidden to common analysis techniques. These patterns can be used by healthcare practitioners to make forecasts, put diagnoses, and set treatments for patients in healthcare organizations.

Collaborative mining and transfer learning for relational data

Science.gov (United States)

Levchuk, Georgiy; Eslami, Mohammed

2015-06-01

Many of the real-world problems, - including human knowledge, communication, biological, and cyber network analysis, - deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

Science.gov (United States)

Özgür, Arzucan; Hur, Junguk; He, Yongqun

2016-01-01

The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A

Arsenic partitioning among particle-size fractions of mine wastes and stream sediments from cinnabar mining districts.

Science.gov (United States)

Silva, Veronica; Loredo, Jorge; Fernández-Martínez, Rodolfo; Larios, Raquel; Ordóñez, Almudena; Gómez, Belén; Rucandio, Isabel

2014-10-01

Tailings from abandoned mercury mines represent an important pollution source by metals and metalloids. Mercury mining in Asturias (north-western Spain) has been carried out since Roman times until the 1970s. Specific and non-specific arsenic minerals are present in the paragenesis of the Hg ore deposit. As a result of intensive mining operations, waste materials contain high concentrations of As, which can be geochemically dispersed throughout surrounding areas. Arsenic accumulation, mobility and availability in soils and sediments are strongly affected by the association of As with solid phases and granular size composition. The objective of this study was to examine phase associations of As in the fine grain size subsamples of mine wastes (La Soterraña mine site) and stream sediments heavily affected by acid mine drainage (Los Rueldos mine site). An arsenic-selective sequential procedure, which categorizes As content into seven phase associations, was applied. In spite of a higher As accumulation in the finest particle-size subsamples, As fractionation did not seem to depend on grain size since similar distribution profiles were obtained for the studied granulometric fractions. The presence of As was relatively low in the most mobile forms in both sites. As was predominantly linked to short-range ordered Fe oxyhydroxides, coprecipitated with Fe and partially with Al oxyhydroxides and associated with structural material in mine waste samples. As incorporated into short-range ordered Fe oxyhydroxides was the predominant fraction at sediment samples, representing more than 80% of total As.

Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

OpenAIRE

Muluken Alemu Yehuala

2015-01-01

Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The ...

Automatic identification in mining

Energy Technology Data Exchange (ETDEWEB)

Puckett, D; Patrick, C [Mine Computers and Electronics Inc., Morehead, KY (United States)

1998-06-01

The feasibility of monitoring the locations and vital statistics of equipment and personnel in surface and underground mining operations has increased with advancements in radio frequency identification (RFID) technology. This paper addresses the use of RFID technology, which is relatively new to the mining industry, to track surface equipment in mine pits, loading points and processing facilities. Specific applications are discussed, including both simplified and complex truck tracking systems and an automatic pit ticket system. This paper concludes with a discussion of the future possibilities of using RFID technology in mining including monitoring heart and respiration rates, body temperatures and exertion levels; monitoring repetitious movements for the study of work habits; and logging air quality via personnel sensors. 10 refs., 5 figs.

Coal Mine Methane in Russia

Energy Technology Data Exchange (ETDEWEB)

NONE

2009-07-01

This paper discusses coal mine methane emissions (CMM) in the Russian Federation and the potential for their productive utilisation. It highlights specific opportunities for cost-effective reductions of CMM from oil and natural gas facilities, coal mines and landfills, with the aim of improving knowledge about effective policy approaches.

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

Science.gov (United States)

Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho

2016-01-23

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.

Radon exposure in abandoned metalliferous mines of South America

Energy Technology Data Exchange (ETDEWEB)

Silva, A.A.R. da; Umisedo, N.; Yoshimura, E.M. [Universidade de Sao Paulo (IF/USP), SP (Brazil). Inst. de Fisica. Lab. de Dosimetria; Anjos, R.M. [Universidade Federal Fluminense (LARA/UFF), Niteroi, RJ (Brazil). Inst. de Fisica. Lab. de Radioecologia; Valladares, D.L.; Velasco, H.; Rizzotto, M. [Universidad Nacional de San Luis (UNSL) (Argentina). Inst. de Matematica Aplicada San Luis

2011-07-01

Since the days of the Spanish and Portuguese conquerors, South America has been closely associated with the metalliferous ore mining. Gold, silver, tin, lead, tungsten, nickel, copper, and palladium ores have been explored over the last centuries. In addition, there has also been the development and promotion of other economic activities related to mining, as the underground mine tourism. A few works have been published on radon levels in the South American mining. In this study, we investigated the radon transport process and its health hazard in two exhausted and abandoned mines in San Luis Province, Argentina. These mines were chosen because they have different physical configurations in their cavities, features which can affect the air flow patterns and radon concentrations. La Carolina gold mine (32 deg 48' 0'' S, 66 deg 60' 0'' W) is currently a blind end system, corresponding to a horizontal excavation into the side of a mountain, with only a main adit. Los Condores wolfram mine (32 deg 33' 25'' S, 65 deg 15' 20'' W) is also a horizontal excavation into the side of a mountain, but has a vertical output (a shaft) at the end of the main gallery. Three different experimental methodologies were used. Radon concentration measurements were performed by CR-39 nuclear track detectors. The distribution of natural radionuclide activities ({sup 40}K, {sup 232}Th and {sup 238}U) was determined from rock samples collected along their main adits, using in laboratory gamma-ray spectrometry. The external gamma dose rate was evaluated using thermoluminescent dosimeters and a portable survey meter. The values for the {sup 222}Rn concentration ranged from 0.43 {+-} 0.04 to 1.48 {+-} 0.12 kBq/m{sup 3} in the Los Condores wolfram mine and from 1.8 {+-} 0.1 to 6.0{+-}0.5 kBq/m{sup 3} in the La Carolina gold mine, indicating that, in this mine, the radon levels exceed up to four times the action level of 1.5 kBq/m{sup 3

Single-section mines carve out a market

International Nuclear Information System (INIS)

Sanda, A.P.

1991-01-01

In the Appalachian states of Pennsylvania, West Virginia, Kentucky and Virginia there are large operations whose complexes are an agglomeration of one and two-section mines; large operators whose own mines are augmented by small contractors; small contractors whose one-section mines collectively make them large operators within this genre; and independent, sole-owner operators of single-contract mines. Finally, there is the totally independent operator who negotiates his own leases, mines his own coal and searches for his own markets. The article profiles 6 single section mines. Mines were chosen on criteria including: the equipment in use; obtaining a representive sample of the states with many small coal mines particularly West Virginia, Virginia and Kentucky; the divergence of operators and situations. The mines chosen were: Elk Run; Kinney Branch Coal Co. No. 5 mine; A ampersand G No. 1 mine; Dotson and Rife Coal Co.; Bullion Hollow Coal Co.; and Bruce Coal. The article includes production rates and mine specifications. 1 tab

Modeling N Cycling during Succession after Forest Disturbance: an Analysis of N Mining and Retention Hypothesis

Science.gov (United States)

Zhou, Z.; Ollinger, S. V.; Ouimette, A.; Lovett, G. M.; Fuss, C. B.; Goodale, C. L.

2017-12-01

Dissolved inorganic nitrogen losses at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, have declined in recent decades, a pattern that counters expectations based on prevailing theory. An unbalanced ecosystem nitrogen (N) budget implies there is a missing component for N sink. Hypotheses to explain this discrepancy include increasing rates of denitrification and accumulation of N in mineral soil pools following N mining by plants. Here, we conducted a modeling analysis fused with field measurements of N cycling, specifically examining the hypothesis relevant to N mining and retention in mineral soils. We included simplified representations of both mechanisms, N mining and retention, in a revised ecosystem process model, PnET-SOM, to evaluate the dynamics of N cycling during succession after forest disturbance at the HBEF. The predicted N mining during the early succession was regulated by a metric representing a potential demand of extra soil N for large wood growth. The accumulation of nitrate in mineral soil pools was a function of the net aboveground biomass accumulation and soil N availability and parameterized based on field 15N tracer incubation data. The predicted patterns of forest N dynamics were consistent with observations. The addition of the new algorithms also improved the predicted DIN export in stream water with an R squared of 0.35 (Ppay back the mined N in mineral soils. Predicted ecosystem N balance showed that N gas loss could account for 14-46% of the total N deposition, the soil mining about 103% during the early succession, and soil retention about 35% at the current forest stage at the HBEF.

Mine drivage in hydraulic mines

Energy Technology Data Exchange (ETDEWEB)

Ehkber, B Ya

1983-09-01

From 20 to 25% of labor cost in hydraulic coal mines falls on mine drivage. Range of mine drivage is high due to the large number of shortwalls mined by hydraulic monitors. Reducing mining cost in hydraulic mines depends on lowering drivage cost by use of new drivage systems or by increasing efficiency of drivage systems used at present. The following drivage methods used in hydraulic mines are compared: heading machines with hydraulic haulage of cut rocks and coal, hydraulic monitors with hydraulic haulage, drilling and blasting with hydraulic haulage of blasted rocks. Mining and geologic conditions which influence selection of the optimum mine drivage system are analyzed. Standardized cross sections of mine roadways driven by the 3 methods are shown in schemes. Support systems used in mine roadways are compared: timber supports, roof bolts, roof bolts with steel elements, and roadways driven in rocks without a support system. Heading machines (K-56MG, GPKG, 4PU, PK-3M) and hydraulic monitors (GMDTs-3M, 12GD-2) used for mine drivage are described. Data on mine drivage in hydraulic coal mines in the Kuzbass are discussed. From 40 to 46% of roadways are driven by heading machines with hydraulic haulage and from 12 to 15% by hydraulic monitors with hydraulic haulage.

Data Mining Solutions for the Business Environment

Directory of Open Access Journals (Sweden)

Ruxandra-Stefania PETRE

2014-02-01

Full Text Available Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making

Water spray ventilator system for continuous mining machines

Science.gov (United States)

Page, Steven J.; Mal, Thomas

1995-01-01

The invention relates to a water spray ventilator system mounted on a continuous mining machine to streamline airflow and provide effective face ventilation of both respirable dust and methane in underground coal mines. This system has two side spray nozzles mounted one on each side of the mining machine and six spray nozzles disposed on a manifold mounted to the underside of the machine boom. The six spray nozzles are angularly and laterally oriented on the manifold so as to provide non-overlapping spray patterns along the length of the cutter drum.

Data mining for the social sciences an introduction

CERN Document Server

Attewell, Paul

2015-01-01

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining

Exploring the Integration of Data Mining and Data Visualization

Science.gov (United States)

Zhang, Yi

2011-01-01

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…

Statistically significant relational data mining :

Energy Technology Data Exchange (ETDEWEB)

Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann; Pinar, Ali; Robinson, David Gerald; Berger-Wolf, Tanya; Bhowmick, Sanjukta; Casleton, Emily; Kaiser, Mark; Nordman, Daniel J.; Wilson, Alyson G.

2014-02-01

This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publications that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

Directory of Open Access Journals (Sweden)

Sungjun Lee

2016-01-01

Full Text Available Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases.

Data Mining Empowers the Generation of a Novel Class of Chromosome-specific DNA Probes

Energy Technology Data Exchange (ETDEWEB)

Zeng, Hui; Weier, Heinz-Ulrich G.; Kwan, Johnson; Wang, Mei; O' Brien, Benjamin

2011-03-08

Probes that allow accurate delineation of chromosome-specific DNA sequences in interphase or metaphase cell nuclei have become important clinical tools that deliver life-saving information about the gender or chromosomal make-up of a product of conception or the probability of an embryo to implant, as well as the definition of tumor-specific genetic signatures. Often such highly specific DNA probes are proprietary in nature and have been the result of extensive probe selection and optimization procedures. We describe a novel approach that eliminates costly and time consuming probe selection and testing by applying data mining and common bioinformatics tools. Similar to a rational drug design process in which drug-protein interactions are modeled in the computer, the rational probe design described here uses a set of criteria and publicly available bioinformatics software to select the desired probe molecules from libraries comprised of hundreds of thousands of probe molecules. Examples describe the selection of DNA probes for the human X and Y chromosomes, both with unprecedented performance, but in a similar fashion, this approach can be applied to other chromosomes or species.

Data Mining Solutions for the Business Environment

OpenAIRE

Ruxandra-Stefania PETRE

2013-01-01

Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The p...

Industrial Mining's flexibility aids in customer satisfaction

Energy Technology Data Exchange (ETDEWEB)

1985-08-01

Mining flexibility and customer specification keeps Industrial Mining of Youngstown, Ohio competitive in a tough environment. The company fills the needs of the small customer who requires a special blend or sized product. Industrial Mining works terrain that was uneconomical to mine before but is now profitable. Draglines and mobile equipment are used for overburden removal for economical and flexible mining with most mining by the contour method. Industrial Mining's preparation plant was constructed in 1979 to enable the company to wash and screen different stoker products and utility coal with a modern on-site lab a quality product can be constantly maintained. A wheel loader is used to feed the hopper from selective stockpiles and this allows blending on the raw side or within the plant.

Associations between pathogen-specific clinical mastitis and somatic cell count patterns

NARCIS (Netherlands)

Haas, de Y.; Veerkamp, R.F.; Barkema, H.W.; Gröhn, Y.T.; Schukken, Y.H.

2004-01-01

Associations were estimated between pathogen-specific cases of clinical mastitis (CM) and somatic cell count (SCC) patterns based on deviations from the typical curve for SCC during lactation and compared with associations between pathogen-specific CM and lactation average SCC. Data from 274 Dutch

Externalities from lignite mining-related dust emissions

International Nuclear Information System (INIS)

Papagiannis, A.; Roussos, D.; Menegaki, M.; Damigos, D.

2014-01-01

During the last three decades, several studies have been conducted in order to assess the external costs of electricity production from fossil fuels, especially coal and lignite. Nevertheless, these studies usually ignore the impacts generated by the upstream mining works. This paper contributes to existing literature and attempts to fill this gap by exploring the externalities of lignite mining owing to the emission of suspended particulate matter. To this end, a ‘bottom-up’ approach is implemented, using as case study the largest operational lignite surface mine at the Lignite Center of Western Macedonia (Greece). The results indicate that annual air pollution externalities of lignite mining are of the order of 3€/ton of lignite, which corresponds to around 5.0 €/MW h. The estimated costs are significantly lower, i.e. up to 80%, when dust deposition is considered in air dispersion models. In any case, these findings should be seen as a starting point for discussion owing to the lack of specific emission rates for Greek lignite mines. - Highlights: • Externalities from lignite mining-related dust emissions are 3 €/t of lignite. • Externalities of mining correspond to around 5.0 €/MW h. • Externalities are significantly lower, up to 80%, if dust deposition is considered. • There is lack of specific dust emission rates for lignite mining. • There are high discrepancies in existing dust emission rates for lignite mining

Mining with Rare Cases

Science.gov (United States)

Weiss, Gary M.

Rare cases are often the most interesting cases. For example, in medical diagnosis one is typically interested in identifying relatively rare diseases, such as cancer, rather than more frequently occurring ones, such as the common cold. In this chapter we discuss the role of rare cases in Data Mining. Specific problems associated with mining rare cases are discussed, followed by a description of methods for addressing these problems.

Determination of Abutment Pressure in Coal Mines with Extremely Thick Alluvium Stratum: A Typical Kind of Rockburst Mines in China

Science.gov (United States)

Zhu, Sitao; Feng, Yu; Jiang, Fuxing

2016-05-01

This paper investigates the abutment pressure distribution in coal mines with extremely thick alluvium stratum (ETAS), which is a typical kind of mines encountering frequent intense rockbursts in China. This occurs due to poor understanding to abutment pressure distribution pattern and the consequent inappropriate mine design. In this study, a theoretical computational model of abutment pressure for ETAS longwall panels is proposed based on the analysis of load transfer mechanisms of key stratum (KS) and ETAS. The model was applied to determine the abutment pressure distribution of LW2302S in Xinjulong Coal Mine; the results of stress and microseismic monitoring verified the rationality of this model. The calculated abutment pressure of LW2302S was also used in the terminal mining line design of LW2301N for rockburst prevention, successfully protecting the main roadway from the adverse influence of the abutment pressure.

Sequential Pattern Mining of Electronic Healthcare Reimbursement Claims: Experiences and Challenges in Uncovering How Patients are Treated by Physicians

Energy Technology Data Exchange (ETDEWEB)

Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL; Hobson, Tanner C [ORNL

2015-01-01

We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). We show that EHRCs are correlated with disease incidence estimates published by the Centers for Disease Control. Further, by analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with autism spectrum disorder (ASD), heart disease (HD) and breast cancer (BC) using sequential pattern mining algorithms. Our analyses reveal that in contrast to treating HD and BC, clinical procedures for ASD diagnoses are highly varied leading up to and after the ASD diagnoses. The discovered clinical procedure sequences also reveal significant differences in the overall costs incurred across different parts of the US, indicating a lack of consensus amongst practitioners in treating ASD patients. We show that a data-driven approach to understand clinical trajectories using EHRC can provide quantitative insights into how to better manage and treat patients. Based on our experience, we also discuss emerging challenges in using EHRC datasets for gaining insights into the state of contemporary healthcare delivery and practice in the US.

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.

Science.gov (United States)

Federer, Callie; Yoo, Minjae; Tan, Aik Choon

2016-12-01

Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov ( https://clinicaltrials.gov/ ), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

Green Mines green energy : establishing productive land on mine tailings

Energy Technology Data Exchange (ETDEWEB)

Tisch, B.; Zinck, J.; Vigneault, B. [Natural Resources Canada, Ottawa, ON (Canada). CANMET Mining and Mineral Sciences Laboratories

2009-02-15

The Green Mines green energy research project was initiated by the CANMET Mining and Mineral Sciences Laboratories of Natural Resources Canada. The objective of the initiative was to demonstrate that organic residuals could be used to remediate mine tailings and establish agriculturally productive land where energy crops such as corn, canola, soy, switchgrass and other species could be grown and harvested specifically as feedstock for the production of green fuels. This paper discussed the scope and progress to date of the Green Mines green energy project. This included discussion about a column leaching study and about effluent treatability and toxicity. Neutralization test results and the results of field trials were presented. The paper concluded with a discussion of next steps. An advisory committee has been established to review annual progress and establish research directions. Overall, preliminary results from the column study suggest that sulphate reduction at the tailings-biosolids interface is occurring, although steady state has not yet been reached after more than one year of testing. 1 tab., 3 figs.

Green Mines green energy : establishing productive land on mine tailings

International Nuclear Information System (INIS)

Tisch, B.; Zinck, J.; Vigneault, B.

2009-01-01

The Green Mines green energy research project was initiated by the CANMET Mining and Mineral Sciences Laboratories of Natural Resources Canada. The objective of the initiative was to demonstrate that organic residuals could be used to remediate mine tailings and establish agriculturally productive land where energy crops such as corn, canola, soy, switchgrass and other species could be grown and harvested specifically as feedstock for the production of green fuels. This paper discussed the scope and progress to date of the Green Mines green energy project. This included discussion about a column leaching study and about effluent treatability and toxicity. Neutralization test results and the results of field trials were presented. The paper concluded with a discussion of next steps. An advisory committee has been established to review annual progress and establish research directions. Overall, preliminary results from the column study suggest that sulphate reduction at the tailings-biosolids interface is occurring, although steady state has not yet been reached after more than one year of testing. 1 tab., 3 figs

BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint

National Research Council Canada - National Science Library

Wang, Jianyong; Karypis, George

2003-01-01

Previous study has shown that mining frequent patterns with length-decreasing support constraint is very helpful in removing some uninteresting patterns based on the observation that short patterns...

The hydrogen mine introduction initiative

Energy Technology Data Exchange (ETDEWEB)

Betournay, M.C.; Howell, B. [Natural Resources Canada, Ottawa, ON (Canada). CANMET Mining and Mineral Sciences Laboratories

2009-07-01

In an effort to address air quality concerns in underground mines, the mining industry is considering the use fuel cells instead of diesel to power mine production vehicles. The immediate issues and opportunities associated with fuel cells use include a reduction in harmful greenhouse gas emissions; reduction in ventilation operating costs; reduction in energy consumption; improved health benefits; automation; and high productivity. The objective of the hydrogen mine introduction initiative (HMII) is to develop and test the range of fundamental and needed operational technology, specifications and best practices for underground hydrogen power applications. Although proof of concept studies have shown high potential for fuel cell use, safety considerations must be addressed, including hydrogen behaviour in confined conditions. This presentation highlighted the issues to meet operational requirements, notably hydrogen production; delivery and storage; mine regulations; and hydrogen behaviour underground. tabs., figs.

Specimen-specific modeling of hip fracture pattern and repair.

Science.gov (United States)

Ali, Azhar A; Cristofolini, Luca; Schileo, Enrico; Hu, Haixiang; Taddei, Fulvia; Kim, Raymond H; Rullkoetter, Paul J; Laz, Peter J

2014-01-22

Hip fracture remains a major health problem for the elderly. Clinical studies have assessed fracture risk based on bone quality in the aging population and cadaveric testing has quantified bone strength and fracture loads. Prior modeling has primarily focused on quantifying the strain distribution in bone as an indicator of fracture risk. Recent advances in the extended finite element method (XFEM) enable prediction of the initiation and propagation of cracks without requiring a priori knowledge of the crack path. Accordingly, the objectives of this study were to predict femoral fracture in specimen-specific models using the XFEM approach, to perform one-to-one comparisons of predicted and in vitro fracture patterns, and to develop a framework to assess the mechanics and load transfer in the fractured femur when it is repaired with an osteosynthesis implant. Five specimen-specific femur models were developed from in vitro experiments under a simulated stance loading condition. Predicted fracture patterns closely matched the in vitro patterns; however, predictions of fracture load differed by approximately 50% due to sensitivity to local material properties. Specimen-specific intertrochanteric fractures were induced by subjecting the femur models to a sideways fall and repaired with a contemporary implant. Under a post-surgical stance loading, model-predicted load sharing between the implant and bone across the fracture surface varied from 59%:41% to 89%:11%, underscoring the importance of considering anatomic and fracture variability in the evaluation of implants. XFEM modeling shows potential as a macro-level analysis enabling fracture investigations of clinical cohorts, including at-risk groups, and the design of robust implants. © 2013 Published by Elsevier Ltd.

WEB STRUCTURE MINING USING PAGERANK, IMPROVED PAGERANK – AN OVERVIEW

Directory of Open Access Journals (Sweden)

V. Lakshmi Praba

2011-03-01

Full Text Available Web Mining is the extraction of interesting and potentially useful patterns and information from Web. It includes Web documents, hyperlinks between documents, and usage logs of web sites. The significant task for web mining can be listed out as Information Retrieval, Information Selection / Extraction, Generalization and Analysis. Web information retrieval tools consider only the text on pages and ignore information in the links. The goal of Web structure mining is to explore structural summary about web. Web structure mining focusing on link information is an important aspect of web data. This paper presents an overview of the PageRank, Improved Page Rank and its working functionality in web structure mining.

Mining and mining authorities in Saarland 2016. Mining economy, mining technology, occupational safety, environmental protection, statistics, mining authority activities. Annual report

International Nuclear Information System (INIS)

2016-01-01

The annual report of the Saarland Upper Mining Authority provides an insight into the activities of mining authorities. Especially, the development of the black coal mining, safety and technology of mining as well as the correlation between mining and environment are stressed.

Evaluation of Documentation Patterns of Trainees and Supervising Physicians Using Data Mining.

Science.gov (United States)

Madhavan, Ramesh; Tang, Chi; Bhattacharya, Pratik; Delly, Fadi; Basha, Maysaa M

2014-09-01

The electronic health record (EHR) includes a rich data set that may offer opportunities for data mining and natural language processing to answer questions about quality of care, key aspects of resident education, or attributes of the residents' learning environment. We used data obtained from the EHR to report on inpatient documentation practices of residents and attending physicians at a large academic medical center. We conducted a retrospective observational study of deidentified patient notes entered over 7 consecutive months by a multispecialty university physician group at an urban hospital. A novel automated data mining technology was used to extract patient note-related variables. A sample of 26 802 consecutive patient notes was analyzed using the data mining and modeling tool Healthcare Smartgrid. Residents entered most of the notes (33%, 8178 of 24 787) between noon and 4 pm and 31% (7718 of 24 787) of notes between 8 am and noon. Attending physicians placed notes about teaching attestations within 24 hours in only 73% (17 843 of 24 443) of the records. Surgical residents were more likely to place notes before noon (P Data related to patient note entry was successfully used to objectively measure current work flow of resident physicians and their supervising faculty, and the findings have implications for physician oversight of residents' clinical work. We were able to demonstrate the utility of a data mining model as an assessment tool in graduate medical education.

Application and Exploration of Big Data Mining in Clinical Medicine.

Science.gov (United States)

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-03-20

To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

The Hazards of Data Mining in Healthcare.

Science.gov (United States)

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.

Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes

OpenAIRE

Anjewierden , Anjo; Kolloffel , Bas; Hulshof , Casper

2007-01-01

In this paper we investigate the application of data mining methods to provide learners with real-time adaptive feedback on the nature and patterns of their on-line communication while learning collaboratively.We derived two models for classifying chat messages using data mining techniques and tested these on an actual data set [16]. The reliability of the classification of chat messages is established by comparing the models performance to that of humans. Results indicate that the classifica...

Personalized privacy-preserving frequent itemset mining using randomized response.

Science.gov (United States)

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.

Cleaning up our mining act: A north-south dialogue

International Nuclear Information System (INIS)

Labonne, B.

2000-01-01

This presentation provides an overview of historic development and economic reality of mining at large with focus on environmental and social impacts. It shows the place mining can take in sustainable development context; the mining cycle and its effects on ecosystems; the specific aspects of mining activities in developing countries. The presentation also provides some recommendations on sound management of natural resources to address the socio-economic and environmental concerns frequently raised by mining activities

In-situ leach mining: the next quantum leap?

International Nuclear Information System (INIS)

Hancock, S.

1988-01-01

The opportunities and problems which in-situ leach mining technology presents to the mining industry are considered. These are exemplified by concerns addressed in the development of a proposal to mine uranium by in-situ leach techniques at Beverley in South Australia. The technique proposed at Beverley will use sulphuric acid with hydrogen peroxide or dissolved oxygen as the lixivient. Pre-treatment of the aquifer will be necessary to remove excess calcium carbonate, and the system will employ a slightly overpumped output of fluid through the wellfield to reduce the risk of excursions of mining solutions. The input and output patterns will also be varied to take account of the hydrogeological conditions such as confining bed thickness and permeability. Much study has been directed towards the post mining condition of the ore zone and the threat it may pose to the water resources of the region. 10 refs., 1 fig

Surface mining and land reclamation in Germany

Energy Technology Data Exchange (ETDEWEB)

Nephew, E.A.

1972-05-01

Mining and land restoration methods as well as planning and regulatory procedures employed in West Germany to ameliorate environmental impacts from large-scale surface mining are described. The Rhineland coalfield in North Rhine Westphalia contains some 55 billion tons of brown-coal (or lignite), making the region one of Europe's most important energy centers. The lignite is extracted from huge, open-pit mines, resulting in large areas of disturbed land. The German reclamation approach is characterized by planning and carrying out the mining process as one continuum from early planning to final restoration of land and its succeeding use. Since the coalfield is located in a populated region with settlements dating back to Roman times, whole villages lying in the path of the mining operations sometimes have to be evacuated and relocated. Even before mining begins, detailed concepts must be worked out for the new landscape which will follow: the topography, the water drainage system, lakes and forests, and the intended land-use pattern are designed and specified in advance. Early, detailed planning makes it possible to coordinate mining and concurrent land reclamation activities. The comprehensive approach permits treating the overall problem as a whole rather than dealing with its separate aspects on a piecemeal basis.

Off-road truck-related accidents in U.S. mines.

Science.gov (United States)

Dindarloo, Saeid R; Pollard, Jonisha P; Siami-Irdemoosa, Elnaz

2016-09-01

Off-road trucks are one of the major sources of equipment-related accidents in the U.S. mining industries. A systematic analysis of all off-road truck-related accidents, injuries, and illnesses, which are reported and published by the Mine Safety and Health Administration (MSHA), is expected to provide practical insights for identifying the accident patterns and trends in the available raw database. Therefore, appropriate safety management measures can be administered and implemented based on these accident patterns/trends. A hybrid clustering-classification methodology using K-means clustering and gene expression programming (GEP) is proposed for the analysis of severe and non-severe off-road truck-related injuries at U.S. mines. Using the GEP sub-model, a small subset of the 36 recorded attributes was found to be correlated to the severity level. Given the set of specified attributes, the clustering sub-model was able to cluster the accident records into 5 distinct groups. For instance, the first cluster contained accidents related to minerals processing mills and coal preparation plants (91%). More than two-thirds of the victims in this cluster had less than 5years of job experience. This cluster was associated with the highest percentage of severe injuries (22 severe accidents, 3.4%). Almost 50% of all accidents in this cluster occurred at stone operations. Similarly, the other four clusters were characterized to highlight important patterns that can be used to determine areas of focus for safety initiatives. The identified clusters of accidents may play a vital role in the prevention of severe injuries in mining. Further research into the cluster attributes and identified patterns will be necessary to determine how these factors can be mitigated to reduce the risk of severe injuries. Analyzing injury data using data mining techniques provides some insight into attributes that are associated with high accuracies for predicting injury severity. Copyright © 2016

Solar Data Mining at Georgia State University

Science.gov (United States)

Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

2016-12-01

In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

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

Science.gov (United States)

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.

Auxiliary mine ventilation manual

International Nuclear Information System (INIS)

Workplace Safety North

2010-01-01

An adequate ventilation system is needed for air quality and handling in a mine and is comprised of many different pieces of equipment for removing contaminated air and supplying fresh air and thereby provide a satisfactory working environment. This manual highlights auxiliary ventilation systems made up of small fans, ducts, tubes, air movers, deflectors and additional air flow controls which distribute fresh air delivered by the primary system to all areas. A review of auxiliary ventilation is provided. Design, operation and management issues are discussed and guidelines are furnished. This manual is limited to underground hard rock operations and does not address directly other, specific auxiliary systems, either in underground coal mines or uranium mines.

Auxiliary mine ventilation manual

Energy Technology Data Exchange (ETDEWEB)

Workplace Safety North

2010-07-01

An adequate ventilation system is needed for air quality and handling in a mine and is comprised of many different pieces of equipment for removing contaminated air and supplying fresh air and thereby provide a satisfactory working environment. This manual highlights auxiliary ventilation systems made up of small fans, ducts, tubes, air movers, deflectors and additional air flow controls which distribute fresh air delivered by the primary system to all areas. A review of auxiliary ventilation is provided. Design, operation and management issues are discussed and guidelines are furnished. This manual is limited to underground hard rock operations and does not address directly other, specific auxiliary systems, either in underground coal mines or uranium mines.

Data Mining and Homeland Security: An Overview

National Research Council Canada - National Science Library

Seifert, Jeffrey W

2008-01-01

.... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

Data Mining and Homeland Security: An Overview

National Research Council Canada - National Science Library

Seifert, Jeffrey W

2007-01-01

.... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

Data Mining and Homeland Security: An Overview

National Research Council Canada - National Science Library

Seifert, Jeffrey W

2006-01-01

.... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

Web Mining of Hotel Customer Survey Data

Directory of Open Access Journals (Sweden)

Richard S. Segall

2008-12-01

Full Text Available This paper provides an extensive literature review and list of references on the background of web mining as applied specifically to hotel customer survey data. This research applies the techniques of web mining to actual text of written comments for hotel customers using Megaputer PolyAnalyst®. Web mining functionalities utilized include those such as clustering, link analysis, key word and phrase extraction, taxonomy, and dimension matrices. This paper provides screen shots of the web mining applications using Megaputer PolyAnalyst®. Conclusions and future directions of the research are presented.

Text Mining in Organizational Research.

Science.gov (United States)

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.

Usage of Data Mining at Financial Decision Making

Directory of Open Access Journals (Sweden)

Levent BORAN

2014-06-01

Full Text Available The knowledge age requires controlling every kind of information. Recognition of patterns in data may provide previously unknown and useful information that can provide competitive advantages. If related techniques are applied on financial statements, it is possible to acquire valuable information about companies’ financial situations. It is considered that data mining could be an alternative of common financial analysis techniques such as vertical analysis, horizontal analysis, trend analysis and ratio analysis. Against existing financial analysis methods, data mining provides some advantages, which are ability of manipulation of huge data and competence of obtaining previously unknown information. There exist two major constraints of data mining implementation that are lack of experts on both data mining and related domains and cost of computer software and hardware used.

Activity recognition from minimal distinguishing subsequence mining

Science.gov (United States)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Web based parallel/distributed medical data mining using software agents

Energy Technology Data Exchange (ETDEWEB)

Kargupta, H.; Stafford, B.; Hamzaoglu, I.

1997-12-31

This paper describes an experimental parallel/distributed data mining system PADMA (PArallel Data Mining Agents) that uses software agents for local data accessing and analysis and a web based interface for interactive data visualization. It also presents the results of applying PADMA for detecting patterns in unstructured texts of postmortem reports and laboratory test data for Hepatitis C patients.

An Evolutionary Algorithm to Mine High-Utility Itemsets

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches.

Science.gov (United States)

Delerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel

2016-01-01

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches.

Directory of Open Access Journals (Sweden)

Sylvain Delerce

Full Text Available Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of

Spontaneous revegetation of mined peatlands in eastern Canada

Energy Technology Data Exchange (ETDEWEB)

Poulin, M.; Rochefort, L.; Quinty, F.; Lavoie, C [Laval University, Quebec, QC (Canada)

2005-05-15

Revegetation patterns of sphagnum recolonization at abandoned mined peatlands are assessed, based on a survey of 26 abandoned harvested peatlands, in the provinces of Quebec and New Brunswick. The impact of local and regional variables and the length of time since abandonment are examined. The vegetation structure of all 2571 trenches and 2595 blocks of abandoned block-cut areas and in all 395 vacuum fields of the mechanically mined areas was recorded. The species at 243 recolonized peat fields (selected by random sampling) were analyzed. The abandoned surfaces were found to be distinctly different depending on whether peat extraction was by hand block-cutting or vacuum mining methods. Block-cut peatlands recovered well; herb cover was similar to that in natural peatlands. Practically no sphagnum species recolonized the vacuum- mined peat fields. The species diversity in abandoned mined peat fields was observed to be high. 72 refs., 3 figs., 5 tabs., 1 app.

Mine Water Treatment in Hongai Coal Mines

Science.gov (United States)

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.

Mine Water Treatment in Hongai Coal Mines

OpenAIRE

Dang Phuong Thao; Dang Vu Chi

2018-01-01

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 ...

Mine planning and scheduling at Ranger Uranium Mine - environmental requirements and economics

International Nuclear Information System (INIS)

Bath, L.J.

1984-01-01

Ranger Uranium Mines operates an open cut located in the Northern Territory. Strict environmental controls govern all operations and the water management requirements have the greatest impact on mine planning. The two main goals of planning are to provide mill feed and to mine sufficient suitable quality waste rock for ongoing construction of the tailings dam. Early planning concentrated on staged development of the pit to provide access to as much ore as possible for a given amount of development. All waste was considered to be suitable construction material. Grade control of crusher feed was the main problem in planning, as wide variations occur in ore grade over relatively short distances. Water management for the site operates a 'no release' system for contaminated waters. Design storage has proven inadequate, and the open cut has been used as the extra storage. As construction of future stages of the tailings dam requires non-mineralised rock materials which meet specific quality criteria, the mine has had to re-examine long term planning and pit development strategies. This has entailed the collection of much data not required under normal mining conditions, such as the assaying of waste drill core. The overall impact on mine planning of the environmental regulations has been to alter the philosophy of earlier planning, making it necessary to create a new strategy for pit development with the accent on exposing waste

Stop dust : a communication support for mining permit application at Bilina opencast mine

International Nuclear Information System (INIS)

Budinsky, V.; Paroha, L.

2010-01-01

This paper reported on a problem solving process called stop dust that has been launched by a brown coal mining company to improve ambient air quality and to reduce health risks for the population living in the area close to the Bilina opencast mine in the Czech Republic. The initiative also involved the efforts of local stakeholders and an independent expert team. The concept for stop dust reflected the fact that particles emitted by households through burning fossil fuels, wood, and litter in old stoves and by road transportation were much smaller and with higher health impacts, than than those emitted by coal mining activity. The paper described the Severoceske doly Company and Bilina Mine. The primary goal of stop dust was also outlined. The goal was to decrease particulate matter levels and related health risks in the communities surrounding Bilina opencast mine through feasible cost-effective measures aimed at background pollution sources. Specific topics that were addressed in the paper included particulate matter and limit values for the protection of human health; comparison of particulate matter to a hair; particulate matter dispersion modelling as part of the Bilina Mine environmental impact assessment process; and the various phases of stop dust. 3 refs., 2 tabs., 5 figs.

Site-Specific Pre-Swelling-Directed Morphing Structures of Patterned Hydrogels.

Science.gov (United States)

Wang, Zhi Jian; Hong, Wei; Wu, Zi Liang; Zheng, Qiang

2017-12-11

Morphing materials have promising applications in various fields, yet how to program the self-shaping process for specific configurations remains a challenge. Herein we show a versatile approach to control the buckling of individual domains and thus the outcome configurations of planar-patterned hydrogels. By photolithography, high-swelling disc gels were positioned in a non-swelling gel sheet; the swelling mismatch resulted in out-of-plain buckling of the disc gels. To locally control the buckling direction, masks with holes were used to guide site-specific swelling of the high-swelling gel under the holes, which built a transient through-thickness gradient and thus directed the buckling during the subsequent unmasked swelling process. Therefore, various configurations of an identical patterned hydrogel can be programmed by the pre-swelling step with different masks to encode the buckling directions of separate domains. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Patterns for Effectively Documenting Frameworks

Science.gov (United States)

Aguiar, Ademar; David, Gabriel

Good design and implementation are necessary but not sufficient pre-requisites for successfully reusing object-oriented frameworks. Although not always recognized, good documentation is crucial for effective framework reuse, and often hard, costly, and tiresome, coming with many issues, especially when we are not aware of the key problems and respective ways of addressing them. Based on existing literature, case studies and lessons learned, the authors have been mining proven solutions to recurrent problems of documenting object-oriented frameworks, and writing them in pattern form, as patterns are a very effective way of communicating expertise and best practices. This paper presents a small set of patterns addressing problems related to the framework documentation itself, here seen as an autonomous and tangible product independent of the process used to create it. The patterns aim at helping non-experts on cost-effectively documenting object-oriented frameworks. In concrete, these patterns provide guidance on choosing the kinds of documents to produce, how to relate them, and which contents to include. Although the focus is more on the documents themselves, rather than on the process and tools to produce them, some guidelines are also presented in the paper to help on applying the patterns to a specific framework.

Optimization in underground mine planning - developments and opportunities

OpenAIRE

Musingwini, C.

2016-01-01

The application of mining-specific and generic optimization techniques in the mining industry is deeply rooted in the discipline of operations research (OR). OR has its origins in the British Royal Air Force and Army around the early 1930s. Its development continued during and after World War II. The application of OR techniques to optimization in the mining industry started to emerge in the early 1960s. Since then, optimization techniques have been applied to solve widely different mine plan...

Mine Water Treatment in Hongai Coal Mines

Directory of Open Access Journals (Sweden)

Dang Phuong Thao

2018-01-01

Full Text Available 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.

Application and Exploration of Big Data Mining in Clinical Medicine

Science.gov (United States)

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-01-01

Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

Rehabilitation of residual pits in post-mining area: a goal of Czech brown coal opencast mining industry

International Nuclear Information System (INIS)

Svoboda, I.

1997-01-01

The closure of surface brown coal mines is a complex process with many specific considerations. In the Czech Republic a special project was proposed to analyze the types of environmental disturbance generated by surface mining operations and suggest procedures for the affected areas. It also aims to propose rehabilitation techniques for residual pits, considering their future use for recreational or development purposes. A Chabarovice mine case study demonstrates how to solve the problem of water flooding and utilization of the future residual lake. 2 figs

Zips : mining compressing sequential patterns in streams

NARCIS (Netherlands)

Hoang, T.L.; Calders, T.G.K.; Yang, J.; Mörchen, F.; Fradkin, D.; Chau, D.H.; Vreeken, J.; Leeuwen, van M.; Faloutsos, C.

2013-01-01

We propose a streaming algorithm, based on the minimal description length (MDL) principle, for extracting non-redundant sequential patterns. For static databases, the MDL-based approach that selects patterns based on their capacity to compress data rather than their frequency, was shown to be

A systematic mapping study of process mining

Science.gov (United States)

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.

Contrasted patterns of age-specific reproduction in long-lived seabirds.

Science.gov (United States)

Berman, M; Gaillard, J-M; Weimerskirch, H

2009-01-22

While the number of studies providing evidence of actuarial senescence is increasing, and covers a wide range of taxa, the process of reproductive senescence remains poorly understood. In fact, quite high reproductive output until the last years of life has been reported in several vertebrate species, so that whether or not reproductive senescence is widespread remains unknown. We compared age-specific changes of reproductive parameters between two closely related species of long-lived seabirds: the small-sized snow petrel Pagodroma nivea, and the medium-sized southern fulmar Fulmarus glacialoides. Both are sympatric in Antarctica. We used an exceptional dataset collected over more than 40 years to assess age-specific variations of both breeding probability and breeding success. We found contrasted age-specific reproductive patterns between the two species. Reproductive senescence clearly occurred from 21 years of age onwards in the southern fulmar, in both breeding probability and success, whereas we did not report any decline in the breeding success of the snow petrel, although a very late decrease in the proportion of breeders occurred at 34 years. Such a contrasted age-specific reproductive pattern was rather unexpected. Differences in life history including size or migratory behaviour are the most likely candidates to account for the difference we reported in reproductive senescence between these sympatric seabird species.

GRAMI: Frequent subgraph and pattern mining in a single large graph

KAUST Repository

Elseidy, M.; Abdelhamid, Ehab; Skiadopoulos, S.; Kalnis, Panos

2014-01-01

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs

Potential socio-economic consequences of mine closure

Directory of Open Access Journals (Sweden)

Marietjie Ackermann

2018-01-01

Full Text Available Background: Mine closures generally reveal negligence on the part of mining houses, not only in terms of the environment, but also the surrounding mining communities. Aim: This article reflects on the findings of research into the socio-economic consequences of mine closure. The research specifically explored how mineworkers’ dependency on their employment at a mine affects their ability to sustain their livelihood. Setting: The research was conducted at the Orkney Mine and the Grootvlei Mine (Springs. Methods: The research was conducted within a naturalistic domain, guided by a relativist orientation, a constructivist ontology and an interpretivist epistemology. Data were collected by means of document analysis, semi-structured interviews, focus group discussion and unstructured observation. Results: From the research findings, it is evident that mine closures, in general, have a devastating effect on the surrounding mining communities as well as on the employees. Mine closures in the case studies gradually depleted the mining communities’ livelihood assets and resulted in the collapse of their coping strategies and livelihood outcomes. It generally affected the communities’ nutrition, health, education, food security, water, shelter, levels of community participation and personal safety. Conclusion: If not managed efficiently and effectively, mine closures may pose significant challenges to the mining industry, government, the environment, national and local economic prosperity and communities in the peripheral areas of mines. This truly amplifies that mine closure, whether temporary or permanent, is an issue that needs to be addressed with responsibility towards all stakeholders, including the mining community and the labour force.

Data Mining and Complex Problems: Case Study in Composite Materials

Science.gov (United States)

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.

Specific and Complete Local Integration of Patterns in Bayesian Networks

Directory of Open Access Journals (Sweden)

Martin Biehl

2017-05-01

Full Text Available We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore, we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the ι -entities in small example systems as a first sanity check and find that ι -entities largely fulfil simple expectations.

Patterns of muscle activity underlying object-specific grasp by the macaque monkey.

Science.gov (United States)

Brochier, T; Spinks, R L; Umilta, M A; Lemon, R N

2004-09-01

During object grasp, a coordinated activation of distal muscles is required to shape the hand in relation to the physical properties of the object. Despite the fundamental importance of the grasping action, little is known of the muscular activation patterns that allow objects of different sizes and shapes to be grasped. In a study of two adult macaque monkeys, we investigated whether we could distinguish between EMG activation patterns associated with grasp of 12 differently shaped objects, chosen to evoke a wide range of grasping postures. Each object was mounted on a horizontal shuttle held by a weak spring (load force 1-2 N). Objects were located in separate sectors of a "carousel," and inter-trial rotation of the carousel allowed sequential presentation of the objects in pseudorandom order. EMG activity from 10 to 12 digit, hand, and arm muscles was recorded using chronically implanted electrodes. We show that the grasp of different objects was characterized by complex but distinctive patterns of EMG activation. Cluster analysis shows that these object-related EMG patterns were specific and consistent enough to identify the object unequivocally from the EMG recordings alone. EMG-based object identification required a minimum of six EMGs from simultaneously recorded muscles. EMG patterns were consistent across recording sessions in a given monkey but showed some differences between animals. These results identify the specific patterns of activity required to achieve distinct hand postures for grasping, and they open the way to our understanding of how these patterns are generated by the central motor network.

Mining the Temporal Dimension of the Information Propagation

Science.gov (United States)

Berlingerio, Michele; Coscia, Michele; Giannotti, Fosca

In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining area has increased very fast. Among the possible related topics, the study of the information propagation in a network attracted the interest of many researchers, also from the industrial world. However, only a few answers to the questions “How does the information propagates over a network, why and how fast?” have been discovered so far. On the other hand, these answers are of large interest, since they help in the tasks of finding experts in a network, assessing viral marketing strategies, identifying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a network with the help of two different techniques: TAS (Temporally Annotated Sequences) mining, aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data, and Graph Mining, which is helpful for locally analyzing the nodes of the networks with their properties. Finally we show preliminary results done in the direction of mining the information propagation over a network, performed on two well known email datasets, that show the power of the combination of these two approaches.

Naturally occurring radioactive materials at New South Wales mines

International Nuclear Information System (INIS)

McLaughlin, Robert

2013-01-01

Until recently mines in New South Wales have been largely exempt from the provisions of the Radiation Control Act with respect to radioactive ore being mined and processed. Legislative changes and the national harmonisation efforts for mine safety regulation have drawn attention to the emerging issue of naturally occurring radioactive material (NORM). While mine operators are already obliged under their duty of care to manage this hazard, specific control measures are increasingly expected by the community and regulators. This applies throughout the whole mine life cycle from exploration right through to rehabilitation.

Morphological response to a North Sea bed depression induced by gas mining

NARCIS (Netherlands)

Fluit, C.C.J.M.; Hulscher, Suzanne J.M.H.

2002-01-01

Gas mining leads to saucer-like surface depressions. In the North Sea, gas is currently mined at several offshore locations. The associated bed depression has a similar spatial extent as offshore tidal sandbanks, which are large-scale bed patterns covering a significant part of the North Sea bottom.

USING WEB MINING IN E-COMMERCE APPLICATIONS

Directory of Open Access Journals (Sweden)

Claudia Elena Dinucă

2011-09-01

Full Text Available Nowadays, the web is an important part of our daily life. The web is now the best medium of doing business. Large companies rethink their business strategy using the web to improve business. Business carried on the Web offers the opportunity to potential customers or partners where their products and specific business can be found. Business presence through a company web site has several advantages as it breaks the barrier of time and space compared with the existence of a physical office. To differentiate through the Internet economy, winning companies have realized that e-commerce transactions is more than just buying / selling, appropriate strategies are key to improve competitive power. One effective technique used for this purpose is data mining. Data mining is the process of extracting interesting knowledge from data. Web mining is the use of data mining techniques to extract information from web data. This article presents the three components of web mining: web usage mining, web structure mining and web content mining.

Coal Mine Methane in Russia [Russian Version

Energy Technology Data Exchange (ETDEWEB)

NONE

2010-07-01

This paper discusses coal mine methane emissions (CMM) in the Russian Federation and the potential for their productive utilisation. It highlights specific opportunities for cost-effective reductions of CMM from oil and natural gas facilities, coal mines and landfills, with the aim of improving knowledge about effective policy approaches.

Consequences of coal mining and burning in the North Bohemian Brown Coal Basin (2). Territorial consequences of coal mining

International Nuclear Information System (INIS)

Stahlik, Z.

1992-01-01

Out of the 1450 km 2 of the North Bohemian Brown Coal Basin, the area of the coal-bearing territory is 850 km 2 . The area occupied by the open pits, spoil banks and mines is nearly 27O km 2 , out of which over 90 km 2 have already been recultivated. Predicted mining development scenarios for the region till 2035 are outlined. The extent of mining will decrease gradually, and land will be reclaimed. The abandoned pits will be filled with water and employed for recreation purposes. The specific features of the individual open pit mines are given. The ways to reduce the adverse environmental impacts of mining are outlined; these include, in particular, desulfurization of existing power plants on the one hand, and energy savings associated with a reduction in mining and power generation activities on the other hand. (J.B.)

Score Mining Rents in Terms of Investment Attractiveness of Peat Mining

Science.gov (United States)

Alexandrov, Gennady; Yablonev, Alexander

2017-11-01

In this article, as determinants in the system factors underlying the investment attractiveness of the peat industry is considered a rental factor, which predetermines the significant differences and peculiarities of the investment climate in the mining business and, in particular, in the sphere of peat mining. In contrast to modern studies treated the essence and role of rents in the economic mechanism, is proposed for a new approach to solving the problems of its formation. Our approach differs in that it, firstly, adequate rental relations, objectively in extractive industries, secondly, provides consensus in the interests of the owner of peat deposits and entrepreneurs, businesses in these deposits and, thus, thirdly, contributes to the creation of a favourable investment climate in the peat extraction industry. In practical terms, in accordance with the proposed approach, we have proposed specific allocation algorithm of mining rents from the profits of peat extraction enterprises.

Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population.

Science.gov (United States)

Eriksson, Robert; Werge, Thomas; Jensen, Lars Juhl; Brunak, Søren

2014-04-01

Data collected for medical, filing and administrative purposes in electronic patient records (EPRs) represent a rich source of individualised clinical data, which has great potential for improved detection of patients experiencing adverse drug reactions (ADRs), across all approved drugs and across all indication areas. The aim of this study was to take advantage of techniques for temporal data mining of EPRs in order to detect ADRs in a patient- and dose-specific manner. We used a psychiatric hospital's EPR system to investigate undesired drug effects. Within one workflow the method identified patient-specific adverse events (AEs) and links these to specific drugs and dosages in a temporal manner, based on integration of text mining results and structured data. The structured data contained precise information on drug identity, dosage and strength. When applying the method to the 3,394 patients in the cohort, we identified AEs linked with a drug in 2,402 patients (70.8 %). Of the 43,528 patient-specific drug substances prescribed, 14,736 (33.9 %) were linked with AEs. From these links we identified multiple ADRs (p patient population, larger doses were prescribed to sedated patients than non-sedated patients; five antipsychotics [corrected] exhibited a significant difference (p<0.05). Finally, we present two cases (p < 0.05) identified by the workflow. The method identified the potentially fatal AE QT prolongation caused by methadone, and a non-described likely ADR between levomepromazine and nightmares found among the hundreds of identified novel links between drugs and AEs (p < 0.05). The developed method can be used to extract dose-dependent ADR information from already collected EPR data. Large-scale AE extraction from EPRs may complement or even replace current drug safety monitoring methods in the future, reducing or eliminating manual reporting and enabling much faster ADR detection.

Coal mine enterprise integration based on strategic alliance

Energy Technology Data Exchange (ETDEWEB)

Zhou, Q.; Sun, J.; Xu, S. [Tsinghua University, Beijing (China). Dept. of Computer Science and Technology

2003-07-01

The relationship between coal mine and related enterprise was analysed. Aiming at the competitive world market as well as the dynamic requirement, a coal mine enterprise integration strategy and a enterprise strategic alliance were proposed for the product providing service business pattern. The modelling method of the enterprise strategic alliance was proposed, including the relationship view model, information view model and business process view model. The idea of enterprise strategic alliance is useful for enterprise integration. 6 refs., 2 figs.

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

International mining forum 2004, new technologies in underground mining, safety in mines proceedings

Energy Technology Data Exchange (ETDEWEB)

Jerzy Kicki; Eugeniusz Sobczyk (eds.)

2004-01-15

The book comprises technical papers that were presented at the International Mining Forum 2004. This event aims to bring together scientists and engineers in mining, rock mechanics, and computer engineering, with a view to explore and discuss international developments in the field. Topics discussed in this book are: trends in the mining industry; new solutions and tendencies in underground mines; rock engineering problems in underground mines; utilization and exploitation of methane; prevention measures for the control of rock bursts in Polish mines; and current problems in Ukrainian coal mines.

Mining Social and Affective Data for Recommendation of Student Tutors

Directory of Open Access Journals (Sweden)

Elisa Boff

2013-03-01

Full Text Available This paper presents a learning environment where a mining algorithm is used to learn patterns of interaction with the user and to represent these patterns in a scheme called item descriptors. The learning environment keeps theoretical information about subjects, as well as tools and exercises where the student can put into practice the knowledge gained. One of the main purposes of the project is to stimulate collaborative learning through the interaction of students with different levels of knowledge. The students' actions, as well as their interactions, are monitored by the system and used to find patterns that can guide the search for students that may play the role of a tutor. Such patterns are found with a particular learning algorithm and represented in item descriptors. The paper presents the educational environment, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors.

INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS

Energy Technology Data Exchange (ETDEWEB)

George Danko

2005-04-01

This report contains a detailed description of the work conducted in the first year of the project on Integrated Robot-Human Control in Mining Operations at University of Nevada, Reno. This project combines human operator control with robotic control concepts to create a hybrid control architecture, in which the strengths of each control method are combined to increase machine efficiency and reduce operator fatigue. The kinematics reconfiguration type differential control of the excavator implemented with a variety of ''software machine kinematics'' is the key feature of the project. This software re-configured excavator is more desirable to execute a given digging task. The human operator retains the master control of the main motion parameters, while the computer coordinates the repetitive movement patterns of the machine links. These repetitive movements may be selected from a pre-defined family of trajectories with different transformations. The operator can make adjustments to this pattern in real time, as needed, to accommodate rapidly-changing environmental conditions. A Bobcat{reg_sign} 435 excavator was retrofitted with electro-hydraulic control valve elements. The modular electronic control was tested and the basic valve characteristics were measured for each valve at the Robotics Laboratory at UNR. Position sensors were added to the individual joint control actuators, and the sensors were calibrated. An electronic central control system consisting of a portable computer, converters and electronic driver components was interfaced to the electro-hydraulic valves and position sensors. The machine is operational with or without the computer control system depending on whether the computer interface is on or off. In preparation for emulated mining tasks tests, typical, repetitive tool trajectories during surface mining operations were recorded at the Newmont Mining Corporation's ''Lone Tree'' mine in Nevada.

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

OpenAIRE

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2012-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present ...

Improving diagnostic accuracy using agent-based distributed data mining system.

Science.gov (United States)

Sridhar, S

2013-09-01

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

A Mining Algorithm for Extracting Decision Process Data Models

Directory of Open Access Journals (Sweden)

Cristina-Claudia DOLEAN

2011-01-01

Full Text Available The paper introduces an algorithm that mines logs of user interaction with simulation software. It outputs a model that explicitly shows the data perspective of the decision process, namely the Decision Data Model (DDM. In the first part of the paper we focus on how the DDM is extracted by our mining algorithm. We introduce it as pseudo-code and, then, provide explanations and examples of how it actually works. In the second part of the paper, we use a series of small case studies to prove the robustness of the mining algorithm and how it deals with the most common patterns we found in real logs.

Mining continuous activity patterns from animal trajectory data

Science.gov (United States)

Wang, Y.; Luo, Ze; Baoping, Yan; Takekawa, John Y.; Prosser, Diann J.; Newman, Scott H.

2014-01-01

The increasing availability of animal tracking data brings us opportunities and challenges to intuitively understand the mechanisms of animal activities. In this paper, we aim to discover animal movement patterns from animal trajectory data. In particular, we propose a notion of continuous activity pattern as the concise representation of underlying similar spatio-temporal movements, and develop an extension and refinement framework to discover the patterns. We first preprocess the trajectories into significant semantic locations with time property. Then, we apply a projection-based approach to generate candidate patterns and refine them to generate true patterns. A sequence graph structure and a simple and effective processing strategy is further developed to reduce the computational overhead. The proposed approaches are extensively validated on both real GPS datasets and large synthetic datasets.

A New Challenge for Information Mining

Directory of Open Access Journals (Sweden)

Roberto Paiano

2017-07-01

Full Text Available In the field of "Data Exploration" many approaches have been developed to solve the problem of management of big data that are also semantically rich. Nowadays, there is a strong need to support the discovery-oriented applications where data discovery is a highly ad hoc interactive process to support the users by assisting the navigation in the data to find interesting objects. In this work starting by a theoretical data exploration system, where we identified the main features that a data exploration system must have to an efficient exploratory experience, we propose a combination of two data exploration techniques faceted navigation and data mining with the aim to improve the discovery information during exploration. This approach is contextualized better in Information Mining. Information mining, in fact, aims at discovering knowledge, i.e. more general patterns within objects or collections of objects.

Sustainable Mining Environment: Technical Review of Post-mining Plans

Directory of Open Access Journals (Sweden)

Restu Juniah

2017-12-01

Full Text Available The mining industry exists because humans need mining commodities to meet their daily needs such as motor vehicles, mobile phones, electronic equipment and others. Mining commodities as mentioned in Government Regulation No. 23 of 2010 on Implementation of Mineral and Coal Mining Business Activities are radioactive minerals, metal minerals, nonmetallic minerals, rocks and coal. Mineral and coal mining is conducted to obtain the mining commodities through production operations. Mining and coal mining companies have an obligation to ensure that the mining environment in particular after the post production operation or post mining continues. The survey research aims to examine technically the post-mining plan in coal mining of PT Samantaka Batubara in Indragiri Hulu Regency of Riau Province towards the sustainability of the mining environment. The results indicate that the post-mining plan of PT Samantaka Batubara has met the technical aspects required in post mining planning for a sustainable mining environment. Postponement of post-mining land of PT Samantaka Batubara for garden and forest zone. The results of this study are expected to be useful and can be used by stakeholders, academics, researchers, practitioners and associations of mining, and the environment.

Recolonization patterns of ants in a rehabilitated lignite mine in central Italy: Potential for the use of Mediterranean ants as indicators of restoration processes

Energy Technology Data Exchange (ETDEWEB)

Ottonetti, L.; Tucci, L.; Santini, G. [University of Florence, Florence (Italy)

2006-03-15

Ant (Hymenoptera: Formicidae) assemblages were sampled with pitfall traps in three different habitats associated with a rehabilitated mine district and in undisturbed forests in Tuscany, Italy. The four habitats were (1) open fields (3-4 years old); (2) a middle-age mixed plantation (10 years); (3) an old-age mixed plantation (20 years); and (4) an oak woodland (40 years) not directly affected by mining activities. The aim of the study was to analyze ant recolonization patterns in order to provide insights on the use of Mediterranean ant fauna as indicators of restoration processes. Species richness and diversity were not significantly different among the four habitats. However, multivariate analyses showed that the assemblages in the different habitats were clearly differentiated, with similarity relationships reflecting a successional gradient among rehabilitated sites. The observed patterns of functional group changes along the gradient broadly accord with those of previous studies in other biogeographic regions. These were (1) a decrease of dominant Dolichoderinae and opportunists; (2) an increase in the proportion of cold-climate specialists; and (3) the appearance of the Cryptic species in the oldest plantations, with a maximum of abundance in the woodland. In conclusion, the results of our study supported the use of Mediterranean ants as a suitable tool for biomonitoring of restoration processes, and in particular, the functional group approach proved a valuable framework to better interpret local trends in terms of global ecological patterns. Further research is, however, needed in order to obtain a reliable classification of Mediterranean ant functional groups.

Critical analysis of the Colombian mining legislation

International Nuclear Information System (INIS)

Vargas P, Elkin; Gonzalez S, Carmen Lucia

2003-01-01

The document analyses the Colombian mining legislation, Act 685 of 2001, based on the reasons expressed by the government and the miners for its conceit and approval. The document tries to determine the developments achieved by this new Mining Code considering international mining competitiveness and its adaptation to the constitutional rules about environment, indigenous communities, decentralization and sustainable development. The analysis formulates general and specific hypothesis about the proposed objectives of the reform, which are confronted with the arguments and critical evaluations of the results. Most hypothesis are not verified, thus demonstrating that the Colombian mining legislation is far from being the necessary instrument to promote mining activities, making it competitive according to international standards and adapted to the principles of sustainable development, healthy environment, community participation, ethnic minorities and regional autonomy

Environmental impact of uranium mining and milling

International Nuclear Information System (INIS)

Dory, A.B.

1981-08-01

The author introduces the subject with an overview of the regulatory requirments and philosophy applied to uranium mines and mills. The special attention given to tailings management is highlighted, and a discussion of the basic environmental concerns is concluded with an itemizing of the main tasks facing the AECB. The extent of the environmental impact of uranium mining, milling and waste management is illustrated with specific details pertaining to mines in the Elliot Lake area. The author concludes that the impact on the ground and surface water system is not alarming, and the impact on air quality is not significant beyond a few hundred metres from the mining facilities. The publicly perceived impact is discussed, followed by a rationale for the continued licensing of new uranium mining operations complete with tailings management facilities

Investigate the causes of transport and tramming accidents on coal mines.

CSIR Research Space (South Africa)

Rushworth, AM

1999-03-01

Full Text Available Transport and tramming accidents on coal mines in South Africa are a major component in the overall pattern of colliery accidents. Furthermore, there is now a widespread acceptance that human error is a common cause of failure in accident patterns...

Pattern of urine specific gravity in exclusively breastfed and water ...

African Journals Online (AJOL)

Background: Exclusive breastfeeding, an essential intervention for the reduction of infant mortality, is not widely practised. A major reason is the issue of thirst, especially in the hot regions of the world. Objective: To describe the pattern of specific gravity of breastfeeding infants aged 0-6 months as a measure of their ...

Contract Mining versus Owner Mining

African Journals Online (AJOL)

Owner

mining companies can concentrate on their core businesses while using specialists for ... 2 Definition of Contract and Owner. Mining ... equipment maintenance, scheduling and budgeting ..... No. Region. Amount Spent on. Contract Mining. ($ billion). Percent of. Total. 1 ... cost and productivity data based on a large range.

Optimization of mining design of Hongwei uranium mine

International Nuclear Information System (INIS)

Wu Sanmao; Yuan Baixiang

2012-01-01

Combined with the mining conditions of Hongwei uranium mine, optimization schemes for hoisting cage, mine drainge,ore transport, mine wastewater treatment, power-supply system,etc are put forward in the mining design of the mine. Optimized effects are analyzed from the aspects of technique, economy, and energy saving and reducing emissions. (authors)

An algorithm, implementation and execution ontology design pattern

NARCIS (Netherlands)

Lawrynowicz, A.; Esteves, D.; Panov, P.; Soru, T.; Dzeroski, S.; Vanschoren, J.

2016-01-01

This paper describes an ontology design pattern for modeling algorithms, their implementations and executions. This pattern is derived from the research results on data mining/machine learning ontologies, but is more generic. We argue that the proposed pattern will foster the development of

Development and prospect of china uranium mining and metallurgy

International Nuclear Information System (INIS)

Que Weimin; Wang Haifeng; Niu Yuqing; Gu Wancheng; Zhang Feifeng

2007-01-01

The development of industry of uranium mining and metallurgy in China has been reviewed generally, emphasizing on investigation approaches and application levels of uranium mining technologies such as in-situ leaching, heap leaching, stope leaching: on the basis of analysis on status of uranium mining and metallurgy and problems existed, also considering the specific features of deposit resources, the development orientation of uranium mining and metallurgy in China is pointed out. The industry of China uranium mining and metallurgy is faced to new opportunity of development and challenge in 21st century, the only way to realize sustainable development of uranium mining and metallurgy and harmonious development between economy and environment is to develop new technology on mining, ore beneficiation and metallurgy, increase the utilization level of uranium resources, low down impact on environment caused by mining and metallurgy. (authors)

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

Directory of Open Access Journals (Sweden)

Reverter Antonio

2008-09-01

Full Text Available Abstract Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing

A specific metabolic pattern related to the hallucinatory activity in schizophrenia

International Nuclear Information System (INIS)

Huret, J.D.; Martinot, J.L.; Lesur, A.; Mazoyer, B.; Pappata, S.; Syrota, A.; Baron, J.C.; Lemperiere, T.

1988-01-01

A clinical and PEI study using 18 F - fluorodesoxyglucose for measuring local cerebral glucose metabolism with the aim of showing a specific pattern related to the hallucinatory activity, is presented in schizophrenic patients all experiencing hallucinations or pseudo-halluccinations

Mining Sector CSR Behaviour: A Developing Country Perspective ...

African Journals Online (AJOL)

The study examined the nature of corporate social responsibility (CSR) in Ghana's gold mining sector, the relationship between company-specific CSR programmes and the initiatives or agreements firms are signatories to. It further analyzed the views of key stakeholders (managers, regulators, mining support organizations, ...

Editorial: Mining in a Sustainable World

Directory of Open Access Journals (Sweden)

Marty Branagan

2014-09-01

Full Text Available Humanity has reaped great benefits from mining. Over the millennia that humans have practiced mining, there have been many obvious improvements in mining’s environmental and social impacts. However, some aspects of mining still involve an element of ecological violence and, in Australia, there is a growing amount of conflict concerned with mining. These two related issues – ‘ecological violence’ and ‘conflict’ – were explored at the ‘Mining in a Sustainable World’ conference on 13 to 15 October 2013 at the University of New England campus in Armidale, Australia. The conference was a joint initiative of the University of New England’s Peace Studies and Australian Centre for Agriculture and Law. Specifically, conference delegates were interested in exploring the work being done to reduce ecological violence and conflict. Articles in this special edition of the International Journal of Rural Law and Policy arose from that conference. This editorial provides an overview of the rationale for the conference and the issues explored.

Knowledge-Based Reinforcement Learning for Data Mining

Science.gov (United States)

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

Assessment of bioaccumulation of REEs by plant species in a mining area by INAA

International Nuclear Information System (INIS)

Hossain Md Anawar; Maria do Carmo Freitas; Nuno Canha; Isabel Dionisio; Ho Manh Dung; Catarina Galinha; Pacheco, A.M.G.

2012-01-01

Native plant species, lichens and tailings, sampled from a copper-sulphide mining area located in southern-eastern Portugal, were analysed by neutron activation analysis (INAA) for determination of rare earth elements (REEs). Values of ΣREEs and individual REEs concentration of tailing samples are higher than those of natural background concentrations. The higher values of REEs are found in modern slags and the mixture of oxidized gossan and sulphide disseminated country rocks when compared with the alluvial sediments contaminated by mine tailings. The total concentrations of light REEs are higher than those of heavy REEs in all tailing samples. Distribution patterns of PAAS-normalized REEs in mine tailings show slightly LREE enriched and flat HREE pattern with negative Eu anomaly. Lichens accumulated higher concentration of lanthanides than vascular plants. The elevated levels of REEs in lichen, native plant species and tailing samples reflect the contamination of REEs in Sao Domingos mining area. The Carlina corymbosa, Erica australis and Lavandula luisierra accumulated the higher amounts of La, Ce and other REEs than the other plant species grown in this mining area. (author)

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

Science.gov (United States)

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

2011-05-01

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

Land Use-Based Landscape Planning and Restoration in Mine Closure Areas

Science.gov (United States)

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

2011-05-01

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

Exploring the significance of human mobility patterns in social link prediction

KAUST Repository

Alharbi, Basma Mohammed

2014-01-01

Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper explores the effect of in-depth mobility patterns. Specifically, we study individuals\\' movement behavior, and quantify mobility on the basis of trip frequency, travel purpose and transportation mode. Our hybrid link prediction model is composed of two modules. The first module extracts mobility patterns, including travel purpose and mode, from raw trajectory data. The second module employs the extracted patterns for link prediction. We evaluate our method on two real data sets, GeoLife [15] and Reality Mining [5]. Experimental results show that our hybrid model significantly improves the accuracy of social link prediction, when comparing to primary topology-based solutions. Copyright 2014 ACM.

Surface Mines, Other - Longwall Mining Panels

Data.gov (United States)

NSGIC Education | GIS Inventory — Coal mining has occurred in Pennsylvania for over a century. A method of coal mining known as Longwall Mining has become more prevalent in recent decades. Longwall...

Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease.

Science.gov (United States)

Omran, Dalia Abd El Hamid; Awad, AbuBakr Hussein; Mabrouk, Mahasen Abd El Rahman; Soliman, Ahmad Fouad; Aziz, Ashraf Omar Abdel

2015-01-01

Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ≥50.3 ng/ml was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (≥50.3 ng/ml). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

Light Rare Earth Elements enrichment in an acidic mine lake (Lusatia, Germany)

International Nuclear Information System (INIS)

Bozau, Elke; Leblanc, Marc; Seidel, Jean Luc; Staerk, Hans-Joachim

2004-01-01

The distribution of Rare Earth Elements (REE) was investigated in the acidic waters (lake and groundwater) of a lignite mining district (Germany). The Fe- and SO 4 -rich lake water (pH 2.7) displays high REE contents (e.g. La∼70 μg/l, Ce∼160 μg/l) and an enrichment of light REE (LREE) in the NASC normalised pattern. Considering the hydrodynamic model and geochemical data, the lake water composition may be calculated as a mixture of inflowing Quaternary and mining dump groundwaters. The groundwater of the dump aquifer is LREE enriched. Nevertheless, the leachates of dump sediments generally have low REE contents and display flat NASC normalised patterns. However, geochemical differences and REE pattern in undisturbed lignite (LREE enriched pattern and low water soluble REE contents) and the weathered lignite of the dumps (flat REE pattern and high water soluble REE contents) suggest that lignite is probably the main REE source rock for the lake water

Numerical modelling of mine workings.

CSIR Research Space (South Africa)

Lightfoot, N

1999-03-01

Full Text Available to cover most of what is required for a practising rock mechanics engineer to be able to use any of these five programs to solve practical mining problems. The chapters on specific programs discuss their individual strengths and weaknesses and highlight... and applications of numerical modelling in the context of the South African gold and platinum mining industries. This includes an example that utilises a number of different numerical 3 modelling programs to solve a single problem. This particular example...

Application of frequent itemsets mining to analyze patterns of one-stop visits in Taiwan.

Directory of Open Access Journals (Sweden)

Chun-Yi Tu

Full Text Available BACKGROUND: The free choice of health care facilities without limitations on frequency of visits within the National Health Insurance in Taiwan gives rise to not only a high number of annual ambulatory visits per capita but also a unique "one-stop shopping"phenomenon, which refers to a patient' visits to several specialties of the same healthcare facility in one day. The visits to multiple physicians would increase the potential risk of polypharmacy. The aim of this study was to analyze the frequency and patterns of one-stop visits in Taiwan. METHODOLOGY/PRINCIPAL FINDINGS: The claims datasets of 1 million nationally representative people within Taiwan's National Health Insurance in 2005 were used to calculate the number of patients with one-stop visits. The frequent itemsets mining was applied to compute the combination patterns of specialties in the one-stop visits. Among the total 13,682,469 ambulatory care visits in 2005, one-stop visits occurred 144,132 times and involved 296,822 visits (2.2% of all visits by 66,294 (6.6% persons. People tended to have this behavior with age and the percentage reached 27.5% (5,662 in 20,579 in the age group ≥80 years. In general, women were more likely to have one-stop visits than men (7.2% vs. 6.0%. Internal medicine plus ophthalmology was the most frequent combination with a visited frequency of 3,552 times (2.5%, followed by cardiology plus neurology with 3,183 times (2.2%. The most frequent three-specialty combination, cardiology plus neurology and gastroenterology, occurred only 111 times. CONCLUSIONS/SIGNIFICANCE: Without the novel computational technique, it would be hardly possible to analyze the extremely diverse combination patterns of specialties in one-stop visits. The results of the study could provide useful information either for the hospital manager to set up integrated services or for the policymaker to rebuild the health care system.

Application for trackless mining technique in Benxi uranium mine

International Nuclear Information System (INIS)

Chen Bingguo

1998-01-01

The author narrates the circumstances achieving constructional target in Benxi Uranium Mine under relying on advance of science and technology and adopting small trackless mining equipment, presents the application of trackless mining equipment at mining small mine and complex mineral deposit and discusses the unique superiority of trackless mining technique in development work, mining preparation work and backstoping

Mining engineer requirements in a German coal mine

Energy Technology Data Exchange (ETDEWEB)

Rauhut, F J

1985-10-01

Basic developments in German coal mines, new definitions of working areas of mining engineers, and groups of requirements in education are discussed. These groups include: requirements of hard-coal mining at great depth and in extended collieries; application of process technology and information systems in semi-automated mines; thinking in processes and systems; organizational changes; future requirements of mining engineers; responsibility of the mining engineer for employees and society.

Entomopathogenic nematode food webs in an ancient, mining pollution gradient in Spain.

Science.gov (United States)

Campos-Herrera, Raquel; Rodríguez Martín, José Antonio; Escuer, Miguel; García-González, María Teresa; Duncan, Larry W; Gutiérrez, Carmen

2016-12-01

Mining activities pollute the environment with by-products that cause unpredictable impacts in surrounding areas. Cartagena-La Unión mine (Southeastern-Spain) was active for >2500years. Despite its closure in 1991, high concentrations of metals and waste residues remain in this area. A previous study using nematodes suggested that high lead content diminished soil biodiversity. However, the effects of mine pollution on specific ecosystem services remain unknown. Entomopathogenic nematodes (EPN) play a major role in the biocontrol of insect pests. Because EPNs are widespread throughout the world, we speculated that EPNs would be present in the mined areas, but at increased incidence with distance from the pollution focus. We predicted that the natural enemies of nematodes would follow a similar spatial pattern. We used qPCR techniques to measure abundance of five EPN species, five nematophagous fungi species, two bacterial ectoparasites of EPNs and one group of free-living nematodes that compete for the insect-cadaver. The study comprised 193 soil samples taken from mining sites, natural areas and agricultural fields. The highest concentrations of iron and zinc were detected in the mined area as was previously described for lead, cadmium and nickel. Molecular tools detected very low numbers of EPNs in samples found to be negative by insect-baiting, demonstrating the importance of the approach. EPNs were detected at low numbers in 13% of the localities, without relationship to heavy-metal concentrations. Only Acrobeloides-group nematodes were inversely related to the pollution gradient. Factors associated with agricultural areas explained 98.35% of the biotic variability, including EPN association with agricultural areas. Our study suggests that EPNs have adapted to polluted habitats that might support arthropod hosts. By contrast, the relationship between abundance of Acrobeloides-group and heavy-metal levels, revealed these taxa as especially well suited bio

Data mining of air traffic control operational errors

Science.gov (United States)

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...

Exploiting Sequential Patterns Found in Users' Solutions and Virtual Tutor Behavior to Improve Assistance in ITS

Science.gov (United States)

Fournier-Viger, Philippe; Faghihi, Usef; Nkambou, Roger; Nguifo, Engelbert Mephu

2010-01-01

We propose to mine temporal patterns in Intelligent Tutoring Systems (ITSs) to uncover useful knowledge that can enhance their ability to provide assistance. To discover patterns, we suggest using a custom, sequential pattern-mining algorithm. Two ways of applying the algorithm to enhance an ITS's capabilities are addressed. The first is to…

Hyperspectral analysis for qualitative and quantitative features related to acid mine drainage at a remediated open-pit mine

Science.gov (United States)

Davies, G.; Calvin, W. M.

2015-12-01

The exposure of pyrite to oxygen and water in mine waste environments is known to generate acidity and the accumulation of secondary iron minerals. Sulfates and secondary iron minerals associated with acid mine drainage (AMD) exhibit diverse spectral properties in the ultraviolet, visible and near-infrared regions of the electromagnetic spectrum. The use of hyperspectral imagery for identification of AMD mineralogy and contamination has been well studied. Fewer studies have examined the impacts of hydrologic variations on mapping AMD or the unique spectral signatures of mine waters. Open-pit mine lakes are an additional environmental hazard which have not been widely studied using imaging spectroscopy. A better understanding of AMD variation related to climate fluctuations and the spectral signatures of contaminated surface waters will aid future assessments of environmental contamination. This study examined the ability of multi-season airborne hyperspectral data to identify the geochemical evolution of substances and contaminant patterns at the Leviathan Mine Superfund site. The mine is located 24 miles southeast of Lake Tahoe and contains remnant tailings piles and several AMD collection ponds. The objectives were to 1) distinguish temporal changes in mineralogy at a the remediated open-pit sulfur mine, 2) identify the absorption features of mine affected waters, and 3) quantitatively link water spectra to known dissolved iron concentrations. Images from NASA's AVIRIS instrument were collected in the spring, summer, and fall seasons for two consecutive years at Leviathan (HyspIRI campaign). Images had a spatial resolution of 15 meters at nadir. Ground-based surveys using the ASD FieldSpecPro spectrometer and laboratory spectral and chemical analysis complemented the remote sensing data. Temporal changes in surface mineralogy were difficult to distinguish. However, seasonal changes in pond water quality were identified. Dissolved ferric iron and chlorophyll

artery disease guidelines with extracted knowledge from data mining

Directory of Open Access Journals (Sweden)

Peyman Rezaei-Hachesu

2017-06-01

Conclusion: Guidelines confirm the achieved results from data mining (DM techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients.

Rock mechanics research in the Coeur d'Alene mining district

Energy Technology Data Exchange (ETDEWEB)

Corp, E. L.

1980-05-15

Over the past 20 years, the Bureau of Mines and mining companies of the Coeur d'Alene district have conducted cooperative research on problems of ground control. For the past six years emphasis has been placed on research to improve deep shaft design and to control rock bursts during cut-and-fill stoping. Finite-element modeling and construction of small-scale circular and rectangular test shafts have shown that circular openings are stable only when stresses are hydrostatic or weakly biaxial. Under a strongly-biaxial horizontal stress field, a rectangular shaft has a greater depth capability if its long axis can be oriented parallel to the major stress and normal to the bedding and joint system. Steel sets appear preferable to wood sets or concrete lining. Based on underground tests at Hecla's Star mine, destressing or preconditioning of the vein rock prior to mining was shown to be an effective means of controlling rock bursts. Drilling and shooting a radial pattern of longholes before stope mining starts has preconditioned or softened the vein material to the extent that seismic energy release during mining is reduced and no bursting occurred. Increased burst and seismic activity while mining about the preconditioned zone points out the need to precondition an entire stope block before mining.

GRAMI: Generalized Frequent Subgraph Mining in Large Graphs

KAUST Repository

El Saeedy, Mohammed El Sayed

2011-07-24

Mining frequent subgraphs is an important operation on graphs. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs or protein-protein interaction in bioinformatics, are modeled as a single large graph. Interesting interactions in such applications may be transitive (e.g., friend of a friend). Existing methods, however, search for frequent isomorphic (i.e., exact match) subgraphs and cannot discover many useful patterns. In this paper we propose GRAMI, a framework that generalizes frequent subgraph mining in a large single graph. GRAMI discovers frequent patterns. A pattern is a graph where edges are generalized to distance-constrained paths. Depending on the definition of the distance function, many instantiations of the framework are possible. Both directed and undirected graphs, as well as multiple labels per vertex, are supported. We developed an efficient implementation of the framework that models the frequency resolution phase as a constraint satisfaction problem, in order to avoid the costly enumeration of all instances of each pattern in the graph. We also implemented CGRAMI, a version that supports structural and semantic constraints; and AGRAMI, an approximate version that supports very large graphs. Our experiments on real data demonstrate that our framework is up to 3 orders of magnitude faster and discovers more interesting patterns than existing approaches.

The design and implementation of web mining in web sites security

Science.gov (United States)

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.

Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

Science.gov (United States)

Abdous, M'hammed; He, Wu

2011-01-01

Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

Mining Together : Large-Scale Mining Meets Artisanal Mining, A Guide for Action

OpenAIRE

World Bank

2009-01-01

The present guide mining together-when large-scale mining meets artisanal mining is an important step to better understanding the conflict dynamics and underlying issues between large-scale and small-scale mining. This guide for action not only points to some of the challenges that both parties need to deal with in order to build a more constructive relationship, but most importantly it sh...

Motif trie: An efficient text index for pattern discovery with don't cares

DEFF Research Database (Denmark)

Grossi, Roberto; Menconi, Giulia; Pisanti, Nadia

2017-01-01

We introduce the motif trie data structure, which has applications in pattern matching and discovery in genomic analysis, plagiarism detection, data mining, intrusion detection, spam fighting and time series analysis, to name a few. Here the extraction of recurring patterns in sequential and text......We introduce the motif trie data structure, which has applications in pattern matching and discovery in genomic analysis, plagiarism detection, data mining, intrusion detection, spam fighting and time series analysis, to name a few. Here the extraction of recurring patterns in sequential...

PLANT DIVERSITY OF THE ZHELTOKAMENSKIY OPEN CAST MINES

Directory of Open Access Journals (Sweden)

Yarova T.A.

2012-11-01

Full Text Available Floristic structure data of soil algae, lichens, mosses, and vascular plants are given. Rare plant species which are protected at the Ukrainian, European, and International levels were revealed. The species list of trees and bushes was conducted. The soil analysis was carried out by such parameters: pH-value, the maintenance of hygroscopic water, the maintenance of mineral substances. Vegetation biomass on the open cast mines sample areas is defined. Ecological analysis of the biotopes of registered algae species was performed. The ecological analysis of the vascular plants species biotopes was carried out.The estimation of the perspective vegetation pattern was suggested for natural restoration of the open cast mines. The plant species are selected according to the ecological and morphological characteristics for plant rehabilitation and planting of open cast mines.

High Performance Data mining by Genetic Neural Network

Directory of Open Access Journals (Sweden)

Dadmehr Rahbari

2013-10-01

Full Text Available Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well incomparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.

Text mining of web-based medical content

CERN Document Server

Neustein, Amy

2014-01-01

Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients. This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information.

French uranium mining sites remediation

International Nuclear Information System (INIS)

Roche, M.

2002-01-01

Following a presentation of the COGEMA's general policy for the remediation of uranium mining sites and the regulatory requirements, the current phases of site remediation operations are described. Specific operations for underground mines, open pits, milling facilities and confining the milled residues to meet long term public health concerns are detailed and discussed in relation to the communication strategies to show and explain the actions of COGEMA. A brief review of the current remediation situation at the various French facilities is finally presented. (author)

Advanced Data Mining of Leukemia Cells Micro-Arrays

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Richard S. Segall

2009-12-01

Full Text Available This paper provides continuation and extensions of previous research by Segall and Pierce (2009a that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM. As Segall and Pierce (2009a and Segall and Pierce (2009b the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a, micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented.

Mining dynamic noteworthy functions in software execution sequences.

Science.gov (United States)

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

2017-01-01

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

Superficial drainage studies in open-pit mines

International Nuclear Information System (INIS)

Teixeira Junior, P.B.; Leite, C.B.B.

1984-01-01

Drainage studies concerning large open-pit mining projects can be of vital importance throughout the mining activity itself as they may assist in avoiding activity interruptions due to drainage problems, therefore representing substantial savings. These studies should, in fact, be carried out from the initial activity stages and shall be considered in operational, project and planning decisions in order to optimize results and reduce costs. This specific study presents a drainage study systematization proposal, enphasazing economic decision criteria. The authors comment on studies of this nature developed at the Caldas uranium mine - NUCLEBRAS. (D.J.M.) [pt

Brick: Mining Pedagogically Interesting Sequential Patterns

NARCIS (Netherlands)

Anjewierden, Anjo; Gijlers, Hannie; Saab, Nadira; de Hoog, Robert; Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John

2011-01-01

One of the goals of the SCY project (www.scy-net.eu) is to make (inquiry) learning environments adaptive. The idea is to develop “pedagogical agents” that monitor learner behaviour through the actions they perform and identify patterns that point to systematic behaviour, or lack thereof. To achieve

Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

Directory of Open Access Journals (Sweden)

Muluken Alemu Yehuala

2015-04-01

Full Text Available Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The classification rule generation process is based on the decision tree and Bayes as a classification technique and the generated rules were studied and evaluated. Data collected from MSEXCEL files and it has been preprocessed for model building. Models were built and tested by using a sample dataset of 11873 regular undergraduate students. Analysis is done by using WEKA 3.7 application software. The research results offer a helpful and constructive recommendations to the academic planners in universities of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve students academic performance. Students able to decide about their field of study before they are enrolled in specific field of study based on the previous experience taken from the research-findings. The research findings indicated that EHEECE Ethiopian Higher Education Entrance Certificate Examination result Sex Number of students in a class number of courses given in a semester and field of study are the major factors affecting the student performances. So on the bases of the research findings the level of student success will increase and it is possible to prevent educational institutions from serious financial strains.

Neighbor Detection Induces Organ-Specific Transcriptomes, Revealing Patterns Underlying Hypocotyl-Specific Growth.

Science.gov (United States)

Kohnen, Markus V; Schmid-Siegert, Emanuel; Trevisan, Martine; Petrolati, Laure Allenbach; Sénéchal, Fabien; Müller-Moulé, Patricia; Maloof, Julin; Xenarios, Ioannis; Fankhauser, Christian

2016-12-01

In response to neighbor proximity, plants increase the growth of specific organs (e.g., hypocotyls) to enhance access to sunlight. Shade enhances the activity of Phytochrome Interacting Factors (PIFs) by releasing these bHLH transcription factors from phytochrome B-mediated inhibition. PIFs promote elongation by inducing auxin production in cotyledons. In order to elucidate spatiotemporal aspects of the neighbor proximity response, we separately analyzed gene expression patterns in the major light-sensing organ (cotyledons) and in rapidly elongating hypocotyls of Arabidopsis thaliana PIFs initiate transcriptional reprogramming in both organs within 15 min, comprising regulated expression of several early auxin response genes. This suggests that hypocotyl growth is elicited by both local and distal auxin signals. We show that cotyledon-derived auxin is both necessary and sufficient to initiate hypocotyl growth, but we also provide evidence for the functional importance of the local PIF-induced response. With time, the transcriptional response diverges increasingly between organs. We identify genes whose differential expression may underlie organ-specific elongation. Finally, we uncover a growth promotion gene expression signature shared between different developmentally regulated growth processes and responses to the environment in different organs. © 2016 American Society of Plant Biologists. All rights reserved.

Sustainable production program in the Mexican mining industry: occupational risks

Directory of Open Access Journals (Sweden)

Andrea Zavala Reyna

2015-07-01

Full Text Available Speaking of mining and sustainability sounds contradictory, as the environmental impact generated by resource extraction is well known. However, there are mining companies that are working to be safe and environmentally friendly. An example of this is presented in this study aimed at identifying occupational risks generated by the activities of a small-scale gold and silver mine located in northwestern Mexico. The methodology followed was a Sustainable Production Program (SPP based on a continuous cycle of five steps in which the tools of cleaner production and pollution prevention are adapted. As a result of this project, it was possible to implement SPP activities: training for workers, use of personal protective equipment and adequate handling of chemicals. As a conclusion, it was verified that SPP application helped this mining company move towards sustainable patterns of production.

Petrological mineralogical and geochemical characterization of the granitoids and fracture fillings developed in Ratones Mines (Spain)

International Nuclear Information System (INIS)

Buil Gutierrez, B.

2002-01-01

The petrological, mineralogical and geochemical characterisation of the granitoids and fracture fillings developed in the Ratones Mine (Caceres, Spain) has been done in order to understand rock-water interaction processes which control water geochemical parameters. Special interest has been devoted to the analysis and interpretation of REE patterns in the solid phase (granitoids and fracture fillings) because they constitute geochemical tracers in water-rock interaction process. Moreover, REE are considered as actinide analogues. In order to characterise the solid phase (granitoids and fracture fillings) several investigation scales (system, outcrop, whole rock, mineral and geochemical components) have been considered and different types of samples have been analysed. These factors control the methodological approach used in this investigation. The analytical methods we have used in this investigation are microscope, qualitative and semi-quantitative methods (XRD, SEM,EDAX) and quantitative methods (ICP-MS, XRF, EM, LAM-IC-MS). The bulk of the granitoids located around the Ratones Mine Belongs to the alkaline feldspar granite-sienogranite lihotype and they show a peraluminous and subalkaline pattern. From the mineralogical point of view, they are composed by quartz, K-feldspar (Or>90%), showing sericitation, moscovitization and turmolinization altherations, alkaline plagioclase (An-=-3%), usually altered to sericite, saussirite and less frequently affected by moscovitization processes, Fe-Al biotite, frequently affected by chloritization processes and sometimes replaced by muscovite, and finally muscovite (>2% celadonite and <4% paragonite) both of primary and secondary origin. The differences observed between the different lithotypes are related with the modal proportion of the principal minerals,with the presence or absence of certain accessory minerals ( turmaline, cordierite), with specific textural patterns, grain size and also with the richness in specific

Researchers and mine managers cooperate for better technology

International Nuclear Information System (INIS)

Anon.

1984-01-01

How cooperation between mine managers and research organizations can result in practical technology and better work methods is discussed. Specific cases of how this close cooperation has helped in the U.K., Swedish blasting experiments, linear array scanners for Australian geologic evaluation, coal preparation in Canada, and deep mine problems in South Africa are given

SOMA: A Proposed Framework for Trend Mining in Large UK Diabetic Retinopathy Temporal Databases

Science.gov (United States)

Somaraki, Vassiliki; Harding, Simon; Broadbent, Deborah; Coenen, Frans

In this paper, we present SOMA, a new trend mining framework; and Aretaeus, the associated trend mining algorithm. The proposed framework is able to detect different kinds of trends within longitudinal datasets. The prototype trends are defined mathematically so that they can be mapped onto the temporal patterns. Trends are defined and generated in terms of the frequency of occurrence of pattern changes over time. To evaluate the proposed framework the process was applied to a large collection of medical records, forming part of the diabetic retinopathy screening programme at the Royal Liverpool University Hospital.

Prognostic significance of specific injury patterns in casualties of traffic-related accidents.

Science.gov (United States)

Calosevic, Srdjan; Lovric, Zvonimir

2015-11-01

Fatal triad and ipsilateral dyad are patterns of pedestrian injuries related to significant mortality in traffic-related accidents. The aim of this research was to investigate the correlation between specific injury patterns and fatal outcome in other participants of traffic-related accidents. This was a retrospective study of traffic-related accidents in the broader area of the city of Osijek in a five-year period from 1995 to 1999. Autopsy results from the Institute of Pathology and Forensic Medicine of the Clinical Hospital Centre Osijek were analysed of individuals who died after their accident. The total severity of injuries was measured using the ISS. Logistic regression analysis was used for assessing the correlation between specific injury patterns and an early outcome from the severe injury. There were 213 individuals included in the study: 72 pedestrians and 141 other participants (drivers, assistant drivers, passengers, cyclists and motorcyclists). A total of129 individuals died on the spot and 84 died in the hospital during the first 48h. Femoral and pelvic fracture, fatal triad and both variants of ipsilateral dyad were related to higher ISS values. Ipsilateral fracture of upper and lower extremities (ipsilateral dyad 1) was associated with a 4.59 times higher risk of an immediate fatal outcome in the total sample. In pedestrians, the risk was 5.99 higher, and in other participants, the risk was 4.11 times higher. Specific skeletal injuries and injury patterns are a significant indicator for total injury severity and related poor prognosis for all participants of traffic-related injuries, not only for pedestrians. In this study, the ipsilateral fracture of upper and lower extremity was related to the largest total severity of injuries and the poorest prognosis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Attenuation of mining-derived pollutants in the hyporheic zone. A review

International Nuclear Information System (INIS)

Gandy, C.J.; Jarvis, A.P.; Smith, J.W.N.

2007-01-01

Mine water pollution is a major cause of surface- and groundwater pollution in former mining districts throughout Europe. It is a potential barrier to achieving good status water bodies, which is a requirement of the EU Water Framework Directive. In the UK, a concerted effort has been made over the last decade or so to address the scientific and practical challenges relating to the remediation of mine water pollution. However, most of this work has focused on remediation of point sources of pollution (typically arising from abandoned mines and shafts), while the behaviour of mine water at the groundwater-surface water interface (the 'hyporheic zone') has received far less attention in relevant scientific and engineering literature. The extent of mine water pollution and capacity for its attenuation at the hyporheic zone has not been well quantified while, furthermore, the complex chemical and microbial processes occurring there (specifically with reference to mining-derived pollutants) have not been investigated in any depth. The absence of such data may relate, in a large part, to the difficulty in physically measuring volumes and concentrations associated with these river inputs/exports. A far greater body of literature addresses biogeochemical processes at the hyporheic zone (especially relating to manganese), albeit many such articles relate to aqueous metal dynamics in general, rather than mine water specifically. This paper presents a review of the natural attenuation processes that may limit the movement and availability of mining-derived pollutants at the groundwater-surface water (GW-SW) interface, and specifically within the hyporheic zone. A substantial part focuses on precipitation and adsorption processes at the hyporheic zone, as well as discussing the role of microbial processes in governing metal ion mobility. (author)

Attenuation of mining-derived pollutants in the hyporheic zone. A review

Energy Technology Data Exchange (ETDEWEB)

Gandy, C.J.; Jarvis, A.P. [Hydrogeochemical Engineering Research and Outreach (HERO), Institute for Research on Environment and Sustainability, Newcastle University, Newcastle upon Tyne, NE1 7RU (United Kingdom); Smith, J.W.N. [Environment Agency, Science Group, Solihull, West Midlands, B92 7HX (United Kingdom); Catchment Science Centre, Sheffield University, Kroto Research Institute, Sheffield, S3 7HQ (United Kingdom)

2007-02-15

Mine water pollution is a major cause of surface- and groundwater pollution in former mining districts throughout Europe. It is a potential barrier to achieving good status water bodies, which is a requirement of the EU Water Framework Directive. In the UK, a concerted effort has been made over the last decade or so to address the scientific and practical challenges relating to the remediation of mine water pollution. However, most of this work has focused on remediation of point sources of pollution (typically arising from abandoned mines and shafts), while the behaviour of mine water at the groundwater-surface water interface (the 'hyporheic zone') has received far less attention in relevant scientific and engineering literature. The extent of mine water pollution and capacity for its attenuation at the hyporheic zone has not been well quantified while, furthermore, the complex chemical and microbial processes occurring there (specifically with reference to mining-derived pollutants) have not been investigated in any depth. The absence of such data may relate, in a large part, to the difficulty in physically measuring volumes and concentrations associated with these river inputs/exports. A far greater body of literature addresses biogeochemical processes at the hyporheic zone (especially relating to manganese), albeit many such articles relate to aqueous metal dynamics in general, rather than mine water specifically. This paper presents a review of the natural attenuation processes that may limit the movement and availability of mining-derived pollutants at the groundwater-surface water (GW-SW) interface, and specifically within the hyporheic zone. A substantial part focuses on precipitation and adsorption processes at the hyporheic zone, as well as discussing the role of microbial processes in governing metal ion mobility. (author)

Automated detection of follow-up appointments using text mining of discharge records.

Science.gov (United States)

Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

2010-06-01

To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

Comparing sets of patterns with the Jaccard index

Directory of Open Access Journals (Sweden)

Sam Fletcher

2018-03-01

Full Text Available The ability to extract knowledge from data has been the driving force of Data Mining since its inception, and of statistical modeling long before even that. Actionable knowledge often takes the form of patterns, where a set of antecedents can be used to infer a consequent. In this paper we offer a solution to the problem of comparing different sets of patterns. Our solution allows comparisons between sets of patterns that were derived from different techniques (such as different classification algorithms, or made from different samples of data (such as temporal data or data perturbed for privacy reasons. We propose using the Jaccard index to measure the similarity between sets of patterns by converting each pattern into a single element within the set. Our measure focuses on providing conceptual simplicity, computational simplicity, interpretability, and wide applicability. The results of this measure are compared to prediction accuracy in the context of a real-world data mining scenario.

Whole field tendencies in transcranial magnetic stimulation: A systematic review with data and text mining.

Science.gov (United States)

Dias, Alvaro Machado; Mansur, Carlos Gustavo; Myczkowski, Martin; Marcolin, Marco

2011-06-01

Transcranial magnetic stimulation (TMS) has played an important role in the fields of psychiatry, neurology and neuroscience, since its emergence in the mid-1980s; and several high quality reviews have been produced since then. Most high quality reviews serve as powerful tools in the evaluation of predefined tendencies, but they cannot actually uncover new trends within the literature. However, special statistical procedures to 'mine' the literature have been developed which aid in achieving such a goal. This paper aims to uncover patterns within the literature on TMS as a whole, as well as specific trends in the recent literature on TMS for the treatment of depression. Data mining and text mining. Currently there are 7299 publications, which can be clustered in four essential themes. Considering the frequency of the core psychiatric concepts within the indexed literature, the main results are: depression is present in 13.5% of the publications; Parkinson's disease in 2.94%; schizophrenia in 2.76%; bipolar disorder in 0.158%; and anxiety disorder in 0.142% of all the publications indexed in PubMed. Several other perspectives are discussed in the article. Copyright © 2011 Elsevier B.V. All rights reserved.

Data mining application in customer relationship management for hospital inpatients.

Science.gov (United States)

Lee, Eun Whan

2012-09-01

This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Exploring the techno-economic feasibility of mine rock waste utilisation in road works: The case of a mining deposit in Ghana.

Science.gov (United States)

Agyeman, Stephen; Ampadu, Samuel I K

2016-02-01

Mine rock waste, which is the rock material removed in order to access and mine ore, is free from gold processing chemical contaminants but presents a significant environmental challenge owing to the large volumes involved. One way of mitigating the environmental and safety challenges posed by the large volume of mine rock waste stockpiled in mining communities is to find uses of this material as a substitute for rock aggregates in construction. This article reports on a study conducted to evaluate the engineering properties of such a mine deposit to determine its suitability for use as road pavement material. Samples of mine rock waste, derived from the granitic and granodioritic intrusive units overlying the gold-bearing metavolcanic rock and volcano-clastic sediments of a gold mining area in Ghana, were obtained from three mine rock waste disposal facilities and subjected to a battery of laboratory tests to determine their physical, mechanical, geotechnical, geometrical and durability properties. The overall conclusion was that the mine rock waste met all the requirements of the Ghana Ministry of Transportation specification for use as aggregates for crushed rock subbase, base and surface dressing chippings for road pavements. The recommendation is to process it into the required sizes for the various applications. © The Author(s) 2015.

Atlas Career Path Guidebook: Patterns and Common Practices in Systems Engineers’ Development

Science.gov (United States)

2018-01-16

text mining principles to be used by systems...statistical and text mining principles facilitate the identification of patterns. Figure 1. Helix methodology for career path analysis In order to...illustrate how text mining algorithms might be used to identify similarities in position titles for systems engineers. In broad

Metal speciation in agricultural soils adjacent to the Irankuh Pb-Zn mining area, central Iran

Science.gov (United States)

Mokhtari, Ahmad Reza; Roshani Rodsari, Parisa; Cohen, David R.; Emami, Adel; Dehghanzadeh Bafghi, Ali Akbar; Khodaian Ghegeni, Ziba

2015-01-01

Mining activities are a significant potential source of metal contamination of soils in surrounding areas, with particular concern for metals dispersed into agricultural area in forms that are bioavailable and which may affect human health. Soils in agricultural land adjacent to Pb-Zn mining operations in the southern part of the Irankuh Mountains contain elevated concentrations for a range of metals associated with the mineralization (including Pb, Zn and As). Total and partial geochemical extraction data from a suite of 137 soil samples is used to establish mineralogical controls on ore-related trace elements and help differentiate spatial patterns that can be related to the effects of mining on the agricultural land soils from general geological and environmental controls. Whereas the patterns for Pb, Zn and As are spatially related to the mining operations they display little correlation with the distribution of secondary Fe + Mn oxyhydroxides or carbonates, suggesting dispersion as dust and in forms with limited bioavailability.

Identifying Learning Patterns of Children at Risk for Specific Reading Disability

Science.gov (United States)

Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

2016-01-01

Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

A multi- sensor system for land mine detection

International Nuclear Information System (INIS)

Megahid, R.M.

2005-01-01

In this review article, description and discussion are given to the methods and techniques which can be used to remove the millions of land mines that are buried in the ground of more than 60 countries worldwide. These buried land mines cause very serious social and economical problems especially in countries such as Egypt with nearly 23 millions land mine contaminating very vast areas in the northern cost of the western desert, north and west of Sinai, Suez Gulf and western cost of the Red Sea. This will challenge scientists in different related disciplines to find a specific, rapid and cost effective detection techniques to remove the millions of land mines from the ground of their countries. It is worth mentioning that the real problem in demining is not defusing the mines but locating them in the ground

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

Science.gov (United States)

2010-07-01

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

Prediction of thermodynamic properties of refrigerants using data mining

International Nuclear Information System (INIS)

Kuecueksille, Ecir Ugur; Selbas, Resat; Sencan, Arzu

2011-01-01

The analysis of vapor compression refrigeration systems requires the availability of simple and efficient mathematical formulations for the determination of thermodynamic properties of refrigerants. The aim of this study is to determine thermodynamic properties as enthalpy, entropy and specific volume of alternative refrigerants using data mining method. Alternative refrigerants used in the study are R134a, R404a, R407c and R410a. The results obtained from data mining have been compared to actual data from the literature. The study shows that the data mining methodology is successfully applicable to determine enthalpy, entropy and specific volume values for any temperature and pressure of refrigerants. Therefore, computation time reduces and simulation of vapor compression refrigeration systems is fairly facilitated.

A novel Neuro-fuzzy classification technique for data mining

Directory of Open Access Journals (Sweden)

Soumadip Ghosh

2014-11-01

Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

Contract Mining versus Owner Mining – The Way Forward | Suglo ...

African Journals Online (AJOL)

Ghana Mining Journal ... By contracting out one or more of their mining operations, the mining companies can concentrate on their core businesses. This paper reviews ... The general trends in the mining industry show that contract mining will be the way forward for most mines under various circumstances in the future.

Multifactor dimensionality reduction analysis identifies specific nucleotide patterns promoting genetic polymorphisms

Directory of Open Access Journals (Sweden)

Arehart Eric

2009-03-01

Full Text Available Abstract Background The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation. Results We modeled the relationship between DNA sequence and observed polymorphisms using the novel multifactor dimensionality reduction (MDR approach. MDR was originally developed to detect synergistic interactions between multiple SNPs that are predictive of disease susceptibility. We initially assembled data from the Broad Institute as a pilot test for the hypothesis that flanking region patterns associate with mutagenesis (n = 2194. We then confirmed and expanded our inquiry with human SNPs within coding regions and their flanking sequences collected from the National Center for Biotechnology Information (NCBI database (n = 29967 and a control set of sequences (coding region not associated with SNP sites randomly selected from the NCBI database (n = 29967. We discovered seven flanking region pattern associations in the Broad dataset which reached a minimum significance level of p ≤ 0.05. Significant models (p Conclusion The present study represents the first use of this computational methodology for modeling nonlinear patterns in molecular genetics. MDR was able to identify distinct nucleotide patterning around sites of mutations dependent upon the observed nucleotide change. We discovered one flanking region set that included five nucleotides clustered around a specific type of SNP site. Based on the strongly associated patterns identified in

Proposta de reflexão teórica e análise de padrões conceituais com data mining Theoretical discussion and conceptual pattern analysis with data mining

Directory of Open Access Journals (Sweden)

Álvaro Machado Dias

2011-08-01

Full Text Available Mais do que uma teoria ou modelo, a Teoria da Mente se refere a um campo de estudos voltado à habilidade de se prospectar intenções alheias. Visando contribuir para a discussão teórica e a interpretação da literatura no tema, o presente estudo apresenta: 1. Um mapa conceitual do campo, baseado em data mining/text mining; 2. Uma abordagem conceitual inovadora e mais eficiente aos estudos de ToM informacional; 3. Uma discussão crítica da extensão e limites dos principais modelos, baseada na análise da literatura com data/text mining e nas perspectivas teóricas anteriormente alinhavadas.More than just a theory or a model, Theory of Mind represents a field of studies concerned with the ability to prospect someone else's intentions. Aiming to contribute to theoretical discussion and the interpretation of the literature on the matter, this study presents: 1. A conceptual map of the field, based on data mining/text mining techniques; 2. A new and advanced conceptual framework focused on informational ToM studies; 3. A critical discussion of the extensions and limits of the most prominent models, based on the outputs of the data/text mining analysis and on the theoretical perspectives that were previously raised.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

Science.gov (United States)

2013-01-01

Background Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the

Mine waste management legislation. Gold mining areas in Romania

Science.gov (United States)

Maftei, Raluca-Mihaela; Filipciuc, Constantina; Tudor, Elena

2014-05-01

Problems in the post-mining regions of Eastern Europe range from degraded land and landscapes, huge insecure dumps, surface cracks, soil pollution, lowering groundwater table, deforestation, and damaged cultural potentials to socio economic problems like unemployment or population decline. There is no common prescription for tackling the development of post-mining regions after mine closure nor is there a common definition of good practices or policy in this field. Key words : waste management, legislation, EU Directive, post mining Rosia Montana is a common oh 16 villages; one of them is also called Rosia Montana, a traditional mining Community, located in the Apuseni Mountains in the North-Western Romania. Beneath part of the village area lays one of the largest gold and silver deposits in Europe. In the Rosia Montana area mining had begun ever since the height of the Roman Empire. While the modern approach to mining demands careful remediation of environmental impacts, historically disused mines in this region have been abandoned, leaving widespread environmental damage. General legislative framework Strict regulations and procedures govern modern mining activity, including mitigation of all environmental impacts. Precious metals exploitation is put under GO no. 190/2000 re-published in 2004. The institutional framework was established and organized based on specific regulations, being represented by the following bodies: • The Ministry of Economy and Commerce (MEC), a public institution which develops the Government policy in the mining area, also provides the management of the public property in the mineral resources area; • The National Agency for the development and implementation of the mining Regions Reconstruction Programs (NAD), responsible with promotion of social mitigation measures and actions; • The Office for Industry Privatization, within the Education Ministry, responsible with privatization of companies under the CEM; • The National

The state of hydraulic mining in China and suggestions for future development

Energy Technology Data Exchange (ETDEWEB)

Anon, A.

1996-11-01

This paper discusses the state of development of hydraulic mining technology in China. The paper conceives that the seam conditions most suitable for hydraulic mining are: geologic faults with many cuts, inclined or gradually inclined thick and very thick seams with stable or moderately stable roofs, where longwall mining cannot be employed or pushed far; inclined or steeply inclined strata of inclination angle above 30{degree} with moderately stable roof, where the roof is stable or moderately stable, the inclination angle is greater than 7{degree}, with irregular production pattern and great variation in the seam thickness, or medium to thick coal seam of relatively more geologic damages and poor stability. Some suggestions are proposed for the future development of hydraulic mining.

Autonomic nervous system response patterns specificity to basic emotions.

Science.gov (United States)

Collet, C; Vernet-Maury, E; Delhomme, G; Dittmar, A

1997-01-12

The aim of this study was to test the assumption that the autonomic nervous system responses to emotional stimuli are specific. A series of six slides was randomly presented to the subjects while six autonomic nervous system (ANS) parameters were recorded: skin conductance, skin potential, skin resistance, skin blood flow, skin temperature and instantaneous respiratory frequency. Each slide induced a basic emotion: happiness, surprise, anger, fear, sadness and disgust. Results have been first considered with reference to electrodermal responses (EDR) and secondly through thermo-vascular and respiratory variations. Classical as well as original indices were used to quantify autonomic responses. The six basic emotions were distinguished by Friedman variance analysis. Thus, ANS values corresponding to each emotion were compared two-by-two. EDR distinguished 13 emotion-pairs out of 15. 10 emotion-pairs were separated by skin resistance as well as skin conductance ohmic perturbation duration indices whereas conductance amplitude was only capable of distinguishing 7 emotion-pairs. Skin potential responses distinguished surprise and fear from sadness, and fear from disgust, according to their elementary pattern analysis in form and sign. Two-by-two comparisons of skin temperature, skin blood flow (estimated by the new non-oscillary duration index) and instantaneous respiratory frequency, enabled the distinction of 14 emotion-pairs out of 15. 9 emotion-pairs were distinguished by the non-oscillatory duration index values. Skin temperature was demonstrated to be different i.e. positive versus negative in response to anger and fear. The instantaneous respiratory frequency perturbation duration index was the only one capable of separating sadness from disgust. From the six ANS parameters study, different autonomic patterns were identified, each characterizing one of the six basic emotion used as inducing signals. No index alone, nor group of parameters (EDR and thermovascular

Graduating the age-specific fertility pattern using Support Vector Machines

Directory of Open Access Journals (Sweden)

Anastasia Kostaki

2009-06-01

Full Text Available A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

Environmental consequences of the Retsof Salt Mine roof collapse

Science.gov (United States)

Yager, Richard M.

2013-01-01

In 1994, the largest salt mine in North America, which had been in operation for more than 100 years, catastrophically flooded when the mine ceiling collapsed. In addition to causing the loss of the mine and the mineral resources it provided, this event formed sinkholes, caused widespread subsidence to land, caused structures to crack and subside, and changed stream flow and erosion patterns. Subsequent flooding of the mine drained overlying aquifers, changed the groundwater salinity distribution (rendering domestic wells unusable), and allowed locally present natural gas to enter dwellings through water wells. Investigations including exploratory drilling, hydrologic and water-quality monitoring, geologic and geophysical studies, and numerical simulation of groundwater flow, salinity, and subsidence have been effective tools in understanding the environmental consequences of the mine collapse and informing decisions about management of those consequences for the future. Salt mines are generally dry, but are susceptible to leaks and can become flooded if groundwater from overlying aquifers or surface water finds a way downward into the mined cavity through hundreds of feet of rock. With its potential to flood the entire mine cavity, groundwater is a constant source of concern for mine operators. The problem is compounded by the viscous nature of salt and the fact that salt mines commonly lie beneath water-bearing aquifers. Salt (for example halite or potash) deforms and “creeps” into the mined openings over time spans that range from years to centuries. This movement of salt can destabilize the overlying rock layers and lead to their eventual sagging and collapse, creating permeable pathways for leakage of water and depressions or openings at land surface, such as sinkholes. Salt is also highly soluble in water; therefore, whenever water begins to flow into a salt mine, the channels through which it flows increase in diameter as the surrounding salt dissolves

Selection of mining method for No.3 uranium ore body in the independent mining area at a uranium mine

International Nuclear Information System (INIS)

Ding Fulong; Ding Dexin; Ye Yongjun

2010-01-01

Mining operation in the existed mining area at a uranium mine is near completion and it is necessary to mine the No.3 uranium ore body in another mining area at the mine. This paper, based on the geological conditions, used analogical method for analyzing the feasible methods and the low cost and high efficiency mining method was suggested for the No.3 ore body in the independent mining area at the uranium mine. (authors)

Simulation of long-term erosion on an abandoned mine site using the SIBERIA landscape evolution model

International Nuclear Information System (INIS)

Hancock, G.; Willgoose, G.; Evans, K.

1999-01-01

The SIBERIA catchment evolution model can simulate the evolution of landforms over many years as a result of runoff and erosion. This study discusses testing of the reliability of the erosion predictions of the model in a field study. Using erosion parameters calibrated from field studies of rainfall and runoff from the waste rock dump batters, the SIBERIA landscape evolution model was calibrated and then used to simulate erosion over 50 years on the abandoned Scinto 6 mine site. Scinto 6 is a former uranium mine located in the Kakadu Region, Northern Territory, Australia. The SIBERIA runs simulated the geomorphic development of the gullies on the man-made batters of the waste rock dump. The waste rock of the mine had been dumped in the characteristic pattern of a flat top and steep sided batters typical of many former and current dumps and there had been significant degradation from both sheet and gully erosion. Traditional erosion models cannot model this type of degradation because their erosion model cannot change the landform, while SIBERIA does change the landform. The gully position, depth volume and morphology on the waste rock dump were compared with that of SIBERIA simulations. The geomorphic development of the waste rock dump indicated that SIBERIA can simulate features that arise from the long-term effect of erosion and also their rate of development on a man-made post-mining landscape over periods of up to 50 years. The detailed results of this specific study will be discussed with specific discussion of the type of data required and the implications of the uncertain erosion physics on the reliability of the predictions

Study on online community user motif using web usage mining

Science.gov (United States)

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.

Electrostatic purification of uranium mine stope atmospheres

International Nuclear Information System (INIS)

Case, G.; Phyper, J.D.; Lowe, L.M.; Chambers, D.B.

1986-01-01

Electrostatic precipitators have been and are currently being used to reduce levels of radioactive aerosols in uranium mine stope atmospheres. Historically, while the electrostatic precipitators have been reported to be successful in reducing levels of radioactive aerosols many practical problems have been encountered with their use in the underground mine environment. Electrical short circuiting appears to have been the major problem with the use of precipitators in humid underground environments. On the basis of literature reviewed for this study it seems that the problems encountered in the past can be overcome. The most likely use of a precipitator in an underground uranium mine is to treat some or all of the air immediately upstream of a work station. The possible locations and uses of a precipitator would vary from work station to work station and from mine to mine. The desirability and cost of using elctrostatic precipitators to purify the air entering a work station are application specific. SENES Consultants therefore is not recommending for or against the use of electrostatic precipitators in underground uranium mines. The information provided in this report can be used however to assist in such determinations. 72 refs

The Northern Manitoba Mining Academy

Science.gov (United States)

Alexandre, Paul

2017-04-01

The Northern Manitoba Mining Academy (NMMA, miningacademy.ca) is a new educational institution located in Flin Flon, Manitoba. It is associated with the University College of the North and is specifically intended to serve the needs of the Northern Manitoban communities with regards to job creation by providing training in a variety of mining, construction, and exploration related areas. NMMA's mission is to provide innovative and responsible solutions for the creation of a knowledgeable, skilled, and sustainable workforce within a vibrant, mineral-rich resource industry. It facilitates strategic training initiatives and research activities in order to strengthen the social, economic, and environmental benefits of a robust mining and resources sector. In terms of education, NMMA offers its own programs, mostly short courses in health and safety, courses organized by the University College of the North (wilderness safety, prospecting, and exploration), and courses organized in association with provincial Industries-Based Safety Programs and Associations (a variety of construction-related trades). However, the programming is not limited to those courses already on the syllabus: the Academy operates on open-doors policy and welcomes people with their unique and diverse needs; it prides itself in its ability to tailor or create specific on-demand courses and deliver them locally in the North. The Northern Manitoba Mining Academy also provides access to its world-class facilities for field-based undergraduate courses, as well as graduate students and researchers doing field work. Full sample preparation facilities are offered to students and scientists in all natural and environmental sciences.

Renewed mining and reclamation: Imapacts on bats and potential mitigation

Energy Technology Data Exchange (ETDEWEB)

Brown, P.E. [Univ. of California, Los Angeles, CA (United States); Berry, R.D. [Brown-Berry Biological Consulting, Bishop, CA (United States)

1997-12-31

Historic mining created new roosting habitat for many bat species. Now the same industry has the potential to adversely impact bats. Contemporary mining operations usually occur in historic districts; consequently the old workings are destroyed by open pit operations. Occasionally, underground techniques are employed, resulting in the enlargement or destruction of the original workings. Even during exploratory operations, historic mine openings can be covered as drill roads are bulldozed, or drills can penetrate and collapse underground workings. Nearby blasting associated with mine construction and operation can disrupt roosting bats. Bats can also be disturbed by the entry of mine personnel to collect ore samples or by recreational mine explorers, since the creation of roads often results in easier access. In addition to roost disturbance, other aspects of renewed mining can have adverse impacts on bat populations, and affect even those bats that do not live in mines. Open cyanide ponds, or other water in which toxic chemicals accumulate, can poison bats and other wildlife. The creation of the pits, roads and processing areas often destroys critical foraging habitat, or change drainage patterns. Finally, at the completion of mining, any historic mines still open may be sealed as part of closure and reclamation activities. The net result can be a loss of bats and bat habitat. Conversely, in some contemporary underground operations, future roosting habitat for bats can be fabricated. An experimental approach to the creation of new roosting habitat is to bury culverts or old tires beneath waste rock. Mining companies can mitigate for impacts to bats by surveying to identify bat-roosting habitat, removing bats prior to renewed mining or closure, protecting non-impacted roost sites with gates and fences, researching to identify habitat requirements and creating new artificial roosts.

Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems

NARCIS (Netherlands)

Romero, C.; Ventura, S.; Zafra, A.; Bra, de P.M.E.

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

Coal graders in Czechoslovakian mines

Energy Technology Data Exchange (ETDEWEB)

Vasek, J.; Klimek, M.

1980-01-01

Problems related to sections of the area of application of graders depending on different mining and geological mining-engineering factors are examined. The principal factors are selected from a general group of influencing factors: dip angle of a formation, separability (shear ability) of coal, characteristics of country rocks, adhesion of coal to rock, tectonic fracturing of a seam, and thickness of a formation. Based on practical and theoretical studies all of the principal factors have been categorized. This allows one to obtain an objective picture of the possibility of using graders under specific conditions by comparing different factors.

Health in uranium mining

Energy Technology Data Exchange (ETDEWEB)

NONE

1964-01-15

Safety in mining radioactive ores, and in milling and treating them, has been a serious preoccupation for some thirty years. Much earlier than this, however, a high incidence of lung cancer had been reported among the miners of the Erzgebirge mountains in the German-Czechoslovak border region (places familiar under the names of Schneeberg and St. Joachims thai). Investigations into deaths from radium poisoning began at these mines in 1937, and the results seemed to indicate a causal connection between the radioactive substances and the development of lung cancer and other diseases. These matters were discussed in Vienna at the symposium on Radiological Health and Safety in Nuclear Materials Mining and Milling, 26-31 August 1963. The symposium was organized by IAEA and co-sponsored by ILO and WHO; some 70 papers were presented. The purpose of the meeting was to collect and compare the very widely scattered research results and practical experience in this field. One conclusion which emerged was that the milling of uranium ore involves no unusual problem. Provided standard controls - as applied to the treatment of other minerals - are strictly enforced, exposure to radiation can be kept to a minimum. In the actual mining of uranium, the problems are only beginning to be clearly defined, but it seems to be well established that exposure of miners to excessive levels of radon will have most serious consequences. In a complicated pattern there are many factors at work, ranging from the physical behaviour of sundry radioactive substances to the personal histories of individual miners. The need for considerably more research was stressed throughout the discussions.

Health in uranium mining

International Nuclear Information System (INIS)

1964-01-01

Safety in mining radioactive ores, and in milling and treating them, has been a serious preoccupation for some thirty years. Much earlier than this, however, a high incidence of lung cancer had been reported among the miners of the Erzgebirge mountains in the German-Czechoslovak border region (places familiar under the names of Schneeberg and St. Joachims thai). Investigations into deaths from radium poisoning began at these mines in 1937, and the results seemed to indicate a causal connection between the radioactive substances and the development of lung cancer and other diseases. These matters were discussed in Vienna at the symposium on Radiological Health and Safety in Nuclear Materials Mining and Milling, 26-31 August 1963. The symposium was organized by IAEA and co-sponsored by ILO and WHO; some 70 papers were presented. The purpose of the meeting was to collect and compare the very widely scattered research results and practical experience in this field. One conclusion which emerged was that the milling of uranium ore involves no unusual problem. Provided standard controls - as applied to the treatment of other minerals - are strictly enforced, exposure to radiation can be kept to a minimum. In the actual mining of uranium, the problems are only beginning to be clearly defined, but it seems to be well established that exposure of miners to excessive levels of radon will have most serious consequences. In a complicated pattern there are many factors at work, ranging from the physical behaviour of sundry radioactive substances to the personal histories of individual miners. The need for considerably more research was stressed throughout the discussions.

Mutielemental concentration and physiological responses of Lavandula pedunculata growing in soils developed on different mine wastes.

Science.gov (United States)

Santos, Erika S; Abreu, Maria Manuela; Saraiva, Jorge A

2016-06-01

This study aimed to: i) evaluate the accumulation and translocation patterns of potentially hazardous elements into the Lavandula pedunculata and their influence in the concentrations of nutrients; and ii) compare some physiological responses associated with oxidative stress (concentration of chlorophylls (Chla, Chlb and total), carotenoids, and total protein) and several components involved in tolerance mechanisms (concentrations of proline and acid-soluble thiols and total/specific activity of catalase (CAT) and superoxide dismutase (SOD)), in plants growing in soils with a multielemental contamination and non-contaminated. Composite samples of soils, developed on mine wastes and/or host rocks, and L. pedunculata (roots and shoots) were collected in São Domingos mine (SE of Portugal) and in a reference area with non-contaminated soils, Corte do Pinto, with the same climatic conditions. São Domingos soils had high total concentrations of several hazardous elements (e.g. As and Pb) but their available fractions were small (mainly Lavandula pedunculata plants are able to survive in soils developed on different mine wastes with multielemental contamination and low fertility showing no symptoms (visible and physiological) of phytotoxicity or deficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Science.gov (United States)

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

Uranium solution mining: comparison of New Mexico with South Texas

International Nuclear Information System (INIS)

Conine, W.D.

1980-01-01

In-situ uranium-leaching or solution-mining operations are currently underway in both south Texas and Wyoming. Mobil Oil Corporation is in the process of applying solution-mining technology, such as that developed at the O'Hern facility in south Texas, to uranium orebodies located near Crownpoint, New Mexico. The O'Hern facility uses an alkaline-leach process to bring the uranium to the surface, where it is removed from solution using ion-exchange resin and chemical precipitation. Line-drive and five-spot well field patterns are used to inject and recover the leach solutions. Although details of ore occurrence in New Mexico differ from those in south Texas, laboratory, engineering-design, and field-hydrology tests indicate that solution mining of uranium should be feasible in New Mexico. To determine the commercial feasibility, Mobil is proceeding with the construction of pilot-plant facilities for a 75-gallon-perminute (gpm) test at an orebody near Crownpoint. The pilot test will use five-spot patterns at various spacings for production of uranium-bearing leachate. Initial surface processing will be the same as that used in south Texas

Intelligent Information Retrieval and Web Mining Architecture Using SOA

Science.gov (United States)

El-Bathy, Naser Ibrahim

2010-01-01

The study of this dissertation provides a solution to a very specific problem instance in the area of data mining, data warehousing, and service-oriented architecture in publishing and newspaper industries. The research question focuses on the integration of data mining and data warehousing. The research problem focuses on the development of…

Treatment of mine-water from decommissioning uranium mines

International Nuclear Information System (INIS)

Fan Quanhui

2002-01-01

Treatment methods for mine-water from decommissioning uranium mines are introduced and classified. The suggestions on optimal treatment methods are presented as a matter of experience with decommissioned Chenzhou Uranium Mine

The Necessity of Public Relations for Sustainable Mining Activities

Science.gov (United States)

Lee, Hyunbock; Ji, Sangwoo

2015-04-01

This paper reports research about the necessity of image making for sustainable mine developments in the Republic of Korea. One of the big risks in mining activities is mining area residents opposing mine developments and operations. Analysis of the media reports on disputes between mining companies and residents can determine causes of opposing mine developments, dispute process, and influences of disputes on processes of mining projects. To do this, civil complaints from 2009 to 2012 and 24 media reports since 2000 on opposing mining activities are analyzed. And, to analyze difficulties of mining companies, the survey is conducted to target to mining companies. 57 representatives of mining companies are participated in the survey. The result of analysis cited that the major reasons of anti-mining activities are environmental degradation and reduced agricultural productivity. And specifically because of water pollution (50%), crop damages (33%), and mining dust pollution (21%), communities of mining area are against mine developments and operations. However, 25% of residents have experience of the damage caused by mining activities and the remaining 75% of residents opposing mining activities simply have anxiety about mining pollution. In the past, construction-oriented, environment-unfriendly mining projects had lasted. And while mine reclamation had been postponed in abandoned mines, mining area residents had suffered from mining pollution. So, mining area residents are highly influenced by the prejudice that mining activities are harmful to mining area communities. Current mining projects in South Korea, unlike the past mining activity, focus on minimizing environmental damage and contributing to mining area communities financially. But, in many case of disputes between mining companies and mining area residents, the both cannot reach an agreements because of the negative prejudice. Moreover, some communities categorically refuse any mining activity. On the

Radioelement migration in natural and mined phosphate terrains. Final report

International Nuclear Information System (INIS)

Osmond, J.K.; Cowart, J.B.; Humphreys, C.L.; Wagner, B.E.

1984-06-01

The phosphatic strata of the Central Florida Phosphate District are enriched in uranium, along with all of its daughters, including thorium-230 and radium-226. Some of the surface and shallow aquifer waters have higher than average concentrations of radium, this being especially pronounced in the down-flow direction of aquifer water movement. However, most of the shallow and deep aquifer waters of the mining district, as well as surface waters, are within normal range in terms of the radioelement content. In the course of mining operations, it appears that the natural pattern is not greatly altered, except in the immediate pit and spoil areas. As a result of mining and processing operation, most of the radioelements accumulate in the waste clays

Development of a Mine Rescue Drilling System (MRDS)

Energy Technology Data Exchange (ETDEWEB)

Raymond, David W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gaither, Katherine N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Polsky, Yarom [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Knudsen, Steven D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Broome, Scott Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Su, Jiann-Cherng [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Blankenship, Douglas A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Costin, Laurence S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-06-01

Sandia National Laboratories (Sandia) has a long history in developing compact, mobile, very high-speed drilling systems and this technology could be applied to increasing the rate at which boreholes are drilled during a mine accident response. The present study reviews current technical approaches, primarily based on technology developed under other programs, analyzes mine rescue specific requirements to develop a conceptual mine rescue drilling approach, and finally, proposes development of a phased mine rescue drilling system (MRDS) that accomplishes (1) development of rapid drilling MRDS equipment; (2) structuring improved web communication through the Mine Safety & Health Administration (MSHA) web site; (3) development of an improved protocol for employment of existing drilling technology in emergencies; (4) deployment of advanced technologies to complement mine rescue drilling operations during emergency events; and (5) preliminary discussion of potential future technology development of specialized MRDS equipment. This phased approach allows for rapid fielding of a basic system for improved rescue drilling, with the ability to improve the system over time at a reasonable cost.

Comprehensive Technical Support for High-Quality Anthracite Production: A Case Study in the Xinqiao Coal Mine, Yongxia Mining Area, China

Directory of Open Access Journals (Sweden)

Wei Zhang

2015-12-01

Full Text Available The effective production of high-quality anthracite has attracted increasing global attention. Based on the coal occurrence in Yongxia Mining Area and mining conditions of a coalface in Xinqiao Coal Mine, we proposed a systematic study on the technical support for the production of high-quality anthracite. Six key steps were explored, including coal falling at the coalface, transport, underground bunker storage, main shaft hoisting, coal preparation on the ground, and railway wagon loading. The study resulted in optimized running parameters for the shearers, and the rotating patterns of the shearer drums was altered (one-way cutting was employed. Mining height and roof supporting intensity were reduced. Besides, loose presplitting millisecond blasting and mechanized mining were applied to upgrade the coal quantity and the lump coal production rate. Additionally, the coalface end transloading, coalface crush, transport systems, underground storage, and main shaft skip unloading processes were improved, and fragmentation-prevention techniques were used in the washing and railway wagon loading processes. As a result, the lump coal production rate was maintained at a high level and fragmentation was significantly reduced. Because of using the parameters and techniques determined in this research, high-quality coal production and increased profits were achieved. The research results could provide theoretical guidance and methodology for other anthracite production bases.

30 CFR 77.1712 - Reopening mines; notification; inspection prior to mining.

Science.gov (United States)

2010-07-01

... to mining. 77.1712 Section 77.1712 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... prior to mining. Prior to reopening any surface coal mine after it has been abandoned or declared... an authorized representative of the Secretary before any mining operations in such mine are...

Data Mining Application in Customer Relationship Management for Hospital Inpatients

Science.gov (United States)

2012-01-01

Objectives This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. Methods A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Results Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. Conclusions To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM. PMID:23115740

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

Science.gov (United States)

He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

2017-03-01

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Development of a diatom-based multimetric index for acid mine drainage impacted depressional wetlands

CSIR Research Space (South Africa)

Riato, L

2018-01-01

Full Text Available Acid mine drainage (AMD) from coal mining in the Mpumalanga Highveld region of South Africa has caused severe chemical and biological degradation of aquatic habitats, specifically depressional wetlands, as mines use these wetlands for storage of AMD...

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

Science.gov (United States)

Chen, Xin

2012-01-01

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

Multi-agents and learning: Implications for Webusage mining

Science.gov (United States)

Lotfy, Hewayda M.S.; Khamis, Soheir M.S.; Aboghazalah, Maie M.

2015-01-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. PMID:26966569

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

Science.gov (United States)

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

EnvMine: A text-mining system for the automatic extraction of contextual information

Directory of Open Access Journals (Sweden)

de Lorenzo Victor

2010-06-01

Full Text Available Abstract Background For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles. So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations from textual sources of any kind. Results EnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings. Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude, thus allowing the calculation of distance between the individual locations. Conclusion EnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical

Reclamation of derelict land: procedure for locating abandoned mine shafts

Energy Technology Data Exchange (ETDEWEB)

1976-01-01

A procedure for locating abandoned shafts has been compiled from the experiences of those familiar with the problem. The procedure begins with a careful study of all the maps, aerial photographs and documents related to the mining activity and may include specialized surveys using geophysical, geochemical and aerial photographic methods when specific conditions are known or are likely to exist at the site. Direct methods, of either excavation, probing or drilling are required in each instance to confirm the location. Most of the methods are illustrated with case histories, and seismic and remote sensing methods are discussed in detail in appendices. Also in appendices, specific sources of information relating to mining are listed. Physical characteristics of mine shafts which are likely to have a bearing on the finding of the shaft are discussed, and an outline of the costs of the methods is presented. A glossary of mining terms used in the document and a detailed bibliography are provided.

TSC mobile mining and extraction technology

Energy Technology Data Exchange (ETDEWEB)

Lavender, W.J. [TSC Company Ltd., Calgary, AB (Canada)

2001-11-01

This Power-Point presentation described an innovative mining and extraction technology developed by Calgary-based TSC Company Ltd. that has provided a major breakthrough in bitumen production from mineable oil sands. The presentation described the process and key mechanical components as demonstrated on oil sands leases. It also described the step change in cost structure and profitability. Oil sands mining provide a hugh resource base with no exploration costs and no decline in production. Despite these advantages, oil sands mining faces the challenge of high capital and operating costs and materials handling. Other challenges include the variability of the ore and environmental impacts. This paper described the fundamentals of the new technology called the Tar Sand Combine (TSC), a continuous mining machine, crusher, cyclone, tailings filter and stacker all in one mobile module. Several viewgraphs were included with the presentation to depict the recovery process as successfully demonstrated at a pilot project. Patent is pending on the process and components. The advantages of the TSC are reduced materials handling, and no tailings ponds are generated since tailings remain where they are mined. The final product is clean bitumen. The specifications of a commercial TSC are: 2000 ton/stream hour mining produce 25,000 bpsd bitumen at 12 per cent ore grade; mined ore bitumen recovery is greater than 95 per cent and the availability factor is 85 per cent. It was concluded that the TSC can maximize oil sands reserves, while providing significant cost savings and environmental benefits. 2 tabs., 24 figs.

Text mining for the biocuration workflow.

Science.gov (United States)

Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

Text mining for the biocuration workflow

Science.gov (United States)

Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

Directory of Open Access Journals (Sweden)

Ahu Sokmenoglu Sohtorik

2017-11-01

to the potentially ‘useful’ and/or ‘valuable’ information patterns and relationships that can be discovered in urban databases by applying data mining algorithms. A knowledge discovery approach to urban analysis through data mining can help us to understand site-specific characteristics of urban environments in a more profound and useful way. On a more specific level, the thesis aims towards ‘knowledge discovery’ in traditional thematic maps published in 2008 by the Istanbul Metropolitan Municipality as a basis of the Master Plan for the Beyoğlu Preservation Area. These thematic maps, which represent urban components, namely buildings, streets, neighbourhoods and their various attributes such as floor space use of the buildings, land price, population density or historical importance, do not really extend our knowledge of Beyoğlu Preservation Area beyond documenting its current state and do not contribute to the interventions presented in the master plan. However it is likely that ‘useful’ and ‘valuable’ information patterns discoverable using data mining algorithms are hidden in them. In accordance with the stated aims, three research questions of the thesis concerns (1 the development of a general process model to adapt the generic process of knowledge discovery using data mining for urban data analysis, (2 the investigation of information patterns and relationships that can be extracted from the traditional thematic maps of the Beyoğlu Preservation Area by further developing and implementing this model and (3 the investigation of how could this ‘relational urban knowledge’ support architects, urban designers or urban planners whilst developing intervention proposals for urban regeneration. A Knowledge Discovery Process Model (KDPM for urban analysis was developed, as an answer to the the first research question. The KDPM for urban analysis is a domain-specific adaptation of the widely accepted process of knowledge discovery in databases

Utilization of coal ash/coal combustion products for mine reclamation

International Nuclear Information System (INIS)

Dolence, R.C.; Giovannitti, E.

1997-01-01

Society's demand for an inexpensive fuel, combined with ignorance of the long term impacts, has left numerous scars on the Pennsylvania landscape. There are over 250,000 acres of abandoned surface mines with dangerous highwalls and water filled pits. About 2,400 miles of streams do not meet water quality standards because of drainage from abandoned mines. There are uncounted households without an adequate water supply due to past mining practices. Mine fires and mine subsidence plague many Pennsylvania communities. The estimated cost to reclaim these past scars is over $15 billion. The beneficial use of coal ash in Pennsylvania for mine reclamation and mine drainage pollution abatement projects increased during the past ten years. The increase is primarily due to procedural and regulatory changes by the Department of Environmental Protection (DEP). Prior to 1986, DEP required a mining permit and a separate waste disposal permit for the use of coal ash in backfilling and reclaiming a surface mine site. In order to eliminate the dual permitting requirements and promote mine reclamation, procedural changes now allow a single permit which authorize both mining and the use of coal ash in reclaiming active and abandoned pits. The actual ash placement, however, must be conducted in accordance with the technical specifications in the solid waste regulations

Output-Sensitive Pattern Extraction in Sequences

DEFF Research Database (Denmark)

Grossi, Roberto; Menconi, Giulia; Pisanti, Nadia

2014-01-01

Genomic Analysis, Plagiarism Detection, Data Mining, Intrusion Detection, Spam Fighting and Time Series Analysis are just some examples of applications where extraction of recurring patterns in sequences of objects is one of the main computational challenges. Several notions of patterns exist...... or extend them causes a loss of significant information (where the number of occurrences changes). Output-sensitive algorithms have been proposed to enumerate and list these patterns, taking polynomial time O(nc) per pattern for constant c > 1, which is impractical for massive sequences of very large length...

Profitability and occupational injuries in U.S. underground coal mines.

Science.gov (United States)

Asfaw, Abay; Mark, Christopher; Pana-Cryan, Regina

2013-01-01

Coal plays a crucial role in the U.S. economy yet underground coal mining continues to be one of the most dangerous occupations in the country. In addition, there are large variations in both profitability and the incidence of occupational injuries across mines. The objective of this study was to examine the association between profitability and the incidence rate of occupational injuries in U.S. underground coal mines between 1992 and 2008. We used mine-specific data on annual hours worked, geographic location, and the number of occupational injuries suffered annually from the employment and accident/injury databases of the Mine Safety and Health Administration, and mine-specific data on annual revenue from coal sales, mine age, workforce union status, and mining method from the U.S. Energy Information Administration. A total of 5669 mine-year observations (number of mines×number of years) were included in our analysis. We used a negative binomial random effects model that was appropriate for analyzing panel (combined time-series and cross-sectional) injury data that were non-negative and discrete. The dependent variable, occupational injury, was measured in three different and non-mutually exclusive ways: all reported fatal and nonfatal injuries, reported nonfatal injuries with lost workdays, and the 'most serious' (i.e. sum of fatal and serious nonfatal) injuries reported. The total number of hours worked in each mine and year examined was used as an exposure variable. Profitability, the main explanatory variable, was approximated by revenue per hour worked. Our model included mine age, workforce union status, mining method, and geographic location as additional control variables. After controlling for other variables, a 10% increase in real total revenue per hour worked was associated with 0.9%, 1.1%, and 1.6% decrease, respectively, in the incidence rates of all reported injuries, reported injuries with lost workdays, and the most serious injuries reported

Acid mine drainage: mining and water pollution issues in British Columbia

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-12-31

The importance of protecting water quality and some of the problems associated with mineral development are described. Negative impacts of mining operations such as sedimentation, water disturbances, and water pollution from waste rock and tailings are considered. Mining wastes, types of water pollution from mining, the legacy of acid mine drainage, predicting acid mine drainage, preventing and mitigating acid mine drainage, examples from the past, and cyanide heap-leaching are discussed. The real costs of mining at the Telkwa open pit coal mine are assessed. British Columbia mines that are known for or are potentially acid generating are shown on a map. 32 refs., 10 figs.

Citation-related reliability analysis for a pilot sample of underground coal mines

Energy Technology Data Exchange (ETDEWEB)

Kinilakodi, H.; Grayson, R.L. [Penn State University, University Park, PA (United States)

2011-05-15

The scrutiny of underground coal mine safety was heightened because of the disasters that occurred in 2006-2007, and more recently in 2010. In the aftermath of the 2006 incidents, the U.S. Congress passed the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which strengthened the existing regulations and mandated new laws to address various issues related to emergency preparedness and response, escape from an emergency situation, and protection of miners. The National Mining Association-sponsored Mine Safety Technology and Training Commission study highlighted the role of risk management in identifying and controlling major hazards, which are elements that could come together and cause a mine disaster. In 2007 MSHA revised its approach to the 'Pattern of Violations' (POV) process in order to target unsafe mines and then force them to remediate conditions in their mines. The POV approach has certain limitations that make it difficult for it to be enforced. One very understandable way to focus on removing threats from major-hazard conditions is to use citation-related reliability analysis. The citation reliability approach, which focuses on the probability of not getting a citation on a given inspector day, is considered an analogue to the maintenance reliability approach, which many mine operators understand and use. In this study, the citation reliability approach was applied to a stratified random sample of 31 underground coal mines to examine its potential for broader application. The results clearly show the best-performing and worst-performing mines for compliance with mine safety standards, and they highlight differences among different mine sizes.

Responsible Mining: A Human Resources Strategy for Mine Development Project

OpenAIRE

Sampathkumar, Sriram (Ram)

2012-01-01

Mining is a global industry. Most mining companies operate internationally, often in remote, challenging environments and consequently frequently have respond to unusual and demanding Human Resource (HR) requirements. It is my opinion that the strategic imperative behind success in mining industry is responsible mining. The purpose of this paper is to examine how an effective HR strategy can be a competitive advantage that contributes to the success of a mining project in the global mining in...

Final environmental impact statement. Marquez uranium mine

International Nuclear Information System (INIS)

1984-01-01

As one of many activities TVA has undertaken to ensure an adequate supply of uranium for these plants, TVA has proposed to underground mine, through its operator, the uranium deposits located in the Canon de Marquez in McKinley County, New Mexico. Construction and operation of the underground mine would be expected to have the following environmental effects: (a) a temporary change in land use for 48.5 hectares from wildlife habitat and recreation to mineral extraction; (b) a minor alteration in topography near the proposed pond sites due to reclamation of waste rock piles; (c) minimal impacts on land due to limited vehicular traffic and road construction; (d) temporary depression of ground water levels in the Westwater Canyon Member of the Morrison Formation in the mine vicinity during mine life; (e) short-term project-induced impacts to surface water and shallow ground water quality; (f) a temporary decrease in air quality in the vicinity of the mining operations due to fugitive dust and exhaust emissions from combustion-driven mining and support vehicles and releases of radon and short-lived radon progeny from ventilation shafts and ore piles; (g) a temporary decrease of plant and animal species at the mine site; (h) a minor and temporary effect on aquatic systems downstream from the mine and settling ponds due to sedimentation; and (i) a minor increase of noise levels in the immediate vicinity of mine shafts and vents. The no action alternative and alternatives for securing uranium ore by other methods were considered but were found insufficient to meet TVA objectives. None of the alternatives explored were environmentally preferable. TVA also evaluated site specific alternatives including the following: different shaft and support building siting, mining techniques, and reclamation options. 25 figures, 20 tables

Challenges in computational statistics and data mining

CERN Document Server

Mielniczuk, Jan

2016-01-01

This volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors’ contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book’s related and often interconnected topics, represent Jacek Koronacki’s research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

Uranium ISR Mine Closure — General Concepts and Model-Based Simulation of Natural Attenuation for South-Australian Mine Sites

Energy Technology Data Exchange (ETDEWEB)

Jeuken, B.; Märten, H.; Woods, P., E-mail: horst.maerten@heathgate.com.au [Heathgate Resources Pty. Ltd. (Heathgate), Adelaide (Australia); Kalka, H.; Nicolai, J. [Umwelt- und Ingenieurtechnik GmbH Dresden (UIT), Dresden (Germany)

2014-05-15

Heathgate has demonstrated the effect of natural attenuation (NA) in post in-situ recovery (ISR) aquifer regions during the operation of the Beverley mine since 2001. Enhanced natural attenuation (ENA) has been considered as the key component of the mine closure concept for the new Beverley Four Mile (BFM) project, complemented by an extensive monitoring program. Data from batch and column tests for BFM core samples was used to calibrate a reactive transport model, whose application in conjunction with the hydrological modelling of the BFM aquifer has shown that NA will result in the restoration of the aquifer in time. ENA within a staged mine development program under the site-specific circumstances is discussed. (author)

Extending mine life

International Nuclear Information System (INIS)

Anon.

1984-01-01

Mine layouts, new machines and techniques, research into problem areas of ground control and so on, are highlighted in this report on extending mine life. The main resources taken into account are coal mining, uranium mining, molybdenum and gold mining

Analysis of the planned post-mining landscape of MIBRAG's open-cast mines with regard to a possible environmental impact of alteration processes in mixed dumps

International Nuclear Information System (INIS)

Jolas, P.; Hofmann, B.

2010-01-01

There has been an increasing body of knowledge with regard to hydro- and geochemical alteration processes in overburden dumps and their impact on groundwater quality in lignite mining and reclamation operations associated with post-mining landscapes in Germany. The operators of the MIBRAG mines have examined issues regarding alteration processes and how they affect the environment and which opportunities exist to actively influence the dumping process. The objectives were to counteract any possible negative impact of the alteration processes. Special emphasis was on the impact caused by oxidation of sulfur containing minerals. This paper presented an analysis of the situation at United Schleenhain Mine and how it reflects on the work to date for MIBRAG's mines. A future outlook was also presented. Specifically, the paper discussed the development of the United Schleenhain mine and the post-mining landscape. The potential for discharge of substances was also evaluated along with acidification. 1 tab., 5 figs.

From university research to innovation Detecting knowledge transfer via text mining

DEFF Research Database (Denmark)

Woltmann, Sabrina; Clemmensen, Line Katrine Harder; Alkærsig, Lars

2016-01-01

and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern...... associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using...... recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent...

AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.

Science.gov (United States)

Zhang, Qingrun; Long, Quan; Ott, Jurg

2014-06-01

Identifying gene-gene interaction is a hot topic in genome wide association studies. Two fundamental challenges are: (1) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and (2) how to test epistatic interaction when all potential combinations are available. We developed AprioriGWAS, which brings two innovations. (1) Based on Apriori, a successful method in field of Frequent Itemset Mining (FIM) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space, AprioriGWAS can efficiently identify genetically associated genotype patterns. (2) To test the hypotheses of epistasis, we adopt a new conditional permutation procedure to obtain reliable statistical inference of Pearson's chi-square test for the [Formula: see text] contingency table generated by associated variants. By applying AprioriGWAS to age-related macular degeneration (AMD) data, we found that: (1) angiopoietin 1 (ANGPT1) and four retinal genes interact with Complement Factor H (CFH). (2) GO term "glycosaminoglycan biosynthetic process" was enriched in AMD interacting genes. The epistatic interactions newly found by AprioriGWAS on AMD data are likely true interactions, since genes interacting with CFH are retinal genes, and GO term enrichment also verified that interaction between glycosaminoglycans (GAGs) and CFH plays an important role in disease pathology of AMD. By applying AprioriGWAS on Bipolar disorder in WTCCC data, we found variants without marginal effect show significant interactions. For example, multiple-SNP genotype patterns inside gene GABRB2 and GRIA1 (AMPA subunit 1 receptor gene). AMPARs are found in many parts of the brain and are the most commonly found receptor in the nervous system. The GABRB2 mediates the fastest inhibitory synaptic transmission in the central nervous system. GRIA1 and GABRB2 are relevant to mental disorders supported by multiple

Treatment of mine water. A German joint project purifies highly charged mine water in Vietnam; Behandlung von Bergbauabwasser. Ein deutsches Verbundprojekt reinigt in Vietnam stark belastete Bergbauabwaesser

Energy Technology Data Exchange (ETDEWEB)

Kurtz, Stefan; Bilek, Felix [GFI Grundwasserforschungsinstitut GmbH, Dresden (Germany); Kochan, Hans-Juergen [eta AG/LUG Enginering GmbH, Cottbus (Germany); Denke, Peter [LMBV international GmbH, Senftenberg (Germany)

2011-09-15

As part of the joint project RAME 'Mining and Environment in Vietnam', a pilot plant for the purification of mine water arises in Vietnam. In cooperation with Vietnamese partners, for the first time an active method for the purification of mine water is used in Vang Danh. The research tasks and development activities necessary for the process development are funded in part by the Federal Ministry of Education and Research (Berlin, Federal Republic of Germany). The construction of the mine water treatment plant is described in addition to the specific national conditions.

Mine subsidence control projects associated with solid waste disposal facilities

International Nuclear Information System (INIS)

Wood, R.M.

1994-01-01

Pennsylvania environmental regulations require applicant's for solid waste disposal permits to provide information regarding the extent of deep mining under the proposed site, evaluations of the maximum subsidence potential, and designs of measures to mitigate potential subsidence impact on the facility. This paper presents three case histories of deep mine subsidence control projects at solid waste disposal facilities. Each case history presents site specific mine grouting project data summaries which include evaluations of the subsurface conditions from drilling, mine void volume calculations, grout mix designs, grouting procedures and techniques, as well as grout coverage and extent of mine void filling evaluations. The case studies described utilized basic gravity grouting techniques to fill the mine voids and fractured strata over the collapsed portions of the deep mines. Grout mixtures were designed to achieve compressive strengths suitable for preventing future mine subsidence while maintaining high flow characteristics to penetrate fractured strata. Verification drilling and coring was performed in the grouted areas to determine the extent of grout coverage and obtain samples of the in-place grout for compression testing. The case histories presented in this report demonstrate an efficient and cost effective technique for mine subsidence control projects

Proceedings of the Sudbury 2003 Mining and the Environment Conference

International Nuclear Information System (INIS)

Spiers, G.; Beckett, P.; Conroy, H.

2003-01-01

Sudbury is considered to be the centre of the Canadian mining industry and has gained a reputation for environmentally sound mining practices that are being met through mine site rehabilitation and regional land reclamation. This international conference provided a forum to exchange ideas and information pertinent to mine reclamation activities. More than 400 delegates, including leading scientists and technical experts from around the world participated at the conference. Approximately 150 papers were presented on a wide range of topics related to mine site rehabilitation issues and environmental protection methods associated with mining. Both current and future challenges faced by the mining industry were discussed with particular reference to the long-term sustainability of the mining process. Topics of discussion included issues pertaining to reclamation and rehabilitation of disturbed lands and waterways as well as specific site reclamation challenges associated with the oil industry. Reclamation activities include a wide range of expertise including plant ecology, forestry, soil science, land use planning, civil and mine engineering, wildlife biology, and reclamation. Six papers from the conference have been processed separately for inclusion in this database. refs., tabs., figs

The N-terminal amphipathic helix of the topological specificity factor MinE is associated with shaping membrane curvature.

Directory of Open Access Journals (Sweden)

Yu-Ling Shih

Full Text Available Pole-to-pole oscillations of the Min proteins in Escherichia coli are required for the proper placement of the division septum. Direct interaction of MinE with the cell membrane is critical for the dynamic behavior of the Min system. In vitro, this MinE-membrane interaction led to membrane deformation; however, the underlying mechanism remained unclear. Here we report that MinE-induced membrane deformation involves the formation of an amphipathic helix of MinE(2-9, which, together with the adjacent basic residues, function as membrane anchors. Biochemical evidence suggested that the membrane association induces formation of the helix, with the helical face, consisting of A2, L3, and F6, inserted into the membrane. Insertion of this helix into the cell membrane can influence local membrane curvature and lead to drastic changes in membrane topology. Accordingly, MinE showed characteristic features of protein-induced membrane tubulation and lipid clustering in in vitro reconstituted systems. In conclusion, MinE shares common protein signatures with a group of membrane trafficking proteins in eukaryotic cells. These MinE signatures appear to affect membrane curvature.

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients.

Science.gov (United States)

Mihelčić, Matej; Šimić, Goran; Babić Leko, Mirjana; Lavrač, Nada; Džeroski, Sašo; Šmuc, Tomislav

2017-01-01

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients.

Directory of Open Access Journals (Sweden)

Matej Mihelčić

Full Text Available Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD. We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A, which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01 were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP in the non-amyloidogenic pathway

Rare earth elements (REE) as natural and applied tracers in the catchment area of Gessental valley, former uranium mining area of Eastern Thuringia, Germany

Science.gov (United States)

Buechel, G.; Merten, D.; Geletneky, J. W.; Kothe, E.

2003-04-01

Between 1947 and 1990 about 113.000 t of uranium were excavated at the former uranium mining site of Ronneburg (Eastern Thuringia, Germany). The legacy consists of more than 200 million m^3 of metasedimentary rocks rich in organic matter, sulfides and heavy metals originally deposited in mining heaps at the surface. The metasedimentary rocks formed under anoxic conditions about a 400 Mio. years ago are now exposed to oxic conditions. The oxidation of markasite and pyrite results in the formation of H_2SO_4. The formation of acid mine drainage (AMD) leads to high concentrations of uranium, rare earth elements (REE) and other heavy metals in surface water, seepage water and groundwater. This mobilization is due to alteration enhanced by high microbial activity and low pH. The tolerance mechanisms towards heavy metal pollution of soil substrate and surface/groundwater has allowed the selection of microbes which have, e.g. specific transporter genes and which are associated to plants in symbiotic interactions like mycorrhiza. In order to follow the processes linking alteration of metasedimentary rocks to biological systems the use of tracers is needed. One group of such tracers occuring in high concentrations in the water phase at the Ronneburg mining site are the REE (La-Lu) which are featured by very similar chemical behaviour. They show smooth but continuous variations of their chemical behaviour as a function of atomic number. For seepage water of the waste rock dump Nordhalde - sampled over a period of two years - the shale normalized REE patterns show enrichment of heavy REE and only minor variations, although the concentration differs. At sampling points in the surface water and in groundwater rather similar REE patterns were observed. Thus, REE can be used as tracers to identify diffuse inflow of REE-rich acid mine drainage of the dumps into the creek and the sediments. The absolute concentrations of REE in the creek and in ground water are up to 1000 times

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

Science.gov (United States)

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

2017-11-28

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

Data mining in soft computing framework: a survey.

Science.gov (United States)

Mitra, S; Pal, S K; Mitra, P

2002-01-01

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Science.gov (United States)

Kathrin Hassler, Sibylle; Weiler, Markus; Blume, Theresa

2018-01-01

Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil-vegetation-atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls.We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290 km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls.Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal

CONTRIBUTION FOR MINING ATMOSPHERE CALCULATION

Directory of Open Access Journals (Sweden)

Franica Trojanović

1989-12-01

Full Text Available Humid air is an unavoidable feature of mining atmosphere, which plays a significant role in defining the climate conditions as well as permitted circumstances for normal mining work. Saturated humid air prevents heat conduction from the human body by means of evaporation. Consequently, it is of primary interest in the mining practice to establish the relative air humidity either by means of direct or indirect methods. Percentage of water in the surrounding air may be determined in various procedures including tables, diagrams or particular calculations, where each technique has its specific advantages and disadvantages. Classical calculation is done according to Sprung's formula, in which case partial steam pressure should also be taken from the steam table. The new method without the use of diagram or tables, established on the functional relation of pressure and temperature on saturated line, is presented here for the first time (the paper is published in Croatian.

Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems

Science.gov (United States)

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…

Discriminative Chemical Patterns: Automatic and Interactive Design.

Science.gov (United States)

Bietz, Stefan; Schomburg, Karen T; Hilbig, Matthias; Rarey, Matthias

2015-08-24

The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.