Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques.

PubMed

Vu, Trung Nghia; Mrzic, Aida; Valkenborg, Dirk; Maes, Evelyne; Lemière, Filip; Goethals, Bart; Laukens, Kris

2014-01-01

Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed "frequent itemset mining" can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks. First a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications. This simple approach offers opportunities to monitor accumulating unexplained mass spectrometry data for emerging new patterns, with possible applications for the development of mass exclusion lists, for the refinement of quality control strategies and for a further interpretation of unexplained spectral peaks in mass spectrometry and tandem mass spectrometry.

30 CFR 49.9 - Mine emergency notification plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... TRAINING MINE RESCUE TEAMS § 49.9 Mine emergency notification plan. (a) Each underground mine shall have a mine rescue notification plan outlining the procedures to follow in notifying the mine rescue teams...

30 CFR 49.9 - Mine emergency notification plan.

Code of Federal Regulations, 2010 CFR

2010-07-01

... TRAINING MINE RESCUE TEAMS § 49.9 Mine emergency notification plan. (a) Each underground mine shall have a mine rescue notification plan outlining the procedures to follow in notifying the mine rescue teams...

Human dynamics in repurchase behavior based on comments mining

NASA Astrophysics Data System (ADS)

Yang, Tian; Feng, Xin; Wu, Ye; Wang, Shengfeng; Xiao, Jinghua

2018-07-01

Hundreds of thousands of individual deals and comments are analyzed to ask: what kinds of patterns appear in their repurchase process? Our results suggest that, in the empirical description, the intervals between two consecutive purchases obey a power-law distribution. Notwithstanding a wide range of individual preferences, shoppers' repurchase behaviors show some similar patterns, called long-scale quiet and short-scale emergence, and the alternating appearance of them form an endless chain in repurchase. In agreement with the empirical results, these short-scale and long-scale patterns suggest an adaptive model with alterable exponents complying with a power-law distribution. And it also implies that each user behaves his own intrinsic pattern such as unique repurchase intensity and silence-emergence cycle, which contributes to customer life-time value from the new view of dynamics and repurchase cycles.

Mining the Temporal Dimension of the Information Propagation

NASA Astrophysics Data System (ADS)

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.

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

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

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

Applying soil science for restoration of post mining degraded landscapes in semi-arid Australia: challenges and opportunities

NASA Astrophysics Data System (ADS)

Muñoz-Rojas, Miriam; Martini, Dylan; Erickson, Todd; Merritt, David; Dixon, Kingsley

2015-04-01

Introduction Current challenges in ecological restoration of post mining environments include the deficit of original topsoil which is frequently lost or damaged, and the lack of soil forming materials. A comprehensive knowledge of soil properties and processes and an adequate management of soil resources are critical to improve the restoration success of these degraded areas. In particular, understanding soil physical, chemical and biological parameters is decisive in environments where water is a limiting factor for seedling establishment and plant survival. To improve the restoration success of biodiverse semi-arid areas disturbed by mining activities (Pilbara region, Western Australia), we conducted experiments to (i) analyse changes in soil physico-chemical properties and soil microbial activity of topsoil stockpiles to optimise its handling and minimise deterioration of nutrients and soil biota, (ii) test climate effects on seedling emergence of native plant species and (iii) assess the potential of mine waste materials as a suitable growth medium for seedling emergence of native plant species under various water regimes. Methods The experimental studies were conducted in controlled environment facilities where air temperature, relative humidity and soil moisture were monitored routinely. Watering regimes were selected to represent rainfall patterns of the area. As a growth media we used material obtained from topsoil stockpiles and waste materials from an active mine site, which were mixed at different ratios. Samples were collected from different parts of the topsoil stockpiles and analysed to determine physical, chemical and biological properties. Results No large discrepancies in physical and chemical values were detected at different positions of the stockpiles. However, microbial activity was highly variable, particularly inside the stockpiles. Seedling emergence on topsoil growth media was highly dependent on climate factors with emergence rates varying significantly (P< 0.001) across species. Highest emergence rates were obtained for Acacia adoxa and Grevillea pyramidalis in the 30°C scenario and adequate soil moisture levels (mean % ± SE 71±5.3 and 80±3.8 respectively). With available water, emergence was above 30% for all species and growth media types (topsoil, waste and mixes of topsoil and waste at 50:50 and 25:75 ratios). However, under drought conditions, emergence severely decreased for all species. In particular, Gossypium robinsonii and Grevillea pyramidalis did not show any response with less than 50% of topsoil in the composition of growth media. Our results suggest that changes in precipitation regimes can have a critical effect on seedling emergence of native plant species from the Pilbara. Understanding soil physico-chemical properties of soil materials and changes in soil moisture related to rainfall patterns and growth media blends are crucial to predict the success of seedling emergence and ultimately achieve biodiverse restoration in semiarid areas. This research is part of a broader multi-study approach, the Restoration Seedbank Initiative project, a partnership between The University of Western Australia, BHP Billiton Iron Ore, and Kings Park and Botanic Garden. Keywords Pilbara region, biodiverse ecosystems, soil microbial activity, topsoil stockpile, dry environments, land rehabilitation.

The Nature and Extent of Instructors' Use of Learning Analytics in Higher Education to Inform Teaching and Learning

ERIC Educational Resources Information Center

King, Janet L.

2017-01-01

The utilization of learning analytics to support teaching and learning has emerged as a newer phenomenon combining instructor-oriented action research, the mining of educational data, and the analyses of statistics and patterns. Learning analytics have documented, quantified and graphically displayed students' interactions, engagement, and…

Combined mining: discovering informative knowledge in complex data.

PubMed

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

2011-06-01

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

A review of contrast pattern based data mining

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

Large-Scale Constraint-Based Pattern Mining

ERIC Educational Resources Information Center

Zhu, Feida

2009-01-01

We studied the problem of constraint-based pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these patterns. For item-set data, we observed robustness of…

A Typology of Communication Dynamics in Families Living a Slow-Motion Technological Disaster.

PubMed

Orom, Heather; Cline, Rebecca J W; Hernandez, Tanis; Berry-Bobovski, Lisa; Schwartz, Ann G; Ruckdeschel, John C

2012-10-01

With increasing numbers of communities harmed by exposures to toxic substances, greater understanding of the psychosocial consequences of these technological disasters is needed. One community living the consequences of a slow-motion technological disaster is Libby, Montana, where, for nearly 70 years, amphibole asbestos-contaminated vermiculite was mined and processed. Former mine employees and Libby area residents continue to cope with the health consequences of occupational and environmental asbestos exposure and with the psychosocial challenges accompanying chronic and often fatal asbestos-related diseases (ARD). Nine focus groups were conducted with Libby area residents. Transcripts were analyzed to explore patterns of family communication about ARD. The following five patterns emerged: Open/Supportive, Silent/Supportive, Open/Conflictual, Silent/Conflictual, and Silent/Denial. Open/Supportive communication included encouragement to be screened for ARD, information about ARD and related disaster topics, and emotional support for people with ARD. In contrast, communication patterns characterized by silence or conflict have the potential to hinder health-promoting communication and increase psychological distress.

78 FR 14592 - Proposed Extension of Existing Information Collection; Emergency Mine Evacuation

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-06

... DEPARTMENT OF LABOR Mine Safety and Health Administration [OMB Control No. 1219-0141] Proposed Extension of Existing Information Collection; Emergency Mine Evacuation AGENCY: Mine Safety and Health... requirements on respondents can be properly assessed. Currently, the Mine Safety and Health Administration is...

Emergence and growth of plant species in coal mine soil

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

Day, A.D.; Mitchell, G.F.; Tucker, T.C.

1979-01-01

Experiments were conducted in the laboratory and greenhouse in Arizona with the following objectives: to evaluate the chemical properties of undisturbed soil, surface-mined coal land (coal mine soil) on the Black Mesa Coal Mine, and Gila loam soil; and to study the emergence of seven plant species in the greenhouse in Gila loam soil and coal mine soil. The pH of coal mine soil (6.2) was lower than the pH of undisturbed soil (7.5) or Gila loam (7.6). The total soluble salts in coal mine soil (3241) and undisturbed soil (4592) were much higher than in Gila loam (378); however,more » coal mine soil was lower in total soluble salts than undisturbed soil. The nitrogen content of coal mine soil was higher than the nitrogen content of undisturbed soil or gila loam. Emergence percentages for seven plant species grown in coal mine soil were similar to emergence percentages for the same species grown in Gila loam. Alfalfa (Medicago sativa L.), barley (Hordeum vulgare L.), and wheat (Triticum aestivum L. em Thell.) had from 84 to 93% emergence in coal mine soil. Indian ricegrass (Oryzopsis hymenoides Roem. and Shult), fourwing saltbush (Atriplex canescens Pursh), yellow sweetclover (Melilotus officinalis Lam.), and winterfat (Euroti lanata Pursh.) emerged <35% in coal mine soil and Gila loam. Plant growth data from forage species grown in the greenhouse indicate that coal mine soil has a lower fertility level than does Gila loam soil. When supplied with optimum soil moisture and plant nutrients, coal mine soil produced approximately the same yields of forage from alfalfa, barley, and wheat as were produced in Gila loam under the same soil-moisture and fertility conditions.« less

30 CFR 48.6 - Experienced miner training.

Code of Federal Regulations, 2011 CFR

2011-07-01

... mine; the check-in and checkout system in effect at the mine; the procedures for riding on and in mine... communication systems, warning signals, and directional signs. (5) Mine map; escapeways; emergency evacuation... escapeway system; the escape, firefighting, and emergency evacuation plans in effect at the mine; and the...

30 CFR 48.6 - Experienced miner training.

Code of Federal Regulations, 2013 CFR

2013-07-01

... mine; the check-in and checkout system in effect at the mine; the procedures for riding on and in mine... communication systems, warning signals, and directional signs. (5) Mine map; escapeways; emergency evacuation... escapeway system; the escape, firefighting, and emergency evacuation plans in effect at the mine; and the...

South and East Asia Report No. 1180

DTIC Science & Technology

1982-08-17

Sundara; DAILY NEWS, 21 Jul 82) 82 Food Stamp Users Increase (DAILY NEWS, 21 Jul 82) 84 Editorial Cautions on Government Spending...patterns, namely food crops agriculture, plantation farming, fisheries, animal husbandry, industry/mining and Sapta Marga villages. The transmigration...cotton and sugarcane. Pakistan has in fact emerged as a net exporter of food - grain. Rice and cotton are substant- ial foreign exclmnge earners

76 FR 35801 - Examinations of Work Areas in Underground Coal Mines and Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-20

..., 1219-AB73 Examinations of Work Areas in Underground Coal Mines and Pattern of Violations AGENCY: Mine... public hearings on the Agency's proposed rules for Examinations of Work Areas in Underground Coal Mines... Underground Coal Mines' submissions, and with ``RIN 1219-AB73'' for Pattern of Violations' submissions...

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

NASA Astrophysics Data System (ADS)

Xu, Xiwei; Zhang, Changhai

2013-12-01

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

The Topological Field Theory of Data: a program towards a novel strategy for data mining through data language

NASA Astrophysics Data System (ADS)

Rasetti, M.; Merelli, E.

2015-07-01

This paper aims to challenge the current thinking in IT for the 'Big Data' question, proposing - almost verbatim, with no formulas - a program aiming to construct an innovative methodology to perform data analytics in a way that returns an automaton as a recognizer of the data language: a Field Theory of Data. We suggest to build, directly out of probing data space, a theoretical framework enabling us to extract the manifold hidden relations (patterns) that exist among data, as correlations depending on the semantics generated by the mining context. The program, that is grounded in the recent innovative ways of integrating data into a topological setting, proposes the realization of a Topological Field Theory of Data, transferring and generalizing to the space of data notions inspired by physical (topological) field theories and harnesses the theory of formal languages to define the potential semantics necessary to understand the emerging patterns.

Prehospital Emergencies in Illegal Gold Mining Sites in French Guiana.

PubMed

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

2018-03-01

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

Study on Mine Emergency Mechanism based on TARP and ICS

NASA Astrophysics Data System (ADS)

Xi, Jian; Wu, Zongzhi

2018-01-01

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

30 CFR 104.1 - Purpose and scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR PATTERN OF VIOLATIONS PATTERN OF... whether a mine operator has established a pattern of significant and substantial (S&S) violations at a mine. It implements section 104(e) of the Federal Mine Safety and Health Act of 1977 (Act) by...

30 CFR 75.1501 - Emergency evacuations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... by the mine operator to take charge during mine emergencies involving a fire, explosion, or gas or... prescribed by MSHA's Office of Educational Policy and Development. The course will include topics such as the... there is a mine emergency which presents an imminent danger to miners due to fire or explosion or gas or...

30 CFR 75.1501 - Emergency evacuations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... by the mine operator to take charge during mine emergencies involving a fire, explosion, or gas or... prescribed by MSHA's Office of Educational Policy and Development. The course will include topics such as the... there is a mine emergency which presents an imminent danger to miners due to fire or explosion or gas or...

30 CFR 75.1501 - Emergency evacuations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... by the mine operator to take charge during mine emergencies involving a fire, explosion, or gas or... prescribed by MSHA's Office of Educational Policy and Development. The course will include topics such as the... there is a mine emergency which presents an imminent danger to miners due to fire or explosion or gas or...

30 CFR 75.1501 - Emergency evacuations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... by the mine operator to take charge during mine emergencies involving a fire, explosion, or gas or... prescribed by MSHA's Office of Educational Policy and Development. The course will include topics such as the... there is a mine emergency which presents an imminent danger to miners due to fire or explosion or gas or...

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

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

NONE

2006-06-15

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

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

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

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

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. Themore » 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.« less

Mining of high utility-probability sequential patterns from uncertain databases

PubMed Central

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

2017-01-01

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

42 CFR 84.3 - Respirators for mine rescue or other emergency use in mines.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 1 2014-10-01 2014-10-01 false Respirators for mine rescue or other emergency use in mines. 84.3 Section 84.3 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY AND HEALTH RESEARCH AND RELATED ACTIVITIES APPROVAL OF RESPIRATORY PROTECTIVE...

42 CFR 84.3 - Respirators for mine rescue or other emergency use in mines.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 1 2010-10-01 2010-10-01 false Respirators for mine rescue or other emergency use in mines. 84.3 Section 84.3 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY AND HEALTH RESEARCH AND RELATED ACTIVITIES APPROVAL OF RESPIRATORY PROTECTIVE...

42 CFR 84.3 - Respirators for mine rescue or other emergency use in mines.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 1 2013-10-01 2013-10-01 false Respirators for mine rescue or other emergency use in mines. 84.3 Section 84.3 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY AND HEALTH RESEARCH AND RELATED ACTIVITIES APPROVAL OF RESPIRATORY PROTECTIVE...

42 CFR 84.3 - Respirators for mine rescue or other emergency use in mines.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 42 Public Health 1 2012-10-01 2012-10-01 false Respirators for mine rescue or other emergency use in mines. 84.3 Section 84.3 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY AND HEALTH RESEARCH AND RELATED ACTIVITIES APPROVAL OF RESPIRATORY PROTECTIVE...

42 CFR 84.3 - Respirators for mine rescue or other emergency use in mines.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Respirators for mine rescue or other emergency use in mines. 84.3 Section 84.3 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY AND HEALTH RESEARCH AND RELATED ACTIVITIES APPROVAL OF RESPIRATORY PROTECTIVE...

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

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

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

The association rules search of Indonesian university graduate’s data using FP-growth algorithm

NASA Astrophysics Data System (ADS)

Faza, S.; Rahmat, R. F.; Nababan, E. B.; Arisandi, D.; Effendi, S.

2018-02-01

The attribute varieties in university graduates data have caused frustrations to the institution in finding the combinations of attributes that often emerge and have high integration between attributes. Association rules mining is a data mining technique to determine the integration of the data or the way of a data set affects another set of data. By way of explanation, there are possibilities in finding the integration of data on a large scale. Frequent Pattern-Growth (FP-Growth) algorithm is one of the association rules mining technique to determine a frequent itemset in an FP-Tree data set. From the research on the search of university graduate’s association rules, it can be concluded that the most common attributes that have high integration between them are in the combination of State-owned High School outside Medan, regular university entrance exam, GPA of 3.00 to 3.49 and over 4-year-long study duration.

78 FR 10637 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Mine Rescue...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-14

... for OMB Review; Comment Request; Mine Rescue Teams, Arrangements for Emergency Medical Assistance, and...) titled, ``Mine Rescue Teams, Arrangements for Emergency Medical Assistance, and Arrangements for... regarding the [[Page 10638

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

PubMed Central

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

2015-01-01

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

A prototype system based on visual interactive SDM called VGC

NASA Astrophysics Data System (ADS)

Jia, Zelu; Liu, Yaolin; Liu, Yanfang

2009-10-01

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

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

PubMed

Quo, Chang F; Kaddi, Chanchala; Phan, John H; Zollanvari, Amin; Xu, Mingqing; Wang, May D; Alterovitz, Gil

2012-07-01

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

Percolator: Scalable Pattern Discovery in Dynamic Graphs

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

Choudhury, Sutanay; Purohit, Sumit; Lin, Peng

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 walkingmore » 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.« less

Mining co-occurrence and sequence patterns from cancer diagnoses in New York State.

PubMed

Wang, Yu; Hou, Wei; Wang, Fusheng

2018-01-01

The goal of this study is to discover disease co-occurrence and sequence patterns from large scale cancer diagnosis histories in New York State. In particular, we want to identify disparities among different patient groups. Our study will provide essential knowledge for clinical researchers to further investigate comorbidities and disease progression for improving the management of multiple diseases. We used inpatient discharge and outpatient visit records from the New York State Statewide Planning and Research Cooperative System (SPARCS) from 2011-2015. We grouped each patient's visit history to generate diagnosis sequences for seven most popular cancer types. We performed frequent disease co-occurrence mining using the Apriori algorithm, and frequent disease sequence patterns discovery using the cSPADE algorithm. Different types of cancer demonstrated distinct patterns. Disparities of both disease co-occurrence and sequence patterns were observed from patients within different age groups. There were also considerable disparities in disease co-occurrence patterns with respect to different claim types (i.e., inpatient, outpatient, emergency department and ambulatory surgery). Disparities regarding genders were mostly found where the cancer types were gender specific. Supports of most patterns were usually higher for males than for females. Compared with secondary diagnosis codes, primary diagnosis codes can convey more stable results. Two disease sequences consisting of the same diagnoses but in different orders were usually with different supports. Our results suggest that the methods adopted can generate potentially interesting and clinically meaningful disease co-occurrence and sequence patterns, and identify disparities among various patient groups. These patterns could imply comorbidities and disease progressions.

Optimal location of emergency stations in underground mine networks using a multiobjective mathematical model.

PubMed

Lotfian, Reza; Najafi, Mehdi

2018-02-26

Background Every year, many mining accidents occur in underground mines all over the world resulting in the death and maiming of many miners and heavy financial losses to mining companies. Underground mining accounts for an increasing share of these events due to their special circumstances and the risks of working therein. Thus, the optimal location of emergency stations within the network of an underground mine in order to provide medical first aid and transport injured people at the right time, plays an essential role in reducing deaths and disabilities caused by accidents Objective The main objective of this study is to determine the location of emergency stations (ES) within the network of an underground coal mine in order to minimize the outreach time for the injured. Methods A three-objective mathematical model is presented for placement of ES facility location selection and allocation of facilities to the injured in various stopes. Results Taking into account the radius of influence for each ES, the proposed model is capable to reduce the maximum time for provision of emergency services in the event of accident for each stope. In addition, the coverage or lack of coverage of each stope by any of the emergency facility is determined by means of Floyd-Warshall algorithm and graph. To solve the problem, a global criterion method using GAMS software is used to evaluate the accuracy and efficiency of the model. Conclusions 7 locations were selected from among 46 candidates for the establishment of emergency facilities in Tabas underground coal mine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Node Linkage Approach for Sequential Pattern Mining

PubMed Central

Navarro, Osvaldo; Cumplido, René; Villaseñor-Pineda, Luis; Feregrino-Uribe, Claudia; Carrasco-Ochoa, Jesús Ariel

2014-01-01

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms. PMID:24933123

Method of locating underground mines fires

DOEpatents

Laage, Linneas; Pomroy, William

1992-01-01

An improved method of locating an underground mine fire by comparing the pattern of measured combustion product arrival times at detector locations with a real time computer-generated array of simulated patterns. A number of electronic fire detection devices are linked thru telemetry to a control station on the surface. The mine's ventilation is modeled on a digital computer using network analysis software. The time reguired to locate a fire consists of the time required to model the mines' ventilation, generate the arrival time array, scan the array, and to match measured arrival time patterns to the simulated patterns.

Efficient discovery of risk patterns in medical data.

PubMed

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

2009-01-01

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

78 FR 79010 - Criteria to Certify Coal Mine Rescue Teams

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-27

... to Certify Coal Mine Rescue Teams AGENCY: Mine Safety and Health Administration, Labor. ACTION... updated the coal mine rescue team certification criteria. The Mine Improvement and New Emergency Response... mine operator to certify the qualifications of a coal mine rescue team is that team members are...

78 FR 35974 - Proposed Information Collection; Comment Request; Coal Mine Rescue Teams; Arrangements for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-14

... Request; Coal Mine Rescue Teams; Arrangements for Emergency Medical Assistance and Transportation for... Part 49, Mine Rescue Teams, Subpart B--Mine Rescue Teams for Underground Coal Mines, sets standards related to the availability of mine rescue teams; alternate mine rescue capability for small and remote...

Research on parallel algorithm for sequential pattern mining

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Qin, Bai; Wang, Yu; Hao, Zhongxiao

2008-03-01

Sequential pattern mining is the mining of frequent sequences related to time or other orders from the sequence database. Its initial motivation is to discover the laws of customer purchasing in a time section by finding the frequent sequences. In recent years, sequential pattern mining has become an important direction of data mining, and its application field has not been confined to the business database and has extended to new data sources such as Web and advanced science fields such as DNA analysis. The data of sequential pattern mining has characteristics as follows: mass data amount and distributed storage. Most existing sequential pattern mining algorithms haven't considered the above-mentioned characteristics synthetically. According to the traits mentioned above and combining the parallel theory, this paper puts forward a new distributed parallel algorithm SPP(Sequential Pattern Parallel). The algorithm abides by the principal of pattern reduction and utilizes the divide-and-conquer strategy for parallelization. The first parallel task is to construct frequent item sets applying frequent concept and search space partition theory and the second task is to structure frequent sequences using the depth-first search method at each processor. The algorithm only needs to access the database twice and doesn't generate the candidated sequences, which abates the access time and improves the mining efficiency. Based on the random data generation procedure and different information structure designed, this paper simulated the SPP algorithm in a concrete parallel environment and implemented the AprioriAll algorithm. The experiments demonstrate that compared with AprioriAll, the SPP algorithm had excellent speedup factor and efficiency.

76 FR 25277 - Examinations of Work Areas in Underground Coal Mines and Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

..., 1219-AB73 Examinations of Work Areas in Underground Coal Mines and Pattern of Violations AGENCY: Mine... four public hearings on the Agency's proposed rules for Examinations of Work Areas in Underground Coal... 1219-AB75'' for Examinations of Work Areas in Underground Coal Mines' submissions, and with ``RIN 1219...

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.

PubMed

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.

Emergency Response to Gold King Mine Release

EPA Pesticide Factsheets

Description of August 5, 2015 release of contaminated waters from the Gold King Mine into Cement Creek and the Animas River, and the resulting emergency response remediation efforts, including monitoring of affected waterways.

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

PubMed

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.

Factors influencing mine rescue team behaviors.

PubMed

Jansky, Jacqueline H; Kowalski-Trakofler, K M; Brnich, M J; Vaught, C

2016-01-01

A focus group study of the first moments in an underground mine emergency response was conducted by the National Institute for Occupational Safety and Health (NIOSH), Office for Mine Safety and Health Research. Participants in the study included mine rescue team members, team trainers, mine officials, state mining personnel, and individual mine managers. A subset of the data consists of responses from participants with mine rescue backgrounds. These responses were noticeably different from those given by on-site emergency personnel who were at the mine and involved with decisions made during the first moments of an event. As a result, mine rescue team behavior data were separated in the analysis and are reported in this article. By considering the responses from mine rescue team members and trainers, it was possible to sort the data and identify seven key areas of importance to them. On the basis of the responses from the focus group participants with a mine rescue background, the authors concluded that accurate and complete information and a unity of purpose among all command center personnel are two of the key conditions needed for an effective mine rescue operation.

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.

ERIC Educational Resources Information Center

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)

Differential affinities of MinD and MinE to anionic phospholipid influence Min Patterning dynamics in vitro

PubMed Central

Vecchiarelli, Anthony G.; Li, Min; Mizuuchi, Michiyo; Mizuuchi, Kiyoshi

2014-01-01

The E. coli Min system forms a cell-pole-to-cell-pole oscillator that positions the divisome at mid-cell. The MinD ATPase binds the membrane and recruits the cell division inhibitor MinC. MinE interacts with and releases MinD (and MinC) from the membrane. The chase of MinD by MinE creates the in vivo oscillator that maintains a low level of the division inhibitor at mid-cell. In vitro reconstitution and visualization of Min proteins on a supported lipid bilayer has provided significant advances in understanding Min patterns in vivo. Here we studied the effects of flow, lipid composition, and salt concentration on Min patterning. Flow and no-flow conditions both supported Min protein patterns with somewhat different characteristics. Without flow, MinD and MinE formed spiraling waves. MinD and, to a greater extent MinE, have stronger affinities for anionic phospholipid. MinD-independent binding of MinE to anionic lipid resulted in slower and narrower waves. MinE binding to the bilayer was also more susceptible to changes in ionic strength than MinD. We find that modulating protein diffusion with flow, or membrane binding affinities with changes in lipid composition or salt concentration, can differentially affect the retention time of MinD and MinE, leading to spatiotemporal changes in Min patterning. PMID:24930948

75 FR 18888 - Mine Rescue Teams and Arrangements for Emergency Medical Assistance and Transportation for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-13

... DEPARTMENT OF LABOR Mine Safety and Health Administration Mine Rescue Teams and Arrangements for... revising the Agency's requirements for mine rescue teams for underground coal mines on February 8, 2008... provisions. Consistent with the Court's decision, MSHA revised its requirements for mine rescue teams for...

Is population flow an unintended consequence of alcohol management plans?

PubMed

Usher, Kim; Woods, Cindy; Lynch, Paul; Pointing, Shane Boris; Budden, Lea; Barker, Ruth; Catchpoole, Jesani; Clough, Alan

2017-03-01

The aim of this study was to gauge whether, and to what extent, population flow occurred as a result of the implementation of alcohol management plans in Indigenous communities. Alcohol management plans involving carriage limits and dry places were introduced into 15 Queensland Indigenous communities between 2002-2004. Controls on alcohol availability were further tightened between 2008-2010, seeing the closure of eight mainly remote community taverns/canteens. A retrospective observational study was undertaken using data from the Queensland Injury Surveillance Unit. Population flow was measured by changing patterns of alcohol-related injuries in a mining region near dry Indigenous communities following the introduction of alcohol management plans and a control mining region distant from Indigenous communities with alcohol management plans. Data were analysed using descriptive and inferential statistics. Logistic regression was used for the comparison of the characteristics between the emergency department presentations. The rates of alcohol-related injury presentations per 1000/population were calculated and age-standardised to the Australian population. Between the five-year periods 2003-2007 and 2008-2012, alcohol-related injury presentations to the Mount Isa emergency department trebled from an age-adjusted average annual rate of 9·5/1000 in the region's population to 27·1/1000 population. In the control region, alcohol-related emergency department injury presentations did not increase to the same degree with age-adjusted average annual rates of 1·42/1000 and 2·21/1000, respectively. The 10-year pattern of emergency department presentations for alcohol-related injuries increased significantly in the Mount Isa region compared with the control region. Further research should investigate the impacts of population flow related to Indigenous community alcohol management plans. Although initiatives such as alcohol management plans have been implemented to reduce alcohol use and related consequences in Indigenous communities, there needs to be a greater consideration of the impact of these policies in nearby towns in the future. © 2016 John Wiley & Sons Ltd.

Exploring patterns of epigenetic information with data mining techniques.

PubMed

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

2013-01-01

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

Abandoned Mine Lands: Site Information

EPA Pesticide Factsheets

A catalogue of mining sites proposed for and listed on the NPL as well as mining sites being cleaned up using the Superfund Alternative Approach. Also mine sites not on the NPL but that have had removal or emergency response cleanup actions.

78 FR 64537 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Coal Mine...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-29

... for OMB Review; Comment Request; Coal Mine Rescue Teams: Arrangements for Emergency Medical Assistance... Administration (MSHA) sponsored information collection request (ICR) titled, ``Coal Mine Rescue Teams... mine rescue team requirements; reporting to the MSHA alternative mine rescue capability for a small and...

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining.

PubMed

Reps, Jenna M; Aickelin, Uwe; Hubbard, Richard B

2016-02-01

To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. We considered six drug families that are commonly associated with myocardial infarction in observational healthcare data, but where the causal relationship ground truth is known (adverse drug reaction or not). We applied emergent pattern mining to find itemsets of drugs and medical events that are associated with the development of myocardial infarction. These are the candidate confounding interaction terms. We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms. The methodology was able to account for signals generated due to confounding and a cox regression with elastic net regularisation correctly ranking the drug families known to be true adverse drug reactions above those that are not. This was not the case without the inclusion of the candidate confounding interaction terms, where confounding leads to a non-adverse drug reaction being ranked highest. The methodology is efficient, can identify high-order confounding interactions and does not require expert input to specify outcome specific confounders, so it can be applied for any outcome of interest to quickly refine its signals. The proposed method shows excellent potential to overcome some forms of confounding and therefore reduce the false positive rate for signal analysis using longitudinal data. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Constraint-based Data Mining

NASA Astrophysics Data System (ADS)

Boulicaut, Jean-Francois; Jeudy, Baptiste

Knowledge Discovery in Databases (KDD) is a complex interactive process. The promising theoretical framework of inductive databases considers this is essentially a querying process. It is enabled by a query language which can deal either with raw data or patterns which hold in the data. Mining patterns turns to be the so-called inductive query evaluation process for which constraint-based Data Mining techniques have to be designed. An inductive query specifies declaratively the desired constraints and algorithms are used to compute the patterns satisfying the constraints in the data. We survey important results of this active research domain. This chapter emphasizes a real breakthrough for hard problems concerning local pattern mining under various constraints and it points out the current directions of research as well.

Introduction to Agent Mining Interaction and Integration

NASA Astrophysics Data System (ADS)

Cao, Longbing

In recent years, more and more researchers have been involved in research on both agent technology and data mining. A clear disciplinary effort has been activated toward removing the boundary between them, that is the interaction and integration between agent technology and data mining. We refer this to agent mining as a new area. The marriage of agents and data mining is driven by challenges faced by both communities, and the need of developing more advanced intelligence, information processing and systems. This chapter presents an overall picture of agent mining from the perspective of positioning it as an emerging area. We summarize the main driving forces, complementary essence, disciplinary framework, applications, case studies, and trends and directions, as well as brief observation on agent-driven data mining, data mining-driven agents, and mutual issues in agent mining. Arguably, we draw the following conclusions: (1) agent mining emerges as a new area in the scientific family, (2) both agent technology and data mining can greatly benefit from agent mining, (3) it is very promising to result in additional advancement in intelligent information processing and systems. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.

Multi-Level Sequential Pattern Mining Based on Prime Encoding

NASA Astrophysics Data System (ADS)

Lianglei, Sun; Yun, Li; Jiang, Yin

Encoding is not only to express the hierarchical relationship, but also to facilitate the identification of the relationship between different levels, which will directly affect the efficiency of the algorithm in the area of mining the multi-level sequential pattern. In this paper, we prove that one step of division operation can decide the parent-child relationship between different levels by using prime encoding and present PMSM algorithm and CROSS-PMSM algorithm which are based on prime encoding for mining multi-level sequential pattern and cross-level sequential pattern respectively. Experimental results show that the algorithm can effectively extract multi-level and cross-level sequential pattern from the sequence database.

A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.

ERIC Educational Resources Information Center

Chen, Ruey-Shun; Hu, Yi-Chung

2003-01-01

Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

PubMed

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

2017-01-01

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

Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences

ERIC Educational Resources Information Center

Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2015-01-01

This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

Best practices for mapping replication origins in eukaryotic chromosomes.

PubMed

Besnard, Emilie; Desprat, Romain; Ryan, Michael; Kahli, Malik; Aladjem, Mirit I; Lemaitre, Jean-Marc

2014-09-02

Understanding the regulatory principles ensuring complete DNA replication in each cell division is critical for deciphering the mechanisms that maintain genomic stability. Recent advances in genome sequencing technology facilitated complete mapping of DNA replication sites and helped move the field from observing replication patterns at a handful of single loci to analyzing replication patterns genome-wide. These advances address issues, such as the relationship between replication initiation events, transcription, and chromatin modifications, and identify potential replication origin consensus sequences. This unit summarizes the technological and fundamental aspects of replication profiling and briefly discusses novel insights emerging from mining large datasets, published in the last 3 years, and also describes DNA replication dynamics on a whole-genome scale. Copyright © 2014 John Wiley & Sons, Inc.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

78 FR 5055 - Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-23

...The Mine Safety and Health Administration (MSHA) is revising the Agency's existing regulation for pattern of violations (POV). MSHA has determined that the existing regulation does not adequately achieve the intent of the Federal Mine Safety and Health Act of 1977 (Mine Act) that the POV provision be used to address mine operators who have demonstrated a disregard for the health and safety of miners. Congress included the POV provision in the Mine Act so that mine operators would manage health and safety conditions at mines and find and fix the root causes of significant and substantial (S&S) violations, protecting the health and safety of miners. The final rule simplifies the existing POV criteria, improves consistency in applying the POV criteria, and more effectively achieves the Mine Act's statutory intent. It also encourages chronic safety violators to comply with the Mine Act and MSHA's health and safety standards.

Health effects of uranium: new research findings.

PubMed

Brugge, Doug; Buchner, Virginia

2011-01-01

Recent plans for a nuclear renaissance in both established and emerging economies have prompted increased interest in uranium mining. With the potential for more uranium mining worldwide and a growth in the literature on the toxicology and epidemiology of uranium and uranium mining, we found it timely to review the current state of knowledge. Here, we present a review of the health effects of uranium mining, with an emphasis on newer findings (2005-2011). Uranium mining can contaminate air, water, and soil. The chemical toxicity of the metal constitutes the primary environmental health hazard, with the radioactivity of uranium a secondary concern. The update of the toxicologic evidence on uranium adds to the established findings regarding nephrotoxicity, genotoxicity, and developmental defects. Additional novel toxicologic findings, including some at the molecular level, are now emerging that raise the biological plausibility of adverse effects on the brain, on reproduction, including estrogenic effects, on gene expression, and on uranium metabolism. Historically, most epidemiology on uranium mining has focused on mine workers and radon exposure. Although that situation is still overwhelmingly true, a smaller emerging literature has begun to form around environmental exposure in residential areas near uranium mining and processing facilities. We present and critique such studies. Clearly, more epidemiologic research is needed to contribute to causal inference. As much damage is irreversible, and possibly cumulative, present efforts must be vigorous to limit environmental uranium contamination and exposure.

Pattern Recognition Using Artificial Neural Network: A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

A Survey of Educational Data-Mining Research

ERIC Educational Resources Information Center

Huebner, Richard A.

2013-01-01

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

Respiratory Emergencies and Management of Mining Accidents

PubMed Central

Özmen, İpek; Aksoy, Emine

2015-01-01

The rapid detection of the reasons for mining accidents that lead to emergency situations is vital for search and rescue work. The control of fire and gas leakage provides an immediate approach for rescue works for deaths or injuries and the detection of who needs resuscitation outside of the mine. The evacuation and recovery operations should be directed by continuous monitoring of the mine environment due to fire and explosion risks. The main toxic gases in mines are carbon monoxide (CO) and carbon dioxide (CO2); the flammable gases are methane (CH4), CO, and hydrogen (H2); the suffocating gases are CO2, nitrogen (N20), and CH4; and the toxic gases are CO, nitrogen oxides (NOx), and hydrogen sulfide (H2S). PMID:29404110

Animal and vegetation patterns in natural and man-made bog pools: implications for restoration

USGS Publications Warehouse

Mazerolle, M.J.; Poulin, M.; Lavoie, C.; Rochefort, L.; Desrochers, A.; Drolet, B.

2006-01-01

1. Peatlands have suffered great losses following drainage for agriculture, forestry, urbanisation, or peat mining, near inhabited areas. We evaluated the faunal and vegetation patterns after restoration of a peatland formerly mined for peat. We assessed whether bog pools created during restoration are similar to natural bog pools in terms of water chemistry, vegetation structure and composition, as well as amphibian and arthropod occurrence patterns. 2. Both avian species richness and peatland vegetation cover at the site increased following restoration. Within bog pools, however, the vegetation composition differed between natural and man-made pools. The cover of low shrubs, Sphagnum moss, submerged, emergent and floating vegetation in man-made pools was lower than in natural pools, whereas pH was higher than in typical bog pools. Dominant plant species also differed between man-made and natural pools. 3. Amphibian tadpoles, juveniles and adults occurred more often in man-made pools than natural bog pools. Although some arthropods, including Coleoptera bog specialists, readily colonised the pools, their abundance was two to 26 times lower than in natural bog pools. Plant introduction in bog pools, at the stocking densities we applied, had no effect on the occurrence of most groups. 4. We conclude that our restoration efforts were partially successful. Peatland-wide vegetation patterns following restoration mimicked those of natural peatlands, but 4 years were not sufficient for man-made pools to fully emulate the characteristics of natural bog pools.

An Incremental High-Utility Mining Algorithm with Transaction Insertion

PubMed Central

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

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

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.

30 CFR 75.1502 - Mine emergency evacuation and firefighting program of instruction.

Code of Federal Regulations, 2010 CFR

2010-07-01

... emergency response; and (iii) The rapid assembly and transportation of necessary miners, fire suppression... self-rescue devices, including hands-on training in the complete donning and transferring of all types of self-rescue devices used at the mine. (3) The deployment, use, and maintenance of refuge...

30 CFR 75.1502 - Mine emergency evacuation and firefighting program of instruction.

Code of Federal Regulations, 2011 CFR

2011-07-01

... emergency response; and (iii) The rapid assembly and transportation of necessary miners, fire suppression... self-rescue devices, including hands-on training in the complete donning and transferring of all types of self-rescue devices used at the mine. (3) The deployment, use, and maintenance of refuge...

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

ERIC Educational Resources Information Center

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…

Ecosystem Health Assessment of Mining Cities Based on Landscape Pattern

NASA Astrophysics Data System (ADS)

Yu, W.; Liu, Y.; Lin, M.; Fang, F.; Xiao, R.

2017-09-01

Ecosystem health assessment (EHA) is one of the most important aspects in ecosystem management. Nowadays, ecological environment of mining cities is facing various problems. In this study, through ecosystem health theory and remote sensing images in 2005, 2009 and 2013, landscape pattern analysis and Vigor-Organization-Resilience (VOR) model were applied to set up an evaluation index system of ecosystem health of mining city to assess the healthy level of ecosystem in Panji District Huainan city. Results showed a temporal stable but high spatial heterogeneity landscape pattern during 2005-2013. According to the regional ecosystem health index, it experienced a rapid decline after a slight increase, and finally it maintained at an ordinary level. Among these areas, a significant distinction was presented in different towns. It indicates that the ecosystem health of Tianjijiedao town, the regional administrative centre, descended rapidly during the study period, and turned into the worst level in the study area. While the Hetuan Town, located in the northwestern suburb area of Panji District, stayed on a relatively better level than other towns. The impacts of coal mining collapse area, land reclamation on the landscape pattern and ecosystem health status of mining cities were also discussed. As a result of underground coal mining, land subsidence has become an inevitable problem in the study area. In addition, the coal mining subsidence area has brought about the destruction of the farmland, construction land and water bodies, which causing the change of the regional landscape pattern and making the evaluation of ecosystem health in mining area more difficult. Therefore, this study provided an ecosystem health approach for relevant departments to make scientific decisions.

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

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

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.

PubMed

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

2018-03-01

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

30 CFR 75.1503 - Use of fire suppression equipment.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Section 75.1503 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Mine Emergencies § 75.1503 Use of...-1(c)(1), and each miner assigned to perform job duties at the job site in the direct line of sight...

Binary Coded Web Access Pattern Tree in Education Domain

ERIC Educational Resources Information Center

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

2008-01-01

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

Emergency Braking of a Mine Hoist in the Context of the Braking System Selection

NASA Astrophysics Data System (ADS)

Wolny, Stanisław

2017-03-01

The paper addresses the selected aspects of the dynamic behaviour of mine hoists during the emergency braking phase. Basing on the model of the hoist and supported by theoretical backgrounds provided by the author (Wolny, 2016), analytical formulas are derived to determine the parameters of the braking system such that during an emergency braking it should guarantee that: - the maximal loading of the hoisting ropes should not exceed the rope breaking force, - deceleration of the conveyances being stopped should not exceed the admissible levels Results of the dynamic analysis of the mine hoist behaviour during an emergency braking phase summarised in this study can be utilised to support the design of conveyance and rope attachments by the fatigue endurance methods, with an aim to adapt it to the specified operational parameters of the hoisting installation (Eurokod 3).

DMT-TAFM: a data mining tool for technical analysis of futures market

NASA Astrophysics Data System (ADS)

Stepanov, Vladimir; Sathaye, Archana

2002-03-01

Technical analysis of financial markets describes many patterns of market behavior. For practical use, all these descriptions need to be adjusted for each particular trading session. In this paper, we develop a data mining tool for technical analysis of the futures markets (DMT-TAFM), which dynamically generates rules based on the notion of the price pattern similarity. The tool consists of three main components. The first component provides visualization of data series on a chart with different ranges, scales, and chart sizes and types. The second component constructs pattern descriptions using sets of polynomials. The third component specifies the training set for mining, defines the similarity notion, and searches for a set of similar patterns. DMT-TAFM is useful to prepare the data, and then reveal and systemize statistical information about similar patterns found in any type of historical price series. We performed experiments with our tool on three decades of trading data fro hundred types of futures. Our results for this data set shows that, we can prove or disprove many well-known patterns based on real data, as well as reveal new ones, and use the set of relatively consistent patterns found during data mining for developing better futures trading strategies.

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

PubMed

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

2017-09-27

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

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

PubMed

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.

Exploration of geo-mineral compounds in granite mining soils using XRD pattern data analysis

NASA Astrophysics Data System (ADS)

Koteswara Reddy, G.; Yarakkula, Kiran

2017-11-01

The purpose of the study was to investigate the major minerals present in granite mining waste and agricultural soils near and away from mining areas. The mineral exploration of representative sub-soil samples are identified by X-Ray Diffractometer (XRD) pattern data analysis. The morphological features and quantitative elementary analysis was performed by Scanning Electron Microscopy-Energy Dispersed Spectroscopy (SEM-EDS).The XRD pattern data revealed that the major minerals are identified as Quartz, Albite, Anorthite, K-Feldspars, Muscovite, Annite, Lepidolite, Illite, Enstatite and Ferrosilite in granite waste. However, in case of agricultural farm soils the major minerals are identified as Gypsum, Calcite, Magnetite, Hematite, Muscovite, K-Feldspars and Quartz. Moreover, the agricultural soils neighbouring mining areas, the minerals are found that, the enriched Mica group minerals (Lepidolite and Illite) the enriched Orthopyroxene group minerals (Ferrosilite and Enstatite). It is observed that the Mica and Orthopyroxene group minerals are present in agricultural farm soils neighbouring mining areas and absent in agricultural farm soils away from mining areas. The study demonstrated that the chemical migration takes place at agricultural farm lands in the vicinity of the granite mining areas.

A parallel algorithm for finding the shortest exit paths in mines

NASA Astrophysics Data System (ADS)

Jastrzab, Tomasz; Buchcik, Agata

2017-11-01

In the paper we study the problem of finding the shortest exit path in an underground mine in case of emergency. Since emergency situations, such as underground fires, can put the miners' lives at risk, the ability to quickly determine the safest exit path is crucial. We propose a parallel algorithm capable of finding the shortest path between the safe exit point and any other point in the mine. The algorithm is also able to take into account the characteristics of individual miners, to make the path determination more reliable.

The landscape for epigenetic/epigenomic biomedical resources

PubMed Central

Shakya, Kabita; O'Connell, Mary J.; Ruskin, Heather J.

2012-01-01

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics. PMID:22874136

Factors affecting the establishment of direct-seeded pine on surface-mine spoils

Treesearch

William T. Plass

1974-01-01

In a greenhouse study the emergence, survival, and growth of seven species of pine were related to chemical and textural characteristics of 12 Kentucky spoils. The results identify three factors that may affect the establishment of direct-seeded pine on surface-mine spoils. First, fine-textured spoil material may restrict seedling emergence. Coarse-textured sandstones...

REMEDIATION TECHNOLOGY EVALUATION AT THE GILT EDGE MINE, SOUTH DAKOTA

EPA Science Inventory

This document reports the findings of the Mine Waste Technology Program's Activity III, Project 29,The Remediation Technology Evaluation Project at the Gilt Edge Mine, S.D. This project consisted of evaluating three emerging acidic waste rock stabilization technologies and compar...

Data Mining and Visualization: Real Time Predictions and Pattern Discovery in Hospital Emergency Rooms and Immigration Data

DTIC Science & Technology

2010-06-01

Decorating Contest, which remained up for the majority of 2010. To all my friends and Beantown and MIT Rugby : I would have never made it without you...Thanks for the memories, a place to live, and all the opportunities you gave me from a rugby perspective and in life. A special thanks to the 294, the...and which are the most different. We use the Cosine Similarity Method for assessing the differences: cos(O) = (4.13) a-b = 3 1 ajbj =a 1 b1 +a 2 b2

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

PubMed

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

2016-01-01

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

Quantification of Operational Risk Using A Data Mining

NASA Technical Reports Server (NTRS)

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

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

An Adaptive Sensor Mining Framework for Pervasive Computing Applications

NASA Astrophysics Data System (ADS)

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.

National Conference on Mining-Influenced Waters: Approaches for Characterization, Source Control and Treatment

EPA Science Inventory

The conference goal was to provide a forum for the exchange of scientific information on current and emerging approaches to assessing characterization, monitoring, source control, treatment and/or remediation on mining-influenced waters. The conference was aimed at mining remedi...

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

Raymond, David W.; Gaither, Katherine N.; Polsky, Yarom

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;more » (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.« less

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

ERIC Educational Resources Information Center

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…

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

PubMed Central

Mande, Sharmila S.

2016-01-01

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

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

PubMed

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

2016-01-01

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

Institutional challenges for mining and sustainability in Peru.

PubMed

Bebbington, Anthony J; Bury, Jeffrey T

2009-10-13

Global consumption continues to generate growth in mining. In lesser developed economies, this growth offers the potential to generate new resources for development, but also creates challenges to sustainability in the regions in which extraction occurs. This context leads to debate on the institutional arrangements most likely to build synergies between mining, livelihoods, and development, and on the socio-political conditions under which such institutions can emerge. Building from a multiyear, three-country program of research projects, Peru, a global center of mining expansion, serves as an exemplar for analyzing the effects of extractive industry on livelihoods and the conditions under which arrangements favoring local sustainability might emerge. This program is guided by three emergent hypotheses in human-environmental sciences regarding the relationships among institutions, knowledge, learning, and sustainability. The research combines in-depth and comparative case study analysis, and uses mapping and spatial analysis, surveys, in-depth interviews, participant observation, and our own direct participation in public debates on the regulation of mining for development. The findings demonstrate the pressures that mining expansion has placed on water resources, livelihood assets, and social relationships. These pressures are a result of institutional conditions that separate the governance of mineral expansion, water resources, and local development, and of relationships of power that prioritize large scale investment over livelihood and environment. A further problem is the poor communication between mining sector knowledge systems and those of local populations. These results are consistent with themes recently elaborated in sustainability science.

Institutional challenges for mining and sustainability in Peru

PubMed Central

Bebbington, Anthony J.; Bury, Jeffrey T.

2009-01-01

Global consumption continues to generate growth in mining. In lesser developed economies, this growth offers the potential to generate new resources for development, but also creates challenges to sustainability in the regions in which extraction occurs. This context leads to debate on the institutional arrangements most likely to build synergies between mining, livelihoods, and development, and on the socio-political conditions under which such institutions can emerge. Building from a multiyear, three-country program of research projects, Peru, a global center of mining expansion, serves as an exemplar for analyzing the effects of extractive industry on livelihoods and the conditions under which arrangements favoring local sustainability might emerge. This program is guided by three emergent hypotheses in human-environmental sciences regarding the relationships among institutions, knowledge, learning, and sustainability. The research combines in-depth and comparative case study analysis, and uses mapping and spatial analysis, surveys, in-depth interviews, participant observation, and our own direct participation in public debates on the regulation of mining for development. The findings demonstrate the pressures that mining expansion has placed on water resources, livelihood assets, and social relationships. These pressures are a result of institutional conditions that separate the governance of mineral expansion, water resources, and local development, and of relationships of power that prioritize large scale investment over livelihood and environment. A further problem is the poor communication between mining sector knowledge systems and those of local populations. These results are consistent with themes recently elaborated in sustainability science. PMID:19805172

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

PubMed Central

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

Long Creek Creek Mine Drainage Study: South Fork Reservation: Final Report

EPA Science Inventory

To characterize water quality in streams affected by historical mining it is necessary to determine the seasonal and spatial distribution patterns of trace metals concentrations. Identification of these patterns is used to identify the trace metals that are of ecological concern ...

30 CFR 57.4362 - Underground rescue and firefighting operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Fire Prevention and Control Firefighting Procedures/alarms/drills § 57.4362 Underground rescue and firefighting operations. Following evacuation of a mine in a fire emergency, only persons wearing and trained...

Anoxia stimulates microbially catalyzed metal release from Animas River sediments.

PubMed

Saup, Casey M; Williams, Kenneth H; Rodríguez-Freire, Lucía; Cerrato, José M; Johnston, Michael D; Wilkins, Michael J

2017-04-19

The Gold King Mine spill in August 2015 released 11 million liters of metal-rich mine waste to the Animas River watershed, an area that has been previously exposed to historical mining activity spanning more than a century. Although adsorption onto fluvial sediments was responsible for rapid immobilization of a significant fraction of the spill-associated metals, patterns of longer-term mobility are poorly constrained. Metals associated with river sediments collected downstream of the Gold King Mine in August 2015 exhibited distinct presence and abundance patterns linked to location and mineralogy. Simulating riverbed burial and development of anoxic conditions, sediment microcosm experiments amended with Animas River dissolved organic carbon revealed the release of specific metal pools coupled to microbial Fe- and SO 4 2- -reduction. Results suggest that future sedimentation and burial of riverbed materials may drive longer-term changes in patterns of metal remobilization linked to anaerobic microbial metabolism, potentially driving decreases in downstream water quality. Such patterns emphasize the need for long-term water monitoring efforts in metal-impacted watersheds.

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

PubMed

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

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

Indirect Measures of Learning Transfer between Real and Virtual Environments

ERIC Educational Resources Information Center

Garrett, Michael; McMahon, Mark

2013-01-01

This paper reports on research undertaken to determine the effectiveness of a 3D simulation environment used to train mining personnel in emergency evacuation procedures, designated the Fires in Underground Mines Evacuation Simulator (FUMES). Owing to the operational constraints of the mining facility, methods for measuring learning transfer were…

33 CFR 334.260 - York River, Va.; naval restricted areas.

Code of Federal Regulations, 2014 CFR

2014-07-01

....; naval restricted areas. (a) The areas—(1) Naval mine service-testing area (prohibited). A rectangular...; and thence along the shore line to the point of beginning. (2) Naval mine service-testing area... case of emergency. Naval authorities are required to publish advance notice of mine-laying and/or...

33 CFR 334.260 - York River, Va.; naval restricted areas.

Code of Federal Regulations, 2013 CFR

2013-07-01

....; naval restricted areas. (a) The areas—(1) Naval mine service-testing area (prohibited). A rectangular...; and thence along the shore line to the point of beginning. (2) Naval mine service-testing area... case of emergency. Naval authorities are required to publish advance notice of mine-laying and/or...

Methane Content Estimation in DuongHuy Coal Mine

NASA Astrophysics Data System (ADS)

Nguyen, Van Thinh; Mijał, Waldemar; Dang, Vu Chi; Nguyen, Thi Tuyet Mai

2018-03-01

Methane hazard has always been considered for underground coal mining as it can lead to methane explosion. In Quang Ninh province, several coal mines such as Mạo Khe coal mine, Khe Cham coal mine, especially Duong Huy mine that have high methane content. Experimental data to examine contents of methane bearing coal seams at different depths are not similar in Duong coal mine. In order to ensure safety, this report has been undertaken to determine a pattern of changing methane contents of coal seams at different exploitation depths in Duong Huy underground coal mine.

Seismic activity in the Sunnyside mining district, Utah, during 1967

USGS Publications Warehouse

Barnes, Barton K.; Dunrud, C. Richard; Hernandez, Jerome

1969-01-01

A seismic monitoring network near Sunnyside, Utah, consisting of a triangular array of seismometer stations that encompasses most of the mine workings in the district, recorded over 50,000 local earth tremors during 1967. About 540 of the tremors were of sufficient magnitude to be accurately located. Most of these were located within 2-3 miles of mine workings and were also near known or suspected faults. The district-wide seismic activity generally consisted of two different patterns--a periodic increase in the daily number of tremors at weekly intervals, and also a less regular and longer term increase and decrease of seismic activity that occurred over a period of weeks or even months. The shorter and more regular pattern can be correlated with the mine work week and seems to result from mining. The longer term activity, however, does not correlate with known mining causes sad therefore seems to be .caused by natural stresses.

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

Greenwalt, R J; Magnoli, D

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

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

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

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

Pattern Mining for Extraction of mentions of Adverse Drug Reactions from User Comments

PubMed Central

Nikfarjam, Azadeh; Gonzalez, Graciela H.

2011-01-01

Rapid growth of online health social networks has enabled patients to communicate more easily with each other. This way of exchange of opinions and experiences has provided a rich source of information about drugs and their effectiveness and more importantly, their possible adverse reactions. We developed a system to automatically extract mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network websites by mining a set of language patterns. The system applied association rule mining on a set of annotated comments to extract the underlying patterns of colloquial expressions about adverse effects. The patterns were tested on a set of unseen comments to evaluate their performance. We reached to precision of 70.01% and recall of 66.32% and F-measure of 67.96%. PMID:22195162

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

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

The Lure of Statistics in Data Mining

ERIC Educational Resources Information Center

Grover, Lovleen Kumar; Mehra, Rajni

2008-01-01

The field of Data Mining like Statistics concerns itself with "learning from data" or "turning data into information". For statisticians the term "Data mining" has a pejorative meaning. Instead of finding useful patterns in large volumes of data as in the case of Statistics, data mining has the connotation of searching for data to fit preconceived…

Source and path identification of metals pollution in a mining area by PMF and rare earth element patterns in road dust.

PubMed

Tian, Shuhan; Liang, Tao; Li, Kexin; Wang, Lingqing

2018-08-15

To better assess pollution and offer efficient protection for local residents, it is necessary to both conduct an exhaustive investigation into pollution levels and quantify its contributing sources and paths. As it is the biggest light rare earth element (REE) reserve in the world, Bayan Obo deposit releases large amounts of heavy metals into the surrounding environment. In this study, road dust from zones located at different distances to the mining area was collected and sieved using seven sizes. This allowed for subsequent analysis of size-dependent influences of mining activities. A receptor model was used to quantitatively assess mine contributions. REE distribution patterns and other REE parameters were compared with those in airborne particulates and the surrounding soil to analyze pollution paths. Results showed that 27 metals were rated as moderately to extremely polluted (2

30 CFR 914.25 - Approval of Indiana abandoned mine land reclamation plan amendments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 26, 1994 Emergency response reclamation program. July 23, 1997 March 16, 1998 Indiana plan §§ 884.13... reclamation plan amendments. 914.25 Section 914.25 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND... STATE INDIANA § 914.25 Approval of Indiana abandoned mine land reclamation plan amendments. The...

30 CFR 914.25 - Approval of Indiana abandoned mine land reclamation plan amendments.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 26, 1994 Emergency response reclamation program. July 23, 1997 March 16, 1998 Indiana plan §§ 884.13... reclamation plan amendments. 914.25 Section 914.25 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND... STATE INDIANA § 914.25 Approval of Indiana abandoned mine land reclamation plan amendments. The...

30 CFR 914.25 - Approval of Indiana abandoned mine land reclamation plan amendments.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 26, 1994 Emergency response reclamation program. July 23, 1997 March 16, 1998 Indiana plan §§ 884.13... reclamation plan amendments. 914.25 Section 914.25 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND... STATE INDIANA § 914.25 Approval of Indiana abandoned mine land reclamation plan amendments. The...

Data mining applications in the context of casemix.

PubMed

Koh, H C; Leong, S K

2001-07-01

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

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.

PubMed

Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C; Greene, Anna C; Greene, Casey S

2016-01-01

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. © The Author 2015. Published by Oxford University Press.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

PubMed Central

Gonzalez, Graciela H.; Tahsin, Tasnia; Goodale, Britton C.; Greene, Anna C.

2016-01-01

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. PMID:26420781

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

PubMed

Barrett, Tanya; Edgar, Ron

2006-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

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

PubMed Central

Ayabe, Yoshiko; Ueno, Takatoshi

2012-01-01

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

Anoxia stimulates microbially catalyzed metal release from Animas River sediments

DOE PAGES

Saup, Casey M.; Williams, Kenneth H.; Rodríguez-Freire, Lucía; ...

2017-03-06

The Gold King Mine spill in August 2015 released 11 million liters of metal-rich mine waste to the Animas River watershed, an area that has been previously exposed to historical mining activity spanning more than a century. Although adsorption onto fluvial sediments was responsible for rapid immobilization of a significant fraction of the spill-associated metals, patterns of longer-term mobility are poorly constrained. Metals associated with river sediments collected downstream of the Gold King Mine in August 2015 exhibited distinct presence and abundance patterns linked to location and mineralogy. Simulating riverbed burial and development of anoxic conditions, sediment microcosm experiments amendedmore » with Animas River dissolved organic carbon revealed the release of specific metal pools coupled to microbial Fe- and SO 4 2-reduction. Results suggest that future sedimentation and burial of riverbed materials may drive longer-term changes in patterns of metal remobilization linked to anaerobic microbial metabolism, potentially driving decreases in downstream water quality. Such patterns emphasize the need for long-term water monitoring efforts in metal-impacted watersheds.« less

Anoxia stimulates microbially catalyzed metal release from Animas River sediments

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

Saup, Casey M.; Williams, Kenneth H.; Rodríguez-Freire, Lucía

The Gold King Mine spill in August 2015 released 11 million liters of metal-rich mine waste to the Animas River watershed, an area that has been previously exposed to historical mining activity spanning more than a century. Although adsorption onto fluvial sediments was responsible for rapid immobilization of a significant fraction of the spill-associated metals, patterns of longer-term mobility are poorly constrained. Metals associated with river sediments collected downstream of the Gold King Mine in August 2015 exhibited distinct presence and abundance patterns linked to location and mineralogy. Simulating riverbed burial and development of anoxic conditions, sediment microcosm experiments amendedmore » with Animas River dissolved organic carbon revealed the release of specific metal pools coupled to microbial Fe- and SO 4 2-reduction. Results suggest that future sedimentation and burial of riverbed materials may drive longer-term changes in patterns of metal remobilization linked to anaerobic microbial metabolism, potentially driving decreases in downstream water quality. Such patterns emphasize the need for long-term water monitoring efforts in metal-impacted watersheds.« less

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

NASA Astrophysics Data System (ADS)

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.

On mining complex sequential data by means of FCA and pattern structures

NASA Astrophysics Data System (ADS)

Buzmakov, Aleksey; Egho, Elias; Jay, Nicolas; Kuznetsov, Sergei O.; Napoli, Amedeo; Raïssi, Chedy

2016-02-01

Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of formal concept analysis and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e. a data reduction of sequential structures) are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analysing interesting patient patterns from a French healthcare data-set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use-case which is the main motivation for this work.

30 CFR 57.4362 - Underground rescue and firefighting operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... MINES Fire Prevention and Control Firefighting Procedures/alarms/drills § 57.4362 Underground rescue and firefighting operations. Following evacuation of a mine in a fire emergency, only persons wearing and trained...

30 CFR 57.4362 - Underground rescue and firefighting operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... MINES Fire Prevention and Control Firefighting Procedures/alarms/drills § 57.4362 Underground rescue and firefighting operations. Following evacuation of a mine in a fire emergency, only persons wearing and trained...

30 CFR 57.4362 - Underground rescue and firefighting operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... MINES Fire Prevention and Control Firefighting Procedures/alarms/drills § 57.4362 Underground rescue and firefighting operations. Following evacuation of a mine in a fire emergency, only persons wearing and trained...

Promising native forbs for seeding on mine spoils

Treesearch

Ardell J. Bjugstad; Warren C. Whitman

1989-01-01

Twenty nine species of perennial forbs and 2 biennial forbs were directly seeded into coal mine spoil materials at Dickinson, North Dakota to determine which species would be most successful for direct seeding into coal mine spoil. Those which showed exceptionally good emergence and vigorous growth of seedlings in a two year study were: white prairie clover (...

30 CFR 886.27 - What special procedures apply to Indian lands not subject to an approved Tribal reclamation program?

Code of Federal Regulations, 2010 CFR

2010-07-01

... OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR ABANDONED MINE LAND... mitigate emergency situations or extreme danger situations arising from past mining practices and begin... Indian tribe and the Bureau of Indian Affairs office having jurisdiction over the Indian lands. (d) If a...

ARSENIC SPECIATION, SEASONAL TRANSFORMATIONS, AND CO-DISTRIBUTION WITH IRON IN A MINE WASTE-INFLUENCED PALUSTRINE EMERGENT WETLAND. (R825399)

EPA Science Inventory

Arsenic is commonly associated with mined ores and thus may be detrimental to naturally occurring wetlands that reside in mine waste-impacted regions. Understanding the relationship between Fe and As in both the aqueous and solid phase is critical for assessing the risk As impose...

Data Mining in Course Management Systems: Moodle Case Study and Tutorial

ERIC Educational Resources Information Center

Romero, Cristobal; Ventura, Sebastian; Garcia, Enrique

2008-01-01

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both…

Porites corals as recorders of mining and environmental impacts: Misima Island, Papua New Guinea

NASA Astrophysics Data System (ADS)

Fallon, Stewart J.; White, Jamie C.; McCulloch, Malcolm T.

2002-01-01

In 1989 open-cut gold mining commenced on Misima Island in Papua New Guinea (PNG). Open-cut mining by its nature causes a significant increase in sedimentation via the exposure of soils to the erosive forces of rain and runoff. This increased sedimentation affected the nearby fringing coral reef to varying degrees, ranging from coral mortality (smothering) to relatively minor short-term impacts. The sediment associated with the mining operation consists of weathered quartz feldspar, greenstone, and schist. These rocks have distinct chemical characteristics (rare earth element patterns and high abundances of manganese, zinc, and lead) and are entering the near-shore environment in considerably higher than normal concentrations. Using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), we analyzed eight colonies (two from high sedimentation, two transitional, two minor, and two unaffected control sites) for Y, La, Ce, Mn, Zn, and Pb. All sites show low steady background levels prior to the commencement of mining in 1988. Subsequently, all sites apart from the control show dramatic increases of Y, La, and Ce associated with the increased sedimentation as well as rapid decreases following the cessation of mining. The elements Zn and Pb exhibit a different behavior, increasing in concentration after 1989 when ore processing began and one year after initial mining operations. Elevated levels of Zn and Pb in corals has continued well after the cessation of mining, indicating ongoing transport into the reef of these metals via sulfate-rich waters. Rare earth element (REE) abundance patterns measured in two corals show significant differences compared to Coral Sea seawater. The corals display enrichments in the light and middle REEs while the heavy REEs are depleted relative to the seawater pattern. This suggests that the nearshore seawater REE pattern is dominated by island sedimentation. Trace element abundances of Misima Island corals clearly record the dramatic changes in the environmental conditions at this site and provide a basis for identifying anthropogenic influences on corals reefs.

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

A primer to frequent itemset mining for bioinformatics

PubMed Central

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

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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.

Data Mining Citizen Science Results

NASA Astrophysics Data System (ADS)

Borne, K. D.

2012-12-01

Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

Development and application of the Safe Performance Index as a risk-based methodology for identifying major hazard-related safety issues in underground coal mines

NASA Astrophysics Data System (ADS)

Kinilakodi, Harisha

The underground coal mining industry has been under constant watch due to the high risk involved in its activities, and scrutiny increased because of the disasters that occurred in 2006-07. In the aftermath of the incidents, the U.S. Congress passed the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which strengthened the existing regulations and mandated new laws to address the various issues related to a safe working environment in the mines. Risk analysis in any form should be done on a regular basis to tackle the possibility of unwanted major hazard-related events such as explosions, outbursts, airbursts, inundations, spontaneous combustion, and roof fall instabilities. One of the responses by the Mine Safety and Health Administration (MSHA) in 2007 involved a new pattern of violations (POV) process to target mines with a poor safety performance, specifically to improve their safety. However, the 2010 disaster (worst in 40 years) gave an impression that the collective effort of the industry, federal/state agencies, and researchers to achieve the goal of zero fatalities and serious injuries has gone awry. The Safe Performance Index (SPI) methodology developed in this research is a straight-forward, effective, transparent, and reproducible approach that can help in identifying and addressing some of the existing issues while targeting (poor safety performance) mines which need help. It combines three injury and three citation measures that are scaled to have an equal mean (5.0) in a balanced way with proportionate weighting factors (0.05, 0.15, 0.30) and overall normalizing factor (15) into a mine safety performance evaluation tool. It can be used to assess the relative safety-related risk of mines, including by mine-size category. Using 2008 and 2009 data, comparisons were made of SPI-associated, normalized safety performance measures across mine-size categories, with emphasis on small-mine safety performance as compared to large- and medium-sized mines. The accident rates (NDL IR, NFDL IR, SM/100) of very small and small mines in 2008 and 2009 were less than those of medium and large mines. The data indicates a heavy occurrence of very severe injuries in a number of very small and small mines. In another application which is a part of this research, the six normalized safety measures and the SPI are used to evaluate the risk that existed at mines in the two years preceding the occurrence of a fatality. This mine safety performance tracking method could have been helpful to the companies, state agency, or MSHA in recognizing and addressing emerging problems with actions that may have been able to prevent high-risk conditions, the fatality, and/or other serious injuries. The approach would have given scrutiny to the risk of mines that encompassed 74% of the fatalities during 2007-2010. In order to assess the SPI as a comparable risk measurement tool, a traditional risk approach is also developed using data embracing frequency and severity in the final equation to analyze the relative risk for all underground coal mines for the years 2007--2010. Then, the SPI is compared with this traditional risk analysis method to demonstrate that the results attained by either method provide the relative safety-related risk of underground coal mines regarding injuries and citations for violations of regulations. The comparison reveals that the SPI does emulate a traditional approach to risk analysis. A correlation coefficient of --0.89 or more was observed between the results of these two methodologies and either can be used to assist companies, the Mine Safety and Health Administration (MSHA), or state agencies in target-ing mines with high risk for serious injuries and elevated citations for remediation of their injury and/or violation experience. The SPI, however, provides a more understandable approach for mine operators to apply using measures compatible with MSHA's enforcement tools. These methodologies form an all-encompassing approach that can be used to assist companies, the MSHA, or state agencies in targeting mines with high risk for serious injuries and elevated citations. Once targeted as high risk, mines can then pursue appropriate intervention to remediate their violation and/or injury experience. This research may help in plugging the gap in the safety system and better pursue the goal of zero fatalities and serious injuries in the underground coal mines.

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

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

Process Mining Online Assessment Data

ERIC Educational Resources Information Center

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

2009-01-01

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

Efficient Mining of Interesting Patterns in Large Biological Sequences

PubMed Central

Rashid, Md. Mamunur; Karim, Md. Rezaul; Jeong, Byeong-Soo

2012-01-01

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time. PMID:23105928

Efficient mining of interesting patterns in large biological sequences.

PubMed

Rashid, Md Mamunur; Karim, Md Rezaul; Jeong, Byeong-Soo; Choi, Ho-Jin

2012-03-01

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

Mechanistic insights of the Min oscillator via cell-free reconstitution and imaging

NASA Astrophysics Data System (ADS)

Mizuuchi, Kiyoshi; Vecchiarelli, Anthony G.

2018-05-01

The MinD and MinE proteins of Escherichia coli self-organize into a standing-wave oscillator on the membrane to help align division at mid-cell. When unleashed from cellular confines, MinD and MinE form a spectrum of patterns on artificial bilayers—static amoebas, traveling waves, traveling mushrooms, and bursts with standing-wave dynamics. We recently focused our cell-free studies on bursts because their dynamics recapitulate many features of Min oscillation observed in vivo. The data unveiled a patterning mechanism largely governed by MinE regulation of MinD interaction with membrane. We proposed that the MinD to MinE ratio on the membrane acts as a toggle switch between MinE-stimulated recruitment and release of MinD from the membrane. In this review, we summarize cell-free data on the Min system and expand upon a molecular mechanism that provides a biochemical explanation as to how these two ‘simple’ proteins can form the remarkable spectrum of patterns.

77 FR 61723 - Felgates Creek and Indian Field Creek Along the York River in Yorktown, VA; Restricted Area

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-11

.... 334.260 York River, Va.; naval restricted areas. (a) The areas--(1) Naval mine service-testing area... thence along the shore line to the point of beginning. (2) Naval mine service-testing area (restricted... delay, except in case of emergency. Naval authorities are required to publish advance notice of mine...

A Tools-Based Approach to Teaching Data Mining Methods

ERIC Educational Resources Information Center

Jafar, Musa J.

2010-01-01

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

A procedure for developing ecosystem loading limits (TMDLs) for selenium in Wastersheds affected by gold mining in Northern Argentina

Treesearch

Dennis A. Lemly

2001-01-01

The Argentina Federal Secretary of Natural Resources oversees a wide array of mining operations conducted on public lands. Recently, selenium has emerged as a contaminant issue associated with several gold mines in the northern mountain ranges. The Secretary's Office contacted me and requested assistance interpreting selenium concentrations and possible impacts on...

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Respiratory parameters at varied altitudes in intermittent mining work.

PubMed

Bacaloni, Alessandro; Zamora Saà, Margarita Cecilia; Sinibaldi, Federica; Steffanina, Alessia; Insogna, Susanna

2018-01-07

Workers in the mining industry in altitude are subjected to several risk factors, e.g., airborne silica and low barometric pressure. The aim of this study has been to assess the risks for this work category, evaluating single risk factors as airborne silica, altitude and work shift, and relating them with cardiovascular and ventilatory parameters. Healthy miners employed in a mining company, Chile, working at varied altitudes, and subjected to unusual work shifts, were evaluated. Cardiovascular and respiratory parameters were investigated. Exposure to airborne silica was evaluated and compared to currently binding exposure limits. At varied altitudes and work shifts, alterations emerged in haemoglobin, ventilation and respiratory parameters, related to employment duration, due to compensatory mechanisms for hypoxia. Haemoglobin increased with altitude, saturation fell down under 90% in the highest mines. The multiple linear regression analysis showed a direct relationship, in the higher mine, between years of exposure to altitude and increased forced vital capacity percent (FVC%), and forced expiratory volume in 1 s (FEV1). An inverse relationship emerged between forced vital capacity (FVC) and years of exposure to airborne silica. In the workplace Mina Subterrànea (MT-3600), statistically significant inverse relationship emerged between the Tiffeneau index and body weight. The working conditions in the mining industry in altitude appeared to be potentially pathogenic; further investigations should be realized integrating risk assessment protocols even in consideration of their undeniable unconventionality. Int J Occup Med Environ Health 2018;31(2):129-138. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

Collaborative mining of graph patterns from multiple sources

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romanoa, John

2016-05-01

Intelligence analysts require automated tools to mine multi-source data, including answering queries, learning patterns of life, and discovering malicious or anomalous activities. Graph mining algorithms have recently attracted significant attention in intelligence community, because the text-derived knowledge can be efficiently represented as graphs of entities and relationships. However, graph mining models are limited to use-cases involving collocated data, and often make restrictive assumptions about the types of patterns that need to be discovered, the relationships between individual sources, and availability of accurate data segmentation. In this paper we present a model to learn the graph patterns from multiple relational data sources, when each source might have only a fragment (or subgraph) of the knowledge that needs to be discovered, and segmentation of data into training or testing instances is not available. Our model is based on distributed collaborative graph learning, and is effective in situations when the data is kept locally and cannot be moved to a centralized location. Our experiments show that proposed collaborative learning achieves learning quality better than aggregated centralized graph learning, and has learning time comparable to traditional distributed learning in which a knowledge of data segmentation is needed.

Collaborative mining and transfer learning for relational data

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

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.

Web usage data mining agent

NASA Astrophysics Data System (ADS)

Madiraju, Praveen; Zhang, Yanqing

2002-03-01

When a user logs in to a website, behind the scenes the user leaves his/her impressions, usage patterns and also access patterns in the web servers log file. A web usage mining agent can analyze these web logs to help web developers to improve the organization and presentation of their websites. They can help system administrators in improving the system performance. Web logs provide invaluable help in creating adaptive web sites and also in analyzing the network traffic analysis. This paper presents the design and implementation of a Web usage mining agent for digging in to the web log files.

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

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

2012-05-01

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

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

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

76 FR 5719 - Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-02

... safety and health record of each mine rather than on a strictly quantitative comparison of mines to... several reservations, given the methodological difficulties involved in estimating the compensating wage...

Constructing and Classifying Email Networks from Raw Forensic Images

DTIC Science & Technology

2016-09-01

data mining for sequence and pattern mining ; in medical imaging for image segmentation; and in computer vision for object recognition” [28]. 2.3.1...machine learning and data mining suite that is written in Python. It provides a platform for experiment selection, recommendation systems, and...predictivemod- eling. The Orange library is a hierarchically-organized toolbox of data mining components. Data filtering and probability assessment are at the

Text-mining and information-retrieval services for molecular biology

PubMed Central

Krallinger, Martin; Valencia, Alfonso

2005-01-01

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators. PMID:15998455

Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)*

PubMed Central

Barrett, Tanya; Edgar, Ron

2006-01-01

Summary The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo. PMID:16888359

Diagnostics of heavy mining equipment during the scheduled preventive maintenance

NASA Astrophysics Data System (ADS)

Drygin, M. Yu; Kuryshkin, N. P.

2018-01-01

Intensification of production, economic globalization and dramatic downgrade of the workers’ professional skills lead to unacceptable technical state of heavy mining equipment. Equipment maintenance outage reaches 84 % of the total downtime, of which emergency maintenance takes up to 36 % of time, that excesses 429 hours per year fr one excavator. It is shown that yearly diagnostics using methods of non-destructive check allows to reduce emergency downtime by 47 %, and 55 % of revealed defects can be eliminated without breaking the technological cycle of the equipment.

130. Photocopied July 1978. 'CAR NO. 8 PARKED AT QUINCY ...

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

130. Photocopied July 1978. 'CAR NO. 8 PARKED AT QUINCY NO. 2 SHAFT.' CAR NO. 8 CARRIED BUREAU OF MINES EMERGENCY TEAM TO QUINCY TO HELP FIGHT AN UNDERGROUND FIRE. JULY 1927. - Quincy Mining Company, Hancock, Houghton County, MI

Spatial and temporal relationships among watershed mining, water quality, and freshwater mussel status in an eastern USA river.

PubMed

Zipper, Carl E; Donovan, Patricia F; Jones, Jess W; Li, Jing; Price, Jennifer E; Stewart, Roger E

2016-01-15

The Powell River of southwestern Virginia and northeastern Tennessee, USA, drains a watershed with extensive coal surface mining, and it hosts exceptional biological richness, including at-risk species of freshwater mussels, downstream of mining-disturbed watershed areas. We investigated spatial and temporal patterns of watershed mining disturbance; their relationship to water quality change in the section of the river that connects mining areas to mussel habitat; and relationships of mining-related water constituents to measures of recent and past mussel status. Freshwater mussels in the Powell River have experienced significant declines over the past 3.5 decades. Over that same period, surface coal mining has influenced the watershed. Water-monitoring data collected by state and federal agencies demonstrate that dissolved solids and associated constituents that are commonly influenced by Appalachian mining (specific conductance, pH, hardness and sulfates) have experienced increasing temporal trends from the 1960s through ~2008; but, of those constituents, only dissolved solids concentrations are available widely within the Powell River since ~2008. Dissolved solids concentrations have stabilized in recent years. Dissolved solids, specific conductance, pH, and sulfates also exhibited spatial patterns that are consistent with dilution of mining influence with increasing distance from mined areas. Freshwater mussel status indicators are correlated negatively with dissolved solids concentrations, spatially and temporally, but the direct causal mechanisms responsible for mussel declines remain unknown. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

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

Exploring the Integration of Data Mining and Data Visualization

ERIC Educational Resources Information Center

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…

Selection of Most Proper Blasting Pattern in Mines Using Linear Assignment Method: Sungun Copper Mine / Wybór Najodpowiedniejszego Schematu Prowadzenia Prac Strzałowych W Kopalni Miedzi Sungun Z Użyciem Metody Przyporządkowania Liniowego

NASA Astrophysics Data System (ADS)

Yari, Mojtaba; Bagherpour, Raheb; Jamali, Saeed; Asadi, Fatemeh

2015-03-01

One of the most important operations in mining is blasting. Improper design of blasting pattern will cause technical and safety problems. Considering impact of results of blasting on next steps of mining, correct pattern selection needs a great cautiousness. In selecting of blasting pattern, technical, economical and safety aspects should be considered. Thus, most appropriate pattern selection can be defined as a Multi Attribute Decision Making (MADM) problem. Linear assignment method is one of the very applicable methods in decision making problems. In this paper, this method was used for the first time to evaluate blasting patterns in mine. In this ranking, safety and technical parameters have been considered to evaluate blasting patterns. Finally, blasting pattern with burden of 3.5 m, spacing of 4.5 m, stemming of 3.8 m and hole length of 12.1 m has been presented as the most suitable pattern obtained from linear assignment model for Sungun Copper Mine. Jedną z najpoważniejszych operacji wykonywanych w ramach prac wydobywczych są prace strzałowe. Niewłaściwe rozplanowanie prac powoduje problemy techniczne i stanowi zagrożenie dla bezpieczeństwa. Z uwagi na potencjalne skutki prac strzałowych i ich wpływ na kolejne etapy procesu wydobycia, właściwe rozplanowanie tych prac wymaga wielkiej uwagi i uwzględnienia kwestii technicznych, ekonomicznych a także bezpieczeństwa pracy. Dlatego też wybór najodpowiedniejszego schematu prowadzenia prac strzałowych zdefiniować można jako wieloatrybutowy problem decyzyjny (MADM - Multi Attribute Decision Making). Metoda przyporządkowania liniowego jest jedną z metod mających zastosowanie w rozwiązywaniu problemów decyzyjnych. W obecnej pracy metoda ta wykorzystana została po raz pierwszy do oceny schematów prowadzenia prac strzałowych w kopalni, w procedurze uwzględniono parametry techniczne oraz parametry związane z bezpieczeństwem. Zaprezentowano wybrany przy pomocy metody najkorzystniejszy schemat prowadzenia prac strzałowych w kopalni miedzi Sungun: nadkład 3.5m, odległości pomiędzy otworami 4.5 m, zastosowana przybitka 3.8 m, długość otworu strzałowego 12.1 m.

Data Mining in Cyber Operations

DTIC Science & Technology

2014-07-01

information processing units intended to mimic the network of neurons in the human brain for performing pattern recognition  Self- organizing maps (SOM...patterns are mined from in order to influence the learning model . An exploratory attack does not alter the training process , but rather uses other...New Jersey: Prentice Hall. 21) Kohonen, T. (1982). Self- organized formation of topologically correct feature maps. Biological Cybernetics , 43, 59–69

A novel approach for acid mine drainage pollution biomonitoring using rare earth elements bioaccumulated in the freshwater clam Corbicula fluminea.

PubMed

Bonnail, Estefanía; Pérez-López, Rafael; Sarmiento, Aguasanta M; Nieto, José Miguel; DelValls, T Ángel

2017-09-15

Lanthanide series have been used as a record of the water-rock interaction and work as a tool for identifying impacts of acid mine drainage (lixiviate residue derived from sulphide oxidation). The application of North-American Shale Composite-normalized rare earth elements patterns to these minority elements allows determining the origin of the contamination. In the current study, geochemical patterns were applied to rare earth elements bioaccumulated in the soft tissue of the freshwater clam Corbicula fluminea after exposure to different acid mine drainage contaminated environments. Results show significant bioaccumulation of rare earth elements in soft tissue of the clam after 14 days of exposure to acid mine drainage contaminated sediment (ΣREE=1.3-8μg/gdw). Furthermore, it was possible to biomonitor different degrees of contamination based on rare earth elements in tissue. The pattern of this type of contamination describes a particular curve characterized by an enrichment in the middle rare earth elements; a homologous pattern (E MREE =0.90) has also been observed when applied NASC normalization in clam tissues. Results of lanthanides found in clams were contrasted with the paucity of toxicity studies, determining risk caused by light rare earth elements in the Odiel River close to the Estuary. The current study purposes the use of clam as an innovative "bio-tool" for the biogeochemical monitoring of pollution inputs that determines the acid mine drainage networks affection. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2007-03-15

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

30 CFR 56.4331 - Firefighting drills.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Firefighting drills. 56.4331 Section 56.4331 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Control Firefighting Procedures/alarms/drills § 56.4331 Firefighting drills. Emergency firefighting drills...

30 CFR 56.4331 - Firefighting drills.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Firefighting drills. 56.4331 Section 56.4331 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Control Firefighting Procedures/alarms/drills § 56.4331 Firefighting drills. Emergency firefighting drills...

30 CFR 56.4331 - Firefighting drills.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Firefighting drills. 56.4331 Section 56.4331 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Control Firefighting Procedures/alarms/drills § 56.4331 Firefighting drills. Emergency firefighting drills...

30 CFR 56.4331 - Firefighting drills.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Firefighting drills. 56.4331 Section 56.4331 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Control Firefighting Procedures/alarms/drills § 56.4331 Firefighting drills. Emergency firefighting drills...

Life priorities in the HIV-positive Asians: a text-mining analysis in young vs. old generation.

PubMed

Chen, Wei-Ti; Barbour, Russell

2017-04-01

HIV/AIDS is one of the most urgent and challenging public health issues, especially since it is now considered a chronic disease. In this project, we used text mining techniques to extract meaningful words and word patterns from 45 transcribed in-depth interviews of people living with HIV/AIDS (PLWHA) conducted in Taipei, Beijing, Shanghai, and San Francisco from 2006 to 2013. Text mining analysis can predict whether an emerging field will become a long-lasting source of academic interest or whether it is simply a passing source of interest that will soon disappear. The data were analyzed by age group (45 and older vs. 44 and younger). The highest ranking fragments in the order of frequency were: "care", "daughter", "disease", "family", "HIV", "hospital", "husband", "medicines", "money", "people", "son", "tell/disclosure", "thought", "want", and "years". Participants in the 44-year-old and younger group were focused mainly on disease disclosure, their families, and their financial condition. In older PLWHA, social supports were one of the main concerns. In this study, we learned that different age groups perceive the disease differently. Therefore, when designing intervention, researchers should consider to tailor an intervention to a specific population and to help PLWHA achieve a better quality of life. Promoting self-management can be an effective strategy for every encounter with HIV-positive individuals.

Patterns of resource exploitation in four coexisting globeflower fly species ( Chiastocheta sp.)

NASA Astrophysics Data System (ADS)

Pompanon, François; Pettex, Emeline; Després, Laurence

2006-03-01

Life history and spatio-temporal patterns of resource utilisation were characterised in four Chiastocheta (Diptera: Anthomyiidae) species, whose larvae compete as seed predators on Trollius europaeus fruits. Interspecific co-occurrence was observed in 80% of the resource patches (= Trollius fruits) in the two communities studied. Isolated larvae from all species had a similar food intake, but differed in development time and size at emergence. Different species exhibit contrasting resource exploitation strategies with specific mining patterns and a partial temporal shift. Two species exhibited particularly singular strategies. C. rotundiventris escaped from strong interactions with other species because it was the first species to develop and the only one to exploit the central pith of Trollius fruits. The key role of this species as the main pollinator of the host-plant appears to be a by-product of constraints imposed by occupying a restricted niche. Although the resource is ephemeral due to seed dispersal, C. dentifera, the last species to oviposit, is not disadvantaged because it has a short development time and rapid food intake. The different patterns can partly explain the stability of Chiastocheta communities, but do not prevent competition to occur at high larval densities.

The Impact of Resource Wealth On Economic Growth, Governance, and Conflict in Afghanistan

DTIC Science & Technology

2013-09-01

charged with developing the mining sector . While analysis of aid programs in the country shows flaws in governance and monetary policies, there are...indications that the incentives induced by the emerging mining sector have triggered a shift toward a future-oriented development strategy amongst...the realization of the country’s economic potential has positively affected government institutions charged with developing the mining sector . While

Mining moving object trajectories in location-based services for spatio-temporal database update

NASA Astrophysics Data System (ADS)

Guo, Danhuai; Cui, Weihong

2008-10-01

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored, and the value of spatio-temporal trajectory data was not fully exploited too. Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS, and they cannot be updated timely and efficiently by manual processing. In this paper we introduce a data mining approach to movement pattern extraction of moving objects, build a model to describe the relationship between movement patterns of LBS mobile objects and their environment, and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining. Experimental evaluation reveals excellent performance of the proposed model and strategy. Our original contribution include formulation of model of interaction between trajectory and its environment, design of spatio-temporal database update strategy based on moving objects data mining, and the experimental application of spatio-temporal database update by mining moving objects trajectories.

Symposium on surface coal mining and reclamation in the Northern Great Plains. Research on shrub establishment in reclamation of surface-mined lands

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

Eddleman, L.E.

1980-01-01

Shrubs are an important component of plant communities in southeastern Montana. Re-establishment of the majority of native shrubs by seed on surface coal mine lands has not been generally successful. Field plantings at the Rosebud Mine of 25 shrub and half-shrub species were initiated in 1978 using seed from native populations. Three species Atriplex canescens, Atriplex nuttallii and Ceratoids lanata establish numerous seedlings in both wet and dry years. No emergence was obtained from 12 species.

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

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

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

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

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

PubMed

Borok, L S

1997-01-01

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

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

ERIC Educational Resources Information Center

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…

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

PubMed

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

2014-01-01

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

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

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

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

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

NASA Astrophysics Data System (ADS)

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.

An index for drought induced financial risk in the mining industry

NASA Astrophysics Data System (ADS)

Bonnafous, L.; Lall, U.; Siegel, J.

2017-02-01

Water scarcity has emerged as a potential risk for mining operations. High capital spending for desalination and water conflicts leading to asset stranding have recently occurred. Investors in mining companies are interested in the exposure to such risks across portfolios of mining assets (whether the practical at-site consequences are foregone production, higher OPEX and CAPEX and ensuing lost revenues, or asset-stranding). In this paper, an index of the potential financial exposure of a portfolio is developed and its application is illustrated. Since the likely loss at each mine is hard to estimate a priori, one needs a proxy for potential loss. The index considers drought duration, severity and frequency (defined by a return-level in years) at each mining asset, and provides a measure of financial exposure through weighing of production or Net Asset Value. Changes in human needs are not considered, but are relevant, and could be incorporated if global data on mine and other water use were available at the appropriate resolution. Potential for contemporaneous drought incidence across sites in a portfolio is considered specifically. Through an appropriate choice of drought thresholds, an analyst can customize a scenario to assess potential losses in production value or profits, or whether conflicts could emerge that would lead to stranded assets or capital expenditure to secure alternate water supplies. Global climate data sets that allow a customized development of such an index are identified, and selected mining company portfolios are scored as to the risk associated with one publicly available drought index.

Coal mine subsidence: proceedings from a citizen's conference

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

Mavrolas, P.; Schechtman, M.

A lay summary of coal-mine subsidence presents non-technical information for people in Illinois' subsidence-prone areas, and describes state and national assistance programs. The report explains mining methods and the effects of subsidence on buildings and farmland. It tells what to do in the event of an emergency and how to buy a home in a questionable area. The five appendices include directories to state and federal agencies. 14 figures, 1 table. (DCK)

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

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

Study of the crater deformation of the CODELCO/Andina mine using the satellite and ground data

NASA Astrophysics Data System (ADS)

Caverlotti-Silva, M. A.; Arellano-Baeza, A. A.

2011-12-01

The correct monitoring of the subsidence of the craters related to the underground mine exploitation is one of the most important endeavors of the satellite remote sensing. The ASTER and LANDSAT satellite images have been used to study the deformation of the crater of the CODELCO/Andina mine, Valparaiso Region, Chile. The high-resolution satellite images were used to detect changes in the lineament patterns related to the subsidence. These results were compared with the ground deformation extracted from the GPS and topography station networks. It was found that sudden changes in the lineament patterns appear when the ground deformation overcomes a definite threshold.

Protein classification using sequential pattern mining.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2006-01-01

Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.

Differentiation of closely related isomers: application of data mining techniques in conjunction with variable wavelength infrared multiple photon dissociation mass spectrometry for identification of glucose-containing disaccharide ions.

PubMed

Stefan, Sarah E; Ehsan, Mohammad; Pearson, Wright L; Aksenov, Alexander; Boginski, Vladimir; Bendiak, Brad; Eyler, John R

2011-11-15

Data mining algorithms have been used to analyze the infrared multiple photon dissociation (IRMPD) patterns of gas-phase lithiated disaccharide isomers irradiated with either a line-tunable CO(2) laser or a free electron laser (FEL). The IR fragmentation patterns over the wavelength range of 9.2-10.6 μm have been shown in earlier work to correlate uniquely with the asymmetry at the anomeric carbon in each disaccharide. Application of data mining approaches for data analysis allowed unambiguous determination of the anomeric carbon configurations for each disaccharide isomer pair using fragmentation data at a single wavelength. In addition, the linkage positions were easily assigned. This combination of wavelength-selective IRMPD and data mining offers a powerful and convenient tool for differentiation of structurally closely related isomers, including those of gas-phase carbohydrate complexes.

The design and implementation of web mining in web sites security

NASA Astrophysics Data System (ADS)

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

2003-06-01

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

Mining Deployment Optimization

NASA Astrophysics Data System (ADS)

Čech, Jozef

2016-09-01

The deployment problem, researched primarily in the military sector, is emerging in some other industries, mining included. The principal decision is how to deploy some activities in space and time to achieve desired outcome while complying with certain requirements or limits. Requirements and limits are on the side constraints, while minimizing costs or maximizing some benefits are on the side of objectives. A model with application to mining of polymetallic deposit is presented. To obtain quick and immediate decision solutions for a mining engineer with experimental possibilities is the main intention of a computer-based tool. The task is to determine strategic deployment of mining activities on a deposit, meeting planned output from the mine and at the same time complying with limited reserves and haulage capacities. Priorities and benefits can be formulated by the planner.

Estimating benthic secondary production from aquatic insect emergence in streams affected by mountaintop removal coal mining, West Virginia, USA

EPA Science Inventory

Mountaintop removal and valley filling is a coal mining method that results in burial of headwater streams. As a result of recent litigation, rapid methods for measuring ecosystem functions are needced for more appropriate permittingand mitigation stra tegies.

43 CFR 3425.1-4 - Emergency leasing.

Code of Federal Regulations, 2011 CFR

2011-10-01

... if the applicant shows: (1) That the coal reserves applied for shall be mined as part of a mining operation that is producing coal on the date of the application, and either: (i) The Federal coal is needed...-4 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT...

77 FR 44685 - Brookwood-Sago Mine Safety Grants

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-30

..., and prevent unsafe working conditions in and around mines. The focus of these grants for the Fiscal...-month period of performance is $250,000. MSHA may award both annual and renewal (two-year) grants. This... the key for proper and safe emergency response and that all miners working underground should be...

Estimating benthic secondary production from aquatic insect emergence in streams affected by mountaintop removal coal mining, West Virginia USA

EPA Science Inventory

Mountaintop removal and valley fill (MTR/VF) coal mining recountours the Appalachian landscape, buries headwater stream channels, and degrades downstream water quality. The goal of this study was to compare benthic community production estimates, based on seasonal insect emergen...

Advanced Concepts: Enabling Future AF Missions Through the Discovery and Demonstration of Emerging Revolutionary Technologies

DTIC Science & Technology

2012-10-03

µmeteoroids, weather, vibrations... Asteroid Mining Breakthrough Physics No known feasible concepts. --- Concept NTF NMS NCA Primary Challenges for Launch...weather, vibrations... Asteroid Mining Breakthrough Physics No known feasible concepts. --- 8 2.2 Microwave Augmentation of Solid Rocket Motors16,17 As...Astronautica, Vol. 52, 1, pp. 49-75, 30 May, 2002. 7. Sonter, M.J., “The Technical and Economic Feasibility of Mining the Near-Earth Asteroids ,” Acta

Occupational exposure to diesel engine exhaust: A literature review

PubMed Central

Pronk, Anjoeka; Coble, Joseph; Stewart, Patricia

2010-01-01

Background Diesel exhaust (DE) is classified as a probable human carcinogen. Aims were to describe the major occupational uses of diesel engines and give an overview of personal DE exposure levels and determinants of exposure as reported in the published literature. Methods Measurements representative of personal DE exposure were abstracted from the literature for the following agents: elemental carbon (EC), particulate matter (PM), carbon monoxide (CO), nitrogen oxide (NO), and nitrogen dioxide (NO2). Information on determinants of exposure was abstracted. Results In total, 3528 EC, 4166 PM, 581 CO, 322 NO, and 1404 NO2 measurements were abstracted. From the 10,001 measurements, 32% represented exposure from on-road vehicles, and 68% from off-road vehicles (30% mining, 15% railroad, and 22% other). Highest levels were reported for enclosed underground work sites where heavy equipment is used: mining, mine maintenance, and construction, (EC: 27-658 μg/m3). Intermediate exposure levels were generally reported for above ground (semi-)enclosed areas where smaller equipment was run: mechanics in a shop, emergency workers in fire stations, distribution workers at a dock, and workers loading/unloading inside a ferry (generally: EC< 50 μg/m3). Lowest levels were reported for enclosed areas separated from the source such as drivers and train crew, or outside such as surface mining, parking attendants, vehicle testers, utility service workers, surface construction and airline ground personnel (EC<25 μg/m3). The other agents showed a similar pattern. Determinants of exposure reported for enclosed situations were ventilation and exhaust after treatment devices. Conclusions Reported DE exposure levels were highest for underground mining and construction, intermediate for working in above ground (semi-)enclosed areas and lowest for working outside or separated from the source. The presented data can be used as a basis for assessing occupational exposure in population-based epidemiological studies and guide future exposure assessment efforts for industrial hygiene and epidemiological studies. PMID:19277070

Occupational exposure to diesel engine exhaust: a literature review.

PubMed

Pronk, Anjoeka; Coble, Joseph; Stewart, Patricia A

2009-07-01

Diesel exhaust (DE) is classified as a probable human carcinogen. Aims were to describe the major occupational uses of diesel engines and give an overview of personal DE exposure levels and determinants of exposure as reported in the published literature. Measurements representative of personal DE exposure were abstracted from the literature for the following agents: elemental carbon (EC), particulate matter (PM), carbon monoxide (CO), nitrogen oxide (NO), and nitrogen dioxide (NO(2)). Information on determinants of exposure was abstracted. In total, 3528 EC, 4166 PM, 581 CO, 322 NO, and 1404 NO(2) measurements were abstracted. From the 10,001 measurements, 32% represented exposure from on-road vehicles and 68% from off-road vehicles (30% mining, 15% railroad, and 22% others). Highest levels were reported for enclosed underground work sites in which heavy equipment is used: mining, mine maintenance, and construction (EC: 27-658 microg/m(3)). Intermediate exposure levels were generally reported for above-ground (semi-) enclosed areas in which smaller equipment was run: mechanics in a shop, emergency workers in fire stations, distribution workers at a dock, and workers loading/unloading inside a ferry (generally: EC<50 microg/m(3)). Lowest levels were reported for enclosed areas separated from the source, such as drivers and train crew, or outside, such as surface mining, parking attendants, vehicle testers, utility service workers, surface construction and airline ground personnel (EC<25 microg/m(3)). The other agents showed a similar pattern. Determinants of exposure reported for enclosed situations were ventilation and exhaust after treatment devices. Reported DE exposure levels were highest for underground mining and construction, intermediate for working in above-ground (semi-) enclosed areas and lowest for working outside or separated from the source. The presented data can be used as a basis for assessing occupational exposure in population-based epidemiological studies and guide future exposure assessment efforts for industrial hygiene and epidemiological studies.

A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

NASA Astrophysics Data System (ADS)

Lee, M. J.; Oh, K. Y.; Joung-ho, L.

2016-12-01

Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The authors appreciate the support that this study has received from `Building application frame of environmental issues, to respond to the latest ICT trends'.

30 CFR 75.1507 - Emergency Response Plan; refuge alternatives.

Code of Federal Regulations, 2011 CFR

2011-07-01

....1507 Emergency Response Plan; refuge alternatives. (a) The Emergency Response Plan (ERP) shall include... request and the District Manager may approve an alternative location in the ERP if mining involves two... constructed prior to an event in a secure space and an isolated atmosphere, the ERP shall specify that— (1...

30 CFR 75.1713 - Emergency medical assistance; first-aid.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Emergency medical assistance; first-aid. 75... Emergency medical assistance; first-aid. [Statutory Provisions] Each operator shall make arrangements in... trained in first-aid and first-aid training shall be made available to all miners. Each coal mine shall...

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

PubMed Central

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H.

2014-01-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance. PMID:25151493

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

PubMed Central

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

2016-01-01

Introduction 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. Methods 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. Results 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 5 years 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. Conclusions 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. Practical application Analyzing injury data using data mining techniques provides some insight into attributes that are associated with high accuracies for predicting injury severity. PMID:27620937

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

PubMed

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 Elsevier Ltd and National Safety Council. All rights reserved.

Selected water-quality data for the Standard Mine, Gunnison County, Colorado, 2006-2007

USGS Publications Warehouse

Verplanck, Philip L.; Manning, Andrew H.; Mast, M. Alisa; Wanty, Richard B.; McCleskey, R. Blaine; Todorov, Todor I.; Adams, Monique

2007-01-01

Mine drainage and underground water samples were collected for analysis of inorganic solutes as part of a 1-year, hydrogeologic investigation of the Standard Mine and vicinity. The U.S. Environmental Protection Agency has listed the Standard Mine in the Elk Creek drainage near Crested Butte, Colorado, as a Superfund Site because discharge from the Standard Mine enters Elk Creek, contributing dissolved and suspended loads of zinc, cadmium, copper, and other metals to Coal Creek, which is the primary drinking-water supply for the town of Crested Butte. Water analyses are reported for mine-effluent samples from Levels 1 and 5 of the Standard Mine, underground samples from Levels 3 and 5 of the Standard Mine, mine effluent from an adit located on the Elk Lode, and two spring samples that emerged from waste-rock material below Level 5 of the Standard Mine and the adit located on the Elk Lode. Reported analyses include field parameters (pH, specific conductance, water temperature, dissolved oxygen, and redox potential) and major constituents and trace elements.

Data Mining and Homeland Security: An Overview

DTIC Science & Technology

2006-01-27

which government agencies should use and mix commercial data with government data, whether data sources are being used for purposes other than those...example, a hardware store may compare their customers’ tool purchases with home ownership, type of CRS-2 3 John Makulowich, “ Government Data Mining...cleaning, data integration, data selection, data transformation , (data mining), pattern evaluation, and knowledge presentation.4 A number of advances in

Data mining of air traffic control operational errors

DOT National Transportation Integrated Search

2006-01-01

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

30 CFR 48.28 - Annual refresher training of miners; minimum courses of instruction; hours of instruction.

Code of Federal Regulations, 2013 CFR

2013-07-01

... are related to the miner's tasks. (2) Transportation controls and communication systems. The course... effect for the transportation of miners and materials; and the use of the mine communication systems... firefighting. The course shall include a review of the mine escape system; escape and emergency evacuation...

30 CFR 48.28 - Annual refresher training of miners; minimum courses of instruction; hours of instruction.

Code of Federal Regulations, 2011 CFR

2011-07-01

... are related to the miner's tasks. (2) Transportation controls and communication systems. The course... effect for the transportation of miners and materials; and the use of the mine communication systems... firefighting. The course shall include a review of the mine escape system; escape and emergency evacuation...

30 CFR 48.28 - Annual refresher training of miners; minimum courses of instruction; hours of instruction.

Code of Federal Regulations, 2012 CFR

2012-07-01

... are related to the miner's tasks. (2) Transportation controls and communication systems. The course... effect for the transportation of miners and materials; and the use of the mine communication systems... firefighting. The course shall include a review of the mine escape system; escape and emergency evacuation...

30 CFR 48.28 - Annual refresher training of miners; minimum courses of instruction; hours of instruction.

Code of Federal Regulations, 2014 CFR

2014-07-01

... are related to the miner's tasks. (2) Transportation controls and communication systems. The course... effect for the transportation of miners and materials; and the use of the mine communication systems... firefighting. The course shall include a review of the mine escape system; escape and emergency evacuation...

Tools for Educational Data Mining: A Review

ERIC Educational Resources Information Center

Slater, Stefan; Joksimovic, Srecko; Kovanovic, Vitomir; Baker, Ryan S.; Gasevic, Dragan

2017-01-01

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will…

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

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

Water spray ventilator system for continuous mining machines

DOEpatents

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.

A Data Mining Approach to Identify Sexuality Patterns in a Brazilian University Population.

PubMed

Waleska Simões, Priscyla; Cesconetto, Samuel; Toniazzo de Abreu, Larissa Letieli; Côrtes de Mattos Garcia, Merisandra; Cassettari Junior, José Márcio; Comunello, Eros; Bisognin Ceretta, Luciane; Aparecida Manenti, Sandra

2015-01-01

This paper presents the profile and experience of sexuality generated from a data mining classification task. We used a database about sexuality and gender violence performed on a university population in southern Brazil. The data mining task identified two relationships between the variables, which enabled the distinction of subgroups that better detail the profile and experience of sexuality. The identification of the relationships between the variables define behavioral models and factors of risk that will help define the algorithms being implemented in the data mining classification task.

Aircraft Mishap Fire Pattern Investigations

DTIC Science & Technology

1985-08-01

AD-AI61 094 AIRC1Arr WSWEA FlREg PATMEN INVESTIGATIONS . Joseph M. Kuchta Mining and industrial Cadre15143 Green latetrutiovalp 𔃻nco 54 Sewickley...ORGANIZATION REPORT NUMSER(S) AFWAL-TR-85-2057 6. NAME OF PERFORMING ORGANIZATION kb. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION Mining and Industrial...IS OBSOLETE. Unc .assi fied SECURITY CLASSIFICATION OF THIS PAGE ( / FOREWARD This report was prepared by the Mining and Industrial Cadre of Green

Ground-water resources and potential hydrologic effects of surface coal mining in the northern Powder River basin, southeastern Montana

USGS Publications Warehouse

Slagle, Steven E.; Lewis, Barney D.; Lee, Roger W.

1985-01-01

The shallow ground-water system in the northern Powder River Basin consists of Upper Cretaceous to Holocene aquifers overlying the Bearpaw Shale--namely, the Fox Hills Sandstone; Hell Creek, Fort Union, and Wasatch Formations; terrace deposits; and alluvium. Ground-water flow above the Bearpaw Shale can be divided into two general flow patterns. An upper flow pattern occurs in aquifers at depths of less than about 200 feet and occurs primarily as localized flow controlled by the surface topography. A lower flow pattern occurs in aquifers at depths from about 200 to 1,200 feet and exhibits a more regional flow, which is generally northward toward the Yellowstone River with significant flow toward the Powder and Tongue Rivers. The chemical quality of water in the shallow ground-water system in the study area varies widely, and most of the ground water does not meet standards for dissolved constituents in public drinking water established by the U.S. Environmental Protection Agency. Water from depths less than 200 feet generally is a sodium sulfate type having an average dissolved-solids concentration of 2,100 milligrams per liter. Sodium bicarbonate water having an average dissolved-solids concentration of 1,400 milligrams per liter is typical from aquifers in the shallow ground-water system at depths between 200 and 1,200 feet. Effects of surface coal mining on the water resources in the northern Powder River Basin are dependent on the stratigraphic location of the mine cut. Where the cut lies above the water-yielding zone, the effects will be minimal. Where the mine cut intersects a water-ielding zone, effects on water levels and flow patterns can be significant locally, but water levels and flow patterns will return to approximate premining conditions after mining ceases. Ground water in and near active and former mines may become more mineralized, owing to the placement of spoil material from the reducing zone in the unsaturated zone where the minerals are subject to oxidation. Regional effects probably will be small because of the limited areal extent of ground-water flow systems where mining is feasible. Results of digital models are presented to illustrate the effects of varying hydraulic properties on water-level changes resulting from mine dewatering. The model simulations were designed to depict maximum-drawdown situations. One simulation indicates that after 20 years of continuous dewatering of an infinite, homogeneous, isotropic aquifer that is 10 feet thick and has an initial potentiometric surface 10 feet above the top of the aquifer, water-level declines greater than 1 foot would generally be limited to within 7.5 miles of the center of the mine excavation; declines greater than 2 feet to within about 6 miles; declines greater than 5 feet to within about 3.7 miles; declines greater than 10 feet to within about 1.7 miles; and declines greater than 15 feet to within 1.2 miles.

Modeling Spatial Dependencies and Semantic Concepts in Data Mining

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

Vatsavai, Raju

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

Data mining in radiology

PubMed Central

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

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining. PMID:25024513

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

PubMed

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

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

Development of a model to determine oxygen consumption when crawling

PubMed Central

Pollard, J.P.; Heberger, J.R.; Dempsey, P.G.

2016-01-01

During a mine disaster or emergency, underground air can quickly become contaminated. In these circumstances, all underground mine workers are taught to don breathable air supply units at the first sign of an emergency. However, no contemporary oxygen consumption data is available for the purposes of designing breathing air supply equipment specifically for mine escape. Further, it would be useful to quantify the oxygen requirements of breathing air supply users for various escape scenarios. To address this need, 14 participants crawled a distance of 305 m each while their breath-by-breath oxygen consumption measurements were taken. Using these data, linear regression models were developed to determine peak and average oxygen consumption rates as well as total oxygen consumption. These models can be used by manufacturers of breathing air supply equipment to aid in the design of devices that would be capable of producing sufficient on-demand oxygen to allow miners to perform self-escape. PMID:26997858

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.

PubMed

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.

Determining Plant – Leaf Miner – Parasitoid Interactions: A DNA Barcoding Approach

PubMed Central

Derocles, Stéphane A. P.; Evans, Darren M.; Nichols, Paul C.; Evans, S. Aifionn; Lunt, David H.

2015-01-01

A major challenge in network ecology is to describe the full-range of species interactions in a community to create highly-resolved food-webs. We developed a molecular approach based on DNA full barcoding and mini-barcoding to describe difficult to observe plant – leaf miner – parasitoid interactions, consisting of animals commonly regarded as agricultural pests and their natural enemies. We tested the ability of universal primers to amplify the remaining DNA inside leaf miner mines after the emergence of the insect. We compared the results of a) morphological identification of adult specimens; b) identification based on the shape of the mines; c) the COI Mini-barcode (130 bp) and d) the COI full barcode (658 bp) fragments to accurately identify the leaf-miner species. We used the molecular approach to build and analyse a tri-partite ecological network of plant – leaf miner – parasitoid interactions. We were able to detect the DNA of leaf-mining insects within their feeding mines on a range of host plants using mini-barcoding primers: 6% for the leaves collected empty and 33% success after we observed the emergence of the leaf miner. We suggest that the low amplification success of leaf mines collected empty was mainly due to the time since the adult emerged and discuss methodological improvements. Nevertheless our approach provided new species-interaction data for the ecological network. We found that the 130 bp fragment is variable enough to identify all the species included in this study. Both COI fragments reveal that some leaf miner species could be composed of cryptic species. The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria. PMID:25710377

Spatial and temporal patterns in trace element deposition to lakes in the Athabasca oil sands region (Alberta, Canada)

NASA Astrophysics Data System (ADS)

Cooke, Colin A.; Kirk, Jane L.; Muir, Derek C. G.; Wiklund, Johan A.; Wang, Xiaowa; Gleason, Amber; Evans, Marlene S.

2017-12-01

The mining and processing of the Athabasca oil sands (Alberta, Canada) has been occurring for decades; however, a lack of consistent regional monitoring has obscured the long-term environmental impact. Here, we present sediment core results to reconstruct spatial and temporal patterns in trace element deposition to lakes in the Athabasca oil sands region. Early mining operations (during the 1970s and 1980s) led to elevated V and Pb inputs to lakes located <50 km from mining operations. Subsequent improvements to mining and upgrading technologies since the 1980s have reduced V and Pb loading to near background levels at many sites. In contrast, Hg deposition increased by a factor of ~3 to all 20 lakes over the 20th century, reflecting global-scale patterns in atmospheric Hg emissions. Base cation deposition (from fugitive dust emissions) has not measurably impacted regional lake sediments. Instead, results from a principal components analysis suggest that the presence of carbonate bedrock underlying lakes located close to development appears to exert a first-order control over lake sediment base cation concentrations and overall lake sediment geochemical composition. Trace element concentrations generally did not exceed Canadian sediment quality guidelines, and no spatial or temporal trends were observed in the frequency of guideline exceedence. Our results demonstrate that early mining efforts had an even greater impact on trace element cycling than has been appreciated previously, placing recent monitoring efforts in a critical long-term context.

Mineralogical controls on mobility of rare earth elements in acid mine drainage environments.

PubMed

Soyol-Erdene, T O; Valente, T; Grande, J A; de la Torre, M L

2018-08-01

Rare earth elements (REE) were analyzed in river waters, acid mine waters, and extracts of secondary precipitates collected in the Iberian Pyrite Belt. The obtained concentrations of the REE in river water and mine waters (acid mine drainage - AMD) were in the range of 0.57 μg/L (Lu) and 2579 μg/L (Ce), which is higher than previously reported in surface waters from the Iberian Pyrite Belt, but are comparable with previous findings from AMD worldwide. Total REE concentrations in river waters were ranged between 297 μg/L (Cobica River) and 7032 μg/L (Trimpancho River) with an average of 2468 μg/L. NASC (North American Shale Composite) normalized REE patterns for river and acid mine waters show clear convex curvatures in middle-REE (MREE) with respect to light- and heavy-REE. During the dissolution experiments of AMD-precipitates, heavy-REE and middle-REE generate the most enriched patterns in the solution. A small number of precipitates did not display MREE enrichment (an index Gd n /Lu n  < 1.0) in NASC normalized pattern and produced relatively lower REE concentrations in extracts. Additionally, very few samples, which mainly contained aluminum sulfates, e.g., pickeringite and alunogen, displayed light-REE enrichment relative to heavy-REE (HREE). In general, the highest retention of REE occurs in samples enriched in magnesium (epsomite or hexahydrite) and aluminum sulfates, mainly pickeringite. Copyright © 2018 Elsevier Ltd. All rights reserved.

Questa Baseline and Pre-Mining Ground-Water Quality Investigation. 17. Geomorphology of the Red River Valley, Taos County, New Mexico, and Influence on Ground-Water Flow in the Shallow Alluvial Aquifer

USGS Publications Warehouse

Vincent, Kirk R.

2008-01-01

In April 2001, the U.S. Geological Survey (USGS) and the New Mexico Environment Department (NMED) began a cooperative study to infer the pre-mining ground-water chemistry at the Molycorp molybdenum mine site in the Red River Valley of north-central New Mexico. This report is one in a series of reports that can be used to determine pre-mining ground-water conditions at the mine site. Molycorp?s Questa molybdenum mine in the Red River Valley, northern New Mexico, is located near the margin of the Questa caldera in a highly mineralized region. The bedrock of the Taos Range surrounding the Red River is composed of Proterozoic rocks of various types, which are intruded and overlain by Oligocene volcanic rocks associated with the Questa caldera. Locally, these rocks were altered by hydrothermal activity. The alteration zones that contain sulfide minerals are particularly important because they constitute the commercial ore bodies of the region and, where exposed to weathering, form sites of rapid erosion referred to as alteration scars. Over the past thousand years, if not over the entire Holocene, erosion rates were spatially variable. Forested hillslopes eroded at about 0.04 millimeter per year, whereas alteration scars eroded at about 2.7 millimeters per year. The erosion rate of the alteration scars is unusually rapid for naturally occurring sites that have not been disturbed by humans. Watersheds containing large alteration scars delivered more sediment to the Red River Valley than the Red River could remove. Consequently, large debris fans, as much as 80 meters thick, developed within the valley. The geomorphology of the Red River Valley has had several large influences on the hydrology of the shallow alluvial aquifer, and those influences were in effect before the onset of mining within the watershed. Several reaches where alluvial ground water emerges to become Red River streamflow were observed by a tracer dilution study conducted in 2001. The aquifer narrows where erosion-resistant bedrock, which tends to form vertical cliffs, restricts the width of the valley bottom. Although the presence of a shallow bedrock sill, overlain by shallow alluvium, is a plausible cause of ground-water emergence, this cause was not demonstrated in the study area. The water-table gradient can locally decrease in the downstream direction because of changes in the hydraulic properties of the alluvium, and this may be a contributing cause of ground-water emergence. However, at one site (near Cabin Springs), ground-water emergence could not be explained by spatial changes in geometric or hydraulic properties of the aquifer. Furthermore, the available evidence demonstrates that ground water flowing through bedrock fractures or colluvium entered the north side of the alluvial aquifer, and is the cause of ground-water emergence. At that location the alluvial aquifer was already flowing full, causing the excess water to emerge into the stream. An indirect consequence of altered rock in the tributary watersheds is the rapid erosion rate of alteration scars combined with the hydraulic properties of sediments shed from those scars. Where alteration scars are large the debris fans at the mouths of the tributary watersheds substantially encroach into the Red River Valley. At such locations debris-fan materials dominate the width and thickness of the alluvium in the valley and reduce the rate of flow of ground water within the Red River alluvial aquifer. Most sites of groundwater emergence are located immediately upstream from or along the margins of debris fans. A substantial fraction of the ground water approaching a debris fan can emerge to become streamflow. This last observation has three implications. First, very little water can flow the entire length of the study area entirely within the alluvial aquifer because the ground water repeatedly contacts debris-fan sediments over that length. Second, it follows that emerging water containing

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

NASA Astrophysics Data System (ADS)

Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

PubMed Central

Seeja, K. R.; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers. PMID:25302317

FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

PubMed

Seeja, K R; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

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

PubMed

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

2015-01-01

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

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

PubMed

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

2017-04-01

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

Reverse and forward engineering of protein pattern formation.

PubMed

Kretschmer, Simon; Harrington, Leon; Schwille, Petra

2018-05-26

Living systems employ protein pattern formation to regulate important life processes in space and time. Although pattern-forming protein networks have been identified in various prokaryotes and eukaryotes, their systematic experimental characterization is challenging owing to the complex environment of living cells. In turn, cell-free systems are ideally suited for this goal, as they offer defined molecular environments that can be precisely controlled and manipulated. Towards revealing the molecular basis of protein pattern formation, we outline two complementary approaches: the biochemical reverse engineering of reconstituted networks and the de novo design, or forward engineering, of artificial self-organizing systems. We first illustrate the reverse engineering approach by the example of the Escherichia coli Min system, a model system for protein self-organization based on the reversible and energy-dependent interaction of the ATPase MinD and its activating protein MinE with a lipid membrane. By reconstituting MinE mutants impaired in ATPase stimulation, we demonstrate how large-scale Min protein patterns are modulated by MinE activity and concentration. We then provide a perspective on the de novo design of self-organizing protein networks. Tightly integrated reverse and forward engineering approaches will be key to understanding and engineering the intriguing phenomenon of protein pattern formation.This article is part of the theme issue 'Self-organization in cell biology'. © 2018 The Author(s).

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

NASA Astrophysics Data System (ADS)

Billa, V. N. Vinay Kumar; Lakshmanna, K.; Rajesh, K.; Reddy, M. Praveen Kumar; Nagaraja, G.; Sudheer, K.

2017-11-01

The ultimate goal of Data Mining is to determine the hidden information which is useful in making decisions using the large databases collected by an organization. This Data Mining involves many tasks that are to be performed during the process. Mining frequent itemsets is the one of the most important tasks in case of transactional databases. These transactional databases contain the data in very large scale where the mining of these databases involves the consumption of physical memory and time in proportion to the size of the database. A frequent pattern mining algorithm is said to be efficient only if it consumes less memory and time to mine the frequent itemsets from the given large database. Having these points in mind in this thesis we proposed a system which mines frequent itemsets in an optimized way in terms of memory and time by using cloud computing as an important factor to make the process parallel and the application is provided as a service. A complete framework which uses a proven efficient algorithm called FIN algorithm. FIN algorithm works on Nodesets and POC (pre-order coding) tree. In order to evaluate the performance of the system we conduct the experiments to compare the efficiency of the same algorithm applied in a standalone manner and in cloud computing environment on a real time data set which is traffic accidents data set. The results show that the memory consumption and execution time taken for the process in the proposed system is much lesser than those of standalone system.

Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes

PubMed Central

Fernández-Llatas, Carlos; Benedi, José-Miguel; García-Gómez, Juan M.; Traver, Vicente

2013-01-01

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection. PMID:24225907

Topographic Maps and Coal Mining.

ERIC Educational Resources Information Center

Raitz, Karl B.

1984-01-01

Geography teachers can illustrate the patterns associated with mineral fuel production, especially coal, by using United States Geological Survey topographic maps, which are illustrated by symbols that indicate mine-related features, such as shafts and tailings. Map reading exercises are presented; an interpretative map key that can facilitate…

Application and Exploration of Big Data Mining in Clinical Medicine.

PubMed

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.

Coal Mining Technology, An Innovative Program.

ERIC Educational Resources Information Center

Wabash Valley Coll., Mt. Carmel, IL.

Described in detail in this report are the processes and procedures involved in the development of a State funded curriculum and program for a new emerging technology, in this instance a Coal Mining Technology Program, to be taught at Wabash Valley College in Illinois. The document provides a step-by-step account of the determination of need,…

The Effectiveness of Web-Based Learning Environment: A Case Study of Public Universities in Kenya

ERIC Educational Resources Information Center

Kirui, Paul A.; Mutai, Sheila J.

2010-01-01

Web mining is emerging in many aspects of e-learning, aiming at improving online learning and teaching processes and making them more transparent and effective. Researchers using Web mining tools and techniques are challenged to learn more about the online students' reshaping online courses and educational websites, and create tools for…

Application of Learning Analytics Using Clustering Data Mining for Students' Disposition Analysis

ERIC Educational Resources Information Center

Bharara, Sanyam; Sabitha, Sai; Bansal, Abhay

2018-01-01

Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research…

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

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

2017-01-01

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

Mercury contamination from historical gold mining in California

USGS Publications Warehouse

Alpers, Charles N.; Hunerlach, Michael P.; May, Jason T.; Hothem, Roger L.

2005-01-01

Mercury contamination from historical gold mines represents a potential risk to human health and the environment. This fact sheet provides background information on the use of mercury in historical gold mining and processing operations in California, with emphasis on historical hydraulic mining areas. It also describes results of recent USGS projects that address the potential risks associated with mercury contamination. Miners used mercury (quicksilver) to recover gold throughout the western United States. Gold deposits were either hardrock (lode, gold-quartz veins) or placer (alluvial, unconsolidated gravels). Underground methods (adits and shafts) were used to mine hardrock gold deposits. Hydraulic, drift, or dredging methods were used to mine the placer gold deposits. Mercury was used to enhance gold recovery in all the various types of mining operations; historical records indicate that more mercury was used and lost at hydraulic mines than at other types of mines. On the basis of USGS studies and other recent work, a better understanding is emerging of mercury distribution, ongoing transport, transformation processes, and the extent of biological uptake in areas affected by historical gold mining. This information has been used extensively by federal, state, and local agencies responsible for resource management and public health in California.

The Perceived Consequences of Gold Mining in Postwar El Salvador: A Qualitative Study.

PubMed

Zakrison, Tanya L; Cabezas, Pedro; Valle, Evan; Kornfeld, Julie; Muntaner, Carles; Soklaridis, Sophie

2015-11-01

We investigated themes related to the health and environmental impacts of gold mining in El Salvador. Over a 1-month period in 2013, we conducted focus groups (n = 32 participants in total) and individual semistructured interviews (n = 11) with community leaders until we achieved thematic saturation. Data collection took place in 4 departments throughout the country. We used a combination of criterion-purposive and snowballing sampling techniques to identify participants. Multiple themes emerged: (1) the fallacy of economic development; (2) critique of mining activities; (3) the creation of mining-related violence, with parallels to El Salvador's civil war; and (4) solutions and alternatives to mining activity. Solutions involved the creation of cooperative microenterprises for sustainable economic growth, political empowerment within communities, and development of local participatory democracies. Gold mining in El Salvador is perceived as a significant environmental and public health threat. Local solutions may be applicable broadly.

Text mining for adverse drug events: the promise, challenges, and state of the art.

PubMed

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H

2014-10-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

Accidents in Coal Mining from Perspective of Risk Theory

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Automated Analysis of Renewable Energy Datasets ('EE/RE Data Mining')

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

Bush, Brian; Elmore, Ryan; Getman, Dan

This poster illustrates methods to substantially improve the understanding of renewable energy data sets and the depth and efficiency of their analysis through the application of statistical learning methods ('data mining') in the intelligent processing of these often large and messy information sources. The six examples apply methods for anomaly detection, data cleansing, and pattern mining to time-series data (measurements from metering points in buildings) and spatiotemporal data (renewable energy resource datasets).

Detecting Malicious Tweets in Twitter Using Runtime Monitoring With Hidden Information

DTIC Science & Technology

2016-06-01

text mining using Twitter streaming API and python [Online]. Available: http://adilmoujahid.com/posts/2014/07/twitter-analytics/ [22] M. Singh, B...sites with 645,750,000 registered users [3] and has open source public tweets for data mining . 2. Malicious Users and Tweets In the modern world...want to data mine in Twitter, and presents the natural language assertions and corresponding rule patterns. It then describes the steps performed using

Numerical linear algebra in data mining

NASA Astrophysics Data System (ADS)

Eldén, Lars

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

Data Mining and Complex Problems: Case Study in Composite Materials

NASA Technical Reports Server (NTRS)

Rabelo, Luis; Marin, Mario

2009-01-01

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

Application of EREP imagery to fracture-related mine safety hazards in coal mining and mining-environmental problems in Indiana. [Indiana and Illinois

NASA Technical Reports Server (NTRS)

Wier, C. E. (Principal Investigator); Powell, R. L.; Amato, R. V.; Russell, O. R.; Martin, K. R.

1975-01-01

The author has identified the following significant results. This investigation evaluated the applicability of a variety of sensor types, formats, and resolution capabilities to the study of both fuel and nonfuel mined lands. The image reinforcement provided by stereo viewing of the EREP images proved useful for identifying lineaments and for mined lands mapping. Skylab S190B color and color infrared transparencies were the most useful EREP imagery. New information on lineament and fracture patterns in the bedrock of Indiana and Illinois extracted from analysis of the Skylab imagery has contributed to furthering the geological understanding of this portion of the Illinois basin.

Constructing Patient Specific Clinical Trajectories from Electronic Healthcare Reimbursement Claims using Sequential Pattern Mining

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

Pullum, Laura L; Hobson, Tanner C

We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). By analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with heart disease (HD) using sequential pattern mining algorithms. Our analyses reveal that the clinical procedures performed on HD patients are highly varied leading up to and after the primary diagnosis. The discovered clinical procedure sequences reveal significant differences in the overall costs incurred across different parts of the US, indicating significant heterogeneity in treating HD patients. We show that a data-driven approachmore » to understand patient specific clinical trajectories constructed from EHRC can provide quantitative insights into how to better manage and treat patients.« less

Educational Data Mining Application for Estimating Students Performance in Weka Environment

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Detecting Underground Mine Voids Using Complex Geophysical Techniques

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

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

2006-12-01

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

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

PubMed

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.

Windblown Dust Deposition Forecasting and Spread of Contamination around Mine Tailings

PubMed Central

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

2017-01-01

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. PMID:29082035

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

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

2003-12-01

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

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

PubMed Central

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

2014-01-01

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

Personalized privacy-preserving frequent itemset mining using randomized response.

PubMed

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

2014-01-01

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

Integration of Text- and Data-Mining Technologies for Use in Banking Applications

NASA Astrophysics Data System (ADS)

Maslankowski, Jacek

Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it's not always easy to find, access, analyze or use (Robb 2004). That is why it is important to use solutions based on text and data mining. This solution is known as duo mining. This leads to improve management based on knowledge owned in organization. The results are interesting. Data mining provides to lead with structuralized data, usually powered from data warehouses. Text mining, sometimes called web mining, looks for patterns in unstructured data — memos, document and www. Integrating text-based information with structured data enriches predictive modeling capabilities and provides new stores of insightful and valuable information for driving business and research initiatives forward.

43 CFR 4.1351 - Preliminary finding by OSM.

Code of Federal Regulations, 2010 CFR

2010-10-01

... APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Request for...(c) of the Act, 30 U.s.c. 1260(c) (federal Program; Federal Lands Program; Federal Program for Indian... or has controlled surface coal mining and reclamation operations with a demonstrated pattern of...

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

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

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

Rare earth elements in freshwater, marine, and terrestrial ecosystems in the eastern Canadian Arctic.

PubMed

MacMillan, Gwyneth Anne; Chételat, John; Heath, Joel P; Mickpegak, Raymond; Amyot, Marc

2017-10-18

Few ecotoxicological studies exist for rare earth elements (REEs), particularly field-based studies on their bioaccumulation and food web dynamics. REE mining has led to significant environmental impacts in several countries (China, Brazil, U.S.), yet little is known about the fate and transport of these contaminants of emerging concern. Northern ecosystems are potentially vulnerable to REE enrichment from prospective mining projects at high latitudes. To understand how REEs behave in remote northern food webs, we measured REE concentrations and carbon and nitrogen stable isotope ratios (∂ 15 N, ∂ 13 C) in biota from marine, freshwater, and terrestrial ecosystems of the eastern Canadian Arctic (N = 339). Wildlife harvesting and tissue sampling was partly conducted by local hunters through a community-based monitoring project. Results show that REEs generally follow a coherent bioaccumulation pattern for sample tissues, with some anomalies for redox-sensitive elements (Ce, Eu). Highest REE concentrations were found at low trophic levels, especially in vegetation and aquatic invertebrates. Terrestrial herbivores, ringed seal, and fish had low total REE levels in muscle tissue (∑REE for 15 elements <0.1 nmol g -1 ), yet accumulation was an order of magnitude higher in liver tissues. Age- and length-dependent REE accumulation also suggest that REE uptake is faster than elimination for some species. Overall, REE bioaccumulation patterns appear to be species- and tissue-specific, with limited potential for biomagnification. This study provides novel data on the behaviour of REEs in ecosystems and will be useful for environmental impact assessment of REE enrichment in northern regions.

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

PubMed

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

2011-05-01

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

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

NASA Astrophysics Data System (ADS)

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.

Solar Data Mining at Georgia State University

NASA Astrophysics Data System (ADS)

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.

Mining Longitudinal Web Queries: Trends and Patterns.

ERIC Educational Resources Information Center

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

2003-01-01

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

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

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

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.

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

Treesearch

John A. Parrotta; Oliver H. Knowles

2001-01-01

Restoring self-sustaining tropical forest ecosystems on surface mined sites is a formidable challenge that requires the integration of proven reclamation techniques and reforestation strategies appropriate to specific site conditions, including landscape biodiversity patterns. Restorationists working in most tropical settings are usually hampered by lack of basic...

Emerging technology becomes an opportunity for EOS

NASA Astrophysics Data System (ADS)

Fargion, Giulietta S.; Harberts, Robert; Masek, Jeffrey G.

1996-11-01

During the last decade, we have seen an explosive growth in our ability to collect and generate data. When implemented, NASA's Earth observing system data information system (EOSDIS) will receive about 50 gigabytes of remotely sensed image data per hour. This will generate an urgent need for new techniques and tools that can automatically and intelligently assist in transforming this abundance of data into useful knowledge. Some emerging technologies that address these challenges include data mining and knowledge discovery in databases (KDD). The most basic data mining application is a content-based search (examples include finding images of particular meteorological phenomena or identifying data that have been previously mined or interpreted). In order that these technologies be effectively exploited for EOSDIS development, a better understanding of data mining and the requirements for using this technology is necessary. The authors are currently undertaking a project exploring the requirements and options of content-based search and data mining for use on EOSDIS. The scope of the project is to develop a prototype with which to investigate user interface concepts, requirements, and designs relevant for EOSDIS core system (ECS) subsystem utilizing these techniques. The goal is to identify a generic handling of these functions. This prototype will help identify opportunities which the earth science community and EOSDIS can use to meet the challenges of collecting, searching, retrieving, and interacting with abundant data resources in highly productive ways.

Perennial forbs for wildlife habitat restoration on mined lands in the northern Great Plains

Treesearch

Ardell J. Bjugstad; Warren C. Whitman

1982-01-01

Research was designed to assess the establishment and growth potential of 30 perennial forbs by seeding and/or transplanting them on coal mine spoil materials over a 2-year period. Five species showed exceptional emergence and vigorous growth from direct seeding. Six species showed vigorous growth with the use of transplanted plants. Seeding resulted in successful...

Penetrating mine injury to the heart with a pericardial tamponade.

PubMed

Maye, J; Marshall, N E

1996-02-01

Cardiac tamponade is a life-threatening emergency. Immediate recognition and surgical intervention is essential to preserve myocardial function and maintain cardiac output. The following is a case report of a penetrating mine injury to the heart with a resulting pericardial tamponade. The positive outcome and the facilities in which the resuscitation and surgical repair occurred make this a unique case report.

Geostatistics: a common link between medical geography, mathematical geology, and medical geology

PubMed Central

Goovaerts, P.

2015-01-01

Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963

Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

PubMed

Goovaerts, P

2014-08-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

NASA Technical Reports Server (NTRS)

Hamilton, Paul Lawrence; Britain, Robert

2010-01-01

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media.

Modeling Patterns of Total Dissolved Solids Release from Central Appalachia, USA, Mine Spoils.

PubMed

Clark, Elyse V; Zipper, Carl E; Daniels, W Lee; Orndorff, Zenah W; Keefe, Matthew J

2017-01-01

Surface mining in the central Appalachian coalfields (USA) influences water quality because the interaction of infiltrated waters and O with freshly exposed mine spoils releases elevated levels of total dissolved solids (TDS) to streams. Modeling and predicting the short- and long-term TDS release potentials of mine spoils can aid in the management of current and future mining-influenced watersheds and landscapes. In this study, the specific conductance (SC, a proxy variable for TDS) patterns of 39 mine spoils during a sequence of 40 leaching events were modeled using a five-parameter nonlinear regression. Estimated parameter values were compared to six rapid spoil assessment techniques (RSATs) to assess predictive relationships between model parameters and RSATs. Spoil leachates reached maximum values, 1108 ± 161 μS cm on average, within the first three leaching events, then declined exponentially to a breakpoint at the 16th leaching event on average. After the breakpoint, SC release remained linear, with most spoil samples exhibiting declines in SC release with successive leaching events. The SC asymptote averaged 276 ± 25 μS cm. Only three samples had SCs >500 μS cm at the end of the 40 leaching events. Model parameters varied with mine spoil rock and weathering type, and RSATs were predictive of four model parameters. Unweathered samples released higher SCs throughout the leaching period relative to weathered samples, and rock type influenced the rate of SC release. The RSATs for SC, total S, and neutralization potential may best predict certain phases of mine spoil TDS release. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Quality Assurance of NCI Thesaurus by Mining Structural-Lexical Patterns

PubMed Central

Abeysinghe, Rashmie; Brooks, Michael A.; Talbert, Jeffery; Licong, Cui

2017-01-01

Quality assurance of biomedical terminologies such as the National Cancer Institute (NCI) Thesaurus is an essential part of the terminology management lifecycle. We investigate a structural-lexical approach based on non-lattice subgraphs to automatically identify missing hierarchical relations and missing concepts in the NCI Thesaurus. We mine six structural-lexical patterns exhibiting in non-lattice subgraphs: containment, union, intersection, union-intersection, inference-contradiction, and inference union. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. We found 809 non-lattice subgraphs with these patterns in the NCI Thesaurus (version 16.12d). Domain experts evaluated a random sample of 50 small non-lattice subgraphs, of which 33 were confirmed to contain errors and make correct suggestions (33/50 = 66%). Of the 25 evaluated subgraphs revealing multiple patterns, 22 were verified correct (22/25 = 88%). This shows the effectiveness of our structurallexical-pattern-based approach in detecting errors and suggesting remediations in the NCI Thesaurus. PMID:29854100

The impact of gold mining on the Witwatersrand on the rivers and karst system of Gauteng and North West Province, South Africa

NASA Astrophysics Data System (ADS)

Durand, J. F.

2012-06-01

The Witwatersrand has been subjected to geological exploration, mining activities, parallel industrial development and associated settlement patterns over the past century. The gold mines brought with them not only development, employment and wealth, but also the most devastating war in the history of South Africa, civil unrest, economical inequality, social uprooting, pollution, negative health impacts and ecological destruction. One of the most consistent and pressing problems caused by mining has been its impact on the water bodies in and adjacent to the Witwatersrand. The dewatering and rewatering of the karstic aquifer overlying and adjacent to the Witwatersrand Supergroup and the pollution caused by Acid Mine Drainage (AMD) are some of the most serious consequences of gold mining in South Africa and will affect the lives of many South Africans.

Discovering amino acid patterns on binding sites in protein complexes

PubMed Central

Kuo, Huang-Cheng; Ong, Ping-Lin; Lin, Jung-Chang; Huang, Jen-Peng

2011-01-01

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes. PMID:21464838

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

PubMed

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

2014-01-01

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

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

PubMed Central

2014-01-01

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

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

PubMed

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 hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

Discovering significant evolution patterns from satellite image time series.

PubMed

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.

Application and Exploration of Big Data Mining in Clinical Medicine

PubMed Central

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

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

PubMed Central

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

2014-01-01

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

Meta-control of combustion performance with a data mining approach

NASA Astrophysics Data System (ADS)

Song, Zhe

Large scale combustion process is complex and proposes challenges of optimizing its performance. Traditional approaches based on thermal dynamics have limitations on finding optimal operational regions due to time-shift nature of the process. Recent advances in information technology enable people collect large volumes of process data easily and continuously. The collected process data contains rich information about the process and, to some extent, represents a digital copy of the process over time. Although large volumes of data exist in industrial combustion processes, they are not fully utilized to the level where the process can be optimized. Data mining is an emerging science which finds patterns or models from large data sets. It has found many successful applications in business marketing, medical and manufacturing domains The focus of this dissertation is on applying data mining to industrial combustion processes, and ultimately optimizing the combustion performance. However the philosophy, methods and frameworks discussed in this research can also be applied to other industrial processes. Optimizing an industrial combustion process has two major challenges. One is the underlying process model changes over time and obtaining an accurate process model is nontrivial. The other is that a process model with high fidelity is usually highly nonlinear, solving the optimization problem needs efficient heuristics. This dissertation is set to solve these two major challenges. The major contribution of this 4-year research is the data-driven solution to optimize the combustion process, where process model or knowledge is identified based on the process data, then optimization is executed by evolutionary algorithms to search for optimal operating regions.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Source Analysis of the Crandall Canyon, Utah, Mine Collapse

DOE PAGES

Dreger, D. S.; Ford, S. R.; Walter, W. R.

2008-07-11

Analysis of seismograms from a magnitude 3.9 seismic event on August 6, 2007 in central Utah reveals an anomalous radiation pattern that is contrary to that expected for a tectonic earthquake, and which is dominated by an implosive component. The results show the seismic event is best modeled as a shallow underground collapse. Interestingly, large transverse surface waves require a smaller additional non-collapse source component that represents either faulting in the rocks above the mine workings or deformation of the medium surrounding the mine.

Geovisualization of Local and Regional Migration Using Web-mined Demographics

NASA Astrophysics Data System (ADS)

Schuermann, R. T.; Chow, T. E.

2014-11-01

The intent of this research was to augment and facilitate analyses, which gauges the feasibility of web-mined demographics to study spatio-temporal dynamics of migration. As a case study, we explored the spatio-temporal dynamics of Vietnamese Americans (VA) in Texas through geovisualization of mined demographic microdata from the World Wide Web. Based on string matching across all demographic attributes, including full name, address, date of birth, age and phone number, multiple records of the same entity (i.e. person) over time were resolved and reconciled into a database. Migration trajectories were geovisualized through animated sprites by connecting the different addresses associated with the same person and segmenting the trajectory into small fragments. Intra-metropolitan migration patterns appeared at the local scale within many metropolitan areas. At the scale of metropolitan area, varying degrees of immigration and emigration manifest different types of migration clusters. This paper presents a methodology incorporating GIS methods and cartographic design to produce geovisualization animation, enabling the cognitive identification of migration patterns at multiple scales. Identification of spatio-temporal patterns often stimulates further research to better understand the phenomenon and enhance subsequent modeling.

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

NASA Technical Reports Server (NTRS)

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

1973-01-01

The author has identified the following significant results. The 70mm black and white infrared photography acquired in March 1973 at an approximate scale of 1:115,000 permits the identification of areas of mine subsidence not readily evident on other films. This is largely due to the high contrast rendition of water and land by this film and the excessive surface moisture conditions prevalent in the area at the time of photography. Subsided areas consist of shallow depressions which have impounded water. Patterns with a regularity indicative of the room and pillar configuration used in subsurface coal mining are evident.

Kinetics of bed fracturing around mine workings

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

Veksler, Yu.A.

1988-03-01

A failure of the bed near the walls of the workings of a mine away from the face occurs gradually over time and in this paper the authors take a kinetic approach to evaluating its development. The influence of certain mine engineering factors on the pattern of bed fracturing is discussed. The effect of the depth of mining is shown. Cracking occurs in the portion of the seam at the face near the ground at some distance from it on the interface between soft and hard coal. The density of the fractured rocks and their response affect the bed fracturingmore » near the stope face.« less

Pattern Discovery and Change Detection of Online Music Query Streams

NASA Astrophysics Data System (ADS)

Li, Hua-Fu

In this paper, an efficient stream mining algorithm, called FTP-stream (Frequent Temporal Pattern mining of streams), is proposed to find the frequent temporal patterns over melody sequence streams. In the framework of our proposed algorithm, an effective bit-sequence representation is used to reduce the time and memory needed to slide the windows. The FTP-stream algorithm can calculate the support threshold in only a single pass based on the concept of bit-sequence representation. It takes the advantage of "left" and "and" operations of the representation. Experiments show that the proposed algorithm only scans the music query stream once, and runs significant faster and consumes less memory than existing algorithms, such as SWFI-stream and Moment.

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

PubMed

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

2017-11-28

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

Text mining patents for biomedical knowledge.

PubMed

Rodriguez-Esteban, Raul; Bundschus, Markus

2016-06-01

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biomedical data mining in clinical routine: expanding the impact of hospital information systems.

PubMed

Müller, Marcel; Markó, Kornel; Daumke, Philipp; Paetzold, Jan; Roesner, Arnold; Klar, Rüdiger

2007-01-01

In this paper we want to describe how the promising technology of biomedical data mining can improve the use of hospital information systems: a large set of unstructured, narrative clinical data from a dermatological university hospital like discharge letters or other dermatological reports were processed through a morpho-semantic text retrieval engine ("MorphoSaurus") and integrated with other clinical data using a web-based interface and brought into daily clinical routine. The user evaluation showed a very high user acceptance - this system seems to meet the clinicians' requirements for a vertical data mining in the electronic patient records. What emerges is the need for integration of biomedical data mining into hospital information systems for clinical, scientific, educational and economic reasons.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

Statistical data mining of streaming motion data for fall detection in assistive environments.

PubMed

Tasoulis, S K; Doukas, C N; Maglogiannis, I; Plagianakos, V P

2011-01-01

The analysis of human motion data is interesting for the purpose of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio or video sensors on the monitored subject (wearable sensors) or the surrounding environment. The output of such sensors is data streams that require real time recognition, especially in emergency situations, thus traditional classification approaches may not be applicable for immediate alarm triggering or fall prevention. This paper presents a statistical mining methodology that may be used for the specific problem of real time fall detection. Visual data captured from the user's environment, using overhead cameras along with motion data are collected from accelerometers on the subject's body and are fed to the fall detection system. The paper includes the details of the stream data mining methodology incorporated in the system along with an initial evaluation of the achieved accuracy in detecting falls.

Frequent Itemset Hiding Algorithm Using Frequent Pattern Tree Approach

ERIC Educational Resources Information Center

Alnatsheh, Rami

2012-01-01

A problem that has been the focus of much recent research in privacy preserving data-mining is the frequent itemset hiding (FIH) problem. Identifying itemsets that appear together frequently in customer transactions is a common task in association rule mining. Organizations that share data with business partners may consider some of the frequent…

Student Consistency and Implications for Feedback in Online Assessment Systems

ERIC Educational Resources Information Center

Madhyastha, Tara M.; Tanimoto, Steven

2009-01-01

Most of the emphasis on mining online assessment logs has been to identify content-specific errors. However, the pattern of general "consistency" is domain independent, strongly related to performance, and can itself be a target of educational data mining. We demonstrate that simple consistency indicators are related to student outcomes,…

Using Syntactic Patterns to Enhance Text Analytics

ERIC Educational Resources Information Center

Meyer, Bradley B.

2017-01-01

Large scale product and service reviews proliferate and are commonly found across the web. The ability to harvest, digest and analyze a large corpus of reviews from online websites is still however a difficult problem. This problem is referred to as "opinion mining." Opinion mining is an important area of research as advances in the…

Data mining application in customer relationship management for hospital inpatients.

PubMed

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 Application in Customer Relationship Management for Hospital Inpatients

PubMed Central

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

"Farming Miners" or "Mining Farmers"?: Diamond Mining and Rural Development in Post-Conflict Sierra Leone

ERIC Educational Resources Information Center

Maconachie, Roy; Binns, Tony

2007-01-01

Sierra Leone is currently emerging from a brutal civil war that lasted most of the 1990s, and now has the dubious distinction of being ranked among the world's poorest countries. As thousands of displaced people move back to their villages, a large proportion of the predominantly farm-based rural population are growing food crops for the first…

Resource Conflicts. Emerging Struggles over Strategic Commodities in Latin America

DTIC Science & Technology

2011-04-01

projects; and (2) the involvement of national 1 On the 2005 case in Ecuador , see Carla D’Nan Bass and...analyze seventeen cases of mining and hydrocarbon conflict in Peru, Bolivia, and Ecuador , highlighting cross- sector variation. I. RESOURCE...largely coopted or even created by the oil companies themselves, common in Ecuador . In contrast to the hydrocarbons case , in the mining sector we

Technological Change and Its Labor Impact in Five Energy Industries. Coal Mining/Oil and Gas Extraction/Petroleum Refining/Petroleum Pipeline Transportation/Electric and Gas Utilities.

ERIC Educational Resources Information Center

Bureau of Labor Statistics (DOL), Washington, DC.

This bulletin appraises major technological changes emerging in five American industries (coal mining, oil and gas extraction, petroleum refining, petroleum pipeline transportation, and electric and gas utilities) and discusses the impact of these changes on productivity and occupations over the next five to ten years. Its separate reports on each…

Design Pattern Mining Using Distributed Learning Automata and DNA Sequence Alignment

PubMed Central

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

High contents of rare earth elements (REEs) in stream waters of a Cu-Pb-Zn mining area.

PubMed

Protano, G; Riccobono, F

2002-01-01

Stream waters draining an old mining area present very high rare earth element (REE) contents, reaching 928 microg/l as the maximum total value (sigmaREE). The middle rare earth elements (MREEs) are usually enriched with respect to both the light (LREEs) and heavy (HREEs) elements of this group, producing a characteristic "roof-shaped" pattern of the shale Post-Archean Australian Shales-normalized concentrations. At the Fenice Capanne Mine (FCM), the most important base metal mine of the study area, the REE source coincides with the mine tailings, mostly the oldest ones composed of iron-rich materials. The geochemical history of the REEs released into Noni stream from wastes in the FCM area is strictly determined by the pH, which controls the REE speciation and in-stream processes. The formation of Al-rich and mainly Fe-rich flocs effectively scavenges the REEs, which are readily and drastically removed from the solution when the pH approaches neutrality. Leaching experiments performed on flocs and waste materials demonstrate that Fe-oxides/oxyhydroxides play a key role in the release of lanthanide elements into stream waters. The origin of the "roof-shaped" REE distribution pattern as well as the peculiar geochemical behavior of some lanthanide elements in the aqueous system are discussed.

The Perceived Consequences of Gold Mining in Postwar El Salvador: A Qualitative Study

PubMed Central

Cabezas, Pedro; Valle, Evan; Kornfeld, Julie; Muntaner, Carles; Soklaridis, Sophie

2015-01-01

Objectives. We investigated themes related to the health and environmental impacts of gold mining in El Salvador. Methods. Over a 1-month period in 2013, we conducted focus groups (n = 32 participants in total) and individual semistructured interviews (n = 11) with community leaders until we achieved thematic saturation. Data collection took place in 4 departments throughout the country. We used a combination of criterion-purposive and snowballing sampling techniques to identify participants. Results. Multiple themes emerged: (1) the fallacy of economic development; (2) critique of mining activities; (3) the creation of mining-related violence, with parallels to El Salvador's civil war; and (4) solutions and alternatives to mining activity. Solutions involved the creation of cooperative microenterprises for sustainable economic growth, political empowerment within communities, and development of local participatory democracies. Conclusions. Gold mining in El Salvador is perceived as a significant environmental and public health threat. Local solutions may be applicable broadly. PMID:26378845

Local Community Perceptions of Mine Site Restoration Using Phytoremediation in Abitibi-Temiscamingue (Quebec).

PubMed

Vodouhe, Fifanou G; Khasa, Damase P

2015-01-01

This work explores factors supporting people perception about mine site restoration and phytoremediation. Phytoremediation is one of the most eco-friendly restoration strategy emerged since the last two decades but studies on local people perception on this restoration strategy are scarce. To fill in this gap, data were collected from mining stakeholders using a structured questionnaire administered through snowball sampling method. We used Multiple Correspondence Analysis as implemented in the software XLSTAT to visualize relationship between participants' characteristics, their view on mine site restoration and phytoremediation. Results clearly show out that people perception on mine site restoration is influenced by mining activities effects on health and region attractiveness. Phytoremediation (65.21%) was rated positively with regard to its environment potential, aesthetic and consideration for future generation followed by fillings and excavating. Restoration strategy costs have no effect on people choice and participants prefer use of shrubs as vegetation component of phytoremediation to reach their restoration objective.

A review on in situ phytoremediation of mine tailings.

PubMed

Wang, Li; Ji, Bin; Hu, Yuehua; Liu, Runqing; Sun, Wei

2017-10-01

Mine tailings are detrimental to natural plant growth due to their physicochemical characteristics, such as high pH, high salinity, low water retention capacity, high heavy metal concentrations, and deficiencies in soil organic matter and fertility. Thus, the remediation of mine tailings has become a key issue in environmental science and engineering. Phytoremediation, an in situ cost-effective technology, is emerging as the most promising remediation method for mine tailings by introducing tolerant plant species. It is particularly effective in dealing with large-area mine tailings with shallow contamination of organic, nutrient and metal pollutants. In this review, the background, concepts and applications of phytoremediation are comprehensively discussed. Furthermore, proper amendments used to improve the physical, chemical and biological properties of mine tailings are systematically reviewed and compared. Emphasis is placed on the types and characteristics of tolerant plants and their role in phytoremediation. Moreover, the role of microorganisms and their mechanism in phytoremediation are also discussed in-depth. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Graph Approach to Mining Biological Patterns in the Binding Interfaces.

PubMed

Cheng, Wen; Yan, Changhui

2017-01-01

Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, we present a graph-mining method for discovering biological patterns in the protein-RNA interfaces. We represented known protein-RNA interfaces using graphs and then discovered graph patterns enriched in the interfaces. Comparison of the discovered graph patterns with UniProt annotations showed that the graph patterns had a significant overlap with residue sites that had been proven crucial for the RNA binding by experimental methods. Using 200 patterns as input features, a support vector machine method was able to classify protein surface patches into RNA-binding sites and non-RNA-binding sites with 84.0% accuracy and 88.9% precision. We built a simple scoring function that calculated the total number of the graph patterns that occurred in a protein-RNA interface. That scoring function was able to discriminate near-native protein-RNA complexes from docking decoys with a performance comparable with that of a state-of-the-art complex scoring function. Our work also revealed possible patterns that might be important for binding affinity.

The Technology Review 10: Emerging Technologies that Will Change the World.

ERIC Educational Resources Information Center

Technology Review, 2001

2001-01-01

Identifies 10 emerging areas of technology that will soon have a profound impact on the economy and on how people live and work: brain-machine interfaces; flexible transistors; data mining; digital rights management; biometrics; natural language processing; microphotonics; untangling code; robot design; and microfluidics. In each area, one…

Statistically significant relational data mining :

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

Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann

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 publicationsmore » 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.« less

Modeling of the Nano- and Picoseismicity Rate Changes Resulting from Static Stress Triggering due to Small (MW2.2) Event Recorded at Mponeng Deep Gold Mine, South Africa

NASA Astrophysics Data System (ADS)

Kozlowska, M.; Orlecka-Sikora, B.; Kwiatek, G.; Boettcher, M. S.; Dresen, G. H.

2014-12-01

Static stress changes following large earthquakes are known to affect the rate and spatio-temporal distribution of the aftershocks. Here we utilize a unique dataset of M ≥ -3.4 earthquakes following a MW 2.2 earthquake in Mponeng gold mine, South Africa, to investigate this process for nano- and pico- scale seismicity at centimeter length scales in shallow, mining conditions. The aftershock sequence was recorded during a quiet interval in the mine and thus enabled us to perform the analysis using Dietrich's (1994) rate and state dependent friction law. The formulation for earthquake productivity requires estimation of Coulomb stress changes due to the mainshock, the reference seismicity rate, frictional resistance parameter, and the duration of aftershock relaxation time. We divided the area into six depth intervals and for each we estimated the parameters and modeled the spatio-temporal patterns of seismicity rates after the stress perturbation. Comparing the modeled patterns of seismicity with the observed distribution we found that while the spatial patterns match well, the rate of modeled aftershocks is lower than the observed rate. To test our model, we used four metrics of the goodness-of-fit evaluation. Testing procedure allowed rejecting the null hypothesis of no significant difference between seismicity rates only for one depth interval containing the mainshock, for the other, no significant differences have been found. Results show that mining-induced earthquakes may be followed by a stress relaxation expressed through aftershocks located on the rupture plane and in regions of positive Coulomb stress change. Furthermore, we demonstrate that the main features of the temporal and spatial distribution of very small, mining-induced earthquakes at shallow depths can be successfully determined using rate- and state-based stress modeling.

Mine fire experiments and simulation with MFIRE

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

Laage, L.W.; Yang, Hang

1995-12-31

A major concern of mine fires is the heat generated ventilation disturbances which can move products of combustion (POC) through unexpected passageways. Fire emergency planning requires simulation of the interaction of the fire and ventilation system to predict the state of the ventilation system and the subsequent distribution of temperatures and POC. Several computer models were developed by the U.S. Bureau of Mines (USBM) to perform this simulation. The most recent, MFIRE, simulates a mine`s ventilation system and its response to altered ventilation parameters such as the development of new mine workings or changes in ventilation control structures, external influencemore » such as varying outside temperatures, and internal influences such as fires. Extensive output allows quantitative analysis of the effects of the proposed alteration to die ventilation system. This paper describes recent USBM research to validate MFIRE`s calculation of temperature distribution in an airway due to a mine fire, as temperatures are the most significant source of ventilation disturbances. Fire tests were conducted at the Waldo Mine near Magdalena, NM. From these experiments, temperature profiles were developed as functions of time and distance from the fire and compared with simulations from MFIRE.« less

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

PubMed

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

2009-11-15

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

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

PubMed

Liu, Shiliang; Li, Wenping; Wang, Qiqing

2018-09-01

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

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

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

Hong, Seokyong; Lee, Sangkeun; Lim, Seung-Hwan

2016-01-01

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons:more » multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.« less

Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes.

PubMed

Leary, Alison; Cook, Rob; Jones, Sarahjane; Smith, Judith; Gough, Malcolm; Maxwell, Elaine; Punshon, Geoffrey; Radford, Mark

2016-12-16

Nursing is a safety critical activity but not easily quantified. This makes the building of predictive staffing models a challenge. The aim of this study was to determine if relationships between registered and non-registered nurse staffing levels and clinical outcomes could be discovered through the mining of routinely collected clinical data. The secondary aim was to examine the feasibility and develop the use of 'big data' techniques commonly used in industry for this area of healthcare and examine future uses. The data were obtained from 1 large acute National Health Service hospital trust in England. Routinely collected physiological, signs and symptom data from a clinical database were extracted, imported and mined alongside a bespoke staffing and outcomes database using Mathmatica V.10. The physiological data consisted of 120 million patient entries over 6 years, the bespoke database consisted of 9 years of daily data on staffing levels and safety factors such as falls. To discover patterns in these data or non-linear relationships that would contribute to modelling. To examine feasibility of this technique in this field. After mining, 40 correlations (p<0.00005) emerged between safety factors, physiological data (such as the presence or absence of nausea) and staffing factors. Several inter-related factors demonstrated step changes where registered nurse availability appeared to relate to physiological parameters or outcomes such as falls and the management of symptoms. Data extraction proved challenging as some commercial databases were not built for extraction of the massive data sets they contain. The relationship between staffing and outcomes appears to exist. It appears to be non-linear but calculable and a data-driven model appears possible. These findings could be used to build an initial mathematical model for acute staffing which could be further tested. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Geological survey of Maryland using EREP flight data. [mining, mapping, Chesapeake Bay islands, coastal water features

NASA Technical Reports Server (NTRS)

Weaver, K. N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Underflight photography has been used in the Baltimore County mined land inventory to determine areas of disturbed land where surface mining of sand and ground clay, or stone has taken place. Both active and abandoned pits and quarries were located. Aircraft data has been used to update cultural features of Calvert, Caroline, St. Mary's, Somerset, Talbot, and Wicomico Counties. Islands have been located and catalogued for comparison with older film and map data for erosion data. Strip mined areas are being mapped to obtain total area disturbed to aid in future mining and reclamation problems. Coastal estuarine and Atlantic Coast features are being studied to determine nearshore bedforms, sedimentary, and erosional patterns, and manmade influence on natural systems.

Using airborne thermal infrared imagery and helicopter EM conductivity to locate mine pools and discharges in the Kettle Creek watershed, north-central Pennsylvania

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

Love, E.; Hammack, R.W.; Harbert, W.P.

2005-11-01

The Kettle Creek watershed contains 50–100-year-old surface and underground coal mines that are a continuing source of acid mine drainage (AMD). To characterize the mining-altered hydrology of this watershed, an airborne reconnaissance was conducted in 2002 using airborne thermal infrared imagery (TIR) and helicopter-mounted electromagnetic (HEM) surveys. TIR uses the temperature differential between surface water and groundwater to locate areas where groundwater emerges at the surface. TIR anomalies located in the survey included seeps and springs, as well as mine discharges. In a follow-up ground investigation, hand-held GPS units were used to locate 103 of the TIR anomalies. Of themore » sites investigated, 26 correlated with known mine discharges, whereas 27 were previously unknown. Seven known mine discharges previously obscured from TIR imagery were documented. HEM surveys were used to delineate the groundwater table and also to locate mine pools, mine discharges, and groundwater recharge zones. These surveys located 12 source regions and flow paths for acidic, metal-containing (conductive) mine drainage; areas containing acid-generating mine spoil; and areas of groundwater recharge and discharge, as well as identifying potential mine discharges previously obscured from TIR imagery by nondeciduous vegetation. Follow-up ground-based electromagnetic surveys verified the results of the HEM survey. Our study suggests that airborne reconnaissance can make the remediation of large watersheds more efficient by focusing expensive ground surveys on small target areas.« less

Using airborne thermal infrared imagery and helicopter EM conductivity to locate mine pools and discharges in the Kettle Creek watershed, north-central Pennsylvania

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

Love, E.; Hammack, R.; Harbert, W.

2005-12-01

The Kettle Creek watershed contains 50-100-year-old surface and underground coal mines that are a continuing source of acid mine drainage (AMD). To characterize the mining-altered hydrology of this watershed, an airborne reconnaissance was conducted in 2002 using airborne thermal infrared imagery (TIR) and helicopter-mounted electromagnetic (HEM) surveys. TIR uses the temperature differential between surface water and groundwater to locate areas where groundwater emerges at the surface. TIR anomalies located in the survey included seeps and springs, as well as mine discharges. In a follow-up ground investigation, hand-held GPS units were used to locate 103 of the TIR anomalies. Of themore » sites investigated, 26 correlated with known mine discharges, whereas 27 were previously unknown. Seven known mine discharges previously obscured from TIR imagery were documented. HEM surveys were used to delineate the groundwater table and also to locate mine pools, mine discharges, and groundwater recharge zones. These surveys located 12 source regions and flow paths for acidic, metal-containing (conductive) mine drainage; areas containing acid-generating mine spoil; and areas of groundwater recharge and discharge, as well as identifying potential mine discharges previously obscured from TIR imagery by nondeciduous vegetation. Follow-up ground-based electromagnetic surveys verified the results of the HEM survey. Our study suggests that airborne reconnaissance can make the remediation of large watersheds more efficient by focusing expensive ground surveys on small target areas.« less

Mining sequential patterns for protein fold recognition.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2008-02-01

Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. Specifically, one of the most efficient SPM algorithms, cSPADE, is employed for the analysis of protein sequence. A classifier uses the extracted sequential patterns to classify proteins in the appropriate fold category. For training and evaluating the proposed method we used the protein sequences from the Protein Data Bank and the annotation of the SCOP database. The method exhibited an overall accuracy of 25% in a classification problem with 36 candidate categories. The classification performance reaches up to 56% when the five most probable protein folds are considered.

Earth Conductivity Estimation from Through-the-Earth Measurements of 94 Coal Mines Using Different Electromagnetic Models

PubMed Central

Yan, Lincan; Waynert, Joseph; Sunderman, Carl

2015-01-01

Through-the-Earth (TTE) communication systems require minimal infrastructure to operate. Hence, they are assumed to be more survivable and more conventional than other underground mine communications systems. This survivability is a major advantage for TTE systems. In 2006, Congress passed the Mine Improvement and New Emergency Response Act (MINER Act), which requires all underground coal mines to install wireless communications systems. The intent behind this mandate is for trapped miners to be able to communicate with surface personnel after a major accident-hence, the interest in TTE communications. To determine the likelihood of establishing a TTE communication link, it would be ideal to be able to predict the apparent conductivity of the overburden above underground mines. In this paper, all 94 mine TTE measurement data collected by Bureau of Mines in the 1970s and early 1980s, are analyzed for the first time to determine the apparent conductivity of the overburden based on three different models: a homogenous half-space model, a thin sheet model, and an attenuation factor or Q-factor model. A statistical formula is proposed to estimate the apparent earth conductivity for a specific mine based on the TTE modeling results given the mine depth and signal frequency. PMID:26213457

Earth Conductivity Estimation from Through-the-Earth Measurements of 94 Coal Mines Using Different Electromagnetic Models.

PubMed

Yan, Lincan; Waynert, Joseph; Sunderman, Carl

2014-10-01

Through-the-Earth (TTE) communication systems require minimal infrastructure to operate. Hence, they are assumed to be more survivable and more conventional than other underground mine communications systems. This survivability is a major advantage for TTE systems. In 2006, Congress passed the Mine Improvement and New Emergency Response Act (MINER Act), which requires all underground coal mines to install wireless communications systems. The intent behind this mandate is for trapped miners to be able to communicate with surface personnel after a major accident-hence, the interest in TTE communications. To determine the likelihood of establishing a TTE communication link, it would be ideal to be able to predict the apparent conductivity of the overburden above underground mines. In this paper, all 94 mine TTE measurement data collected by Bureau of Mines in the 1970s and early 1980s, are analyzed for the first time to determine the apparent conductivity of the overburden based on three different models: a homogenous half-space model, a thin sheet model, and an attenuation factor or Q-factor model. A statistical formula is proposed to estimate the apparent earth conductivity for a specific mine based on the TTE modeling results given the mine depth and signal frequency.

Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream.

PubMed

Byrne, Patrick; Runkel, Robert L; Walton-Day, Katherine

2017-07-01

Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream

USGS Publications Warehouse

Byrne, Patrick; Runkel, Robert L.; Walton-Day, Katie

2017-01-01

Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

Electromagnetic system for detection and localization of miners caught in mine accidents

NASA Astrophysics Data System (ADS)

Pronenko, Vira; Dudkin, Fedir

2016-12-01

The profession of a miner is one of the most dangerous in the world. Among the main causes of fatalities in underground coal mines are the delayed alert of the accident and the lack of information regarding the actual location of the miners after the accident. In an emergency situation (failure or destruction of underground infrastructure), personnel search behind and beneath blockage needs to be performed urgently. However, none of the standard technologies - radio-frequency identification (RFID), Digital Enhanced Cordless Telecommunications (DECT), Wi-Fi, emitting cables, which use the stationary technical devices in mines - provide information about the miners location with the necessary precision. The only technology that is able to provide guaranteed delivery of messages to mine personnel, regardless of their location and under any destruction in the mine, is low-frequency radio technology, which is able to operate through the thickness of rocks even if they are wet. The proposed new system for miner localization is based on solving the inverse problem of determining the magnetic field source coordinates using the data of magnetic field measurements. This approach is based on the measurement of the magnetic field radiated by the miner's responder beacon using two fixed and spaced three-component magnetic field receivers and the inverse problem solution. As a result, a working model of the system for miner's beacon search and localization (MILES - MIner's Location Emergency System) was developed and successfully tested. This paper presents the most important aspects of this development and the results of experimental tests.

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

PubMed

Bekhuis, Tanja

2006-04-03

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.

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

PubMed

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

2014-10-01

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

Land Use, Water Quality, and Incidence of Buruli Ulcer in Gold-Mining Regions of Ghana

NASA Astrophysics Data System (ADS)

Hagarty, J.; Voegborlo, R.; Smithwick, E. A.; Singha, K.

2011-12-01

Buruli ulcer, an emerging bacterial disease caused by Mycobacterium ulcerans, affects populations in many equatorial countries, predominantly in western Africa. Occurring in over thirty countries worldwide, it is the third most common Mycobacterial disease after tuberculosis and leprosy. The disease causes ulcerative lesions and can lead to severe deformity if untreated. While methods of treatment for Buruli ulcer are well known and have a high rate of success, the mode of transmission of Buruli ulcer remains elusive. Multiple hypotheses have been put forward in the search for the vector for this disease. Studies of Buruli ulcer to date seem to conclude that water is, in some way, closely related to the transmission of this disease. In particular, changes in water quality due to changes in land use may contribute to the emergence of Buruli ulcer. We hypothesize that stagnant pools, especially those with low dissolved oxygen and high metals, nitrogen, and phosphorus concentrations, will provide a favorable environment for M. ulcerans growth and transmission. To explore how climate, land use, and soil and water quality interact to create a favorable environment for Buruli ulcer emergence, we explore seasonal and annual variability in rainfall and temperature, land use, and physical and chemical properties of soil and water at five sites within the country: four in the southern part of the country (three Buruli-endemic communities and one control) and one non-endemic community in the north. The southern control accounts for differences between endemic and non-endemic communities with similar land uses and geological setting. The northern community has experienced massive floods in recent years, and we suspect that, due to this, Buruli ulcer may start to appear in the community. Results from groundwater data indicate that aquifer rock type does not strongly correlate with groundwater chemistry and that groundwater chemistry does not relate to incidence of Buruli ulcer, thus highlighting that the problems are likely largely surface water based. Analyses of rainfall data collected from eleven stations throughout Ghana show that patterns of annual rainfall do not vary greatly between Buruli-endemic and non-endemic areas, suggesting that normal rainfall patterns do not affect incidence of disease, and that event-based precipitation may be a driving factor for the onset of Buruli ulcer. Analysis of localized soil and water chemistry is ongoing, with samples collected from mining pits, farms, rivers, ponds, swamps, and wells in our five communities within Ghana.

Mining Interactions in Immersive Learning Environments for Real-Time Student Feedback

ERIC Educational Resources Information Center

Kennedy, Gregor; Ioannou, Ioanna; Zhou, Yun; Bailey, James; O'Leary, Stephen

2013-01-01

The analysis and use of data generated by students' interactions with learning systems or programs--learning analytics--has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in…

30 CFR 946.25 - Approval of Virginia abandoned mine land reclamation plan amendments.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., 1987 November 13, 1987 VR 480-03-19.884.13(c) (2), (5), (6), (7), (d)(1), (2); Establish emergency... CFR 884.13(a), (b), (c)(1), (c)(2), (c)(3), (c)(4), (c)(5), (c)(6), (c)(7), (d)(1), (d)(2), (d)(3), (d... Division of Mined Land Reclamation, P.O. Drawer 900, Big Stone Gap, Virginia 24219, or (2) Office of...

Landfill mining from a deposit of the chlorine/organochlorine industry as source of dioxin contamination of animal feed and assessment of the responsible processes.

PubMed

Torres, João Paulo Machado; Leite, Claudio; Krauss, Thomas; Weber, Roland

2013-04-01

In 1997, the Polychlorinated dibenzo-para-dioxin (PCDD)/Polychlorinated dibenzofuran (PCDF) concentrations in dairy products in Germany and other European countries increased. The PCDD/PCDF source was contaminated lime used in Brazilian citrus pulp pellets. The contaminated lime was mined from an industrial dump site. However, the detailed origin of the PCDD/PCDFs in the lime was not revealed. This paper investigates the contamination origin and describes the link between lime milk from the dumpsite of a chlorine/organochlorine industry and the contaminated lime. The contaminated lime stem from mining at the corporate landfill of Solvay Indupa in Sao Paulo. The landfill was used for 40 years for deposition of production residues and closed in 1996. The factory operated/operates at least two processes with potentially high PCDD/PCDFs releases namely the oxychlorination process for production of ethylene dichloride (EDC) and the chlor-alkali process. The main landfilled waste was lime milk (1.4 million tons) from the vinyl chloride monomer production (via the acetylene process) along with residues from other processes. The PCDD/PCDF fingerprint revealed that most samples from the chemical landfill showed an EDC PCDD/PCDF pattern with a characteristic octachlorodibenzofuran dominance. The PCDD/PCDF pattern of a Rio Grande sediment samples downstream the facility showed a chlor-alkali pattern with a minor impact of the EDC pattern. The case highlights that PCDD/PCDF- and persistent organic pollutants-contaminated sites need to be identified in a comprehensive manner as required by the Stockholm Convention (article 6) and controlled for their impact on the environment and human health. Landfill mining and reuse of materials from contaminated deposits should be prohibited.

Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease.

PubMed

Emoto, Takuo; Yamashita, Tomoya; Kobayashi, Toshio; Sasaki, Naoto; Hirota, Yushi; Hayashi, Tomohiro; So, Anna; Kasahara, Kazuyuki; Yodoi, Keiko; Matsumoto, Takuya; Mizoguchi, Taiji; Ogawa, Wataru; Hirata, Ken-Ichi

2017-01-01

The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.

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

USGS Publications Warehouse

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

1983-01-01

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

Extracting nursing practice patterns from structured labor and delivery data sets.

PubMed

Hall, Eric S; Thornton, Sidney N

2007-10-11

This study was designed to demonstrate the feasibility of a computerized care process model that provides real-time case profiling and outcome forecasting. A methodology was defined for extracting nursing practice patterns from structured point-of-care data collected using the labor and delivery information system at Intermountain Healthcare. Data collected during January 2006 were retrieved from Intermountain Healthcare's enterprise data warehouse for use in the study. The knowledge discovery in databases process provided a framework for data analysis including data selection, preprocessing, data-mining, and evaluation. Development of an interactive data-mining tool and construction of a data model for stratification of patient records into profiles supported the goals of the study. Five benefits of the practice pattern extraction capability, which extend to other clinical domains, are listed with supporting examples.

A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things.

PubMed

Xiao, Yun; Wang, Xin; Eshragh, Faezeh; Wang, Xuanhong; Chen, Xiaojiang; Fang, Dingyi

2017-05-11

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.

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

NASA Astrophysics Data System (ADS)

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 less than in seepage water due to mixing and (co)precipitation of REE. Lu/La and Sm/La relations show a significant decrease with increasing distance from the dump caused by preferential (co)precipitation of heavy REE with amorphous Fe-hydroxides along the Gessenbach. Thus, REE patterns can not only be used as tracers but also to study processes. In contrast to the patterns of the seepage, the REE patterns of the Silurian rocks as determined by LA-ICP-MS feature rather flat patterns with enrichment of middle REE (Sm - Dy). Results from batch experiments show preferentially leaching of heavy REE for all investigated source rocks. The highest absolute concentrations of REE appear in the eluates of the Silurian 'Ockerkalk'. Since the REE pattern closely reflects the pattern found in the seepage water it is assumed to be the most important source for the occurence of the REE pattern observed in seepage water. Studies of microbial heavy metal retention were performed by direct incubation of seepage water using well characterized fungal and bacterial strains. Using the bacterium Escherichia coli for incubation of seepage water sorption of heavy metals to biomass was observed. Use of the fungus Schizophyllum commune for incubation, however, has a much more pronounced effect including significant fractionation of REE pointing to the possibility of a specific active uptake mechanism. Bioextraction with bacteria and fungal mycelia might be an alternative to plant growth and phytoextraction and might be preferable for AMD water treatment since no soil substrate is necessary. Future research must be directed towards genes for active transport, intra- or extracellular storage proteins and their application. Biotechnological use of such genes in, e.g., strains of E. coli, might yield highly useful bioremediation strains that can help to reduce the ecological effects of pollution resulting from former mining activities.

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

PubMed

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

2014-10-01

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

Polymorphic SSR markers for Plasmopara obducens (Peronosporaceae), the newly emergent downy mildew pathogen of Impatiens (Balsaminaceae)

USDA-ARS?s Scientific Manuscript database

Premise of the study: Microsatellite markers were developed for Plasmopara obducens, the causal agent of the newly emergent downy mildew disease of Impatiens walleriana. Methods and Results: A 151.2 Mb draft genome assembly was generated from P. obducens using Illumina technology and mined to identi...

Mine-Resistant Ambush-Protection vehicles

NASA Image and Video Library

2014-02-13

CAPE CANAVERAL, Fla. – One of four new emergency egress vehicles, called Mine-Resistant Ambush-Protection, or MRAP, vehicles sits near space shuttle-era M-113 vehicles at the Maintenance and Operations Facility at NASA’s Kennedy Space Center in Florida. The MRAPs arrived from the U.S. Army Red River Depot in Texarkana, Texas in December 2013. The vehicles were processed in and then transported to the Rotation, Processing and Surge Facility near the Vehicle Assembly Building for temporary storage. The Ground Systems Development and Operations Program at Kennedy led the efforts to an emergency egress vehicle that future astronauts could quickly use to leave the Launch Complex 39 area in case of an emergency. During crewed launches of NASA’s Space Launch System and Orion spacecraft, the MRAP will be stationed by the slidewire termination area at the pad. In case of an emergency, the crew will ride a slidewire to the ground and immediately board the MRAP for safe egress from the pad. The new vehicles replace the M-113 vehicles that were used during the Space Shuttle Program. Photo credit: NASA/Kim Shiflett

Mine-Resistant Ambush-Protection vehicles

NASA Image and Video Library

2014-02-13

CAPE CANAVERAL, Fla. – One of four new emergency egress vehicles, called Mine-Resistant Ambush-Protection, or MRAP, vehicles is driven to the Maintenance and Operations Facility at Kennedy Space Center in Florida. The MRAPs arrived from the U.S. Army Red River Depot in Texarkana, Texas in December 2013. The vehicles were processed in and then transported to the Rotation, Processing and Surge Facility near the Vehicle Assembly Building for temporary storage. The Ground Systems Development and Operations Program at Kennedy led the efforts to an emergency egress vehicle that future astronauts could quickly use to leave the Launch Complex 39 area in case of an emergency. During crewed launches of NASA’s Space Launch System and Orion spacecraft, the MRAP will be stationed by the slidewire termination area at the pad. In case of an emergency, the crew will ride a slidewire to the ground and immediately board the MRAP for safe egress from the pad. The new vehicles replace the M-113 vehicles that were used during the Space Shuttle Program. Photo credit: NASA/Kim Shiflett

Mine-Resistant Ambush-Protection vehicles

NASA Image and Video Library

2014-02-13

CAPE CANAVERAL, Fla. – A URS Federal Services worker pulls down the steps to the entrance of one of the four new emergency egress vehicles, called Mine-Resistant Ambush-Protected, or MRAP, vehicles at the Maintenance and Operations Facility at NASA’s Kennedy Space Center in Florida. The MRAPs arrived from the U.S. Army Red River Depot in Texarkana, Texas in December 2013. The vehicles were processed in and then transported to the Rotation, Processing and Surge Facility near the Vehicle Assembly Building for temporary storage. The Ground Systems Development and Operations Program at Kennedy led the efforts to an emergency egress vehicle that future astronauts could quickly use to leave the Launch Complex 39 area in case of an emergency. During crewed launches of NASA’s Space Launch System and Orion spacecraft, the MRAP will be stationed by the slidewire termination area at the pad. In case of an emergency, the crew will ride a slidewire to the ground and immediately board the MRAP for safe egress from the pad. The new vehicles replace the M-113 vehicles that were used during the Space Shuttle Program. Photo credit: NASA/Kim Shiflett

Mine-Resistant Ambush-Protection vehicles

NASA Image and Video Library

2014-02-13

CAPE CANAVERAL, Fla. – A view of the interior of one of four new emergency egress vehicles, called Mine-Resistant Ambush-Protected, or MRAP, vehicles is shown. The MRAPs are at the Maintenance and Operations Facility at NASA’s Kennedy Space Center in Florida. The MRAPs arrived from the U.S. Army Red River Depot in Texarkana, Texas in December 2013. The vehicles were processed in and then transported to the Rotation, Processing and Surge Facility near the Vehicle Assembly Building for temporary storage. The Ground Systems Development and Operations Program at Kennedy led the efforts to an emergency egress vehicle that future astronauts could quickly use to leave the Launch Complex 39 area in case of an emergency. During crewed launches of NASA’s Space Launch System and Orion spacecraft, the MRAP will be stationed by the slidewire termination area at the pad. In case of an emergency, the crew will ride a slidewire to the ground and immediately board the MRAP for safe egress from the pad. The new vehicles replace the M-113 vehicles that were used during the Space Shuttle Program. Photo credit: NASA/Kim Shiflett

Thermal infrared remote sensing in assessing groundwater and surface-water resources related to Hannukainen mining development site, northern Finland

NASA Astrophysics Data System (ADS)

Rautio, Anne B.; Korkka-Niemi, Kirsti I.; Salonen, Veli-Pekka

2018-02-01

Mining development sites occasionally host complicated aquifer systems with notable connections to natural surface water (SW) bodies. A low-altitude thermal infrared (TIR) imaging survey was conducted to identify hydraulic connections between aquifers and rivers and to map spatial surface temperature patterns along the subarctic rivers in the proximity of the Hannukainen mining development area, northern Finland. In addition to TIR data, stable isotopic compositions ( δ 18O, δD) and dissolved silica concentrations were used as tracers to verify the observed groundwater (GW) discharge into the river system. Based on the TIR survey, notable GW discharge into the main river channel and its tributaries (61 km altogether) was observed and over 500 GW discharge sites were located. On the basis of the survey, the longitudinal temperature patterns of the studied rivers were found to be highly variable. Hydrological and hydrogeological information is crucial in planning and siting essential mining operations, such as tailing areas, in order to prevent any undesirable environmental impacts. The observed notable GW discharge was taken into consideration in the planning of the Hannukainen mining development area. The results of this study support the use of TIR imagery in GW-SW interaction and environmental studies in extensive and remote areas with special concerns for water-related issues but lacking the baseline research.

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

NASA Astrophysics Data System (ADS)

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 (P<0.01) aganist observations. Predicted mining processes had an average rate of 7.4 kgNha-1yr-1 and Predicted rates of N retention processes were 5.2 kgNha-1yr-1, both of which were in line with estimates only based on field data. The predicted trend of low DIN export could continue for another 70 years to pay 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.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

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 fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

Using a Data Mining Approach to Develop a Student Engagement-Based Institutional Typology. IR Applications, Volume 18, February 8, 2009

ERIC Educational Resources Information Center

Luan, Jing; Zhao, Chun-Mei; Hayek, John C.

2009-01-01

Data mining provides both systematic and systemic ways to detect patterns of student engagement among students at hundreds of institutions. Using traditional statistical techniques alone, the task would be significantly difficult--if not impossible--considering the size and complexity in both data and analytical approaches necessary for this…

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Large Scale Data Mining to Improve Usability of Data: An Intelligent Archive Testbed

NASA Technical Reports Server (NTRS)

Ramapriyan, Hampapuram; Isaac, David; Yang, Wenli; Morse, Steve

2005-01-01

Research in certain scientific disciplines - including Earth science, particle physics, and astrophysics - continually faces the challenge that the volume of data needed to perform valid scientific research can at times overwhelm even a sizable research community. The desire to improve utilization of this data gave rise to the Intelligent Archives project, which seeks to make data archives active participants in a knowledge building system capable of discovering events or patterns that represent new information or knowledge. Data mining can automatically discover patterns and events, but it is generally viewed as unsuited for large-scale use in disciplines like Earth science that routinely involve very high data volumes. Dozens of research projects have shown promising uses of data mining in Earth science, but all of these are based on experiments with data subsets of a few gigabytes or less, rather than the terabytes or petabytes typically encountered in operational systems. To bridge this gap, the Intelligent Archives project is establishing a testbed with the goal of demonstrating the use of data mining techniques in an operationally-relevant environment. This paper discusses the goals of the testbed and the design choices surrounding critical issues that arose during testbed implementation.

The Application of Data Mining Techniques to Create Promotion Strategy for Mobile Phone Shop

NASA Astrophysics Data System (ADS)

Khasanah, A. U.; Wibowo, K. S.; Dewantoro, H. F.

2017-12-01

The number of mobile shop is growing very fast in various regions in Indonesia including in Yogyakarta due to the increasing demand of mobile phone. This fact leads high competition among the mobile phone shops. In these conditions the mobile phone shop should have a good promotion strategy in order to survive in competition, especially for a small mobile phone shop. To create attractive promotion strategy, the companies/shops should know their customer segmentation and the buying pattern of their target market. These kind of analysis can be done using Data mining technique. This study aims to segment customer using Agglomerative Hierarchical Clustering and know customer buying pattern using Association Rule Mining. This result conducted in a mobile shop in Sleman Yogyakarta. The clustering result shows that the biggest customer segment of the shop was male university student who come on weekend and from association rule mining, it can be concluded that tempered glass and smart phone “x” as well as action camera and waterproof monopod and power bank have strong relationship. This results that used to create promotion strategies which are presented in the end of the study.

Mining dynamic noteworthy functions in software execution sequences.

PubMed

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

2017-01-01

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

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

USGS Publications Warehouse

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

2009-01-01

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

Development and application of biotechnologies in the metal mining industry.

PubMed

Johnson, D Barrie

2013-11-01

Metal mining faces a number of significant economic and environmental challenges in the twenty-first century for which established and emerging biotechnologies may, at least in part, provide the answers. Bioprocessing of mineral ores and concentrates is already used in variously engineered formats to extract base (e.g., copper, cobalt, and nickel) and precious (gold and silver) metals in mines throughout the world, though it remains a niche technology. However, current projections of an increasing future need to use low-grade primary metal ores, to reprocess mine wastes, and to develop in situ leaching technologies to extract metals from deep-buried ore bodies, all of which are economically more amenable to bioprocessing than conventional approaches (e.g., pyrometallurgy), would suggest that biomining will become more extensively utilized in the future. Recent research has also shown that bioleaching could be used to process a far wider range of metal ores (e.g., oxidized ores) than has previously been the case. Biotechnologies are also being developed to control mine-related pollution, including securing mine wastes (rocks and tailings) by using "ecological engineering" approaches, and also to remediate and recover metals from waste waters, such as acid mine drainage. This article reviews the current status of biotechnologies within the mining sector and considers how these may be developed and applied in future years.

Mercury and methylmercury concentrations and loads in the Cache Creek watershed, California

USGS Publications Warehouse

Domagalski, Joseph L.; Alpers, Charles N.; Slotton, D.G.; Suchanek, T.H.; Ayers, S.M.

2004-01-01

Concentrations and loads of total mercury and methylmercury were measured in streams draining abandoned mercury mines and in the proximity of geothermal discharge in the Cache Creek watershed of California during a 17-month period from January 2000 through May 2001. Rainfall and runoff were lower than long-term averages during the study period. The greatest loading of mercury and methylmercury from upstream sources to downstream receiving waters, such as San Francisco Bay, generally occurred during or after winter rainfall events. During the study period, loads of mercury and methylmercury from geothermal sources tended to be greater than those from abandoned mining areas, a pattern attributable to the lack of large precipitation events capable of mobilizing significant amounts of either mercury-laden sediment or dissolved mercury and methylmercury from mine waste. Streambed sediments of Cache Creek are a significant source of mercury and methylmercury to downstream receiving bodies of water. Much of the mercury in these sediments is the result of deposition over the last 100-150 years by either storm-water runoff, from abandoned mines, or continuous discharges from geothermal areas. Several geochemical constituents were useful as natural tracers for mining and geothermal areas, including the aqueous concentrations of boron, chloride, lithium and sulfate, and the stable isotopes of hydrogen and oxygen in water. Stable isotopes of water in areas draining geothermal discharges showed a distinct trend toward enrichment of 18O compared with meteoric waters, whereas much of the runoff from abandoned mines indicated a stable isotopic pattern more consistent with local meteoric water. ?? 2004 Elsevier B.V. All rights reserved.

Development of electric drive for centrifugal mine pumps in Solikamsk Potassium Mine Group Based on Industrial OMRON Controller

NASA Astrophysics Data System (ADS)

Kostarev, S. N.; Sereda, T. G.; Tatarnikova, N. A.; Kochetova, O. V.

2018-03-01

The electric drive for automation pumping out of filtration waters in the Second Solikamsk Potasssium Mine Group is developed. The emergency situation of flooding of the Mine has been considered in the course of development of the Upper Kama deposits of potash-magnesium salts. The functional scheme of automation of a drive of the pump is developed. The scheme is stipulated with manual and automatic control. To decrease the risk of flooding of mine, it is recommended to establish gauges of both bottom and top level control of a brine and other equipment in the collector of a brine: the gauge of measurementof a level, the gauge of the signal system of a level, the gauge of the pump control, the gauge of the signal system of a level with remote data transmission. For regulation of the charge of sewage, the P-regulator with the executive mechanism is stipulated. The ladder diagram of a pump control is developed to improve the work of centrifugal pumps and to prevent the cases of mines flooding.

Sustainable Remediation of Legacy Mine Drainage: A Case Study of the Flight 93 National Memorial.

PubMed

Emili, Lisa A; Pizarchik, Joseph; Mahan, Carolyn G

2016-03-01

Pollution from mining activities is a global environmental concern, not limited to areas of current resource extraction, but including a broader geographic area of historic (legacy) and abandoned mines. The pollution of surface waters from acid mine drainage is a persistent problem and requires a holistic and sustainable approach to addressing the spatial and temporal complexity of mining-specific problems. In this paper, we focus on the environmental, socio-economic, and legal challenges associated with the concurrent activities to remediate a coal mine site and to develop a national memorial following a catastrophic event. We provide a conceptual construct of a socio-ecological system defined at several spatial, temporal, and organizational scales and a critical synthesis of the technical and social learning processes necessary to achieving sustainable environmental remediation. Our case study is an example of a multi-disciplinary management approach, whereby collaborative interaction of stakeholders, the emergence of functional linkages for information exchange, and mediation led to scientifically informed decision making, creative management solutions, and ultimately environmental policy change.

[Spatiotemporal patterns and driving forces of land use change in industrial relocation area: a case study of old industrial area in Tiexi of Shenyang, Northeast China].

PubMed

Wang, Mei-Ling; Bing, Long-Fei; Xi, Feng-Ming; Wu, Rui; Geng, Yong

2013-07-01

Based on the QuickBird remote sensing images and with the support of GIS, this paper analyzed the spatiotemporal characteristics of land use change and its driving forces in old industrial area of Tiexi, Shenyang City of Liaoning Province in 2000-2010. During the study period, the industrial and mining warehouse land pattern had the greatest change, evolving from the historical pattern of residential land in the south and of industrial land in the north into residential land as the dominant land use pattern. In the last decade, the residential land area increased by 9%, mainly transferred from the industrial and mining warehouse land located in the north of Jianshe Road, while the industrial and mining warehouse land area decreased by 20%. The land areas for the commercial service and for the administrative and public services were increased by 1.3% and 3.1%, respectively. The land area for construction had a greater change, with an overall change rate being 76.9%. The land use change rate in 2000-2005 was greater than that in 2005-2010. National development strategies and policies, regional development planning, administrative reform, and industrial upgrading were the main driving forces of the land use change in old industrial area of Tiexi.

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

PubMed Central

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Liu, Ze; Xu, Jing

2016-01-01

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system. PMID:26771615

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

PubMed Central

2016-01-01

Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter’s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. Results While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. PMID:27956376

Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data

NASA Astrophysics Data System (ADS)

Espinoza Molina, D.; Datcu, M.

2015-04-01

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform's goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

Spatial patterns of cadmium and lead deposition on and adjacent to National Park Service lands in the vicinity of Red Dog Mine, Alaska.

PubMed

Hasselbach, L; Ver Hoef, J M; Ford, J; Neitlich, P; Crecelius, E; Berryman, S; Wolk, B; Bohle, T

2005-09-15

Heavy metal escapement associated with ore trucks is known to occur along the DeLong Mountain Regional Transportation System (DMTS) haul road corridor in Cape Krusenstern National Monument, northwest Alaska. Heavy metal concentrations in Hylocomium splendens moss (n = 226) were used in geostatistical models to predict the extent and pattern of atmospheric deposition of Cd and Pb on Monument lands. A stratified grid-based sample design was used with more intensive sampling near mine-related activity areas. Spatial predictions were used to produce maps of concentration patterns, and to estimate the total area in 10 moss concentration categories. Heavy metal levels in moss were highest immediately adjacent to the DMTS haul road (Cd > 24 mg/kg dw; Pb > 900 mg/kg dw). Spatial regression analyses indicated that heavy metal deposition decreased with the log of distance from the DMTS haul road and the DMTS port site. Analysis of subsurface soil suggested that observed patterns of heavy metal deposition reflected in moss were not attributable to subsurface lithology at the sample points. Further, moss Pb concentrations throughout the northern half of the study area were high relative to concentrations previously reported from other Arctic Alaska sites. Collectively, these findings indicate the presence of mine-related heavy metal deposition throughout the northern portion of Cape Krusenstern National Monument. Geospatial analyses suggest that the Pb depositional area extends 25 km north of the haul road to the Kisimilot/Iyikrok hills, and possibly beyond. More study is needed to determine whether higher moss heavy metal concentrations in the northernmost portion of the study area reflect deposition from mining-related activities, weathering from mineralized Pb/Zn outcrops in the broader region, or a combination of the two. South of the DMTS haul road, airborne deposition appears to be constrained by the Tahinichok Mountains. Heavy metal levels continue to diminish south of the mountains, reaching a minimum in the southernmost portion of the study area near the Igichuk Hills (45 km from the haul road). The influence of the mine site was not studied.

Estimating the Importance of Terrorists in a Terror Network

NASA Astrophysics Data System (ADS)

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

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

Historical archaeology at the Clarkson Mine, an eastern Ohio mining complex

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

Keener, C.S.

2003-07-01

This study examines the Clarkson Mine (33BL333), an eastern Ohio coal mine complex dating to the 1910s to 1920s, situated along Wheeling Creek. The results of preliminary surveys and the subsequent mitigation of four structures at the site are presented. The historical archaeology conducted at the site demonstrates the significant research possibilities inherent at many of these early industrial mine complexes. Of particular interest is the findings of depositional patterning around residential structures that revealed the influence of architecture on where and how items were deposited on the land surface. The ceramic and faunal assemblage were analyzed and provide significantmore » details on socioeconomic attributes associated with the workers or staff. Artifacts recovered at the site provide an excellent diagnostic framework from which other similarly aged sites can be compared and dated. The findings at the Clarkson Mine are also placed into a more regional perspective and compared with other contemporary studies.« less

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

PubMed Central

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2015-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods. PMID:25938136

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

PubMed

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

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

Graph Mining Meets the Semantic Web

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

Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluatemore » the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.« less

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

PubMed

Huang, Yin-Fu; Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

PubMed Central

Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. PMID:23737711

Common themes in changing vector-borne disease scenarios.

PubMed

Molyneux, David H

2003-01-01

The impact of climate change on disease patterns is controversial. However, global burden of disease studies suggest that infectious diseases will contribute a proportionately smaller burden of disease over the next 2 decades as non-communicable diseases emerge as public health problems. However, infectious diseases contribute proportionately more in the poorest quintile of the population. Notwithstanding the different views of the impact of global warming on vector-borne infections this paper reviews the conditions which drive the changing epidemiology of these infections and suggests that such change is linked by common themes including interactions of generalist vectors and reservoir hosts at interfaces with humans, reduced biodiversity associated with anthropogenic environmental changes, increases in Plasmodium falciparum: P. vivax ratios and well-described land use changes such as hydrological, urbanization, agricultural, mining and forest-associated impacts (extractive activities, road building, deforestation and migration) which are seen on a global scale.

Mercury Contamination from Historic Gold Mining in California

USGS Publications Warehouse

Alpers, Charles N.; Hunerlach, Michael P.

2000-01-01

Mercury contamination from historic gold mines represents a potential risk to human health and the environment. This fact sheet provides background information on the use of mercury in historic gold mining and processing operations in California, and describes a new USGS project that addresses the potential risks associated with mercury from these sources, with emphasis on historic hydraulic mining areas. Miners used mercury (quicksilver) to recover gold throughout the western United States at both placer (alluvial) and hardrock (lode) mines. The vast majority of mercury lost to the environment in California was from placer-goldmines, which used hydraulic, drift, and dredging methods. At hydraulic mines, placer ores were broken down with monitors (or water cannons, fig. 1) and the resulting slurry was directed throughsluices and drainage tunnels, where goldparticles combined with liquid mercury to form gold?mercury amalgam. Loss ofmercury in this process was 10 to 30 percent per season (Bowie, 1905), resulting in highly contaminated sediments at mine sites (fig. 2). Elevated mercury concentrations in present-day mine waters and sediments indicate thathundreds to thousands of pounds of mercury remain at each of the many sites affected by hydraulic mining. High mercury levels in fish, amphibians, and invertebrates downstream of the hydraulic mines are a consequence of historic mercury use. On the basis of USGS studies and other recent work, a better understanding is emerging of mercury distribution, ongoing transport, transformation processes, and the extent of biological uptake in areas affected by historic gold mining. This information will be useful to agencies responsible for prudent land and resource management and for protecting public health.

A Note on Interfacing Object Warehouses and Mass Storage Systems for Data Mining Applications

NASA Technical Reports Server (NTRS)

Grossman, Robert L.; Northcutt, Dave

1996-01-01

Data mining is the automatic discovery of patterns, associations, and anomalies in data sets. Data mining requires numerically and statistically intensive queries. Our assumption is that data mining requires a specialized data management infrastructure to support the aforementioned intensive queries, but because of the sizes of data involved, this infrastructure is layered over a hierarchical storage system. In this paper, we discuss the architecture of a system which is layered for modularity, but exploits specialized lightweight services to maintain efficiency. Rather than use a full functioned database for example, we use light weight object services specialized for data mining. We propose using information repositories between layers so that components on either side of the layer can access information in the repositories to assist in making decisions about data layout, the caching and migration of data, the scheduling of queries, and related matters.

A comparison of Eichhornia crassipes (Pontederiaceae) and Sphagnum quinquefarium (Sphagnaceae) in treatment of acid mine water

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

Falbo, M.B.; Weaks, T.E.

Tests were conducted under greenhouse conditions to evaluate the ability of Eichhornia crassipes (Pontederiaceae) and Sphagnum quinquefarium (Sphagnaceae) to ameliorate acid mine water discharged from coal operations. In addition, the survivorship and growth rate of E. crassipes (water-hyacinth), cultured in toxic acid mine water, were determined. The results of both short- and long-term studies indicated that E. crassipes readily reduced levels of heavy metals in acid mine water while the plants exhibited few signs of toxicity. Patterns of reduction of pollutants, for both E. crassipes and S. quinquefarium indicated that treatment efficiency could be improved by the periodic harvesting ofmore » plants. It is suggested that the ease with which water-hyacinths can be introduced into wetlands and harvested cannot be economically duplicated with other plants currently in use in treating acid mine water.« less

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

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

Activity recognition from minimal distinguishing subsequence mining

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

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

Sleep Patterns of Naval Aviation Personnel Conducting Mine Hunting Operations

DTIC Science & Technology

2006-09-01

Personnel Conducting Mine Hunting Operations 6. AUTHOR(S) Bennett Solberg 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES...Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND...human performance , resulting in predictable changes not only on the individual level but also on the system as a whole. This descriptive study

Population Change in West Virginia 1950-1970. West Virginia University Agricultural and Forestry Experiment Station Bulletin 658.

ERIC Educational Resources Information Center

Sizer, Leonard M.

Growth patterns of the national economy during the 1950's and 1960's have not been shared by the state of West Virginia; towns and rural areas have lost population and job opportunities have declined. The switch to petroleum products and advanced mining technology displaced many coal mine workers. A national food surplus and the difficulty in…

Practice-Relevant Pedagogy for Mining Software Engineering Curricula Assets

DTIC Science & Technology

2007-06-20

permits the application of the Lean methods by virtually grouping shared services into eWorkcenters to which only non-routine requests are routed...engineering can be applied to IT shared services improvement and provide precise system improvement methods to complement the ITIL best practice. This...Vertical� or internal service- chain of primary business functions and enabling shared services Framework results - Mined patterns that relate

Development of floristic diversity in 10-year-old restoration forests on a bauxite mined site in Amazonia.

Treesearch

J. A. Parrotta; O. H. Knowles; J.M. Wunderle Jr.

1997-01-01

Patterns of plant and animal diversity were studied in a 10-year-old native species reforestation area at a bauxite-mined site at porto Trombetas in western Para State, Brazil. Understorey and overstorey floristic composition and structure, understorey light conditions, forest floor development and soil properties were evaluated in a total of 38 78.5-m2

Exploring Online Students' Self-Regulated Learning with Self-Reported Surveys and Log Files: A Data Mining Approach

ERIC Educational Resources Information Center

Cho, Moon-Heum; Yoo, Jin Soung

2017-01-01

Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…

Pattern formation in mass conserving reaction-diffusion systems

NASA Astrophysics Data System (ADS)

Brauns, Fridtjof; Halatek, Jacob; Frey, Erwin

We present a rigorous theoretical framework able to generalize and unify pattern formation for quantitative mass conserving reaction-diffusion models. Mass redistribution controls chemical equilibria locally. Separation of diffusive mass redistribution on the level of conserved species provides a general mathematical procedure to decompose complex reaction-diffusion systems into effectively independent functional units, and to reveal the general underlying bifurcation scenarios. We apply this framework to Min protein pattern formation and identify the mechanistic roles of both involved protein species. MinD generates polarity through phase separation, whereas MinE takes the role of a control variable regulating the existence of MinD phases. Hence, polarization and not oscillations is the generic core dynamics of Min proteins in vivo. This establishes an intrinsic mechanistic link between the Min system and a broad class of intracellular pattern forming systems based on bistability and phase separation (wave-pinning). Oscillations are facilitated by MinE redistribution and can be understood mechanistically as relaxation oscillations of the polarization direction.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

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

Discovery of Disease Co-occurrence Patterns from Electronic Healthcare Reimbursement Claims Data

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

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

Effective public health surveillance is important for national secu- rity. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build effective bio- surveillance systems that can track, monitor and report such events in a timely manner. Additionally, there is a need to identify sus- ceptible geographic regions/populations where these diseases may have a significant impact and design preemptive strategies to tackle them. With the digitization of health related information through electronic health records (EHR) and electronic healthcare claim re- imbursements (eHCR), there is a tremendous opportunity to ex- ploit these datasetsmore » for public health surveillance. In this paper, we present our analysis on the use of eHCR data for bio-surveillance by studying the 2009-2010 H1N1 pandemic flu season. We present a novel approach to extract spatial and temporal patterns of flu in- cidence across the United States (US) from eHCRs and find that a small, but distinct set of break-out patterns govern the flu and asthma incidence rates across the entire country. Further, we ob- serve a distinct temporal lag in the onset of flu when compared to asthma across geographic regions in the US. The patterns extracted from the data collectively indicate how these break-out patterns are coupled, even though the flu represents an infectious disease whereas asthma represents a typical chronic condition. Taken to- gether, our approach demonstrates how mining eHCRs can provide novel insights in tackling public health concerns.« less

Odiel River, acid mine drainage and current characterisation by means of univariate analysis.

PubMed

Sainz, A; Grande, J A; de la Torre, M L

2003-04-01

Water pollution caused by sulfide oxidation responds to two geochemical processes: a natural one of temporal patterns, and the 'acid mine drainage', an accelerated process derived from the extractive activity. The Odiel River is located in Southwestern Spain; it flows to the south and into the Atlantic Ocean after joining the Tinto River near its mouth, forming a common estuary. There are three kinds of metallic mining in the Odiel River Basin: manganese, gold and silver, and pyrite mining, the latter being the most important in this basin, which is the object of this study. The main objective of the present study is centred in the characterisation of the sources responsible for the 'acid mine drainage' processes in the Odiel River Basin, through the sampling and subsequent chemical and statistical analyses of water samples collected in three types of sources: mine dumps, active mines and abandoned mines. The main conclusion is that mean pH values in the target area are remarkably lower than those in other active and abandoned mines outside of the study zone. On the contrary, mean values for heavy metal sulfates are much higher. Regarding mine dumps, mean values for pH, sulfates and heavy metals are within a similar range to those data known for areas outside the study zone. Copyright 2003 Elsevier Science Ltd.

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

PubMed

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

2018-04-26

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

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

PubMed Central

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

2018-01-01

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

Stickler takes the helm at MSHA

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

Fiscor, S.

2006-12-15

New agency director of the USA's Mine Safety and Health Administration (MSHA), Richard E. Stickler, inherits responsibility for implementing the Mine Improvement and New Emergency Response (MINER) Act amidst one of the US coal industry's most unsafe periods. The article gives an outline of the new leader's career in the coal industry and presents his views on the importance and the implementation of the Act. Stickler's No.1 priority is to do everything he can to protect the health and safety of America's miners. He believes that the goal for everyone in the mining community should be to achieve zero injuriesmore » and zero fatalities, and put an end to occupational illness.« less

Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways

ERIC Educational Resources Information Center

Wang, Xueli

2016-01-01

This research focuses on course-taking patterns of beginning community college students enrolled in one or more non-remedial science, technology, engineering, and mathematics (STEM) courses during their first year of college, and how these patterns are mapped against upward transfer in STEM fields of study. Drawing upon postsecondary transcript…

Changes in the Extent of Surface Mining and Reclamation in the Central Appalachians Detected Using a 1976-2006 Landsat Time Series

NASA Technical Reports Server (NTRS)

Townsend, Philip A.; Helmers, David P.; Kingdon, Clayton C.; McNeil, Brenden E.; de Beurs, Kirsten M.; Eshleman, Keith N.

2009-01-01

Surface mining and reclamation is the dominant driver of land cover land use change (LCLUC) in the Central Appalachian Mountain region of the Eastern U.S. Accurate quantification of the extent of mining activities is important for assessing how this LCLUC affects ecosystem services such as aesthetics, biodiversity, and mitigation of flooding.We used Landsat imagery from 1976, 1987, 1999 and 2006 to map the extent of surface mines and mine reclamation for eight large watersheds in the Central Appalachian region of West Virginia, Maryland and Pennsylvania. We employed standard image processing techniques in conjunction with a temporal decision tree and GIS maps of mine permits and wetlands to map active and reclaimed mines and track changes through time. For the entire study area, active surface mine extent was highest in 1976, prior to implementation of the Surface Mine Control and Reclamation Act in 1977, with 1.76% of the study area in active mines, declining to 0.44% in 2006. The most extensively mined watershed, Georges Creek in Maryland, was 5.45% active mines in 1976, declining to 1.83% in 2006. For the entire study area, the area of reclaimed mines increased from 1.35% to 4.99% from 1976 to 2006, and from 4.71% to 15.42% in Georges Creek. Land cover conversion to mines and then reclaimed mines after 1976 was almost exclusively from forest. Accuracy levels for mined and reclaimed cover was above 85% for all time periods, and was generally above 80% for mapping active and reclaimed mines separately, especially for the later time periods in which good accuracy assessment data were available. Among other implications, the mapped patterns of LCLUC are likely to significantly affect watershed hydrology, as mined and reclaimed areas have lower infiltration capacity and thus more rapid runoff than unmined forest watersheds, leading to greater potential for extreme flooding during heavy rainfall events.

Your place or mine? A phylogenetic comparative analysis of marital residence in Indo-European and Austronesian societies

PubMed Central

Fortunato, Laura; Jordan, Fiona

2010-01-01

Accurate reconstruction of prehistoric social organization is important if we are to put together satisfactory multidisciplinary scenarios about, for example, the dispersal of human groups. Such considerations apply in the case of Indo-European and Austronesian, two large-scale language families that are thought to represent Neolithic expansions. Ancestral kinship patterns have mostly been inferred through reconstruction of kin terminologies in ancestral proto-languages using the linguistic comparative method, and through geographical or distributional arguments based on the comparative patterns of kin terms and ethnographic kinship ‘facts’. While these approaches are detailed and valuable, the processes through which conclusions have been drawn from the data fail to provide explicit criteria for systematic testing of alternative hypotheses. Here, we use language trees derived using phylogenetic tree-building techniques on Indo-European and Austronesian vocabulary data. With these trees, ethnographic data and Bayesian phylogenetic comparative methods, we statistically reconstruct past marital residence and infer rates of cultural change between different residence forms, showing Proto-Indo-European to be virilocal and Proto-Malayo-Polynesian uxorilocal. The instability of uxorilocality and the rare loss of virilocality once gained emerge as common features of both families. PMID:21041215

An investigation into heterogeneity in a single vein-type uranium ore deposit: Implications for nuclear forensics.

PubMed

Keatley, A C; Scott, T B; Davis, S; Jones, C P; Turner, P

2015-12-01

Minor element composition and rare earth element (REE) concentrations in nuclear materials are important as they are used within the field of nuclear forensics as an indicator of sample origin. However recent studies into uranium ores and uranium ore concentrates (UOCs) have shown significant elemental and isotopic heterogeneity from a single mine site such that some sites have shown higher variation within the mine site than that seen between multiple sites. The elemental composition of both uranium and gangue minerals within ore samples taken along a single mineral vein in South West England have been measured and reported here. The analysis of the samples was undertaken to determine the extent of the localised variation in key elements. Energy Dispersive X-ray spectroscopy (EDS) was used to analyse the gangue mineralogy and measure major element composition. Minor element composition and rare earth element (REE) concentrations were measured by Electron Probe Microanalysis (EPMA). The results confirm that a number of key elements, REE concentrations and patterns used for origin location do show significant variation within mine. Furthermore significant variation is also visible on a meter scale. In addition three separate uranium phases were identified within the vein which indicates multiple uranium mineralisation events. In light of these localised elemental variations it is recommended that representative sampling for an area is undertaken prior to establishing the REE pattern that may be used to identify the originating mine for an unknown ore sample and prior to investigating impact of ore processing on any arising REE patterns. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Daranpob, Ammarin; Yang, Y. Jeffrey; Jin, Kang-Ren

2009-09-01

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

Natural forest expansion on reclaimed coal mines in Northern Spain: the role of native shrubs as suitable microsites.

PubMed

Alday, Josu G; Zaldívar, Pilar; Torroba-Balmori, Paloma; Fernández-Santos, Belén; Martínez-Ruiz, Carolina

2016-07-01

The characterization of suitable microsites for tree seedling establishment and growth is one of the most important tasks to achieve the restoration of native forest using natural processes in disturbed sites. For that, we assessed the natural Quercus petraea forest expansion in a 20-year-old reclaimed open-cast mine under sub-Mediterranean climate in northern Spain, monitoring seedling survival, growth, and recruitment during 5 years in three contrasting environments (undisturbed forest, mine edge, and mine center). Seedling density and proportion of dead branches decreased greatly from undisturbed forest towards the center of the mine. There was a positive effect of shrubs on Q. petraea seedling establishment in both mine environments, which increase as the environment undergoes more stress (from the mine edge to the center of the mine), and it was produced by different shrub structural features in each mine environment. Seedling survival reduction through time in three environments did not lead to a density reduction because there was a yearly recruitment of new seedlings. Seedling survival, annual growth, and height through time were greater in mine sites than in the undisturbed forest. The successful colonization patterns and positive neighbor effect of shrubs on natural seedlings establishment found in this study during the first years support the use of shrubs as ecosystem engineers to increase heterogeneity in micro-environmental conditions on reclaimed mine sites, which improves late-successional Quercus species establishment.

Upland and wetland vegetation establishment on coal slurry in northern Missouri

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

Skeel, V.A.; Nawrot, J.R.

Since the Cooperative Wildlife Research Laboratory`s (CWRL) Mined Land Reclamation Program`s first establishment of a wetland on slurry in 1976, industry, state, and federal agency interest in reclamation alternatives for inactive slurry has increased. CWRL has been involved in pre-reclamation site characterization and monitoring for inactive slurry impoundments throughout Illinois, Indiana, Kansas, Kentucky, Missouri, and Washington. Geochemical site characterization of three slurry impoundments at the AECI Bee Veer Mine located near Macon, Missouri began in April 1990. A substrate sampling grid was established for all slurry impoundments with a centerline orientated parallel to the discharge to decant flow pattern. Surfacemore » (0--6 in.) and subsurface (30--36 in.) slurry samples were collected annually and analyzed for acid-base balance, immediate acidity macro- and micro-nutrients, potential phytotoxic metallic ions and salts, and texture. Water table elevations and water quality were monitored quarterly from shallow ({le}12 ft.) piezometers. General reclamation plans included annual (3 years) incremental limestone amendments (35--50 tons/acre) and direct vegetation establishment. Cool and warm season grasses dominate vegetation cover in upland habitats (slurry cell RDA1) while wetland habitats (palustrine emergent seasonally-permanently inundated) have been established in slurry cells (RDA2 and RDA3). Isolated hot spots continue to be amended with limestone and supplemental vegetation establishment is scheduled.« less

A review on data mining and continuous optimization applications in computational biology and medicine.

PubMed

Weber, Gerhard-Wilhelm; Ozöğür-Akyüz, Süreyya; Kropat, Erik

2009-06-01

An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology. It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions.

Microarray data and gene expression statistics for Saccharomyces cerevisiae exposed to simulated asbestos mine drainage.

PubMed

Driscoll, Heather E; Murray, Janet M; English, Erika L; Hunter, Timothy C; Pivarski, Kara; Dolci, Elizabeth D

2017-08-01

Here we describe microarray expression data (raw and normalized), experimental metadata, and gene-level data with expression statistics from Saccharomyces cerevisiae exposed to simulated asbestos mine drainage from the Vermont Asbestos Group (VAG) Mine on Belvidere Mountain in northern Vermont, USA. For nearly 100 years (between the late 1890s and 1993), chrysotile asbestos fibers were extracted from serpentinized ultramafic rock at the VAG Mine for use in construction and manufacturing industries. Studies have shown that water courses and streambeds nearby have become contaminated with asbestos mine tailings runoff, including elevated levels of magnesium, nickel, chromium, and arsenic, elevated pH, and chrysotile asbestos-laden mine tailings, due to leaching and gradual erosion of massive piles of mine waste covering approximately 9 km 2 . We exposed yeast to simulated VAG Mine tailings leachate to help gain insight on how eukaryotic cells exposed to VAG Mine drainage may respond in the mine environment. Affymetrix GeneChip® Yeast Genome 2.0 Arrays were utilized to assess gene expression after 24-h exposure to simulated VAG Mine tailings runoff. The chemistry of mine-tailings leachate, mine-tailings leachate plus yeast extract peptone dextrose media, and control yeast extract peptone dextrose media is also reported. To our knowledge this is the first dataset to assess global gene expression patterns in a eukaryotic model system simulating asbestos mine tailings runoff exposure. Raw and normalized gene expression data are accessible through the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) Database Series GSE89875 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89875).

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Distribution characteristics of rare earth elements in children's scalp hair from a rare earths mining area in southern China.

PubMed

Tong, Shi-Lu; Zhu, Wang-Zhao; Gao, Zhao-Hua; Meng, Yu-Xiu; Peng, Rui-Ling; Lu, Guo-Cheng

2004-01-01

In order to demonstrate the validity of using scalp hair rare earth elements (REEs) content as a biomarker of human REEs exposure, data were collected on REEs exposure levels from children aged 11-15 years old and living in an ion-adsorptive type light REEs (LREEs) mining and surrounding areas in southern China. Sixty scalp hair samples were analyzed by ICP-MS for 16 REEs (La Lu, Y and Sc). Sixteen REEs contents in the samples from the mining area (e.g., range: La: 0.14-6.93 microg/g; Nd: 0.09-5.27 microg/g; Gd: 12.2-645.6ng/g; Lu: 0.2-13.3 ng/g; Y: 0.03-1.27 microg/g; Sc: 0.05-0.30 microg/g) were significantly higher than those from the reference area (range: La: 0.04-0.40 microg/g; Nd: 0.04-0.32 microg/g; Gd: 8.3-64.6 ng/g; Lu: 0.4-3.3ng/g; Y: 0.03-0.29 microg/g; Sc: 0.11-0.36 microg/g) and even much higher than those published in the literature. The distribution pattern of REEs in scalp hair from the mining area was very similar to that of REEs in the mine and the atmosphere shrouding that area. In conclusion, the scalp hair REEs contents may indicate not only quantitatively but also qualitatively (distribution pattern) the absorption of REEs from environmental exposure into human body. The children living in this mining area should be regarded as a high-risk group with REEs (especially LREEs) exposure, and their health status should be examined from a REEs health risk assessment perspective.

Land mine detection using multispectral image fusion

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

Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.

1995-03-29

Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a varietymore » of physical properties that are more separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts. We use a supervised learning pattern recognition approach to detecting the metal and plastic land mines. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the spectral bands add value to the detection system. The most important features from the various sensors are fused using a supervised learning pattern classifier (the probabilistic neural network). We present results of experiments to detect land mines from real data collected from an airborne platform, and evaluate the usefulness of fusing feature information from multiple spectral bands.« less

Nanoseismicity and picoseismicity rate changes from static stress triggering caused by a Mw 2.2 earthquake in Mponeng gold mine, South Africa

NASA Astrophysics Data System (ADS)

Kozłowska, Maria; Orlecka-Sikora, Beata; Kwiatek, Grzegorz; Boettcher, Margaret S.; Dresen, Georg

2015-01-01

Static stress changes following large earthquakes are known to affect the rate and distribution of aftershocks, yet this process has not been thoroughly investigated for nanoseismicity and picoseismicity at centimeter length scales. Here we utilize a unique data set of M ≥ -3.4 earthquakes following a Mw 2.2 earthquake in Mponeng gold mine, South Africa, that was recorded during a quiet interval in the mine to investigate if rate- and state-based modeling is valid for shallow, mining-induced seismicity. We use Dieterich's (1994) rate- and state-dependent formulation for earthquake productivity, which requires estimation of four parameters: (1) Coulomb stress changes due to the main shock, (2) the reference seismicity rate, (3) frictional resistance parameter, and (4) the duration of aftershock relaxation time. Comparisons of the modeled spatiotemporal patterns of seismicity based on two different source models with the observed distribution show that while the spatial patterns match well, the rate of modeled aftershocks is lower than the observed rate. To test our model, we used three metrics of the goodness-of-fit evaluation. The null hypothesis, of no significant difference between modeled and observed seismicity rates, was only rejected in the depth interval containing the main shock. Results show that mining-induced earthquakes may be followed by a stress relaxation expressed through aftershocks located on the rupture plane and in regions of positive Coulomb stress change. Furthermore, we demonstrate that the main features of the temporal and spatial distributions of very small, mining-induced earthquakes can be successfully determined using rate- and state-based stress modeling.

76 FR 37826 - Public Land Order No. 7773; Emergency Withdrawal of Public and National Forest System Lands...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-28

...] Public Land Order No. 7773; Emergency Withdrawal of Public and National Forest System Lands, Coconino and... Forest System lands from location and entry under the 1872 Mining Law for a period of 6 months under the... described above aggregate approximately 1,010,776 acres public and National Forest System lands in Coconino...

Benthic invertebrate communities and their responses to selected environmental factors in the Kanawha River basin, West Virginia, Virginia, and North Carolina

USGS Publications Warehouse

Chambers, Douglas B.; Messinger, Terence

2001-01-01

The effects of selected environmental factors on the composition and structure of benthic invertebrate communities in the Kanawha River Basin of West Virginia, Virginia and North Carolina were investigated in 1997 and 1998. Environmental factors investigated include physiography, land-use pattern, streamwater chemistry, streambed- sediment chemistry, and habitat characteristics. Land-use patterns investigated include coal mining, agriculture, and low intensity rural-residential patterns, at four main stem and seven tributary sites throughout the basin. Of the 37 sites sampled, basin size and physiography most strongly affected benthic invertebrate-community structure. Land-use practices also affected invertebrate community structure in these basins. The basins that differed most from the minimally affected reference condition were those basins in which coal mining was the dominant nonforest land use, as determined by comparing invertebrate- community metric values among sites. Basins in which agriculture was important were more similar to the reference condition. The effect of coal mining upon benthic invertebrate communities was further studied at 29 sites and the relations among invertebrate communities and the selected environmental factors of land use, streamwater chemistry, streambed- sediment chemistry, and habitat characteristics analyzed. Division of coal-mining synoptic-survey sites based on invertebrate-community composition resulted in two groups?one with more than an average production of 9,000 tons of coal per square mile per year since 1980, and one with lesser or no recent coal production. The group with significant recent coal production showed higher levels of community impairment than the group with little or no recent coal production. Median particle size of streambed sediment, and specific conductance and sulfate concentration of streamwater were most strongly correlated with effects on invertebrate communities. These characteristics were related to mining intensity, as measured by thousands of tons of coal produced per square mile of drainage area.

Phytostabilization of mine tailings in arid and semiarid environments--an emerging remediation technology.

PubMed

Mendez, Monica O; Maier, Raina M

2008-03-01

Unreclaimed mine tailings sites are a worldwide problem, with thousands of unvegetated, exposed tailings piles presenting a source of contamination for nearby communities. Tailings disposal sites in arid and semiarid environments are especially subject to eolian dispersion and water erosion. Phytostabilization, the use of plants for in situ stabilization of tailings and metal contaminants, is a feasible alternative to costly remediation practices. In this review we emphasize considerations for phytostabilization of mine tailings in arid and semiarid environments, as well as issues impeding its long-term success. We reviewed literature addressing mine closures and revegetation of mine tailings, along with publications evaluating plant ecology, microbial ecology, and soil properties of mine tailings. Data were extracted from peer-reviewed articles and books identified in Web of Science and Agricola databases, and publications available through the U.S. Department of Agriculture, U.S. Environmental Protection Agency, and the United Nations Environment Programme. Harsh climatic conditions in arid and semiarid environments along with the innate properties of mine tailings require specific considerations. Plants suitable for phytostabilization must be native, be drought-, salt-, and metal-tolerant, and should limit shoot metal accumulation. Factors for evaluating metal accumulation and toxicity issues are presented. Also reviewed are aspects of implementing phytostabilization, including plant growth stage, amendments, irrigation, and evaluation. Phytostabilization of mine tailings is a promising remedial technology but requires further research to identify factors affecting its long-term success by expanding knowledge of suitable plant species and mine tailings chemistry in ongoing field trials.

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

Kartsaklis, Christos; Hernandez, Oscar R

Interrogating the structure of a program for patterns of interest is attractive to the broader spectrum of software engineering. The very approach by which a pattern is constructed remains a concern for the source code mining community. This paper presents a pattern programming model, for the C and Fortran programming languages, using a compiler directives approach. We discuss our specification, called HERCULES/PL, throughout a number of examples and show how different patterns can be constructed, plus some preliminary results.

15 CFR 970.2503 - Suspension of exploration activities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Pre... environment. Upon receipt of notice of the emergency order, the United States citizen engaged in the...

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

PubMed Central

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

2015-01-01

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

Health-Mining: a Disease Management Support Service based on Data Mining and Rule Extraction.

PubMed

Bei, Andrea; De Luca, Stefano; Ruscitti, Giancarlo; Salamon, Diego

2005-01-01

The disease management is the collection of the processes aimed to control the health care and improving the quality at same time reducing the overall cost of the procedures. Our system, Health-Mining, is a Decision Support System with the objective of controlling the adequacy of hospitalization and therapies, determining the effective use of standard guidelines and eventually identifying better ones emerged from the medical practice (Evidence Based Medicine). In realizing the system, we have the aim of creation of a path to admissions- appropriateness criteria construction, valid at an international level. A main goal of the project is rule extraction and the identification of the rules adequate in term of efficacy, quality and cost reduction, especially in the view of fast changing technologies and medicines. We tested Health-Mining in a real test case for an Italian Region, Regione Veneto, on the installation of pacemaker and ICD.

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

PubMed

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

2017-08-01

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

Monitoring the growth or decline of vegetation on mine dumps

NASA Technical Reports Server (NTRS)

Gilbertson, B. P. (Principal Investigator)

1975-01-01

The author has identified the following signficant results. It was established that particular mine dumps throughout the entire test area can be detected and identified. It was also established that patterns of vegetative growth on the mine dumps can be recognized from a simple visual analysis of photographic images. Because vegetation tends to occur in patches on many mine dumps, it is unsatisfactory to classify complete dumps into categories of percentage vegetative cover. A more desirable approach is to classify the patches of vegetation themselves. The coarse resolution of conventional densitometers restricts the accuracy of this procedure, and consequently a direct analysis of ERTS CCT's is preferred. A set of computer programs was written to perform the data reading and manipulating functions required for basic CCT analysis.

An intelligent knowledge mining model for kidney cancer using rough set theory.

PubMed

Durai, M A Saleem; Acharjya, D P; Kannan, A; Iyengar, N Ch Sriman Narayana

2012-01-01

Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.

A Recommendation System to Facilitate Business Process Modeling.

PubMed

Deng, Shuiguang; Wang, Dongjing; Li, Ying; Cao, Bin; Yin, Jianwei; Wu, Zhaohui; Zhou, Mengchu

2017-06-01

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Resource Conflicts: Emerging Struggles over Strategic Commodities in Latin America. Phase 2

DTIC Science & Technology

2012-10-01

Madre de Dios , between local communities and Mobil, which sought to develop a natural gas project. In the late 1990s, the audiencia for that project was...for instance, that Grupo de México is not a member of the International Council on Mining and Metals (ICMM), created in 2001 “to advance the mining...Experiment Reconsidered, ed. Cynthia McClintock and Abraham F. Lowenthal (Princeton, NJ: Princeton University Press, 1983), 187–192. 7 Comisión de la Verdad

Moving Equipment and Workers to Mine Construction Site at a Logistically Challenged Area

NASA Astrophysics Data System (ADS)

Tikasz, Laszlo; Biroscak, Dennis; Pentiah, Scheale Duvah; McCulloch, Robert I.

Social sensitivity of habitants, minimal impact on the environment, low-grade infrastructure, high altitude, frequent rock slides combined with expectations for the timely moving of equipment and workers are some of the challenges emerging from the current construction of a mine. Starting with traditional planning, and experiencing issues in the early phase of the construction, a traffic simulator was requested by the Procurement Department in order to validate daily-weekly schedules and predict likely delays or blockages on the long-term.

Analysis of Human Mobility Based on Cellular Data

NASA Astrophysics Data System (ADS)

Arifiansyah, F.; Saptawati, G. A. P.

2017-01-01

Nowadays not only adult but even teenager and children have then own mobile phones. This phenomena indicates that the mobile phone becomes an important part of everyday’s life. Based on these indication, the amount of cellular data also increased rapidly. Cellular data defined as the data that records communication among mobile phone users. Cellular data is easy to obtain because the telecommunications company had made a record of the data for the billing system of the company. Billing data keeps a log of the users cellular data usage each time. We can obtained information from the data about communication between users. Through data visualization process, an interesting pattern can be seen in the raw cellular data, so that users can obtain prior knowledge to perform data analysis. Cellular data processing can be done using data mining to find out human mobility patterns and on the existing data. In this paper, we use frequent pattern mining and finding association rules to observe the relation between attributes in cellular data and then visualize them. We used weka tools for finding the rules in stage of data mining. Generally, the utilization of cellular data can provide supporting information for the decision making process and become a data support to provide solutions and information needed by the decision makers.

Occupancy schedules learning process through a data mining framework

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

D'Oca, Simona; Hong, Tianzhen

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Succession on regraded placer mine spoil in Alaska, USA, in relation to initial site characteristics

USGS Publications Warehouse

Densmore, R.V.

1994-01-01

This study evaluated the rate and pattern of natural succession on regraded placer mine spoil in relation to initial substrate characteristics. The study site was the Glen Creek watershed of the Kantishna mining area of Denali National Park and Preserve, Alaska. After regrading, twelve 0.01-ha plots were established and substrate characteristics were measured. Natural plant succession was evaluated after five growing seasons. Three successional patterns were identified on the basis of plant community characteristics using cluster analysis, and were related to substrate characteristics. First, a riparian plant community with vigorous Salix alaxensis and Alnus crispa grew rapidly on topsoil that had been spread over the regraded spoil. Second, a similar plant community with less vigorous S. alaxensis developed more slowly on unprocessed spoil and spoil amended with a small amount of topsoil. Third, processed spoil remained almost bare of vegetation, although S. alaxensis was able to establish and persist in a stunted growth form. In contrast, Alnus crispa had difficulty establishing on processed spoil, but the few established seedlings grew well. Several substrate variables, including the proportion of silt and clay vs. sand, total nitrogen, and water retention capacity, were good predictors of the rate and pattern of succession. Total nitrogen was the best single predictor for the number of vigorous S. alaxensis.

Occupancy schedules learning process through a data mining framework

DOE PAGES

D'Oca, Simona; Hong, Tianzhen

2014-12-17

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Geochemistry of rare earth elements in minesoils from São Domingos mining district (Iberian Pyrite Belt)

NASA Astrophysics Data System (ADS)

Delgado, Joaquin; Perez-Lopez, Rafael; Nieto, Jose Miguel; Ayora, Carles

2010-05-01

The São Domingos mine is one of the most emblematic mining districts in the lower part of the Guadiana River Basin (SW of Iberian Peninsula). It is located in Portugal (about 5 km from the Spanish border), in the northern sector of the Iberian Pyrite Belt (IPB), one of the largest metallogenetic provinces of massive sulphides in the world. Although mining activity has ceased at present, the large-scale exploitation of this deposit between the second half of the XIX century and the first half of the XX century, has favoured the production of enormous waste dumps, where oxidation of pyrite and associated sulphides is resulting in the production of acid mine drainage (AMD). Mining wastes, minesoils, and acid mine drainage have been analyzed for their major ions and rare earth elements (REE) with the aim of understanding the REE mobility during sulphide weathering so that lanthanoid series can be used both as a proxy for the extent of water-rock interaction and as a tool for identifying impacts of AMD on natural ecosystems. Chemical speciation of REE in extracts from minesoils indicates that REE sulphate complexes (mainly LnSO4+) are the primary aqueous form (60-90%), and free ionic species (Ln3+, 10-40%) are the next most abundant form of soil water-soluble fraction and controls the REE speciation model. The REE from this fraction have NASC-normalized patterns with middle-REE (MREE) enriched signature compared to the light-REE (LREE) and heavy-REE (HREE), showing convex MREE-signatures and convexity index values of +1.29 +/- 1.13. These results are consistent with the typical REE fractionation patterns reported for AMD. Poorly crystalline iron oxyhydroxysulphates act as a source of labile MREE by dissolution and/or desorption processes and could explain the MREE-enriched signatures in solution.

The taxonomy statistic uncovers novel clinical patterns in a population of ischemic stroke patients.

PubMed

Tukiendorf, Andrzej; Kaźmierski, Radosław; Michalak, Sławomir

2013-01-01

In this paper, we describe a simple taxonomic approach for clinical data mining elaborated by Marczewski and Steinhaus (M-S), whose performance equals the advanced statistical methodology known as the expectation-maximization (E-M) algorithm. We tested these two methods on a cohort of ischemic stroke patients. The comparison of both methods revealed strong agreement. Direct agreement between M-S and E-M classifications reached 83%, while Cohen's coefficient of agreement was κ = 0.766(P < 0.0001). The statistical analysis conducted and the outcomes obtained in this paper revealed novel clinical patterns in ischemic stroke patients. The aim of the study was to evaluate the clinical usefulness of Marczewski-Steinhaus' taxonomic approach as a tool for the detection of novel patterns of data in ischemic stroke patients and the prediction of disease outcome. In terms of the identification of fairly frequent types of stroke patients using their age, National Institutes of Health Stroke Scale (NIHSS), and diabetes mellitus (DM) status, when dealing with rough characteristics of patients, four particular types of patients are recognized, which cannot be identified by means of routine clinical methods. Following the obtained taxonomical outcomes, the strong correlation between the health status at moment of admission to emergency department (ED) and the subsequent recovery of patients is established. Moreover, popularization and simplification of the ideas of advanced mathematicians may provide an unconventional explorative platform for clinical problems.

Mining dynamic noteworthy functions in software execution sequences

PubMed Central

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

Sharma, Anuj; Kumar Panigrahi, Prabin

2012-02-01

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

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

PubMed

Mkandawire, Martin

2013-11-01

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

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

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

Renewed mining and reclamation: Imapacts on bats and potential mitigation

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

Brown, P.E.; Berry, R.D.

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 personnelmore » 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.« less

Geo-Spatial Characterization of Soil Mercury and Arsenic at a High-Altitude Bolivian Gold Mine.

PubMed

Johnson, Glen D; Pavilonis, Brian; Caravanos, Jack; Grassman, Jean

2018-02-01

Soil mercury concentrations at a typical small-scale mine site in the Bolivian Andes were elevated (28-737 mg/kg or ppm) in localized areas where mercury amalgams were either formed or vaporized to release gold, but was not detectable beyond approximately 10 m from its sources. Arsenic was measurable, exceeding known background levels throughout the mine site (77-137,022 ppm), and was also measurable through the local village of Ingenio (36-1803 ppm). Although arsenic levels were high at all surveyed locations, its spatial pattern followed mercury, being highest where mercury was high.

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

PubMed

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

2015-11-01

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

The New Space Age in the making: Emergence of exo-mining, exo-burials and exo-marketing

NASA Astrophysics Data System (ADS)

Capova, Klara Anna

2016-10-01

At the beginning of the 21st century we witness considerable global developments in space exploration and a new era has begun: the New Space Age. The principal symbols of that age are firstly internationalization of space activities, secondly commercial utilization of space technologies, and lastly emergence of outer space economy. This paper presents selected signposts of the New Space Age. Three cases of recent outer space enterprises: recovery of asteroid resources (exo-mining), post-cremation memorial spaceflight (exo-burials) and first extraterrestrial advert (exo-marketing), are introduced in order to emphasize the monetary and social dimension of commercial application of space technologies. To give an illustration of these trends, this paper provides a brief socioculturally minded account of three outer space undertakings that are interpreted as signposts of the new era.

Advancing Science through Mining Libraries, Ontologies, and Communities*

PubMed Central

Evans, James A.; Rzhetsky, Andrey

2011-01-01

Life scientists today cannot hope to read everything relevant to their research. Emerging text-mining tools can help by identifying topics and distilling statements from books and articles with increased accuracy. Researchers often organize these statements into ontologies, consistent systems of reality claims. Like scientific thinking and interchange, however, text-mined information (even when accurately captured) is complex, redundant, sometimes incoherent, and often contradictory: it is rooted in a mixture of only partially consistent ontologies. We review work that models scientific reason and suggest how computational reasoning across ontologies and the broader distribution of textual statements can assess the certainty of statements and the process by which statements become certain. With the emergence of digitized data regarding networks of scientific authorship, institutions, and resources, we explore the possibility of accounting for social dependences and cultural biases in reasoning models. Computational reasoning is starting to fill out ontologies and flag internal inconsistencies in several areas of bioscience. In the not too distant future, scientists may be able to use statements and rich models of the processes that produced them to identify underexplored areas, resurrect forgotten findings and ideas, deconvolute the spaghetti of underlying ontologies, and synthesize novel knowledge and hypotheses. PMID:21566119

Mining the Geophysical Research Abstracts Corpus: Mapping the impact of Free and Open Source Software on the EGU Divisions

NASA Astrophysics Data System (ADS)

Löwe, Peter; Klump, Jens; Robertson, Jesse

2015-04-01

Text mining is commonly employed as a tool in data science to investigate and chart emergent information from corpora of research abstracts, such as the Geophysical Research Abstracts (GRA) published by Copernicus. In this context current standards, such as persistent identifiers like DOI and ORCID, allow us to trace, cite and map links between journal publications, the underlying research data and scientific software. This network can be expressed as a directed graph which enables us to chart networks of cooperation and innovation, thematic foci and the locations of research communities in time and space. However, this approach of data science, focusing on the research process in a self-referential manner, rather than the topical work, is still in a developing stage. Scientific work presented at the EGU General Assembly is often the first step towards new approaches and innovative ideas to the geospatial community. It represents a rich, deep and heterogeneous source of geoscientific thought. This corpus is a significant data source for data science, which has not been analysed on this scale previously. In this work, the corpus of the Geophysical Research Abstracts is used for the first time as a data base for analyses of topical text mining. For this, we used a sturdy and customizable software framework, based on the work of Schmitt et al. [1]. For the analysis we used the High Performance Computing infrastructure of the German Research Centre for Geosciences GFZ in Potsdam, Germany. Here, we report on the first results from the analysis of the continuous spreading the of use of Free and Open Source Software Tools (FOSS) within the EGU communities, mapping the general increase of FOSS-themed GRA articles in the last decade and the developing spatial patterns of involved parties and FOSS topics. References: [1] Schmitt, L. M., Christianson, K.T, Gupta R..: Linguistic Computing with UNIX Tools, in Kao, A., Poteet S.R. (Eds.): Natural Language processing and Text Mining, Springer, 2007. doi:10.1007/978-1-84628-754-1_12.

A comprehensive review on privacy preserving data mining.

PubMed

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

2015-01-01

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

Real-time intelligent decision making with data mining

NASA Astrophysics Data System (ADS)

Gupta, Deepak P.; Gopalakrishnan, Bhaskaran

2004-03-01

Database mining, widely known as knowledge discovery and data mining (KDD), has attracted lot of attention in recent years. With the rapid growth of databases in commercial, industrial, administrative and other applications, it is necessary and interesting to extract knowledge automatically from huge amount of data. Almost all the organizations are generating data and information at an unprecedented rate and they need to get some useful information from this data. Data mining is the extraction of non-trivial, previously unknown and potentially useful patterns, trends, dependence and correlation known as association rules among data values in large databases. In last ten to fifteen years, data mining spread out from one company to the other to help them understand more about customers' aspect of quality and response and also distinguish the customers they want from those they do not. A credit-card company found that customers who complete their applications in pencil rather than pen are more likely to default. There is a program that identifies callers by purchase history. The bigger the spender, the quicker the call will be answered. If you feel your call is being answered in the order in which it was received, think again. Many algorithms assume that data is static in nature and mine the rules and relations in that data. But for a dynamic database e.g. in most of the manufacturing industries, the rules and relations thus developed among the variables/items no longer hold true. A simple approach may be to mine the associations among the variables after every fixed period of time. But again, how much the length of this period should be, is a question to be answered. The next problem with the static data mining is that some of the relationships that might be of interest from one period to the other may be lost after a new set of data is used. To reflect the effect of new data set and current status of the association rules where some of the strong rules might become weak and vice versa, there is a need to develop an efficient algorithm to adapt to the current patterns and associations. Some work has been done in developing the association rules for incremental database but to the best of the author"s knowledge no work has been done to do the same for periodic cause and effect analysis for online association rules in manufacturing industries. The present research attempts to answer these questions and develop an algorithm that can display the association rules online, find the periodic patterns in the data and detect the root cause of the problem.

Information and communication technology and climate change adaptation: Evidence from selected mining companies in South Africa

PubMed Central

Nhamo, Godwell

2016-01-01

The mining sector is a significant contributor to the gross domestic product of many global economies. Given the increasing trends in climate-induced disasters and the growing desire to find lasting solutions, information and communication technology (ICT) has been introduced into the climate change adaptation mix. Climate change-induced extreme weather events such as flooding, drought, excessive fog, and cyclones have compounded the environmental challenges faced by the mining sector. This article presents the adoption of ICT innovation as part of the adaptation strategies towards reducing the mining sector’s vulnerability and exposure to climate change disaster risks. Document analysis and systematic literature review were adopted as the methodology. Findings from the study reflect how ICT intervention orchestrated changes in communication patterns which are tailored towards the reduction in climate change vulnerability and exposure. The research concludes with a proposition that ICT intervention must be part of the bigger and ongoing climate change adaptation agenda in the mining sector.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

Sensor feature fusion for detecting buried objects

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

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

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

Phytostabilization of Mine Tailings in Arid and Semiarid Environments—An Emerging Remediation Technology

PubMed Central

Mendez, Monica O.; Maier, Raina M.

2008-01-01

Objective Unreclaimed mine tailings sites are a worldwide problem, with thousands of unvegetated, exposed tailings piles presenting a source of contamination for nearby communities. Tailings disposal sites in arid and semiarid environments are especially subject to eolian dispersion and water erosion. Phytostabilization, the use of plants for in situ stabilization of tailings and metal contaminants, is a feasible alternative to costly remediation practices. In this review we emphasize considerations for phytostabilization of mine tailings in arid and semiarid environments, as well as issues impeding its long-term success. Data sources We reviewed literature addressing mine closures and revegetation of mine tailings, along with publications evaluating plant ecology, microbial ecology, and soil properties of mine tailings. Data extraction Data were extracted from peer-reviewed articles and books identified in Web of Science and Agricola databases, and publications available through the U.S. Department of Agriculture, U.S. Environmental Protection Agency, and the United Nations Environment Programme. Data synthesis Harsh climatic conditions in arid and semiarid environments along with the innate properties of mine tailings require specific considerations. Plants suitable for phytostabilization must be native, be drought-, salt-, and metal-tolerant, and should limit shoot metal accumulation. Factors for evaluating metal accumulation and toxicity issues are presented. Also reviewed are aspects of implementing phytostabilization, including plant growth stage, amendments, irrigation, and evaluation. Conclusions Phytostabilization of mine tailings is a promising remedial technology but requires further research to identify factors affecting its long-term success by expanding knowledge of suitable plant species and mine tailings chemistry in ongoing field trials. PMID:18335091

Real-time incident detection using social media data.

DOT National Transportation Integrated Search

2016-05-09

The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems : is labor-intensive. This research project mines tweet texts to extract incident information on bot...

Classification of patients by severity grades during triage in the emergency department using data mining methods.

PubMed

Zmiri, Dror; Shahar, Yuval; Taieb-Maimon, Meirav

2012-04-01

To test the feasibility of classifying emergency department patients into severity grades using data mining methods. Emergency department records of 402 patients were classified into five severity grades by two expert physicians. The Naïve Bayes and C4.5 algorithms were applied to produce classifiers from patient data into severity grades. The classifiers' results over several subsets of the data were compared with the physicians' assessments, with a random classifier, and with a classifier that selects the maximal-prevalence class. Positive predictive value, multiple-class extensions of sensitivity and specificity combinations, and entropy change. The mean accuracy of the data mining classifiers was 52.94 ± 5.89%, significantly better (P < 0.05) than the mean accuracy of a random classifier (34.60 ± 2.40%). The entropy of the input data sets was reduced through classification by a mean of 10.1%. Allowing for classification deviations of one severity grade led to mean accuracy of 85.42 ± 1.42%. The classifiers' accuracy in that case was similar to the physicians' consensus rate. Learning from consensus records led to better performance. Reducing the number of severity grades improved results in certain cases. The performance of the Naïve Bayes and C4.5 algorithms was similar; in unbalanced data sets, Naïve Bayes performed better. It is possible to produce a computerized classification model for the severity grade of triage patients, using data mining methods. Learning from patient records regarding which there is a consensus of several physicians is preferable to learning from each physician's patients. Either Naïve Bayes or C4.5 can be used; Naïve Bayes is preferable for unbalanced data sets. An ambiguity in the intermediate severity grades seems to hamper both the physicians' agreement and the classifiers' accuracy. © 2010 Blackwell Publishing Ltd.

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Discovering Visual Scanning Patterns in a Computerized Cancellation Test

ERIC Educational Resources Information Center

Huang, Ho-Chuan; Wang, Tsui-Ying

2013-01-01

The purpose of this study was to develop an attention sequential mining mechanism for investigating the sequential patterns of children's visual scanning process in a computerized cancellation test. Participants had to locate and cancel the target amongst other non-targets in a structured form, and a random form with Chinese stimuli. Twenty-three…

Using temporal mining to examine the development of lymphedema in breast cancer survivors.

PubMed

Green, Jason M; Paladugu, Sowjanya; Shuyu, Xu; Stewart, Bob R; Shyu, Chi-Ren; Armer, Jane M

2013-01-01

Secondary lymphedema is a lifetime risk for breast cancer survivors and can severely affect quality of life. Early detection and treatment are crucial for successful lymphedema management. Limb volume measurements can be utilized not only to diagnose lymphedema but also to track progression of limb volume changes before lymphedema, which has the potential to provide insight into the development of this condition. This study aims to identify commonly occurring patterns in limb volume changes in breast cancer survivors before the development of lymphedema and to determine if there were differences in these patterns between certain patient subgroups. Furthermore, pattern differences were studied between patients who developed lymphedema quickly and those whose onset was delayed. A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (n = 232). Patterns were filtered initially by support and confidence values, and then t tests were used to determine statistical significance of the remaining patterns. Higher body mass index and the presence of postoperative swelling are supported as risk factors for lymphedema. In addition, a difference in trajectory to the lymphedema state was observed. The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.

Comparison of coseismic near-field and off-fault surface deformation patterns of the 1992 Mw 7.3 Landers and 1999 Mw 7.1 Hector Mine earthquakes: Implications for controls on the distribution of surface strain

NASA Astrophysics Data System (ADS)

Milliner, C. W. D.; Dolan, J. F.; Hollingsworth, J.; Leprince, S.; Ayoub, F.

2016-10-01

Subpixel correlation of preevent and postevent air photos reveal the complete near-field, horizontal surface deformation patterns of the 1992 Mw 7.3 Landers and 1999 Mw 7.1 Hector Mine ruptures. Total surface displacement values for both earthquakes are systematically larger than "on-fault" displacements from geologic field surveys, indicating significant distributed, inelastic deformation occurred along these ruptures. Comparison of these two data sets shows that 46 ± 10% and 39 ± 22% of the total surface deformation were distributed over fault zones averaging 154 m and 121 m in width for the Landers and Hector Mine events, respectively. Spatial variations of distributed deformation along both ruptures show correlations with the type of near-surface lithology and degree of fault complexity; larger amounts of distributed shear occur where the rupture propagated through loose unconsolidated sediments and areas of more complex fault structure. These results have basic implications for geologic-geodetic rate comparisons and probabilistic seismic hazard analysis.

Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

PubMed Central

Torre, Emiliano; Picado-Muiño, David; Denker, Michael; Borgelt, Christian; Grün, Sonja

2013-01-01

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains. PMID:24167487

[Vegetation spatial and temporal dynamic characteristics based on NDVI time series trajectories in grassland opencast coal mining].

PubMed

Jia, Duo; Wang, Cang Jiao; Mu, Shou Guo; Zhao, Hua

2017-06-18

The spatiotemporal dynamic patterns of vegetation in mining area are still unclear. This study utilized time series trajectory segmentation algorithm to fit Landsat NDVI time series which generated from fusion images at the most prosperous period of growth based on ESTARFM algorithm. Combining with the shape features of the fitted trajectory, this paper extracted five vegetation dynamic patterns including pre-disturbance type, continuous disturbance type, stabilization after disturbance type, stabilization between disturbance and recovery type, and recovery after disturbance type. The result indicated that recovery after disturbance type was the dominant vegetation change pattern among the five types of vegetation dynamic pattern, which accounted for 55.2% of the total number of pixels. The follows were stabilization after disturbance type and continuous disturbance type, accounting for 25.6% and 11.0%, respectively. The pre-disturbance type and stabilization between disturbance and recovery type accounted for 3.5% and 4.7%, respectively. Vegetation disturbance mainly occurred from 2004 to 2009 in Shengli mining area. The onset time of stable state was 2008 and the spatial locations mainlydistributed in open-pit stope and waste dump. The reco-very state mainly started since the year of 2008 and 2010, while the areas were small and mainly distributed at the periphery of open-pit stope and waste dump. Duration of disturbance was mainly 1 year. The duration of stable period usually sustained 7 years. The duration of recovery state of the type of stabilization between disturbances continued 2 to 5 years, while the type of recovery after disturbance often sustained 8 years.

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

PubMed Central

Ismail, Walaa N.; Hassan, Mohammad Mehedi

2017-01-01

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. PMID:28445441

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

PubMed

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.

Astroinformatics, data mining and the future of astronomical research

NASA Astrophysics Data System (ADS)

Brescia, Massimo; Longo, Giuseppe

2013-08-01

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

Hospitalization patterns associated with Appalachian coal mining.

PubMed

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.

Characterization and Modeling of Dust Emissions from an Instrumented Mine Tailings Site

NASA Astrophysics Data System (ADS)

Betterton, E. A.; Stovern, M.; Saez, A.; Csavina, J. L.; Felix Villar, O. I.; Field, J. P.; Rine, K. P.; Russell, M. R.; Saliba, P.

2012-12-01

Mining operations are potential sources of airborne particulate metal and metalloid contaminants through both direct smelter emissions and wind erosion of mine tailings. The warmer, drier conditions predicted for the Southwestern US by climate models may make contaminated atmospheric dust and aerosols increasingly important, due to potential deleterious effects on human health and ecology. Dust emissions and dispersion of contaminants from the Iron King Mine tailings in Dewey-Humboldt, Arizona, a Superfund site, are currently being investigated through in situ field measurements and computational fluid dynamics modeling. These tailings are heavily contaminated with lead and arsenic. We report on the chemical characterization of atmospheric dust and aerosol sampled near the mine tailings. Instrumented eddy flux towers were also setup on the mine tailings to give both spatial and temporal dust observations. The eddy flux towers have multiple DUSTTRAK monitors as well as weather stations. These in situ observations allow us to assess spatial distribution of suspended particulate. Using the DUSTTRAK flux tower observations at 10-second resolution in conjunction with a computational fluid dynamics model, we have been able to model dust transport from the mine tailings to downwind areas. In order to improve the accuracy of the dust transport simulations both regional topographical features and local weather patterns have been incorporated into the model simulations.

Drilling and blasting parameters in sublevel caving in Sheregesh mine

NASA Astrophysics Data System (ADS)

Eremenko, AA; Filippov, VN; Konurin, AI; Khmelinin, AP; Baryshnikov, DV; Khristolyubov, EA

2018-03-01

The factors that influence geomechanical state of rock mass in Sheregesh Mine are determined. The authors discuss a variant of geotechnology with fan drilling. The drill-hole patterns and drilling-and-blasting parameters are presented. The revealed causes of low-quality fragmentation of rocks include the presence of closed and open fractures at different distances from drill-hole mouths, both in case of rings and fans, as well as the blocking of drill-holes with rocks.

International strategic mineral issues summary report: tungsten

USGS Publications Warehouse

Werner, Antony B.T.; Sinclair, W. David; Amey, Earle B.

1998-01-01

In 1995, China and the former Soviet Union accounted for over three-fourths of the world's mine production of tungsten. China alone produced about two-thirds of world output. Given its vast resources, China will likely maintain its prominent role in world tungsten supply. By the year 2020, changes in supply patterns are likely to result from declining output from individual deposits in Australia, Austria, and Portugal and the opening of new mines in Canada, China, and the United Kingdom.

Data mining of space heating system performance in affordable housing

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

Ren, Xiaoxin; Yan, Da; Hong, Tianzhen

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems inmore » terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.« less

Data mining of space heating system performance in affordable housing

DOE PAGES

Ren, Xiaoxin; Yan, Da; Hong, Tianzhen

2015-02-16

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems inmore » terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.« less

Developing and Implementing the Data Mining Algorithms in RAVEN

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

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantificationmore » analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.« less

[Exploring the clinical characters of Shugan Jieyu capsule through text mining].

PubMed

Pu, Zheng-Ping; Xia, Jiang-Ming; Xie, Wei; He, Jin-Cai

2017-09-01

The study was main to explore the clinical characters of Shugan Jieyu capsule through text mining. The data sets of Shugan Jieyu capsule were downloaded from CMCC database by the method of literature retrieved from May 2009 to Jan 2016. Rules of Chinese medical patterns, diseases, symptoms and combination treatment were mined out by data slicing algorithm, and they were demonstrated in frequency tables and two dimension based network. Then totally 190 literature were recruited. The outcomess suggested that SC was most frequently correlated with liver Qi stagnation. Primary depression, depression due to brain disease, concomitant depression followed by physical diseases, concomitant depression followed by schizophrenia and functional dyspepsia were main diseases treated by Shugan Jieyu capsule. Symptoms like low mood, psychic anxiety, somatic anxiety and dysfunction of automatic nerve were mainy relieved bv Shugan Jieyu capsule.For combination treatment. Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. The research suggested that syndrome types and mining results of Shugan Jieyu capsule were almost the same as its instructions. Syndrome of malnutrition of heart spirit was the potential Chinese medical pattern of Shugan Jieyu capsule. Primary comorbid anxiety and depression, concomitant comorbid anxiety and depression followed by physical diseases, and postpartum depression were potential diseases treated by Shugan Jieyu capsule.For combination treatment, Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. Copyright© by the Chinese Pharmaceutical Association.

Analysis of In-Flight Collision Process During V-Type Firing Pattern in Surface Blasting Using Simple Physics

NASA Astrophysics Data System (ADS)

Chouhan, Lalit Singh; Raina, Avtar K.

2015-10-01

Blasting is a unit operation in Mine-Mill Fragmentation System (MMFS) and plays a vital role in mining cost. One of the goals of MMFS is to achieve optimum fragment size at minimal cost. Blast fragmentation optimization is known to result in better explosive energy utilization. Fragmentation depends on the rock, explosive and blast design variables. If burden, spacing and type of explosive used in a mine are kept constant, the firing sequence of blast-holes plays a vital role in rock fragmentation. To obtain smaller fragmentation size, mining professionals and relevant publications recommend V- or extended V-pattern of firing sequence. In doing so, it is assumed that the in-flight air collision breaks larger rock fragments into smaller ones, thus aiding further fragmentation. There is very little support to the phenomenon of breakage during in-flight collision of fragments during blasting in published literature. In order to assess the breakage of in-flight fragments due to collision, a mathematical simulation was carried over using basic principles of physics. The calculations revealed that the collision breakage is dependent on velocity of fragments, mass of fragments, the strength of the rock and the area of fragments over which collision takes place. For higher strength rocks, the in-flight collision breakage is very difficult to achieve. This leads to the conclusion that the concept demands an in-depth investigation and validation.

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

PubMed

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

2013-11-01

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

The role of conflict minerals, artisanal mining, and informal trading networks in African intrastate and regional conflicts

USGS Publications Warehouse

Chirico, Peter G.; Malpeli, Katherine C.

2014-01-01

The relationship between natural resources and armed conflict gained public and political attention in the 1990s, when it became evident that the mining and trading of diamonds were connected with brutal rebellions in several African nations. Easily extracted resources such as alluvial diamonds and gold have been and continue to be exploited by rebel groups to fund their activities. Artisanal and small-scale miners operating under a quasi-legal status often mine these mineral deposits. While many African countries have legalized artisanal mining and established flow chains through which production is intended to travel, informal trading networks frequently emerge in which miners seek to evade taxes and fees by selling to unauthorized buyers. These networks have the potential to become international in scope, with actors operating in multiple countries. The lack of government control over the artisanal mining sector and the prominence of informal trade networks can have severe social, political, and economic consequences. In the past, mineral extraction fuelled violent civil wars in Sierra Leone, Liberia, and Angola, and it continues to do so today in several other countries. The significant influence of the informal network that surrounds artisanal mining is therefore an important security concern that can extend across borders and have far-reaching impacts.

A framework for periodic outlier pattern detection in time-series sequences.

PubMed

Rasheed, Faraz; Alhajj, Reda

2014-05-01

Periodic pattern detection in time-ordered sequences is an important data mining task, which discovers in the time series all patterns that exhibit temporal regularities. Periodic pattern mining has a large number of applications in real life; it helps understanding the regular trend of the data along time, and enables the forecast and prediction of future events. An interesting related and vital problem that has not received enough attention is to discover outlier periodic patterns in a time series. Outlier patterns are defined as those which are different from the rest of the patterns; outliers are not noise. While noise does not belong to the data and it is mostly eliminated by preprocessing, outliers are actual instances in the data but have exceptional characteristics compared with the majority of the other instances. Outliers are unusual patterns that rarely occur, and, thus, have lesser support (frequency of appearance) in the data. Outlier patterns may hint toward discrepancy in the data such as fraudulent transactions, network intrusion, change in customer behavior, recession in the economy, epidemic and disease biomarkers, severe weather conditions like tornados, etc. We argue that detecting the periodicity of outlier patterns might be more important in many sequences than the periodicity of regular, more frequent patterns. In this paper, we present a robust and time efficient suffix tree-based algorithm capable of detecting the periodicity of outlier patterns in a time series by giving more significance to less frequent yet periodic patterns. Several experiments have been conducted using both real and synthetic data; all aspects of the proposed approach are compared with the existing algorithm InfoMiner; the reported results demonstrate the effectiveness and applicability of the proposed approach.

42 CFR 455.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... any source, including but not limited to the following: (1) Fraud hotline complaints. (2) Claims data mining. (3) Patterns identified through provider audits, civil false claims cases, and law enforcement...

42 CFR 455.2 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... any source, including but not limited to the following: (1) Fraud hotline complaints. (2) Claims data mining. (3) Patterns identified through provider audits, civil false claims cases, and law enforcement...

42 CFR 455.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... any source, including but not limited to the following: (1) Fraud hotline complaints. (2) Claims data mining. (3) Patterns identified through provider audits, civil false claims cases, and law enforcement...

Fluvial transport and surface enrichment of arsenic in semi-arid mining regions: examples from the Mojave Desert, California.

PubMed

Kim, Christopher S; Stack, David H; Rytuba, James J

2012-07-01

As a result of extensive gold and silver mining in the Mojave Desert, southern California, mine wastes and tailings containing highly elevated arsenic (As) concentrations remain exposed at a number of former mining sites. Decades of weathering and erosion have contributed to the mobilization of As-enriched tailings, which now contaminate surrounding communities. Fluvial transport plays an intermittent yet important and relatively undocumented role in the migration and dispersal of As-contaminated mine wastes in semi-arid climates. Assessing the contribution of fluvial systems to tailings mobilization is critical in order to assess the distribution and long-term exposure potential of tailings in a mining-impacted environment. Extensive sampling, chemical analysis, and geospatial mapping of dry streambed (wash) sediments, tailings piles, alluvial fans, and rainwater runoff at multiple mine sites have aided the development of a conceptual model to explain the fluvial migration of mine wastes in semi-arid climates. Intense and episodic precipitation events mobilize mine wastes downstream and downslope as a series of discrete pulses, causing dispersion both down and lateral to washes with exponential decay behavior as distance from the source increases. Accordingly a quantitative model of arsenic concentrations in wash sediments, represented as a series of overlapping exponential power-law decay curves, results in the acceptable reproducibility of observed arsenic concentration patterns. Such a model can be transferable to other abandoned mine lands as a predictive tool for monitoring the fate and transport of arsenic and related contaminants in similar settings. Effective remediation of contaminated mine wastes in a semi-arid environment requires addressing concurrent changes in the amounts of potential tailings released through fluvial processes and the transport capacity of a wash.