Understanding Human Motion Skill with Peak Timing Synergy

NASA Astrophysics Data System (ADS)

Ueno, Ken; Furukawa, Koichi

The careful observation of motion phenomena is important in understanding the skillful human motion. However, this is a difficult task due to the complexities in timing when dealing with the skilful control of anatomical structures. To investigate the dexterity of human motion, we decided to concentrate on timing with respect to motion, and we have proposed a method to extract the peak timing synergy from multivariate motion data. The peak timing synergy is defined as a frequent ordered graph with time stamps, which has nodes consisting of turning points in motion waveforms. A proposed algorithm, PRESTO automatically extracts the peak timing synergy. PRESTO comprises the following 3 processes: (1) detecting peak sequences with polygonal approximation; (2) generating peak-event sequences; and (3) finding frequent peak-event sequences using a sequential pattern mining method, generalized sequential patterns (GSP). Here, we measured right arm motion during the task of cello bowing and prepared a data set of the right shoulder and arm motion. We successfully extracted the peak timing synergy on cello bowing data set using the PRESTO algorithm, which consisted of common skills among cellists and personal skill differences. To evaluate the sequential pattern mining algorithm GSP in PRESTO, we compared the peak timing synergy by using GSP algorithm and the one by using filtering by reciprocal voting (FRV) algorithm as a non time-series method. We found that the support is 95 - 100% in GSP, while 83 - 96% in FRV and that the results by GSP are better than the one by FRV in the reproducibility of human motion. Therefore we show that sequential pattern mining approach is more effective to extract the peak timing synergy than non-time series analysis approach.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Binding intensity and metal partitioning in soils affected by mining and smelting activities in Minas Gerais, Brazil.

PubMed

Lopes, G; Costa, E T S; Penido, E S; Sparks, D L; Guilherme, L R G

2015-09-01

Mining and smelting activities are potential sources of heavy metal contamination, which pose a threat to human health and ecological systems. This study investigated single and sequential extractions of Zn, Pb, and Cd in Brazilian soils affected by mining and smelting activities. Soils from a Zn mining area (soils A, B, C, D, E, and the control soil) and a tailing from a smelting area were collected in Minas Gerais state, Brazil. The samples were subjected to single (using Mehlich I solution) and sequential extractions. The risk assessment code (RAC), the redistribution index (U ts ), and the reduced partition index (I R ) have been applied to the sequential extraction data. Zinc and Cd, in soil samples from the mining area, were found mainly associated with carbonate forms. This same pattern did not occur for Pb. Moreover, the Fe-Mn oxides and residual fractions had important contributions for Zn and Pb in those soils. For the tailing, more than 70 % of Zn and Cd were released in the exchangeable fraction, showing a much higher mobility and availability of these metals at this site, which was also supported by results of RAC and I R . These differences in terms of mobility might be due to different chemical forms of the metals in the two sites, which are attributable to natural occurrence as well as ore processing.

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

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

Personalized Recommendation of Learning Material Using Sequential Pattern Mining and Attribute Based Collaborative Filtering

ERIC Educational Resources Information Center

Salehi, Mojtaba; Nakhai Kamalabadi, Isa; Ghaznavi Ghoushchi, Mohammad Bagher

2014-01-01

Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover,…

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.

Spatio-Temporal Mining of PolSAR Satellite Image Time Series

NASA Astrophysics Data System (ADS)

Julea, A.; Meger, N.; Trouve, E.; Bolon, Ph.; Rigotti, C.; Fallourd, R.; Nicolas, J.-M.; Vasile, G.; Gay, M.; Harant, O.; Ferro-Famil, L.

2010-12-01

This paper presents an original data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the frequent grouped sequential patterns, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Preliminary experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont-Blanc test-site are used to illustrate the proposed approach.

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.

Preliminary results of sequential extraction experiments for selenium on mine waste and stream sediments from Vermont, Maine, and New Zealand

USGS Publications Warehouse

Piatak, N.M.; Seal, R.R.; Sanzolone, R.F.; Lamothe, P.J.; Brown, Z.A.

2006-01-01

We report the preliminary results of sequential partial dissolutions used to characterize the geochemical distribution of selenium in stream sediments, mine wastes, and flotation-mill tailings. In general, extraction schemes are designed to extract metals associated with operationally defined solid phases. Total Se concentrations and the mineralogy of the samples are also presented. Samples were obtained from the Elizabeth, Ely, and Pike Hill mines in Vermont, the Callahan mine in Maine, and the Martha mine in New Zealand. These data are presented here with minimal interpretation or discussion. Further analysis of the data will be presented elsewhere.

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.

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

NASA Astrophysics Data System (ADS)

Zhao, Lifang; Jia, Jin

2018-02-01

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

Sequential Extraction Results and Mineralogy of Mine Waste and Stream Sediments Associated With Metal Mines in Vermont, Maine, and New Zealand

USGS Publications Warehouse

Piatak, N.M.; Seal, R.R.; Sanzolone, R.F.; Lamothe, P.J.; Brown, Z.A.; Adams, M.

2007-01-01

We report results from sequential extraction experiments and the quantitative mineralogy for samples of stream sediments and mine wastes collected from metal mines. Samples were from the Elizabeth, Ely Copper, and Pike Hill Copper mines in Vermont, the Callahan Mine in Maine, and the Martha Mine in New Zealand. The extraction technique targeted the following operationally defined fractions and solid-phase forms: (1) soluble, adsorbed, and exchangeable fractions; (2) carbonates; (3) organic material; (4) amorphous iron- and aluminum-hydroxides and crystalline manganese-oxides; (5) crystalline iron-oxides; (6) sulfides and selenides; and (7) residual material. For most elements, the sum of an element from all extractions steps correlated well with the original unleached concentration. Also, the quantitative mineralogy of the original material compared to that of the residues from two extraction steps gave insight into the effectiveness of reagents at dissolving targeted phases. The data are presented here with minimal interpretation or discussion and further analyses and interpretation will be presented elsewhere.

Improving Hospital-wide Patient Scheduling Decisions by Clinical Pathway Mining.

PubMed

Gartner, Daniel; Arnolds, Ines V; Nickel, Stefan

2015-01-01

Recent research has highlighted the need for solving hospital-wide patient scheduling problems. Inpatient scheduling, patient activities have to be scheduled on scarce hospital resources such that temporal relations between activities (e.g. for recovery times) are ensured. Common objectives are, among others, the minimization of the length of stay (LOS). In this paper, we consider a hospital-wide patient scheduling problem with LOS minimization based on uncertain clinical pathways. We approach the problem in three stages: First, we learn most likely clinical pathways using a sequential pattern mining approach. Second, we provide a mathematical model for patient scheduling and finally, we combine the two approaches. In an experimental study carried out using real-world data, we show that our approach outperforms baseline approaches on two metrics.

Comparison between BIDE, PrefixSpan, and TRuleGrowth for Mining of Indonesian Text

NASA Astrophysics Data System (ADS)

Sa'adillah Maylawati, Dian; Irfan, Mohamad; Budiawan Zulfikar, Wildan

2017-01-01

Mining proscess for Indonesian language still be an interesting research. Multiple of words representation was claimed can keep the meaning of text better than bag of words. In this paper, we compare several sequential pattern algortihm, among others BIDE (BIDirectional Extention), PrefixSpan, and TRuleGrowth. All of those algorithm produce frequent word sequence to keep the meaning of text. However, the experiment result, with 14.006 of Indonesian tweet from Twitter, shows that BIDE can produce more efficient frequent word sequence than PrefixSpan and TRuleGrowth without missing the meaning of text. Then, the average of time process of PrefixSpan is faster than BIDE and TRuleGrowth. In the other hand, PrefixSpan and TRuleGrowth is more efficient in using memory than BIDE.

The synergic role of sociotechnical and personal characteristics on work injuries in mines.

PubMed

Paul, P S; Maiti, J

2008-05-01

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.

Mining patterns in persistent surveillance systems with smart query and visual analytics

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.; Shirkhodaie, Amir

2013-05-01

In Persistent Surveillance Systems (PSS) the ability to detect and characterize events geospatially help take pre-emptive steps to counter adversary's actions. Interactive Visual Analytic (VA) model offers this platform for pattern investigation and reasoning to comprehend and/or predict such occurrences. The need for identifying and offsetting these threats requires collecting information from diverse sources, which brings with it increasingly abstract data. These abstract semantic data have a degree of inherent uncertainty and imprecision, and require a method for their filtration before being processed further. In this paper, we have introduced an approach based on Vector Space Modeling (VSM) technique for classification of spatiotemporal sequential patterns of group activities. The feature vectors consist of an array of attributes extracted from generated sensors semantic annotated messages. To facilitate proper similarity matching and detection of time-varying spatiotemporal patterns, a Temporal-Dynamic Time Warping (DTW) method with Gaussian Mixture Model (GMM) for Expectation Maximization (EM) is introduced. DTW is intended for detection of event patterns from neighborhood-proximity semantic frames derived from established ontology. GMM with EM, on the other hand, is employed as a Bayesian probabilistic model to estimated probability of events associated with a detected spatiotemporal pattern. In this paper, we present a new visual analytic tool for testing and evaluation group activities detected under this control scheme. Experimental results demonstrate the effectiveness of proposed approach for discovery and matching of subsequences within sequentially generated patterns space of our experiments.

Sequential pattern data mining and visualization

DOEpatents

Wong, Pak Chung [Richland, WA; Jurrus, Elizabeth R [Kennewick, WA; Cowley, Wendy E [Benton City, WA; Foote, Harlan P [Richland, WA; Thomas, James J [Richland, WA

2011-12-06

One or more processors (22) are operated to extract a number of different event identifiers therefrom. These processors (22) are further operable to determine a number a display locations each representative of one of the different identifiers and a corresponding time. The display locations are grouped into sets each corresponding to a different one of several event sequences (330a, 330b, 330c. 330d, 330e). An output is generated corresponding to a visualization (320) of the event sequences (330a, 330b, 330c, 330d, 330e).

Sequential pattern data mining and visualization

DOEpatents

Wong, Pak Chung [Richland, WA; Jurrus, Elizabeth R [Kennewick, WA; Cowley, Wendy E [Benton City, WA; Foote, Harlan P [Richland, WA; Thomas, James J [Richland, WA

2009-05-26

One or more processors (22) are operated to extract a number of different event identifiers therefrom. These processors (22) are further operable to determine a number a display locations each representative of one of the different identifiers and a corresponding time. The display locations are grouped into sets each corresponding to a different one of several event sequences (330a, 330b, 330c. 330d, 330e). An output is generated corresponding to a visualization (320) of the event sequences (330a, 330b, 330c, 330d, 330e).

Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

PubMed

Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

2015-11-01

Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

Trace elements and Pb isotopes in soils and sediments impacted by uranium mining.

PubMed

Cuvier, A; Pourcelot, L; Probst, A; Prunier, J; Le Roux, G

2016-10-01

The purpose of this study is to evaluate the contamination in As, Ba, Co, Cu, Mn, Ni, Sr, V, Zn and REE, in a high uranium activity (up to 21,000Bq∙kg(-1)) area, downstream of a former uranium mine. Different geochemical proxies like enrichment factor and fractions from a sequential extraction procedure are used to evaluate the level of contamination, the mobility and the availability of the potential contaminants. Pb isotope ratios are determined in the total samples and in the sequential leachates to identify the sources of the contaminants and to determine the mobility of radiogenic Pb in the context of uranium mining. In spite of the large uranium contamination measured in the soils and the sediments (EF≫40), trace element contamination is low to moderate (2

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

Influence of ore processing activity on Hg, As and Sb contamination and fractionation in soils in a former mining site of Monte Amiata ore district (Italy).

PubMed

Protano, Giuseppe; Nannoni, Francesco

2018-05-01

A geochemical study was carried out at the former Abbadia San Salvatore (ASS) mining site of the Monte Amiata ore district (Italy). Hg, As and Sb total contents and fractionation using a sequential extraction procedure were determined in soil and mining waste samples. Ore processing activities provided a different contribution to Hg contamination and concentration in soil fractions, influencing its behaviour as volatility and availability. Soils of roasting zone showed the highest Hg contamination levels mainly due to the deposition of Hg released as Hg 0 by furnaces during cinnabar roasting. High Hg contents were also measured in waste from the lower part of mining dump due to the presence of cinnabar. The fractionation pattern suggested that Hg was largely as volatile species in both uncontaminated and contaminated soils and mining waste, and concentrations of these Hg species increased as contamination increased. These findings were in agreement with the fact that the ASS mining site is characterized by high Hg concentrations in the air and the presence of Hg 0 liquid droplets in soil. Volatile Hg species were also prevalent in uncontaminated soils likely because the Monte Amiata region is an area characterized by anomalous fluxes of gaseous Hg from natural and anthropogenic inputs. At the ASS mining site soils were also contaminated by Sb, while As contents were comparable with its local background in soil. In all soil and waste samples Sb and As were preferentially in residual fraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

Geochemistry and mineralogy of arsenic in mine wastes and stream sediments in a historic metal mining area in the UK.

PubMed

Rieuwerts, J S; Mighanetara, K; Braungardt, C B; Rollinson, G K; Pirrie, D; Azizi, F

2014-02-15

Mining generates large amounts of waste which may contain potentially toxic elements (PTE), which, if released into the wider environment, can cause air, water and soil pollution long after mining operations have ceased. The fate and toxicological impact of PTEs are determined by their partitioning and speciation and in this study, the concentrations and mineralogy of arsenic in mine wastes and stream sediments in a former metal mining area of the UK are investigated. Pseudo-total (aqua-regia extractable) arsenic concentrations in all samples from the mining area exceeded background and guideline values by 1-5 orders of magnitude, with a maximum concentration in mine wastes of 1.8×10(5)mgkg(-1) As and concentrations in stream sediments of up to 2.5×10(4)mgkg(-1) As, raising concerns over potential environmental impacts. Mineralogical analysis of the wastes and sediments was undertaken by scanning electron microscopy (SEM) and automated SEM-EDS based quantitative evaluation (QEMSCAN®). The main arsenic mineral in the mine waste was scorodite and this was significantly correlated with pseudo-total As concentrations and significantly inversely correlated with potentially mobile arsenic, as estimated from the sum of exchangeable, reducible and oxidisable arsenic fractions obtained from a sequential extraction procedure; these findings correspond with the low solubility of scorodite in acidic mine wastes. The work presented shows that the study area remains grossly polluted by historical mining and processing and illustrates the value of combining mineralogical data with acid and sequential extractions to increase our understanding of potential environmental threats. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of drying-wetting and freezing-thawing cycle on leachability of metallic elements in mine soils

NASA Astrophysics Data System (ADS)

Bang, H.; Kim, J.; Hyun, S.

2016-12-01

Mine leachate derived from contaminated mine sites with metallic elements can pose serious risks on human society and environment. Only labile fraction of metallic elements in mine soils is subject to leaching and movement by rainfall. Lability of metallic element in soil is a function of bond strengths between metal and soil surfaces, which is influenced by environmental condition (e.g., rainfall intensity, duration, temperature, etc.) The purpose of this study was to elucidate the effects of various climate conditions on the leaching patterns and lability of metallic elements in mine soils. To do this, two mine soils were sampled from two abandoned mine sites located in Korea. Leaching test were conducted using batch decant-refill method. Various climatic conditions were employed in leaching test such as (1) oven drying (40oC) - wetting cycles, (2) air drying (20oC) - wetting cycle, and (3) freezing (-40oC) - thawing cycles. Duration of drying and freezing were varied from 4 days to 2 weeks. Concentration of metallic elements, pH, Eh and concentration of dissolved iron and sulfate in leachate from each leaching process was measured. To identify the changes of labile fraction in mine soils after each of drying or freezing period, sequential extraction procedure (five fraction) was used to compare labile fraction (i.e., F1 + F2) of metallic elements. The concentration of metallic elements in mine leachate was increased after drying and freezing procedure. The amounts of released metallic element from mine soils was changed depending on their drying or freezing period. In addition, labile fraction of metallic elements in soil was also changed after drying and freezing. The changes in labile fraction after drying and freezing might be due to the increased soil surface area by pore water volume expansion. Further study is therefore needed to evaluate the impact of altered physical properties of soils such as hydration of soil surface area and shrinking by drying and freezing cycles.

Uranium decay daughters from isolated mines: Accumulation and sources.

PubMed

Cuvier, A; Panza, F; Pourcelot, L; Foissard, B; Cagnat, X; Prunier, J; van Beek, P; Souhaut, M; Le Roux, G

2015-11-01

This study combines in situ gamma spectrometry performed at different scales, in order to accurately locate the contamination pools, to identify the concerned radionuclides and to determine the distribution of the contaminants from soil to bearing phase scale. The potential mobility of several radionuclides is also evaluated using sequential extraction. Using this procedure, an accumulation area located downstream of a former French uranium mine and concentrating a significant fraction of radioactivity is highlighted. We report disequilibria in the U-decay chains, which are likely related to the processes implemented on the mining area. Coupling of mineralogical analyzes with sequential extraction allow us to highlight the presence of barium sulfate, which may be the carrier of the Ra-226 activities found in the residual phase (Ba(Ra)SO4). In contrast, uranium is essentially in the reducible fraction and potentially trapped in clay-iron coatings located on the surface of minerals. Copyright © 2015 Elsevier Ltd. All rights reserved.

DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data.

PubMed

Hollunder, Jens; Friedel, Maik; Kuiper, Martin; Wilhelm, Thomas

2010-04-01

Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape. Source code, pre-compiled binaries for different systems, a comprehensive tutorial, case studies and many additional datasets are freely available at http://www.ifr.ac.uk/dass/gui/. DASS-GUI is implemented in Qt.

Chimney subsidence development in the Colorado Springs coal field, Colorado

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

Matheson, G.M.; Pearson, M.L.

1985-01-01

Mining in the Colorodo Springs coal field took place from the 1880's to 1940's. The depth of mining in the coal field varied from about 10 meters to over 150 meters. Review of sequential historical aerial photographs from 1937 to 1960 indicated about 2400 chimney subsidence sinkholes had developed throughout the study area. Statistical analyses of the location and size of these sinkholes with respect to the time since mining, depth of mining, mined thickness and type of mining indicated definite trends in the time of occurrence, size, and location of these features. This data is valuable in the assessmentmore » of potential future subsidence in this and other areas of similar mining conditions.« less

Involvement of Working Memory in College Students' Sequential Pattern Learning and Performance

ERIC Educational Resources Information Center

Kundey, Shannon M. A.; De Los Reyes, Andres; Rowan, James D.; Lee, Bern; Delise, Justin; Molina, Sabrina; Cogdill, Lindsay

2013-01-01

When learning highly organized sequential patterns of information, humans and nonhuman animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential…

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…

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

C-quence: a tool for analyzing qualitative sequential data.

PubMed

Duncan, Starkey; Collier, Nicholson T

2002-02-01

C-quence is a software application that matches sequential patterns of qualitative data specified by the user and calculates the rate of occurrence of these patterns in a data set. Although it was designed to facilitate analyses of face-to-face interaction, it is applicable to any data set involving categorical data and sequential information. C-quence queries are constructed using a graphical user interface. The program does not limit the complexity of the sequential patterns specified by the user.

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...

Microwave assisted hard rock cutting

DOEpatents

Lindroth, David P.; Morrell, Roger J.; Blair, James R.

1991-01-01

An apparatus for the sequential fracturing and cutting of subsurface volume of hard rock (102) in the strata (101) of a mining environment (100) by subjecting the volume of rock to a beam (25) of microwave energy to fracture the subsurface volume of rock by differential expansion; and , then bringing the cutting edge (52) of a piece of conventional mining machinery (50) into contact with the fractured rock (102).

Mine countermeasures (MCM) sensor technology drivers

NASA Astrophysics Data System (ADS)

Skinner, David P.

1995-06-01

In recent years, MCM has moved to the forefront of the Navy's attention. This paper describes the general problems that drive the technology requirements of classical sea mine countermeasure (MCM) sensors for those working outside of this specialized area. Sensor requirements for MCM are compared with those for antisubmarine warfare. This highlights the unique environmental issues and crucial false target problems. The elimination of false targets, not mine detection, is the principal driver of MCM sensor requirements and places special emphasis on the technologies needed for the sequential operations of detection, classification, and identification.

Leachability of Arsenic and Heavy Metals from Mine Tailings of Abandoned Metal Mines

PubMed Central

Lim, Mihee; Han, Gi-Chun; Ahn, Ji-Whan; You, Kwang-Suk; Kim, Hyung-Seok

2009-01-01

Mine tailings from an abandoned metal mine in Korea contained high concentrations of arsenic (As) and heavy metals [e.g., As: 67,336, Fe: 137,180, Cu: 764, Pb: 3,572, and Zn: 12,420 (mg/kg)]. US EPA method 6010 was an effective method for analyzing total arsenic and heavy metals concentrations. Arsenic in the mine tailings showed a high residual fraction of 89% by a sequential extraction. In Toxicity Characteristic Leaching Procedure (TCLP) and Korean Standard Leaching Test (KSLT), leaching concentrations of arsenic and heavy metals were very low [e.g., As (mg/L): 0.4 for TCLP and 0.2 for KSLT; cf. As criteria (mg/L): 5.0 for TCLP and 1.5 for KSLT]. PMID:20049231

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

PubMed Central

Liu, Zhao; Zhu, Yunhong; Wu, Chenxue

2016-01-01

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502

Arsenic release from arsenopyrite weathering: insights from sequential extraction and microscopic studies.

PubMed

Basu, Ankan; Schreiber, Madeline E

2013-11-15

At a former As mine site, arsenopyrite oxidation has resulted in formation of scorodite and As-bearing iron hydroxide, both in host rock and mine tailings. Electron microprobe analysis documents that arsenopyrite weathers along two pathways: one that involves formation of sulfur, and one that does not. In both pathways, arsenopyrite oxidizes to form scorodite, which dissolves incongruently to form As-bearing iron hydroxides. From a mass balance perspective, arsenopyrite oxidation to scorodite conserves As, but as scorodite dissolves incongruently to iron hydroxides, As is released to solution, resulting in elevated As concentrations in the headwater stream adjacent to the site. The As-bearing iron hydroxide is the dominant solid phase reservoir of As in mine tailings and stream sediment, as suggested by sequential extraction. This As-bearing iron hydroxide is stable under the aerobic and pH 4-6 conditions at the site; however, changes in biogeochemical conditions resulting from sediment burial or future remedial efforts, which could promote As release from this reservoir due to reductive dissolution, should be avoided. Copyright © 2012 Elsevier B.V. All rights reserved.

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...

Test pattern generation for ILA sequential circuits

NASA Technical Reports Server (NTRS)

Feng, YU; Frenzel, James F.; Maki, Gary K.

1993-01-01

An efficient method of generating test patterns for sequential machines implemented using one-dimensional, unilateral, iterative logic arrays (ILA's) of BTS pass transistor networks is presented. Based on a transistor level fault model, the method affords a unique opportunity for real-time fault detection with improved fault coverage. The resulting test sets are shown to be equivalent to those obtained using conventional gate level models, thus eliminating the need for additional test patterns. The proposed method advances the simplicity and ease of the test pattern generation for a special class of sequential circuitry.

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.

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

NASA Astrophysics Data System (ADS)

Jakubowski, Jacek

2014-12-01

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

Cadmium partition in river sediments from an area affected by mining activities.

PubMed

Vasile, Georgiana D; Vlădescu, Luminiţa

2010-08-01

In this paper, the cadmium distribution in Certej River sediments in an area seriously affected by intense mining activities has been studied. The main objective of this study was the evaluation of partition of this metal into different operational defined fractions by sequential extractions. Community Bureau of Reference (BCR) sequential extraction was used to isolate different fractions. The sediment quality was assessed both upstream and downstream the pollution input points, along the Certej River, in order to reveal a possible accumulation of cadmium in sediments and the seasonal changes in cadmium concentrations in BCR sediment phases. Our results reveal that most of the cadmium content is divided between both the soluble and iron and manganese hydrated oxide fractions. Based on total cadmium concentrations in sediments, the enrichment factors were estimated using aluminum as normalizing element and the regression curve Cd/Al corresponding to the geochemical background of the studied area.

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

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

PubMed

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

2018-06-24

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

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

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Discovering Sentinel Rules for Business Intelligence

NASA Astrophysics Data System (ADS)

Middelfart, Morten; Pedersen, Torben Bach

This paper proposes the concept of sentinel rules for multi-dimensional data that warns users when measure data concerning the external environment changes. For instance, a surge in negative blogging about a company could trigger a sentinel rule warning that revenue will decrease within two months, so a new course of action can be taken. Hereby, we expand the window of opportunity for organizations and facilitate successful navigation even though the world behaves chaotically. Since sentinel rules are at the schema level as opposed to the data level, and operate on data changes as opposed to absolute data values, we are able to discover strong and useful sentinel rules that would otherwise be hidden when using sequential pattern mining or correlation techniques. We present a method for sentinel rule discovery and an implementation of this method that scales linearly on large data volumes.

Mining reflective continuing medical education data for family physician learning needs.

PubMed

Lewis, Denice Colleen; Pluye, Pierre; Rodriguez, Charo; Grad, Roland

2016-04-06

A mixed methods research (sequential explanatory design) studied the potential of mining the data from the consumers of continuing medical education (CME) programs, for the developers of CME programs. The quantitative data generated by family physicians, through applying the information assessment method to CME content, was presented to key informants from the CME planning community through a qualitative description study.The data were revealed to have many potential applications including supporting the creation of CME content, CME program planning and personal learning portfolios.

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

NASA Technical Reports Server (NTRS)

Rohloff, Kurt

2010-01-01

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

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.

Geochemical characteristics of Au in the water systemfrom abandoned gold mines area

NASA Astrophysics Data System (ADS)

Cho, Kanghee; Kim, Bongju; Kim, Byungjoo; Park, Cheonyoung; Choi, Nagchoul

2013-04-01

The AMD (acid mine drainage) poses a threat not only to the aquatic life in mountain streams and rivers, but can also contaminate groundwater and downstream water bodies. Besides pyrite, sulfides of copper, zinc, cadmium, lead and arsenic in the drainage tunnels and tailings piles also undergo similar geochemical reactions, releasing toxic metals and more H+ into the mine drainage. The fate of gold in the AMD system is reduced and precipitated with iron oxides by oxidation-reduction reaction between ferrous/ferric iron and Au3+/Au0. The objective of this study was to investigate the influence of the transport characteristic on the distance through distribution of heavy metals and gold on the interrelation between acid mine drainage and sediments in the abandoned Gwang-yang gold mine, Korea. We conducted to confirm the chemical (chemical analysis and sequential extraction) and mineralogical property (XRD, SEM-EDS and polarization microscope) from AMD, sediments and tailing samples. The result of chemical analysis showed that Fe contents in the AMD and sediments from the upstream to the downstream ranged of 10.99 to 18.60 mg/L and 478.74 to 542.98 mg/kg, respectively. Also the contents of Au and As in the sediment were respectively ranged from 14.06 to 22.85 g/t and 0.245 to 0.612 mg/kg. In XRD analysis of the sediments, x-ray diffracted d-value belong to quartz, geothite was observed. The results of SEM-EDS analysis revealed that iron hydroxide were observed in the sediment and tailing. The result of sequential extraction for Au from the sediment showed that Au predominated in 26 to 27% of Organic matter fraction(STEP 4), and 24 to 25% of Residual fraction(STEP 5).

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

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

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.

Environmental assessment and management of metal-rich wastes generated in acid mine drainage passive remediation systems.

PubMed

Macías, Francisco; Caraballo, Manuel A; Nieto, José Miguel

2012-08-30

As acid mine drainage (AMD) remediation is increasingly faced by governments and mining industries worldwide, the generation of metal-rich solid residues from the treatments plants is concomitantly raising. A proper environmental management of these metal-rich wastes requires a detailed characterization of the metal mobility as well as an assessment of this new residues stability. The European standard leaching test EN 12457-2, the US EPA TCLP test and the BCR sequential extraction procedure were selected to address the environmental assessment of dispersed alkaline substrate (DAS) residues generated in AMD passive treatment systems. Significant discrepancies were observed in the hazardousness classification of the residues according to the TCLP or EN 12457-2 test. Furthermore, the absence of some important metals (like Fe or Al) in the regulatory limits employed in both leaching tests severely restricts their applicability for metal-rich wastes. The results obtained in the BCR sequential extraction suggest an important influence of the landfill environmental conditions on the metals released from the wastes. To ensure a complete stability of the pollutants in the studied DAS-wastes the contact with water or any other leaching solutions must be avoided and a dry environment needs to be provided in the landfill disposal selected. Copyright © 2012 Elsevier B.V. All rights reserved.

Probabilistic Seeking Prediction in P2P VoD Systems

NASA Astrophysics Data System (ADS)

Wang, Weiwei; Xu, Tianyin; Gao, Yang; Lu, Sanglu

In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user's next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

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

Leaching characteristics of vanadium in mine tailings and soils near a vanadium titanomagnetite mining site.

PubMed

Yang, Jinyan; Tang, Ya; Yang, Kai; Rouff, Ashaki A; Elzinga, Evert J; Huang, Jen-How

2014-01-15

A series of column leaching experiments were performed to understand the leaching behaviour and the potential environmental risk of vanadium in a Panzhihua soil and vanadium titanomagnetite mine tailings. Results from sequential extraction experiments indicated that the mobility of vanadium in both the soil and the mine tailings was low, with <1% of the total vanadium readily mobilised. Column experiments revealed that only <0.1% of vanadium in the soil and mine tailing was leachable. The vanadium concentrations in the soil leachates did not vary considerably, but decreased with the leachate volume in the mine tailing leachates. This suggests that there was a smaller pool of leachable vanadium in the mine tailings compared to that in the soil. Drought and rewetting increased the vanadium concentrations in the soil and mine tailing leachates from 20μgL(-1) to 50-90μgL(-1), indicating the potential for high vanadium release following periods of drought. Experiments with soil columns overlain with 4, 8 and 20% volume mine tailings/volume soil exhibited very similar vanadium leaching behaviour. These results suggest that the transport of vanadium to the subsurface is controlled primarily by the leaching processes occurring in soils. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of musical training on sound pattern processing in high-school students.

PubMed

Wang, Wenjung; Staffaroni, Laura; Reid, Errold; Steinschneider, Mitchell; Sussman, Elyse

2009-05-01

Recognizing melody in music involves detection of both the pitch intervals and the silence between sequentially presented sounds. This study tested the hypothesis that active musical training in adolescents facilitates the ability to passively detect sequential sound patterns compared to musically non-trained age-matched peers. Twenty adolescents, aged 15-18 years, were divided into groups according to their musical training and current experience. A fixed order tone pattern was presented at various stimulus rates while electroencephalogram was recorded. The influence of musical training on passive auditory processing of the sound patterns was assessed using components of event-related brain potentials (ERPs). The mismatch negativity (MMN) ERP component was elicited in different stimulus onset asynchrony (SOA) conditions in non-musicians than musicians, indicating that musically active adolescents were able to detect sound patterns across longer time intervals than age-matched peers. Musical training facilitates detection of auditory patterns, allowing the ability to automatically recognize sequential sound patterns over longer time periods than non-musical counterparts.

Speciation and leachability of copper in mine tailings from porphyry copper mining: influence of particle size.

PubMed

Hansen, Henrik K; Yianatos, Juan B; Ottosen, Lisbeth M

2005-09-01

Mine tailing from the El Teniente-Codelco copper mine situated in VI Region of Chile was analysed in order to evaluate the mobility and speciation of copper in the solid material. Mine tailing was sampled after the rougher flotation circuits, and the copper content was measured to 1150 mg kg (-1) dry matter. This tailing was segmented into fractions of different size intervals: 0-38, 38-45, 45-53, 53-75, 75-106, 106-150, 150-212, and >212 microm, respectively. Copper content determination, sequential chemical extraction, and desorption experiments were carried out for each size interval in order to evaluate the speciation of copper. It was found that the particles of smallest size contained 50-60% weak acid leachable copper, whereas only 32% of the copper found in largest particles could be leached in weak acid. Copper oxides and carbonates were the dominating species in the smaller particles, and the larger particles contained considerable amounts of sulphides.

Method for gasification of deep, thin coal seams. [DOE patent

DOEpatents

Gregg, D.W.

1980-08-29

A method of gasification of coal in deep, thin seams by using controlled bending subsidence to confine gas flow to a region close to the unconsumed coal face is given. The injection point is moved sequentially around the perimeter of a coal removal area from a production well to sweep out the area to cause the controlled bending subsidence. The injection holes are drilled vertically into the coal seam through the overburden or horizontally into the seam from an exposed coal face. The method is particularly applicable to deep, thin seams found in the eastern United States and at abandoned strip mines where thin seams were surface mined into a hillside or down a modest dip until the overburden became too thick for further mining.

Method for gasification of deep, thin coal seams

DOEpatents

Gregg, David W.

1982-01-01

A method of gasification of coal in deep, thin seams by using controlled bending subsidence to confine gas flow to a region close to the unconsumed coal face. The injection point is moved sequentially around the perimeter of a coal removal area from a production well to sweep out the area to cause the controlled bending subsidence. The injection holes are drilled vertically into the coal seam through the overburden or horizontally into the seam from an exposed coal face. The method is particularly applicable to deep, thin seams found in the eastern United States and at abandoned strip mines where thin seams were surface mined into a hillside or down a modest dip until the overburden became too thick for further mining.

Sequential infiltration synthesis for enhancing multiple-patterning lithography

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

Darling, Seth B.; Elam, Jeffrey W.; Tseng, Yu-Chih

Simplified methods of multiple-patterning photolithography using sequential infiltration synthesis to modify the photoresist such that it withstands plasma etching better than unmodified resist and replaces one or more hard masks and/or a freezing step in MPL processes including litho-etch-litho-etch photolithography or litho-freeze-litho-etch photolithography.

An integrated pipeline of open source software adapted for multi-CPU architectures: use in the large-scale identification of single nucleotide polymorphisms.

PubMed

Jayashree, B; Hanspal, Manindra S; Srinivasan, Rajgopal; Vigneshwaran, R; Varshney, Rajeev K; Spurthi, N; Eshwar, K; Ramesh, N; Chandra, S; Hoisington, David A

2007-01-01

The large amounts of EST sequence data available from a single species of an organism as well as for several species within a genus provide an easy source of identification of intra- and interspecies single nucleotide polymorphisms (SNPs). In the case of model organisms, the data available are numerous, given the degree of redundancy in the deposited EST data. There are several available bioinformatics tools that can be used to mine this data; however, using them requires a certain level of expertise: the tools have to be used sequentially with accompanying format conversion and steps like clustering and assembly of sequences become time-intensive jobs even for moderately sized datasets. We report here a pipeline of open source software extended to run on multiple CPU architectures that can be used to mine large EST datasets for SNPs and identify restriction sites for assaying the SNPs so that cost-effective CAPS assays can be developed for SNP genotyping in genetics and breeding applications. At the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), the pipeline has been implemented to run on a Paracel high-performance system consisting of four dual AMD Opteron processors running Linux with MPICH. The pipeline can be accessed through user-friendly web interfaces at http://hpc.icrisat.cgiar.org/PBSWeb and is available on request for academic use. We have validated the developed pipeline by mining chickpea ESTs for interspecies SNPs, development of CAPS assays for SNP genotyping, and confirmation of restriction digestion pattern at the sequence level.

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.

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.

Two Tales of Time: Uncovering the Significance of Sequential Patterns among Contribution Types in Knowledge-Building Discourse

ERIC Educational Resources Information Center

Chen, Bodong; Resendes, Monica; Chai, Ching Sing; Hong, Huang-Yao

2017-01-01

As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish "productive" threads of knowledge-building discourse. A database of Grade 1-6 knowledge-building discourse was first coded for…

Judgments Relative to Patterns: How Temporal Sequence Patterns Affect Judgments and Memory

ERIC Educational Resources Information Center

Kusev, Petko; Ayton, Peter; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Stewart, Neil; Chater, Nick

2011-01-01

RESix experiments studied relative frequency judgment and recall of sequentially presented items drawn from 2 distinct categories (i.e., city and animal). The experiments show that judged frequencies of categories of sequentially encountered stimuli are affected by certain properties of the sequence configuration. We found (a) a "first-run…

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.

The Effects of Partial Reinforcement in the Acquisition and Extinction of Recurrent Serial Patterns.

ERIC Educational Resources Information Center

Dockstader, Steven L.

The purpose of these 2 experiments was to determine whether sequential response pattern behavior is affected by partial reinforcement in the same way as other behavior systems. The first experiment investigated the partial reinforcement extinction effects (PREE) in a sequential concept learning task where subjects were required to learn a…

Contribution of Implicit Sequence Learning to Spoken Language Processing: Some Preliminary Findings with Hearing Adults

ERIC Educational Resources Information Center

Conway, Christopher M.; Karpicke, Jennifer; Pisoni, David B.

2007-01-01

Spoken language consists of a complex, sequentially arrayed signal that contains patterns that can be described in terms of statistical relations among language units. Previous research has suggested that a domain-general ability to learn structured sequential patterns may underlie language acquisition. To test this prediction, we examined the…

Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques

ERIC Educational Resources Information Center

Sonnenberg, Christoph; Bannert, Maria

2015-01-01

According to research examining self-regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing…

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

USGS Publications Warehouse

Karacan, C.O.; Olea, R.A.; Goodman, G.

2012-01-01

Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control.This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines.Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may change up to 3. MMscf per acre and, in a multi-panel district, may total 9. Bcf of methane within the gas emission zone. Therefore, ventilation and gas capture systems should be designed accordingly. In addition, rock displacements within the gas emission zone are spatially distributed. From an engineering and practical point of view, spatial distributions of GIP and distributions of rock displacements should be correlated with in-mine emissions and gob gas venthole productions. ?? 2011.

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

PubMed Central

Karacan, C. Özgen; Olea, Ricardo A.; Goodman, Gerrit

2015-01-01

Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control. This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines. Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may change up to 3 MMscf per acre and, in a multi-panel district, may total 9 Bcf of methane within the gas emission zone. Therefore, ventilation and gas capture systems should be designed accordingly. In addition, rock displacements within the gas emission zone are spatially distributed. From an engineering and practical point of view, spatial distributions of GIP and distributions of rock displacements should be correlated with in-mine emissions and gob gas venthole productions. PMID:26435558

Sequential stages and distribution patterns of aging-related tau astrogliopathy (ARTAG) in the human brain.

PubMed

Kovacs, Gabor G; Xie, Sharon X; Robinson, John L; Lee, Edward B; Smith, Douglas H; Schuck, Theresa; Lee, Virginia M-Y; Trojanowski, John Q

2018-06-11

Aging-related tau astrogliopathy (ARTAG) describes tau pathology in astrocytes in different locations and anatomical regions. In the present study we addressed the question of whether sequential distribution patterns can be recognized for ARTAG or astroglial tau pathologies in both primary FTLD-tauopathies and non-FTLD-tauopathy cases. By evaluating 687 postmortem brains with diverse disorders we identified ARTAG in 455. We evaluated frequencies and hierarchical clustering of anatomical involvement and used conditional probability and logistic regression to model the sequential distribution of ARTAG and astroglial tau pathologies across different brain regions. For subpial and white matter ARTAG we recognize three and two patterns, respectively, each with three stages initiated or ending in the amygdala. Subependymal ARTAG does not show a clear sequential pattern. For grey matter (GM) ARTAG we recognize four stages including a striatal pathway of spreading towards the cortex and/or amygdala, and the brainstem, and an amygdala pathway, which precedes the involvement of the striatum and/or cortex and proceeds towards the brainstem. GM ARTAG and astrocytic plaque pathology in corticobasal degeneration follows a predominantly frontal-parietal cortical to temporal-occipital cortical, to subcortical, to brainstem pathway (four stages). GM ARTAG and tufted astrocyte pathology in progressive supranuclear palsy shows a striatum to frontal-parietal cortical to temporal to occipital, to amygdala, and to brainstem sequence (four stages). In Pick's disease cases with astroglial tau pathology an overlapping pattern with PSP can be appreciated. We conclude that tau-astrogliopathy type-specific sequential patterns cannot be simplified as neuron-based staging systems. The proposed cytopathological and hierarchical stages provide a conceptual approach to identify the initial steps of the pathogenesis of tau pathologies in ARTAG and primary FTLD-tauopathies.

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.

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

PubMed

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

2012-11-01

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

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

PubMed

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

2014-10-01

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

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.

Endogenous Sequential Cortical Activity Evoked by Visual Stimuli

PubMed Central

Miller, Jae-eun Kang; Hamm, Jordan P.; Jackson, Jesse; Yuste, Rafael

2015-01-01

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time. PMID:26063915

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

Stabilization of the As-contaminated soil from the metal mining areas in Korea.

PubMed

Ko, Myoung-Soo; Kim, Ju-Yong; Bang, Sunbeak; Lee, Jin-Soo; Ko, Ju-In; Kim, Kyoung-Woong

2012-01-01

The stabilization efficiencies of arsenic (As) in contaminated soil were evaluated using various additives such as limestone, steel mill slag, granular ferric hydroxide (GFH), and mine sludge collected from an acid mine drainage treatment system. The soil samples were collected from the Chungyang area, where abandoned Au-Ag mines are located. Toxicity characteristic leaching procedure, synthetic precipitation leaching procedure, sequential extraction analysis, aqua regia digestion, cation exchange capacity, loss on ignition, and particle size distribution were conducted to assess the physical and chemical characteristics of highly arsenic-contaminated soils. The total concentrations of arsenic in the Chungyang area soil ranged up to 145 mg/kg. After the stabilization tests, the removal percentages of dissolved As(III) and As(V) were found to differ from the additives employed. Approximately 80 and 40% of the As(V) and As(III), respectively, were removed with the use of steel mill slag. The addition of limestone had a lesser effect on the removal of arsenic from solution. However, more than 99% of arsenic was removed from solution within 24 h when using GFH and mine sludge, with similar results observed when the contaminated soils were stabilized using GFH and mine sludge. These results suggested that GFH and mine sludge may play a significant role on the arsenic stabilization. Moreover, this result showed that mine sludge can be used as a suitable additive for the stabilization of arsenic.

Effect of mining and related activities on the sediment trace element geochemistry of Lake Coeur D'Alene, Idaho, USA. Part I: Surface sediments

USGS Publications Warehouse

Horowitz, Arthur J.; Elrick, Kent A.; Cook, Robert B.

1993-01-01

During the summer of 1989 surface sediment samples were collected in Lake Coeur d'Alene, the Coeur d'Alene River and the St Joe River, Idaho, at a density of approximately one sample per square kilometre. Additional samples were collected from the banks of the South Fork of the Coeur d'Alene and the Coeur d'Alene Rivers in 1991. All the samples were collected to determine trace element concentrations, partitioning and distribution patterns, and to relate them to mining, mining related and discharge operations that have occurred in the Coeur d'Alene district since the 1880s, some of which are ongoing.Most of the surface sediments in Lake Coeur d'Alene north of Conkling Point and Carey Bay are substantially enriched in Ag, As, Cu, Cd, Hg, Pb, Sb and Zn relative to unaffected sediments in the southern portion of the lake near the St Joe River. All the trace element enriched sediments are extremely fine grained (mean grain sizes « 63 μm). Most of the enriched trace elements, based on both the chemical analyses of separated heavy and light mineral fractions and a two step sequential extraction procedure, are associated with an operationally defined Fe oxide phase; much smaller percentages are associated either with operationally defined organics/sulphides or refractory phases.The presence, concentration and distribution of the Fe oxides and heavy minerals indicates that a substantial portion of the enriched trace elements are probably coming from the Coeur d'Alene River, which is serving as a point source. Within the lake, this relatively simple point source pattern is complicated by a combination of (1) the formation of trace element rich authigenic Fe oxides that appear to have reprecipitated from material solubilized from anoxic bed sediments and (2) physical remobilization by currents and wind driven waves. The processes that have caused the trace element enrichment in the surface sediments of Lake Coeur d'Alene are likely to continue for the foreseeable future.

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.

Sequential patterns of essential trace elements composition in Gracilaria verrucosa and its generated products

NASA Astrophysics Data System (ADS)

Izzati, Munifatul; Haryanti, Sri; Parman, Sarjana

2018-05-01

Gracilaria widely known as a source of essential trace elements. However this red seaweeds also has great potential for being developed into commercial products. This study examined the sequential pattern of essential trace elements composition in fresh Gracilaria verrucosa and a selection of its generated products, nemely extracted agar, Gracilaria salt and Gracilaria residue. The sample was collected from a brackish water pond, located in north part Semarang, Central Java. The collected sample was then dried under the sun, and subsequently processed into aformentioned generated products. The Gracilaria salt was obtain by soaking the sun dried Gracilaria overnight in fresh water overnight. The resulted salt solution was then boiled leaving crystal salt. Extracted agar was obtained with alkali agar extraction method. The rest of remaining material was considered as Gracilaria residue. The entire process was repeated 3 times. The compositin of trace elements was examined using ICP-MS Spectrometry. Collected data was then analyzed by ANOVA single factor. Resulting sequential pattern of its essential trace elements composition was compared. A regular table salt was used as controls. Resuts from this study revealed that Gracilaria verrucosa and its all generated products all have similarly patterned the composition of essential trace elements, where Mn>Zn>Cu>Mo. Additionally this pattern is similar to different subspecies of Gracilaria from different location and and different season. However, Gracilaria salt has distinctly different pattern of sequential essential trace elements composition compared to table salt.

Sequential Transition Patterns of Preschoolers' Social Interactions during Child-Initiated Play: Is Parallel-Aware Play a Bidirectional Bridge to Other Play States?

ERIC Educational Resources Information Center

Robinson, Clyde C.; Anderson, Genan T.; Porter, Christin L.; Hart, Craig, H.; Wouden-Miller, Melissa

2003-01-01

Explored the simultaneous sequential transition patterns of preschoolers' social play within classroom settings. Found that the proportion of social-play states did not vary during play episodes even when accounting for type of activity center, gender, and SES. Found a reciprocal relationship between parallel-aware and other social-play states…

Natural resource inventories and management applications in the Great Basin. [Nevada vegetation and wildlands

NASA Technical Reports Server (NTRS)

Tueller, P. T.; Lorain, G.; Halvorson, R. M.

1974-01-01

ERTS-1 resolution capabilities and repetitive coverage have allowed the acquisition of several statewide inventories of natural resource features not previously completed or that could not be completed in any other way. Familiarity with landform, tone, pattern and other converging factors, along with multidate imagery, has been required. Nevada's vegetation has been mapped from ERTS-1. Dynamic characteristics of the landscape have been studied. Sequential ERTS-1 imagery has proved its usefulness for mapping vegetation, following vegetation phenology changes, monitoring changes in lakes and reservoirs (including water quality), determining changes in surface mining use, making fire fuel estimates and determining potential hazard, mapping the distribution of rain and snow events, making range readiness determinations, monitoring marshland management practices and other uses. Feasibility has been determined, but details of incorporating the data in management systems awaits further research and development. The need is to accurately define the steps necessary to extract required or usable information from ERTS imagery and fit it into on-going management programs.

Implications of Emerging Data Mining

NASA Astrophysics Data System (ADS)

Kulathuramaiyer, Narayanan; Maurer, Hermann

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

Dysfunction of bulbar central pattern generator in ALS patients with dysphagia during sequential deglutition.

PubMed

Aydogdu, Ibrahim; Tanriverdi, Zeynep; Ertekin, Cumhur

2011-06-01

The aim of this study is to investigate a probable dysfunction of the central pattern generator (CPG) in dysphagic patients with ALS. We investigated 58 patients with ALS, 23 patients with PD, and 33 normal subjects. The laryngeal movements and EMG of the submental muscles were recorded during sequential water swallowing (SWS) of 100ml of water. The coordination of SWS and respiration was also studied in some normal cases and ALS patients. Normal subjects could complete the SWS optimally within 10s using 7 swallows, while in dysphagic ALS patients, the total duration and the number of swallows were significantly increased. The novel finding was that the regularity and rhythmicity of the swallowing pattern during SWS was disorganized to irregular and arhythmic pattern in 43% of the ALS patients. The duration and speed of swallowing were the most sensitive parameters for the disturbed oropharyngeal motility during SWS. The corticobulbar control of swallowing is insufficient in ALS, and the swallowing CPG cannot work very well to produce segmental muscle activation and sequential swallowing. CPG dysfunction can result in irregular and arhythmical sequential swallowing in ALS patients with bulbar plus pseudobulbar types. The arhythmical SWS pattern can be considered as a kind of dysfunction of CPG in human ALS cases with dysphagia. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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.

A sequential analysis of classroom discourse in Italian primary schools: the many faces of the IRF pattern.

PubMed

Molinari, Luisa; Mameli, Consuelo; Gnisci, Augusto

2013-09-01

A sequential analysis of classroom discourse is needed to investigate the conditions under which the triadic initiation-response-feedback (IRF) pattern may host different teaching orientations. The purpose of the study is twofold: first, to describe the characteristics of classroom discourse and, second, to identify and explore the different interactive sequences that can be captured with a sequential statistical analysis. Twelve whole-class activities were video recorded in three Italian primary schools. We observed classroom interaction as it occurs naturally on an everyday basis. In total, we collected 587 min of video recordings. Subsequently, 828 triadic IRF patterns were extracted from this material and analysed with the programme Generalized Sequential Query (GSEQ). The results indicate that classroom discourse may unfold in different ways. In particular, we identified and described four types of sequences. Dialogic sequences were triggered by authentic questions, and continued through further relaunches. Monologic sequences were directed to fulfil the teachers' pre-determined didactic purposes. Co-constructive sequences fostered deduction, reasoning, and thinking. Scaffolding sequences helped and sustained children with difficulties. The application of sequential analyses allowed us to show that interactive sequences may account for a variety of meanings, thus making a significant contribution to the literature and research practice in classroom discourse. © 2012 The British Psychological Society.

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.

Sources and fates of heavy metals in a mining-impacted stream: Temporal variability and the role of iron oxides

PubMed Central

Schaider, Laurel A.; Senn, David B.; Estes, Emily R.; Brabander, Daniel J.; Shine, James P.

2014-01-01

Heavy metal contamination of surface waters at mining sites often involves complex interactions of multiple sources and varying biogeochemical conditions. We compared surface and subsurface metal loading from mine waste pile runoff and mine drainage discharge and characterized the influence of iron oxides on metal fate along a 0.9-km stretch of Tar Creek (Oklahoma, USA), which drains an abandoned Zn/Pb mining area. The importance of each source varied by metal: mine waste pile runoff contributed 70% of Cd, while mine drainage contributed 90% of Pb, and both sources contributed similarly to Zn loading. Subsurface inputs accounted for 40% of flow and 40-70% of metal loading along this stretch. Streambed iron oxide aggregate material contained highly elevated Zn (up to 27,000 μg g−1), Pb (up to 550 μg g−1) and Cd (up to 200 μg g−1) and was characterized as a heterogeneous mixture of iron oxides, fine-grain mine waste, and organic material. Sequential extractions confirmed preferential sequestration of Pb by iron oxides, as well as substantial concentrations of Zn and Cd in iron oxide fractions, with additional accumulation of Zn, Pb, and Cd during downstream transport. Comparisons with historical data show that while metal concentrations in mine drainage have decreased by more than an order of magnitude in recent decades, the chemical composition of mine waste pile runoff has remained relatively constant, indicating less attenuation and increased relative importance of pile runoff. These results highlight the importance of monitoring temporal changes at contaminated sites associated with evolving speciation and simultaneously addressing surface and subsurface contamination from both mine waste piles and mine drainage. PMID:24867708

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.

Considering User's Access Pattern in Multimedia File Systems

NASA Astrophysics Data System (ADS)

Cho, KyoungWoon; Ryu, YeonSeung; Won, Youjip; Koh, Kern

2002-12-01

Legacy buffer cache management schemes for multimedia server are grounded at the assumption that the application sequentially accesses the multimedia file. However, user access pattern may not be sequential in some circumstances, for example, in distance learning application, where the user may exploit the VCR-like function(rewind and play) of the system and accesses the particular segments of video repeatedly in the middle of sequential playback. Such a looping reference can cause a significant performance degradation of interval-based caching algorithms. And thus an appropriate buffer cache management scheme is required in order to deliver desirable performance even under the workload that exhibits looping reference behavior. We propose Adaptive Buffer cache Management(ABM) scheme which intelligently adapts to the file access characteristics. For each opened file, ABM applies either the LRU replacement or the interval-based caching depending on the Looping Reference Indicator, which indicates that how strong temporally localized access pattern is. According to our experiment, ABM exhibits better buffer cache miss ratio than interval-based caching or LRU, especially when the workload exhibits not only sequential but also looping reference property.

Judgments relative to patterns: how temporal sequence patterns affect judgments and memory.

PubMed

Kusev, Petko; Ayton, Peter; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Stewart, Neil; Chater, Nick

2011-12-01

Six experiments studied relative frequency judgment and recall of sequentially presented items drawn from 2 distinct categories (i.e., city and animal). The experiments show that judged frequencies of categories of sequentially encountered stimuli are affected by certain properties of the sequence configuration. We found (a) a first-run effect whereby people overestimated the frequency of a given category when that category was the first repeated category to occur in the sequence and (b) a dissociation between judgments and recall; respondents may judge 1 event more likely than the other and yet recall more instances of the latter. Specifically, the distribution of recalled items does not correspond to the frequency estimates for the event categories, indicating that participants do not make frequency judgments by sampling their memory for individual items as implied by other accounts such as the availability heuristic (Tversky & Kahneman, 1973) and the availability process model (Hastie & Park, 1986). We interpret these findings as reflecting the operation of a judgment heuristic sensitive to sequential patterns and offer an account for the relationship between memory and judged frequencies of sequentially encountered stimuli.

[Experimental study on acid mine drainage treatment using mine tailings of Xiangsi Valley, Tongling, China].

PubMed

Zhang, Nan; Chen, Tian-Hu; Zhou, Yue-Fei; Li, Shao-Jie; Jin, Jie; Wang, Yan-Ming

2012-04-01

Mine tailings in Xiangsi Valley, Tongling, China, is a typical skarn-type tailing with high contents of carbonates. This study designed dynamic leaching experiments to investigate the efficiency of this tailing under the acid mine drainage treatment. During 80 d trial period, the physical and chemical properties of influents were fixed and the effluents were monitored. After the trial, the speciation of Fe, Cu and Zn in solid was analyzed. The results showed that during the trial period, pH value maintained above 7.5. Moreover, the concentrations of Cu, Zn, Fe ions in effluents kept below 0.1, 0.4 and 1 mg x L(-1), respectively. In addition, the permeability coefficient of experimental column kept decreasing during the experimental period (from 0.23 cm x s(-1) to 0.10 cm x s(-1)). Five-step sequential extraction method was employed to study the distribution of elements at different depths. The results showed that Cu2+, Zn2+ were removed mainly through sorption and precipitation. This study indicates that Tongling skarn mine tailings have strong acid neutralization as well as heavy metal binding capacities. Therefore, the authors suggest that this mine tailing, which used to be waste, has a potential in AMD control and treatment.

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.

Sequential, progressive, equal-power, reflective beam-splitter arrays

NASA Astrophysics Data System (ADS)

Manhart, Paul K.

2017-11-01

The equations to calculate equal-power reflectivity of a sequential series of beam splitters is presented. Non-sequential optical design examples are offered for uniform illumination using diode lasers. Objects created using Boolean operators and Swept Surfaces can create objects capable of reflecting light into predefined elevation and azimuth angles. Analysis of the illumination patterns for the array are also presented.

Psychosocial service needs of pediatric transport accident survivors: Using clinical data-mining to establish demographic and service usage characteristics.

PubMed

Manguy, Alys-Marie; Joubert, Lynette; Bansemer, Leah

2016-09-01

The objectives in this article are the exploration of demographic and service usage data gained through clinical data mining audit and suggesting recommendations for social work service delivery model and future research. The method is clinical data-mining audit of 100 sequentially sampled cases gathering quantitative demographic and service usage data. Descriptive analysis of file audit data raised interesting trends with potential to inform service delivery and usage; the key areas of the results included patient demographics, family involvement and impact, and child safety and risk issues. Transport accidents involving children often include other family members. Care planning must take into account psychosocial issues including patient and family emotional responses, availability of primary carers, and other practical needs that may impact on recovery and discharge planning. This study provides evidence to plan for further research and development of more integrated models of care.

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.

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.

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

Learning multiple variable-speed sequences in striatum via cortical tutoring.

PubMed

Murray, James M; Escola, G Sean

2017-05-08

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

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 ...

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.

Spatial-simultaneous and spatial-sequential working memory in individuals with Down syndrome: the effect of configuration.

PubMed

Carretti, Barbara; Lanfranchi, Silvia; Mammarella, Irene C

2013-01-01

Earlier research showed that visuospatial working memory (VSWM) is better preserved in Down syndrome (DS) than verbal WM. Some differences emerged, however, when VSWM performance was broken down into its various components, and more recent studies revealed that the spatial-simultaneous component of VSWM is more impaired than the spatial-sequential one. The difficulty of managing more than one item at a time is also evident when the information to be recalled is structured. To further analyze this issue, we investigated the advantage of material being structured in spatial-simultaneous and spatial-sequential tasks by comparing the performance of a group of individuals with DS and a group of typically-developing children matched for mental age. Both groups were presented with VSWM tasks in which both the presentation format (simultaneous vs. sequential) and the type of configuration (pattern vs. random) were manipulated. Findings indicated that individuals with DS took less advantage of the pattern configuration in the spatial-simultaneous task than TD children; in contrast, the two groups' performance did not differ in the pattern configuration of the spatial-sequential task. Taken together, these results confirmed difficulties relating to the spatial-simultaneous component of VSWM in individuals with DS, supporting the importance of distinguishing between different components within this system. The findings are discussed in terms of factors influencing this specific deficit. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling eye gaze patterns in clinician-patient interaction with lag sequential analysis.

PubMed

Montague, Enid; Xu, Jie; Chen, Ping-Yu; Asan, Onur; Barrett, Bruce P; Chewning, Betty

2011-10-01

The aim of this study was to examine whether lag sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multiuser health care settings in which trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Nonverbal communication patterns are important aspects of clinician-patient interactions and may affect patient outcomes. The eye gaze behaviors of clinicians and patients in 110 videotaped medical encounters were analyzed using the lag sequential method to identify significant behavior sequences. Lag sequential analysis included both event-based lag and time-based lag. Results from event-based lag analysis showed that the patient's gaze followed that of the clinician, whereas the clinician's gaze did not follow the patient's. Time-based sequential analysis showed that responses from the patient usually occurred within 2 s after the initial behavior of the clinician. Our data suggest that the clinician's gaze significantly affects the medical encounter but that the converse is not true. Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs.

Modeling Eye Gaze Patterns in Clinician-Patient Interaction with Lag Sequential Analysis

PubMed Central

Montague, E; Xu, J; Asan, O; Chen, P; Chewning, B; Barrett, B

2011-01-01

Objective The aim of this study was to examine whether lag-sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multi-user health care settings where trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Background Nonverbal communication patterns are important aspects of clinician-patient interactions and may impact patient outcomes. Method Eye gaze behaviors of clinicians and patients in 110-videotaped medical encounters were analyzed using the lag-sequential method to identify significant behavior sequences. Lag-sequential analysis included both event-based lag and time-based lag. Results Results from event-based lag analysis showed that the patients’ gaze followed that of clinicians, while clinicians did not follow patients. Time-based sequential analysis showed that responses from the patient usually occurred within two seconds after the initial behavior of the clinician. Conclusion Our data suggest that the clinician’s gaze significantly affects the medical encounter but not the converse. Application Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs. PMID:22046723

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.

Environmental Impacts Of Zirab Coal Washing Plant, Mazandaran, Iran

NASA Astrophysics Data System (ADS)

Moore, F.; Esmaeili, A.

2009-04-01

Extraction and beneficiation operations associated with coal mining increase the rate of chemical reaction of waste material to air and water media. Zirab coal washing plant is located on the bank of the Cherat stream in Mazandaran province, Iran. coal Mined from central Alborz coalfield mines is not suitable for use in Iranian Steel Corporation. Hence, coal ash content is reduced by physical and chemical processes in this plant. These processes leave a large quantity of liquid and solid wastes that accumulate in waste dump and tailing dam. sediment and water samples taken from Sheshrudbar and Cherat streams and also from Talar river show high concentration of Cd, Mo and As in water samples of coal washing plant and the associated drainage. Eh-pH diagrams revealed the chemical species of elements in water. The enrichment factor and geoaccumulation index show that Cd, Hg, Mo and V are enriched in bottom sediments of the coal washing plant and decrease with increasing distance from the plant. Sequential extraction analysis Results of three sediment samples of Cherat stream show that silicate bound is the major phase in samples taken before and after the plant, but adjacent to the plant, organic bound is dominant. The high concentration of Cd and Mo in the water soluble phase, is noticeable and may result in high mobility and bioavailability of these elements. Mann-Whitney and Wilcoxon tests on six samples, before and after the coal washing plant support the obtained results. Keywords: Zirab; coal washing plant; Sequential extraction analysis; Mann-whitney; Wilcoxon; Enrichment factor; Geoaccumulation index.

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.

Application of dynamic topic models to toxicogenomics data.

PubMed

Lee, Mikyung; Liu, Zhichao; Huang, Ruili; Tong, Weida

2016-10-06

All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data. A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms. We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

Finding Spatio-Temporal Patterns in Large Sensor Datasets

ERIC Educational Resources Information Center

McGuire, Michael Patrick

2010-01-01

Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is…

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

A new method for discovering behavior patterns among animal movements

USGS Publications Warehouse

Wang, Y.; Luo, Ze; Takekawa, John Y.; Prosser, Diann J.; Xiong, Y.; Newman, S.; Xiao, X.; Batbayar, N.; Spragens, Kyle A.; Balachandran, S.; Yan, B.

2016-01-01

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

A new method for discovering behavior patterns among animal movements.

PubMed

Wang, Yuwei; Luo, Ze; Takekawa, John; Prosser, Diann; Xiong, Yan; Newman, Scott; Xiao, Xiangming; Batbayar, Nyambayar; Spragens, Kyle; Balachandran, Sivananinthaperumal; Yan, Baoping

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

A new method for discovering behavior patterns among animal movements

PubMed Central

Wang, Yuwei; Luo, Ze; Takekawa, John; Prosser, Diann; Xiong, Yan; Newman, Scott; Xiao, Xiangming; Batbayar, Nyambayar; Spragens, Kyle; Balachandran, Sivananinthaperumal; Yan, Baoping

2016-01-01

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets. PMID:27217810

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…

Geochemical fractionation of metals and metalloids in tailings and appraisal of environmental pollution in the abandoned Musina Copper Mine, South Africa.

PubMed

Gitari, M W; Akinyemi, S A; Ramugondo, L; Matidza, M; Mhlongo, S E

2018-04-30

The economic benefits of mining industry have often overshadowed the serious challenges posed to the environments through huge volume of tailings generated and disposed in tailings dumps. Some of these challenges include the surface and groundwater contamination, dust, and inability to utilize the land for developmental purposes. The abandoned copper mine tailings in Musina (Limpopo province, South Africa) was investigated for particle size distribution, mineralogy, physicochemical properties using arrays of granulometric, X-ray diffraction, and X-ray fluorescence analyses. A modified Community Bureau of Reference (BCR) sequential chemical extraction method followed by inductively coupled plasma mass spectrometry/atomic emission spectrometry (ICP-MS/AES) technique was employed to assess bioavailability of metals. Principal component analysis was performed on the sequential extraction data to reveal different loadings and mobilities of metals in samples collected at various depths. The pH ranged between 7.5 and 8.5 (average ≈ 8.0) indicating alkaline medium. Samples composed mostly of poorly grated sands (i.e. 50% fine sand) with an average permeability of about 387.6 m/s. Samples have SiO 2 /Al 2 O 3 and Na 2 O/(Al 2 O 3  + SiO 2 ) ratios and low plastic index (i.e. PI ≈ 2.79) suggesting non-plastic and very low dry strength. Major minerals were comprised of quartz, epidote, and chlorite while the order of relative abundance of minerals in minor quantities is plagioclase > muscovite > hornblende > calcite > haematite. The largest percentage of elements such as As, Cd and Cr was strongly bound to less extractable fractions. Results showed high concentration and easily extractable Cu in the Musina Copper Mine tailings, which indicates bioavailability and poses environmental risk and potential health risk of human exposure. Principal component analysis revealed Fe-oxide/hydroxides, carbonate and clay components, and copper ore process are controlling the elements distribution.

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

Field-scale study of the influence of differing remediation strategies on trace metal geochemistry in metal mine tailings from the Irish Midlands.

PubMed

Perkins, William T; Bird, Graham; Jacobs, Suzanne R; Devoy, Cora

2016-03-01

Mine tailings represent a globally significant source of potentially harmful elements (PHEs) to the environment. The management of large volumes of mine tailings represents a major challenge to the mining industry and environmental managers. This field-scale study evaluates the impact of two highly contrasting remediation approaches to the management and stabilisation of mine tailings. The geochemistry of the tailings, overlying amendment layers and vegetation are examined in the light of the different management approaches. Pseudo-total As, Cd and Pb concentrations and solid-state partitioning (speciation), determined via sequential extraction, were established for two Tailings Management Facilities (TMFs) in Ireland subjected to the following: (1) a 'walk-away' approach (Silvermines) and (2) application of an amendment layer (Galmoy). PHE concentrations in roots and herbage of grasses growing on the TMFs were also determined. Results identify very different PHE concentration profiles with depth through the TMFs and the impact of remediation approach on concentrations and their potential bioavailability in the rooting zone of grass species. Data also highlight the importance of choice of grass species in remediation approaches and the benefits of relatively shallow-rooting Agrostis capillaris and Festuca rubra varieties. In addition, data from the Galmoy TMF indicate the importance of regional soil geochemistry for interpreting the influence of the PHE geochemistry of capping and amendment layers applied to mine tailings.

Solid-phase partitioning of mercury in artisanal gold mine tailings from selected key areas in Mindanao, Philippines, and its implications for mercury detoxification.

PubMed

Opiso, Einstine M; Aseneiro, John Paul J; Banda, Marybeth Hope T; Tabelin, Carlito B

2018-03-01

The solid-phase partitioning of mercury could provide necessary data in the identification of remediation techniques in contaminated artisanal gold mine tailings. This study was conducted to determine the total mercury content of mine wastes and identify its solid-phase partitioning through selective sequential extraction coupled with cold vapour atomic absorption spectroscopy. Samples from mine tailings and the carbon-in-pulp (CIP) process were obtained from selected key areas in Mindanao, Philippines. The results showed that mercury use is still prevalent among small-scale gold miners in the Philippines. Tailings after ball mill-gravity concentration (W-BM and Li-BM samples) from Mt Diwata and Libona contained high levels of mercury amounting to 25.024 and 6.5 mg kg -1 , respectively. The most prevalent form of mercury in the mine tailings was elemental/amalgamated mercury, followed by water soluble, exchangeable, organic and strongly bound phases, respectively. In contrast, mercury content of carbon-in-pulp residues were significantly lower at only 0.3 and 0.06 mg kg -1 for P-CIP (Del Pilar) and W-CIP (Mt Diwata), respectively. The bulk of mercury in P-CIP samples was partitioned in residual fraction while in W-CIP samples, water soluble mercury predominated. Overall, this study has several important implications with regards to mercury detoxification of contaminated mine tailings from Mindanao, Philippines.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Data mining 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.

Some sequential, distribution-free pattern classification procedures with applications

NASA Technical Reports Server (NTRS)

Poage, J. L.

1971-01-01

Some sequential, distribution-free pattern classification techniques are presented. The decision problem to which the proposed classification methods are applied is that of discriminating between two kinds of electroencephalogram responses recorded from a human subject: spontaneous EEG and EEG driven by a stroboscopic light stimulus at the alpha frequency. The classification procedures proposed make use of the theory of order statistics. Estimates of the probabilities of misclassification are given. The procedures were tested on Gaussian samples and the EEG responses.

Do weirs affect the physical and geochemical mobility of toxic metals in mining-impacted floodplain sediments?

NASA Astrophysics Data System (ADS)

Bulcock, Amelia; Coleman, Alexandra; Whitfield, Elizabeth; Andres Lopez-Tarazon, Jose; Byrne, Patrick; Whitfield, Greg

2015-04-01

Weirs are common river structures designed to modify river channel hydraulics and hydrology for purposes of navigation, flood defence, irrigation and hydrometry. By design, weirs constrain natural flow processes and affect sediment flux and river channel forms leading to homogenous river habitats and reduced biodiversity. The recent movement towards catchment-wide river restoration, driven by the EU Water Framework Directive, has recognised weirs as a barrier to good ecological status. However, the removal of weirs to achieve more 'natural' river channels and flow processes is inevitably followed by a period of adjustment to the new flow regime and sediment flux. This period of adjustment can have knock-on effects that may increase flood risk, sedimentation and erosion until the river reaches a state of geomorphological equilibrium. Many catchments in the UK contain a legacy of toxic metals in floodplain sediments due to historic metal mining activities. The consequences of weir removal in these catchments may be to introduce 'stored' mine wastes into the river system with severe implications for water quality and biodiversity. The purpose of this study is to investigate the potential impact of a weir on the physical and geochemical mobilisation of mine wastes in the formerly mined River Twymyn catchment, Wales. Our initial investigations have shown floodplain and riverbed sediments to be grossly contaminated (up to 15,500 mg/kg Pb) compared to soil from a pre-mining Holocene terrace (180 mg/kg Pb). Geomorphological investigations also suggest that weir removal will re-establish more dynamic river channel processes resulting in lateral migration of the channel and erosion of contaminated floodplain sediments. These data will be used as a baseline for more detailed investigations of the potential impact of weirs on the physical and geochemical mobilisation of contaminated sediments. We have two specific objectives. (1) Geomorphological assessments will use unmanned aerial vehicle (UAV) photographic surveys, historical aerial photographs, ground-based topographic surveys, surface and subsurface particle size determination, bed stability and sediment entrainment assessment, together with discharge and sediment (both suspended and bedload) monitoring to establish the effect of the weir on patterns of sediment flux and the physical transport of metal contaminants. 2D and 1D models (IBER, HEC-RAS) of the weir-affected reach will investigate sediment and metal flux following weir removal. (2) The physicochemical speciation and geochemical stability of contaminated floodplain sediments will be characterised using bulk chemistry, mineralogical (XRD, SEM) and speciation methods (sequential extractions, electron microprobe analysis).

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

Trace Metal Content of Sediments Close to Mine Sites in the Andean Region

PubMed Central

Yacoub, Cristina; Pérez-Foguet, Agustí; Miralles, Nuria

2012-01-01

This study is a preliminary examination of heavy metal pollution in sediments close to two mine sites in the upper part of the Jequetepeque River Basin, Peru. Sediment concentrations of Al, As, Cd, Cu, Cr, Fe, Hg, Ni, Pb, Sb, Sn, and Zn were analyzed. A comparative study of the trace metal content of sediments shows that the highest concentrations are found at the closest points to the mine sites in both cases. The sediment quality analysis was performed using the threshold effect level of the Canadian guidelines (TEL). The sediment samples analyzed show that potential ecological risk is caused frequently at both sites by As, Cd, Cu, Hg, Pb, and Zn. The long-term influence of sediment metals in the environment is also assessed by sequential extraction scheme analysis (SES). The availability of metals in sediments is assessed, and it is considered a significant threat to the environment for As, Cd, and Sb close to one mine site and Cr and Hg close to the other mine site. Statistical analysis of sediment samples provides a characterization of both subbasins, showing low concentrations of a specific set of metals and identifies the main characteristics of the different pollution sources. A tentative relationship between pollution sources and possible ecological risk is established. PMID:22606058

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.

Sequential Learning and Recognition of Comprehensive Behavioral Patterns Based on Flow of People

NASA Astrophysics Data System (ADS)

Gibo, Tatsuya; Aoki, Shigeki; Miyamoto, Takao; Iwata, Motoi; Shiozaki, Akira

Recently, surveillance cameras have been set up everywhere, for example, in streets and public places, in order to detect irregular situations. In the existing surveillance systems, as only a handful of surveillance agents watch a large number of images acquired from surveillance cameras, there is a possibility that they may miss important scenes such as accidents or abnormal incidents. Therefore, we propose a method for sequential learning and the recognition of comprehensive behavioral patterns in crowded places. First, we comprehensively extract a flow of people from input images by using optical flow. Second, we extract behavioral patterns on the basis of change-point detection of the flow of people. Finally, in order to recognize an observed behavioral pattern, we draw a comparison between the behavioral pattern and previous behavioral patterns in the database. We verify the effectiveness of our approach by placing a surveillance camera on a campus.

Time-sequential observation of spindle and phragmoplast orientation in BY-2 cells with altered cortical actin microfilament patterning.

PubMed

Kojo, Kei H; Yasuhara, Hiroki; Hasezawa, Seiichiro

2014-01-01

Precise division plane determination is essential for plant development. At metaphase, a dense actin microfilament meshwork appears on both sides of the cell center, forming a characteristic cortical actin microfilament twin peak pattern in BY-2 cells. We previously reported a strong correlation between altered cortical actin microfilament patterning and an oblique mitotic spindle orientation, implying that these actin microfilament twin peaks play a role in the regulation of mitotic spindle orientation. In the present study, time-sequential observation was used to reveal the progression from oblique phragmoplast to oblique cell plate orientation in cells with altered cortical actin microfilament patterning. In contrast to cells with normal actin microfilament twin peaks, oblique phragmoplast reorientation was rarely observed in cells with altered cortical actin microfilament patterning. These results support the important roles of cortical actin microfilament patterning in division plane orientation.

Time-sequential observation of spindle and phragmoplast orientation in BY-2 cells with altered cortical actin microfilament patterning.

PubMed

Kojo, Kei H; Yasuhara, Hiroki; Hasezawa, Seiichiro

2014-06-18

Precise division plane determination is essential for plant development. At metaphase, a dense actin microfilament meshwork appears on both sides of the cell center, forming a characteristic cortical actin microfilament twin peak pattern in BY-2 cells. We previously reported a strong correlation between altered cortical actin microfilament patterning and an oblique mitotic spindle orientation, implying that these actin microfilament twin peaks play a role in the regulation of mitotic spindle orientation. In the present study, time-sequential observation was used to reveal the progression from oblique phragmoplast to oblique cell plate orientation in cells with altered cortical actin microfilament patterning. In contrast to cells with normal actin microfilament twin peaks, oblique phragmoplast reorientation was rarely observed in cells with altered cortical actin microfilament patterning. These results support the important roles of cortical actin microfilament patterning in division plane orientation.

On the uniqueness of color patterns in raptor feathers

USGS Publications Warehouse

Ellis, D.H.

2009-01-01

For this study, I compared sequentially molted feathers for a few captive raptors from year to year and symmetrically matched feathers (left/right pairs) for many raptors to see if color patterns of sequential feather pairs were identical or if symmetrical pairs were mirror-image identical. Feather pairs were found to be identical only when without color pattern (e.g., the all-white rectrices of Bald Eagles [Haliaeetus leucocephalus]). Complex patterns were not closely matched, but some simple patterns were sometimes closely matched, although not identical. Previous claims that complex color patterns in feather pairs are fingerprint-identical (and therefore that molted feathers from wild raptors can be used to identify breeding adults from year to year with certainty) were found to be untrue: each feather is unique. Although it is unwise to be certain of bird of origin using normal feathers, abnormal feathers can often be so used. ?? 2009 The Raptor Research Foundation, Inc.

TRUNCATED RANDOM MEASURES

DTIC Science & Technology

2018-01-12

sequential representations, a method is required for deter- mining which to use for the application at hand and, once a representation is selected, for...DISTRIBUTION UNLIMITED Methods , Assumptions, and Procedures 3.1 Background 3.1.1 CRMs and truncation Consider a Poisson point process on R+ := [0...the heart of the study of truncated CRMs. They provide an itera- tive method that can be terminated at any point to yield a finite approximation to the

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.

Support for distinct subcomponents of spatial working memory: a double dissociation between spatial-simultaneous and spatial-sequential performance in unilateral neglect.

PubMed

Wansard, Murielle; Bartolomeo, Paolo; Bastin, Christine; Segovia, Fermín; Gillet, Sophie; Duret, Christophe; Meulemans, Thierry

2015-01-01

Over the last decade, many studies have demonstrated that visuospatial working memory (VSWM) can be divided into separate subsystems dedicated to the retention of visual patterns and their serial order. Impaired VSWM has been suggested to exacerbate left visual neglect in right-brain-damaged individuals. The aim of this study was to investigate the segregation between spatial-sequential and spatial-simultaneous working memory in individuals with neglect. We demonstrated that patterns of results on these VSWM tasks can be dissociated. Spatial-simultaneous and sequential aspects of VSWM can be selectively impaired in unilateral neglect. Our results support the hypothesis of multiple VSWM subsystems, which should be taken into account to better understand neglect-related deficits.

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.

Content-Aware DataGuide with Incremental Index Update using Frequently Used Paths

NASA Astrophysics Data System (ADS)

Sharma, A. K.; Duhan, Neelam; Khattar, Priyanka

2010-11-01

Size of the WWW is increasing day by day. Due to the absence of structured data on the Web, it becomes very difficult for information retrieval tools to fully utilize the Web information. As a solution to this problem, XML pages come into play, which provide structural information to the users to some extent. Without efficient indexes, query processing can be quite inefficient due to an exhaustive traversal on XML data. In this paper an improved content-centric approach of Content-Aware DataGuide, which is an indexing technique for XML databases, is being proposed that uses frequently used paths from historical query logs to improve query performance. The index can be updated incrementally according to the changes in query workload and thus, the overhead of reconstruction can be minimized. Frequently used paths are extracted using any Sequential Pattern mining algorithm on subsequent queries in the query workload. After this, the data structures are incrementally updated. This indexing technique proves to be efficient as partial matching queries can be executed efficiently and users can now get the more relevant documents in results.

Understanding flood-induced water chemistry variability extracting temporal patterns with the LDA method

NASA Astrophysics Data System (ADS)

Aubert, A. H.; Tavenard, R.; Emonet, R.; De Lavenne, A.; Malinowski, S.; Guyet, T.; Quiniou, R.; Odobez, J.; Merot, P.; Gascuel-odoux, C.

2013-12-01

Studying floods has been a major issue in hydrological research for years, both in quantitative and qualitative hydrology. Stream chemistry is a mix of solutes, often used as tracers, as they originate from various sources in the catchment and reach the stream by various flow pathways. Previous studies (for instance (1)) hypothesized that stream chemistry reaction to a rainfall event is not unique but varies seasonally, and according to the yearly meteorological conditions. Identifying a typology of flood temporal chemical patterns is a way to better understand catchment processes at the flood and seasonal time scale. We applied a probabilistic model (Latent Dirichlet Allocation or LDA (2)) mining recurrent sequential patterns from a dataset of floods. A set of 472 floods was automatically extracted from a daily 12-year long record of nitrate, dissolved organic carbon, sulfate and chloride concentrations. Rainfall, discharge, water table depth and temperature are also considered. Data comes from a long-term hydrological observatory (AgrHys, western France) located at Kervidy-Naizin. From each flood, a document has been generated that is made of a set of "hydrological words". Each hydrological word corresponds to a measurement: it is a triplet made of the considered variable, the time at which the measurement is made (relative to the beginning of the flood), and its magnitude (that can be low, medium or high). The documents and the number of pattern to be mined are used as input data to the LDA algorithm. LDA relies on spotting co-occurrences (as an alternative to the more traditional study of correlation) between words that appear within the flood documents. It has two nice properties that are its ability to easily deal with missing data and its additive property that allows a document to be seen as a mixture of several flood patterns. The output of LDA is a set of patterns easily represented in graphics. These patterns correspond to typical reactions to rainfall events. The patterns themselves are carefully studied, as well as their repartition along the year and along the 12 years of the dataset. We would recommend the use of such model to any study based on patterns or signature extraction. It could be well suited to compare different geographical locations and analyzing the resulting different pattern distributions. (1) Aubert, A.H., Gascuel-Odoux, C., Gruau, G., Akkal, N., Faucheux, M., Fauvel, Y., Grimaldi, C., Hamon, Y., Jaffrezic, A., Lecoz Boutnik, M., Molenat, J., Petitjean, P., Ruiz, L., Merot, Ph. (2013), Solute transport dynamics in small, shallow groundwater-dominated agricultural catchments: insights from a high-frequency, multisolute 10 yr-long monitoring study. Hydrol. Earth Syst. Sci., 17(4): 1379-1391. (2) Aubert, A.H., Tavenard, R, Emonet, R., de Lavenne, A., Malinowski, S., Guyet, T., Quiniou, R., Odobez, J.-M., Merot, Ph., Gascuel-Odoux, C., submitted to WRR. Clustering with a probabilistic method newly applied in hydrology: application on flood events from water quality time-series.

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation - A comparative study for spatial ultimate analyses data.

PubMed

Karacan, C Özgen; Olea, Ricardo A

2018-03-01

Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an "as-received" basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.

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.

Bedroom Rape: Sequences of Sexual Behavior in Stranger Assaults

ERIC Educational Resources Information Center

Fossi, Julia J.; Clarke, David D.; Lawrence, Claire

2005-01-01

This article examines the sequential, temporal, and interactional aspects of sexual assaults using sequential analysis. Fourteen statements taken from victims of bedroom-based assaults were analyzed to provide a comprehensive account of the behavioral patterns of individuals in sexually charged conflict situations. The cases were found to vary in…

Visual exploration and analysis of human-robot interaction rules

NASA Astrophysics Data System (ADS)

Zhang, Hui; Boyles, Michael J.

2013-01-01

We present a novel interaction paradigm for the visual exploration, manipulation and analysis of human-robot interaction (HRI) rules; our development is implemented using a visual programming interface and exploits key techniques drawn from both information visualization and visual data mining to facilitate the interaction design and knowledge discovery process. HRI is often concerned with manipulations of multi-modal signals, events, and commands that form various kinds of interaction rules. Depicting, manipulating and sharing such design-level information is a compelling challenge. Furthermore, the closed loop between HRI programming and knowledge discovery from empirical data is a relatively long cycle. This, in turn, makes design-level verification nearly impossible to perform in an earlier phase. In our work, we exploit a drag-and-drop user interface and visual languages to support depicting responsive behaviors from social participants when they interact with their partners. For our principal test case of gaze-contingent HRI interfaces, this permits us to program and debug the robots' responsive behaviors through a graphical data-flow chart editor. We exploit additional program manipulation interfaces to provide still further improvement to our programming experience: by simulating the interaction dynamics between a human and a robot behavior model, we allow the researchers to generate, trace and study the perception-action dynamics with a social interaction simulation to verify and refine their designs. Finally, we extend our visual manipulation environment with a visual data-mining tool that allows the user to investigate interesting phenomena such as joint attention and sequential behavioral patterns from multiple multi-modal data streams. We have created instances of HRI interfaces to evaluate and refine our development paradigm. As far as we are aware, this paper reports the first program manipulation paradigm that integrates visual programming interfaces, information visualization, and visual data mining methods to facilitate designing, comprehending, and evaluating HRI interfaces.

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…

A novel sequential process for remediating rare-earth wastewater.

PubMed

Cui, Mingcan; Jang, Min; Kang, Kyounglim; Kim, Dukmin; Snyder, Shane A; Khim, Jeehyeong

2016-02-01

A novel and economic sequential process consisting of precipitation, adsorption, and oxidation was developed to remediate actual rare-earth (RE) wastewater containing various toxic pollutants, including radioactive species. In the precipitation step, porous air stones (PAS) containing waste oyster shell (WOS), PASWOS, was prepared and used to precipitate most heavy metals with >97% removal efficiencies. The SEM-EDS analysis revealed that PAS plays a key role in preventing the surface coating of precipitants on the surface of WOS and in releasing the dissolved species of WOS successively. For the adsorption step, a polyurethane (PU) impregnated by coal mine drainage sludge (CMDS), PUCMDS, was synthesized and applied to deplete fluoride (F), arsenic (As), uranium (U), and thorium (Th) that remained after precipitation. The continuous-mode sequential process using PAS(WOS), PU(CMDS), and ozone (O3) had 99.9-100% removal efficiencies of heavy metals, 99.3-99.9% of F and As, 95.8-99.4% of U and Th, and 92.4% of COD(Cr) for 100 days. The sequential process can treat RE wastewater economically and effectively without stirred-tank reactors, pH controller, continuous injection of chemicals, and significant sludge generation, as well as the quality of the outlet met the EPA recommended limits. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Heavy metals contamination and their risk assessment around the abandoned base metals and Au-Ag mines in Korea

NASA Astrophysics Data System (ADS)

Chon, Hyo-Taek

2017-04-01

Heavy metals contamination in the areas of abandoned Au-Ag and base metal mines in Korea was investigated in order to assess the level of metal pollution, and to draw general summaries about the fate of toxic heavy metals in different environments. Efforts have been made to compare the level of heavy metals, chemical forms, and plant uptake of heavy metals in each mine site. In the base-metals mine areas, significant levels of Cd, Cu, Pb and Zn were found in mine dump soils developed over mine waste materials and tailings. Leafy vegetables tend to accumulate heavy metals(in particular, Cd and Zn) higher than other crop plants, and high metal concentrations in rice crops may affect the local residents' health. In the Au-Ag mining areas, arsenic would be the most characteristic contaminant in the nearby environment. Arsenic and heavy metals were found to be mainly associated with sulfide gangue minerals, and the mobility of these metals would be enhanced by the effect of continuing weathering and oxidation. According to the sequential extraction of metals in soils, most heavy metals were identified as non-residual chemical forms, and those are very susceptible to the change of ambient conditions of a nearby environment. The concept of pollution index(PI) of soils gives important information on the extent and degree of multi-element contamination, and can be applied to the evaluation of mine soils before their agricultural use and remediation. The risk assessment process comprising exposure assessment, dose-response assessment, and risk characterization was discussed, and the results of non-cancer risk of As, Cd, and Zn, and those of cancer risk of As were suggested.

Sequential Adaptive Multi-Modality Target Detection and Classification Using Physics Based Models

DTIC Science & Technology

2006-09-01

estimation," R. Raghuram, R. Raich and A.O. Hero, IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing, Toulouse France, June 2006, <http...can then be solved using off-the-shelf classifiers such as radial basis functions, SVM, or kNN classifier structures. When applied to mine detection we...stage waveform selection for adaptive resource constrained state estimation," 2006 IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing

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…

Sequential contrast-enhanced MR imaging of the penis.

PubMed

Kaneko, K; De Mouy, E H; Lee, B E

1994-04-01

To determine the enhancement patterns of the penis at magnetic resonance (MR) imaging. Sequential contrast material-enhanced MR images of the penis in a flaccid state were obtained in 16 volunteers (12 with normal penile function and four with erectile dysfunction). Subjects with normal erectile function showed gradual and centrifugal enhancement of the corpora cavernosa, while those with erectile dysfunction showed poor enhancement with abnormal progression. Sequential contrast-enhanced MR imaging provides additional morphologic information for the evaluation of erectile dysfunction.

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.

Sequential pattern formation governed by signaling gradients

NASA Astrophysics Data System (ADS)

Jörg, David J.; Oates, Andrew C.; Jülicher, Frank

2016-10-01

Rhythmic and sequential segmentation of the embryonic body plan is a vital developmental patterning process in all vertebrate species. However, a theoretical framework capturing the emergence of dynamic patterns of gene expression from the interplay of cell oscillations with tissue elongation and shortening and with signaling gradients, is still missing. Here we show that a set of coupled genetic oscillators in an elongating tissue that is regulated by diffusing and advected signaling molecules can account for segmentation as a self-organized patterning process. This system can form a finite number of segments and the dynamics of segmentation and the total number of segments formed depend strongly on kinetic parameters describing tissue elongation and signaling molecules. The model accounts for existing experimental perturbations to signaling gradients, and makes testable predictions about novel perturbations. The variety of different patterns formed in our model can account for the variability of segmentation between different animal species.

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.

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.

Similar Neural Correlates for Language and Sequential Learning: Evidence from Event-Related Brain Potentials

PubMed Central

Christiansen, Morten H.; Conway, Christopher M.; Onnis, Luca

2011-01-01

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (a) a sequential learning task involving complex structured sequences, and (b) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language, and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally. PMID:23678205

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...

Biaxially mechanical tuning of 2-D reversible and irreversible surface topologies through simultaneous and sequential wrinkling.

PubMed

Yin, Jie; Yagüe, Jose Luis; Boyce, Mary C; Gleason, Karen K

2014-02-26

Controlled buckling is a facile means of structuring surfaces. The resulting ordered wrinkling topologies provide surface properties and features desired for multifunctional applications. Here, we study the biaxially dynamic tuning of two-dimensional wrinkled micropatterns under cyclic mechanical stretching/releasing/restretching simultaneously or sequentially. A biaxially prestretched PDMS substrate is coated with a stiff polymer deposited by initiated chemical vapor deposition (iCVD). Applying a mechanical release/restretch cycle in two directions loaded simultaneously or sequentially to the wrinkled system results in a variety of dynamic and tunable wrinkled geometries, the evolution of which is investigated using in situ optical profilometry, numerical simulations, and theoretical modeling. Results show that restretching ordered herringbone micropatterns, created through sequential release of biaxial prestrain, leads to reversible and repeatable surface topography. The initial flat surface and the same wrinkled herringbone pattern are obtained alternatively after cyclic release/restretch processes, owing to the highly ordered structure leaving no avenue for trapping irregular topological regions during cycling as further evidenced by the uniformity of strains distributions and negligible residual strain. Conversely, restretching disordered labyrinth micropatterns created through simultaneous release shows an irreversible surface topology whether after sequential or simultaneous restretching due to creation of irregular surface topologies with regions of highly concentrated strain upon formation of the labyrinth which then lead to residual strains and trapped topologies upon cycling; furthermore, these trapped topologies depend upon the subsequent strain histories as well as the cycle. The disordered labyrinth pattern varies after each cyclic release/restretch process, presenting residual shallow patterns instead of achieving a flat state. The ability to dynamically tune the highly ordered herringbone patterning through mechanical stretching or other actuation makes these wrinkles excellent candidates for tunable multifunctional surfaces properties such as reflectivity, friction, anisotropic liquid flow or boundary layer control.

A Sequential Analysis of Parent-Child Interactions in Anxious and Nonanxious Families

ERIC Educational Resources Information Center

Williams, Sarah R.; Kertz, Sarah J.; Schrock, Matthew D.; Woodruff-Borden, Janet

2012-01-01

Although theoretical work has suggested that reciprocal behavior patterns between parent and child may be important in the development of childhood anxiety, most empirical work has failed to consider the bidirectional nature of interactions. The current study sought to address this limitation by utilizing a sequential approach to exploring…

Children's Reproduction of Modeled Sequential Actions. Final Report.

ERIC Educational Resources Information Center

Uzgiris, Ina C.

This paper describes seven interrelated studies concerned with children's understanding of sequential actions and with the effects of observing a model on this understanding. A total of 546 elementary and secondary school students served as subjects for the studies. The tasks for all of the studies involved deriving the pattern for a sequence from…

Sequential and Simultaneous Processing in Children with Learning Disabilities: An Attempted Replication.

ERIC Educational Resources Information Center

Bain, Sherry K.

1993-01-01

Analysis of Kaufman Assessment Battery for Children (K-ABC) Sequential and Simultaneous Processing scores of 94 children (ages 6-12) with learning disabilities produced factor patterns generally supportive of the traditional K-ABC Mental Processing structure with the exception of Spatial Memory. The sample exhibited relative processing strengths…

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

Mercury speciation in the Mt. Amiata mining district (Italy): interplay between urban activities and mercury contamination

USGS Publications Warehouse

Rimondi, Valentina; Bardelli, Fabrizio; Benvenuti, Marco; Costagliola, Pilario; Gray, John E.; Lattanzi, Pierfranco

2014-01-01

A fundamental step to evaluate the biogeochemical and eco-toxicological significance of Hg dispersion in the environment is to determine speciation of Hg in solid matrices. In this study, several analytical techniques such as scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), sequential chemical extractions (SCEs), and X-ray absorption spectroscopy (XANES) were used to identify Hg compounds and Hg speciation in samples collected from the Mt. Amiata Hg mining district, southern Tuscany, Italy. Different geological materials, such as mine waste calcine (retorted ore), soil, stream sediment, and stream water suspended particulate matter were analyzed. Results show that the samples were generally composed of highly insoluble Hg compounds such as sulphides (HgS, cinnabar and metacinnabar), and more soluble Hg halides such as those associated with the mosesite group. Other moderately soluble Hg compounds, HgCl2, HgO and Hg0, were also identified in stream sediments draining the mining area. The presence of these minerals suggests active and continuous runoff of soluble Hg compounds from calcines, where such Hg compounds form during retorting, or later in secondary processes. Specifically, we suggest that, due to the proximity of Hg mines to the urban center of Abbadia San Salvatore, the influence of other anthropogenic activities was a key factor for Hg speciation, resulting in the formation of unusual Hg-minerals such as mosesite.

Sequential associative memory with nonuniformity of the layer sizes.

PubMed

Teramae, Jun-Nosuke; Fukai, Tomoki

2007-01-01

Sequence retrieval has a fundamental importance in information processing by the brain, and has extensively been studied in neural network models. Most of the previous sequential associative memory embedded sequences of memory patterns have nearly equal sizes. It was recently shown that local cortical networks display many diverse yet repeatable precise temporal sequences of neuronal activities, termed "neuronal avalanches." Interestingly, these avalanches displayed size and lifetime distributions that obey power laws. Inspired by these experimental findings, here we consider an associative memory model of binary neurons that stores sequences of memory patterns with highly variable sizes. Our analysis includes the case where the statistics of these size variations obey the above-mentioned power laws. We study the retrieval dynamics of such memory systems by analytically deriving the equations that govern the time evolution of macroscopic order parameters. We calculate the critical sequence length beyond which the network cannot retrieve memory sequences correctly. As an application of the analysis, we show how the present variability in sequential memory patterns degrades the power-law lifetime distribution of retrieved neural activities.

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

Mobility of radionuclides and trace elements in soil from legacy NORM and undisturbed naturally 232Th-rich sites.

PubMed

Mrdakovic Popic, Jelena; Meland, Sondre; Salbu, Brit; Skipperud, Lindis

2014-05-01

Investigation of radionuclides (232Th and 238U) and trace elements (Cr, As and Pb) in soil from two legacy NORM (former mining sites) and one undisturbed naturally 232Th-rich site was conducted as a part of the ongoing environmental impact assessment in the Fen Complex area (Norway). The major objectives were to determine the radionuclide and trace element distribution and mobility in soils as well as to analyze possible differences between legacy NORM and surrounding undisturbed naturally 232Th-rich soils. Inhomogeneous soil distribution of radionuclides and trace elements was observed for each of the investigated sites. The concentration of 232Th was high (up to 1685 mg kg(-1), i.e., ∼7000 Bq kg(-1)) and exceeded the screening value for the radioactive waste material in Norway (1 Bq g(-1)). Based on the sequential extraction results, the majority of 232Th and trace elements were rather inert, irreversibly bound to soil. Uranium was found to be potentially more mobile, as it was associated with pH-sensitive soil phases, redox-sensitive amorphous soil phases and soil organic compounds. Comparison of the sequential extraction datasets from the three investigated sites revealed increased mobility of all analyzed elements at the legacy NORM sites in comparison with the undisturbed 232Th-rich site. Similarly, the distribution coefficients Kd (232Th) and Kd (238U) suggested elevated dissolution, mobility and transportation at the legacy NORM sites, especially at the decommissioned Nb-mining site (346 and 100 L kg(-1) for 232Th and 238U, respectively), while the higher sorption of radionuclides was demonstrated at the undisturbed 232Th-rich site (10,672 and 506 L kg(-1) for 232Th and 238U, respectively). In general, although the concentration ranges of radionuclides and trace elements were similarly wide both at the legacy NORM and at the undisturbed 232Th-rich sites, the results of soil sequential extractions together with Kd values supported the expected differences between sites as the consequences of previous mining operations. Hence, mobility and possible elevated bioavailability at the legacy NORM site could be expected and further risk assessment should take this into account when decisions about the possible intervention measures are made.

How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules

PubMed Central

Zhang, Kechen

2017-01-01

A neural network with symmetric reciprocal connections always admits a Lyapunov function, whose minima correspond to the memory states stored in the network. Networks with suitable asymmetric connections can store and retrieve a sequence of memory patterns, but the dynamics of these networks cannot be characterized as readily as that of the symmetric networks due to the lack of established general methods. Here, a reduction method is developed for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories, as in a Hopfield network. The method projects the original activity pattern of the network to a low-dimensional space such that sequential memory retrievals in the original network correspond to periodic oscillations in the reduced system. The reduced system is self-contained and provides quantitative information about the stability and speed of sequential memory retrievals in the original network. The time evolution of the overlaps between the network state and the stored memory patterns can also be determined from extended reduced systems. The reduction procedure can be summarized by a few reduction rules, which are applied to several network models, including coupled networks and networks with time-delayed connections, and the analytical solutions of the reduced systems are confirmed by numerical simulations of the original networks. Finally, a local learning rule that provides an approximation to the connection weights involving the pseudoinverse is also presented. PMID:24877729

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/.

Building emotional resilience over 14 sessions of emotion focused therapy: Micro-longitudinal analyses of productive emotional patterns.

PubMed

Pascual-Leone, A; Yeryomenko, N; Sawashima, T; Warwar, S

2017-05-04

Pascual-Leone and Greenberg's sequential model of emotional processing has been used to explore process in over 24 studies. This line of research shows emotional processing in good psychotherapy often follows a sequential order, supporting a saw-toothed pattern of change within individual sessions (progressing "2-steps-forward, 1-step-back"). However, one cannot assume that local in-session patterns are scalable across an entire course of therapy. Thus, the primary objective of this exploratory study was to consider how the sequential patterns identified by Pascual-Leone, may apply across entire courses of treatment. Intensive emotion coding in two separate single-case designs were submitted for quantitative analyses of longitudinal patterns. Comprehensive coding in these cases involved recording observations for every emotional event in an entire course of treatment (using the Classification of Affective-Meaning States), which were then treated as a 9-point ordinal scale. Applying multilevel modeling to each of the two cases showed significant patterns of change over a large number of sessions, and those patterns were either nested at the within-session level or observed at the broader session-by-session level of change. Examining successful treatment cases showed several theoretically coherent kinds of temporal patterns, although not always in the same case. Clinical or methodological significance of this article: This is the first paper to demonstrate systematic temporal patterns of emotion over the course of an entire treatment. (1) The study offers a proof of concept that longitudinal patterns in the micro-processes of emotion can be objectively derived and quantified. (2) It also shows that patterns in emotion may be identified on the within-session level, as well as the session-by-session level of analysis. (3) Finally, observed processes over time support the ordered pattern of emotional states hypothesized in Pascual-Leone and Greenberg's ( 2007 ) model of emotional processing.

Impact of commercial garden growth substratum and NPK-fertilizer on copper fractionation in a copper-mine tailing

NASA Astrophysics Data System (ADS)

Charles, A.; Karam, A.; Jaouich, A.

2009-04-01

Organic amendment and NPK-fertilizer could affect the distribution of copper (Cu) among Cu-mine tailing compounds and hence the availability or phytotoxicity of Cu to plants. A laboratory incubation experiment was conducted to investigate the forms of Cu in a Cu-mine tailing (pH 7.70) amended with a commercial garden growth substratum (GGS) containing peat moss and natural mycorrhizae (Glomus intraradices) in combination with a commercial NPK-fertilizer (20-20-20), by a sequential extraction method. There were eight treatments after the combination of four rates of GGS (0, 12.4, 50 and 100 g/kg tailing) and two rates of fertilizer (0 and 20 g/kg tailing). At the end of a 52-week incubation period, tailing Cu was sequentially extracted to fractionate Cu into five operationally defined geochemical forms, namely ‘water-soluble' (Cu-sol), ‘exchangeable' (Cu-exc), ‘specifically adsorbed on carbonates or carbonate-bound' (Cu-car), ‘organic-bound' (Cu-org) and ‘residual' (Cu-res) fractions. After treatments, the most labile Cu pool (Cu-sol + Cu-exc) represented about 0.94 % of the total Cu, the Cu-car and Cu-org accounted for 22.7 and 5.0% of total Cu, and the residual Cu accounted for nearly 71.3% of total Cu. Compared with the control, the application of GGS decreased Cu-car and increased CuORG whereas the addition of fertilizer increased Cu-sol + Cu-exc and decreased Cu-carb. Fertilizer-treated tailings had the highest amount of Cu-sol + Cu-exc. High rates of GGS resulted in Cu-org levels in GGS-treated tailings which were more than 2.0-2.8 times those obtained in the untreated tailing (control). The partition of Cu in GGS-treated tailings followed the order: Cu-sol + Cu-exc < Cu-car < Cu-org < Cu-res. This study suggests that NPK-fertilizer promotes the formation of labile Cu forms in the calcite-containing Cu-mine tailing. GGS in the tailing matrix acts as effective sorbent for Cu.

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.

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

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.

Sequential Pattern Analysis: Method and Application in Exploring How Students Develop Concept Maps

ERIC Educational Resources Information Center

Chiu, Chiung-Hui; Lin, Chien-Liang

2012-01-01

Concept mapping is a technique that represents knowledge in graphs. It has been widely adopted in science education and cognitive psychology to aid learning and assessment. To realize the sequential manner in which students develop concept maps, most research relies upon human-dependent, qualitative approaches. This article proposes a method for…

Sequential Research Needs in Evolving Disciplines of Social Practice.

ERIC Educational Resources Information Center

Knowles, Malcolm S.

The author suggests that the emerging fields of social practice (such as recreation, social work, and adult education) must all go through a sequential pattern of research needs, first superficially, and then in ever deeper cycles. The six phases of these research needs are: definition of the field (survey and descriptive studies, census studies,…

How Cognitive Styles Affect the Learning Behaviors of Online Problem-Solving Based Discussion Activity: A Lag Sequential Analysis

ERIC Educational Resources Information Center

Wu, Sheng-Yi; Hou, Huei-Tse

2015-01-01

Cognitive styles play an important role in influencing the learning process, but to date no relevant study has been conducted using lag sequential analysis to assess knowledge construction learning patterns based on different cognitive styles in computer-supported collaborative learning activities in online collaborative discussions. This study…

A gossip based information fusion protocol for distributed frequent itemset mining

NASA Astrophysics Data System (ADS)

Sohrabi, Mohammad Karim

2018-07-01

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.

Isotopically constrained lead sources in fugitive dust from unsurfaced roads in the southeast Missouri mining district.

PubMed

Witt, Emitt C; Pribil, Michael J; Hogan, John P; Wronkiewicz, David J

2016-09-01

The isotopic composition of lead (Pb) in fugitive dust suspended by a vehicle from 13 unsurfaced roads in Missouri was measured to identify the source of Pb within an established long-term mining area. A three end-member model using (207)Pb/(206)Pb and concentration as tracers resulted in fugitive dust samples plotting in the mixing field of well characterized heterogeneous end members. End members selected for this investigation include the (207)Pb/(206)Pb for 1) a Pb-mixture representing mine tailings, 2) aerosol Pb-impacted soils within close proximity to the Buick secondary recycling smelter, and 3) an average of soils, rock cores and drill cuttings representing the background conditions. Aqua regia total concentrations and (207)Pb/(206)Pb of mining area dust suggest that 35.4-84.3% of the source Pb in dust is associated with the mine tailings mixture, 9.1-52.7% is associated with the smelter mixture, and 0-21.6% is associated with background materials. Isotope ratios varied minimally within the operational phases of sequential extraction suggesting that mixing of all three Pb mixtures occurs throughout. Labile forms of Pb were attributed to all three end members. The extractable carbonate phase had as much as 96.6% of the total concentration associated with mine tailings, 51.8% associated with smelter deposition, and 34.2% with background. The next most labile geochemical phase (Fe + Mn Oxides) showed similar results with as much as 85.3% associated with mine tailings, 56.8% associated with smelter deposition, and 4.2% associated with the background soil. Published by Elsevier Ltd.

Sulfide oxidation and acid mine drainage formation within two active tailings impoundments in the Golden Quadrangle of the Apuseni Mountains, Romania.

PubMed

Sima, Mihaela; Dold, Bernhard; Frei, Linda; Senila, Marin; Balteanu, Dan; Zobrist, Jurg

2011-05-30

Sulfidic mine tailings have to be classified as one of the major source of hazardous materials leading to water contamination. This study highlights the processes leading to sulfide oxidation and acid mine drainage (AMD) formation in the active stage of two tailings impoundments located in the southern part of the Apuseni Mountains, in Romania, a well-known region for its long-term gold-silver and metal mining activity. Sampling was undertaken when both impoundments were still in operation in order to assess their actual stage of oxidation and long-term behavior in terms of the potential for acid mine drainage generation. Both tailings have high potential for AMD formation (2.5 and 3.7 wt.% of pyrite equivalent, respectively) with lesser amount of carbonates (5.6 and 3.6 wt.% of calcite equivalent) as neutralization potential (ABA=-55.6 and -85.1 tCaCO(3)/1000 t ) and showed clear signs of sulfide oxidation yet during operation. Sequential extraction results indicate a stronger enrichment and mobility of elements in the oxidized tailings: Fe as Fe(III) oxy-hydroxides and oxides (transformation from sulfide minerals, leaching in oxidation zone), Ca mainly in water soluble and exchangeable form where gypsum and calcite are dissolved and higher mobility of Cu for Ribita and Pb for Mialu. Two processes leading to the formation of mine drainage at this stage could be highlighted (1) a neutral Fe(II) plume forming in the impoundment with ferrihydrite precipitation at its outcrop and (2) acid mine drainage seeping in the unsaturated zone of the active dam, leading to the formation of schwertmannite at its outcrop. Copyright © 2011 Elsevier B.V. All rights reserved.

Selective sequential precipitation of dissolved metals in mine drainage from coal mine

NASA Astrophysics Data System (ADS)

Yim, Giljae; Bok, Songmin; Ji, Sangwoo; Oh, Chamteut; Cheong, Youngwook; Han, Youngsoo; Ahn, Joosung

2017-04-01

In abandoned mines in Korea, a large amount of mine drainage continues to flow out and spread pollution. In purification of the mine drainage a massive amount of sludge is generated as waste. Since this metal sludge contains high Fe, Al and Mn oxides, developing the treatment method to recover homogeneous individual metal with high purity may beneficial to recycle waste metals as useful resources and reduce the amount of sludge production. In this regard, we established a dissolved metals selective precipitation process to treat Waryong Industry's mine drainage. The process that selectively precipitates metals dissolved in mine drainage is a continuous Fe-buffer-Al process, and each process consists of the neutralization tank, the coagulation tank, and the settling tank. Based on this process, this study verified the operational applicability of the Fe and Al selective precipitation. Our previous study revealed that high-purity Fe and Al precipitates could be recovered at a flow rate of 1.5 ton/day, while the lower purity was achieved when the rate was increased to about 3 ton/day due to the difficulty in reagent dosage control. In the current study was conducted to increase the capacity of the system to recover Fe and Al as high-purity precipitates at a flow rate of 10 ton/day with the ensured continuous operations by introducing an automatic reagent injection system. The previous study had a difficulty in controlling the pH and operating system continuously due to the manually controlled reagent injection system. To upgrade this and ensure the optimal pH in a stable way, a continuous reagent injection system was installed. The result of operation of the 10 ton/day system confirmed that the scaled-up process could maintain the stable recovery rates and purities of precipitates on site.

Isotopically constrained lead sources in fugitive dust from unsurfaced roads in the southeast Missouri mining district

USGS Publications Warehouse

Witt, Emitt C.; Pribil, Michael; Hogan, John P; Wronkiewicz, David

2016-01-01

The isotopic composition of lead (Pb) in fugitive dust suspended by a vehicle from 13 unsurfaced roads in Missouri was measured to identify the source of Pb within an established long-term mining area. A three end-member model using 207Pb/206Pb and concentration as tracers resulted in fugitive dust samples plotting in the mixing field of well characterized heterogeneous end members. End members selected for this investigation include the 207Pb/206Pb for 1) a Pb-mixture representing mine tailings, 2) aerosol Pb-impacted soils within close proximity to the Buick secondary recycling smelter, and 3) an average of soils, rock cores and drill cuttings representing the background conditions. Aqua regia total concentrations and 207Pb/206Pb of mining area dust suggest that 35.4–84.3% of the source Pb in dust is associated with the mine tailings mixture, 9.1–52.7% is associated with the smelter mixture, and 0–21.6% is associated with background materials. Isotope ratios varied minimally within the operational phases of sequential extraction suggesting that mixing of all three Pb mixtures occurs throughout. Labile forms of Pb were attributed to all three end members. The extractable carbonate phase had as much as 96.6% of the total concentration associated with mine tailings, 51.8% associated with smelter deposition, and 34.2% with background. The next most labile geochemical phase (Fe + Mn Oxides) showed similar results with as much as 85.3% associated with mine tailings, 56.8% associated with smelter deposition, and 4.2% associated with the background soil.

Double-blind photo lineups using actual eyewitnesses: an experimental test of a sequential versus simultaneous lineup procedure.

PubMed

Wells, Gary L; Steblay, Nancy K; Dysart, Jennifer E

2015-02-01

Eyewitnesses (494) to actual crimes in 4 police jurisdictions were randomly assigned to view simultaneous or sequential photo lineups using laptop computers and double-blind administration. The sequential procedure used in the field experiment mimicked how it is conducted in actual practice (e.g., using a continuation rule, witness does not know how many photos are to be viewed, witnesses resolve any multiple identifications), which is not how most lab experiments have tested the sequential lineup. No significant differences emerged in rates of identifying lineup suspects (25% overall) but the sequential procedure produced a significantly lower rate (11%) of identifying known-innocent lineup fillers than did the simultaneous procedure (18%). The simultaneous/sequential pattern did not significantly interact with estimator variables and no lineup-position effects were observed for either the simultaneous or sequential procedures. Rates of nonidentification were not significantly different for simultaneous and sequential but nonidentifiers from the sequential procedure were more likely to use the "not sure" response option than were nonidentifiers from the simultaneous procedure. Among witnesses who made an identification, 36% (41% of simultaneous and 32% of sequential) identified a known-innocent filler rather than a suspect, indicating that eyewitness performance overall was very poor. The results suggest that the sequential procedure that is used in the field reduces the identification of known-innocent fillers, but the differences are relatively small.

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

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Diagnostic Utility of the Bannatyne WISC-III Pattern. Learning Disabilities Practice

ERIC Educational Resources Information Center

Smith, Courtney B.; Watkins, Marley W.

2004-01-01

Regrouping Wechsler Intelligence Scale for Children-Third Edition (WISC-III) subtests into Bannatyne's spatial, conceptual, and sequential patterns has been thought by many to identify children with learning disabilities (LD). This study investigated the prevalence and diagnostic utility of WISC-III Bannatyne patterns by comparing 1,302 children…

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.

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

PubMed

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

2012-01-01

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

A sequential coalescent algorithm for chromosomal inversions

PubMed Central

Peischl, S; Koch, E; Guerrero, R F; Kirkpatrick, M

2013-01-01

Chromosomal inversions are common in natural populations and are believed to be involved in many important evolutionary phenomena, including speciation, the evolution of sex chromosomes and local adaptation. While recent advances in sequencing and genotyping methods are leading to rapidly increasing amounts of genome-wide sequence data that reveal interesting patterns of genetic variation within inverted regions, efficient simulation methods to study these patterns are largely missing. In this work, we extend the sequential Markovian coalescent, an approximation to the coalescent with recombination, to include the effects of polymorphic inversions on patterns of recombination. Results show that our algorithm is fast, memory-efficient and accurate, making it feasible to simulate large inversions in large populations for the first time. The SMC algorithm enables studies of patterns of genetic variation (for example, linkage disequilibria) and tests of hypotheses (using simulation-based approaches) that were previously intractable. PMID:23632894

Text Classification for Organizational Researchers

PubMed Central

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

2017-01-01

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output. PMID:29881249

Primary prevention of lead poisoning in rural Native American children: behavioral outcomes from a community-based intervention in a former mining region.

PubMed

Kegler, Michelle C; Malcoe, Lorraine Halinka; Fedirko, Veronika

2010-01-01

The current study examined the effectiveness of a community-based lay health advisor intervention, combined with youth engagement, in improving lead poisoning prevention behaviors and associated beliefs in a rural Native American population located in and near a Superfund site containing mining waste. Three sequential (1997, 2000, and 2004) cross-sectional assessments involving in-person interviews with Native American and White caregivers of young children were conducted. Results showed significant improvements over time for Native American, but not for White, for children washing their hands before meals and snacks, and for annual blood lead testing of both Native American and White children. Findings lend support to the value of community-based education for primary prevention of lead poisoning in Native American and rural communities.

The Evolution of Gene Regulatory Networks that Define Arthropod Body Plans.

PubMed

Auman, Tzach; Chipman, Ariel D

2017-09-01

Our understanding of the genetics of arthropod body plan development originally stems from work on Drosophila melanogaster from the late 1970s and onward. In Drosophila, there is a relatively detailed model for the network of gene interactions that proceeds in a sequential-hierarchical fashion to define the main features of the body plan. Over the years, we have a growing understanding of the networks involved in defining the body plan in an increasing number of arthropod species. It is now becoming possible to tease out the conserved aspects of these networks and to try to reconstruct their evolution. In this contribution, we focus on several key nodes of these networks, starting from early patterning in which the main axes are determined and the broad morphological domains of the embryo are defined, and on to later stage wherein the growth zone network is active in sequential addition of posterior segments. The pattern of conservation of networks is very patchy, with some key aspects being highly conserved in all arthropods and others being very labile. Many aspects of early axis patterning are highly conserved, as are some aspects of sequential segment generation. In contrast, regional patterning varies among different taxa, and some networks, such as the terminal patterning network, are only found in a limited range of taxa. The growth zone segmentation network is ancient and is probably plesiomorphic to all arthropods. In some insects, it has undergone significant modification to give rise to a more hardwired network that generates individual segments separately. In other insects and in most arthropods, the sequential segmentation network has undergone a significant amount of systems drift, wherein many of the genes have changed. However, it maintains a conserved underlying logic and function. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

The Effect of Sequential Dependence on the Sampling Distributions of KR-20, KR-21, and Split-Halves Reliabilities.

ERIC Educational Resources Information Center

Sullins, Walter L.

Five-hundred dichotomously scored response patterns were generated with sequentially independent (SI) items and 500 with dependent (SD) items for each of thirty-six combinations of sampling parameters (i.e., three test lengths, three sample sizes, and four item difficulty distributions). KR-20, KR-21, and Split-Half (S-H) reliabilities were…

THE PARADOX OF SIGN LANGUAGE MORPHOLOGY

PubMed Central

Aronoff, Mark; Meir, Irit; Sandler, Wendy

2011-01-01

Sign languages have two strikingly different kinds of morphological structure: sequential and simultaneous. The simultaneous morphology of two unrelated sign languages, American and Israeli Sign Language, is very similar and is largely inflectional, while what little sequential morphology we have found differs significantly and is derivational. We show that at least two pervasive types of inflectional morphology, verb agreement and classifier constructions, are iconically grounded in spatiotemporal cognition, while the sequential patterns can be traced to normal historical development. We attribute the paucity of sequential morphology in sign languages to their youth. This research both brings sign languages much closer to spoken languages in their morphological structure and shows how the medium of communication contributes to the structure of languages.* PMID:22223926

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.

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.

Shifts in microbial community composition and function in the acidification of a lead/zinc mine tailings.

PubMed

Chen, Lin-Xing; Li, Jin-Tian; Chen, Ya-Ting; Huang, Li-Nan; Hua, Zheng-Shuang; Hu, Min; Shu, Wen-Sheng

2013-09-01

In an attempt to link the microbial community composition and function in mine tailings to the generation of acid mine drainage, we simultaneously explored the geochemistry and microbiology of six tailings collected from a lead/zinc mine, i.e. primary tailings (T1), slightly acidic tailings (T2), extremely acidic tailings (T3, T4 and T5) and orange-coloured oxidized tailings (T6). Geochemical results showed that the six tailings (from T1 to T6) likely represented sequential stages of the acidification process of the mine tailings. 16S rRNA pyrosequencing revealed a contrasting microbial composition between the six tailings: Proteobacteria-related sequences dominated T1-T3 with relative abundance ranging from 56 to 93%, whereas Ferroplasma-related sequences dominated T4-T6 with relative abundance ranging from 28 to 58%. Furthermore, metagenomic analysis of the microbial communities of T2 and T6 indicated that the genes encoding key enzymes for microbial carbon fixation, nitrogen fixation and sulfur oxidation in T2 were largely from Thiobacillus and Acidithiobacillus, Methylococcus capsulatus, and Thiobacillus denitrificans respectively; while those in T6 were mostly identified in Acidithiobacillus and Leptospirillum, Acidithiobacillus and Leptospirillum, and Acidithiobacillus respectively. The microbial communities in T2 and T6 harboured more genes suggesting diverse metabolic capacities for sulfur oxidation/heavy metal detoxification and tolerating low pH respectively. © 2013 John Wiley & Sons Ltd and Society for Applied Microbiology.

Sequential infiltration synthesis for advanced lithography

DOEpatents

Darling, Seth B.; Elam, Jeffrey W.; Tseng, Yu-Chih; Peng, Qing

2015-03-17

A plasma etch resist material modified by an inorganic protective component via sequential infiltration synthesis (SIS) and methods of preparing the modified resist material. The modified resist material is characterized by an improved resistance to a plasma etching or related process relative to the unmodified resist material, thereby allowing formation of patterned features into a substrate material, which may be high-aspect ratio features. The SIS process forms the protective component within the bulk resist material through a plurality of alternating exposures to gas phase precursors which infiltrate the resist material. The plasma etch resist material may be initially patterned using photolithography, electron-beam lithography or a block copolymer self-assembly process.

Knowing what to respond in the future does not cancel the influence of past events.

PubMed

Tubau, Elisabet; López-Moliner, Joan

2009-05-29

Everyday tasks seldom involve isolate actions but sequences of them. We can see whether previous actions influence the current one by exploring the response time to controlled sequences of stimuli. Specifically, depending on the response-stimulus temporal interval (RSI), different mechanisms have been proposed to explain sequential effects in two-choice serial response tasks. Whereas an automatic facilitation mechanism is thought to produce a benefit for response repetitions at short RSIs, subjective expectancies are considered to replace the automatic facilitation at longer RSIs, producing a cost-benefit pattern: repetitions are faster after other repetitions but they are slower after alternations. However, there is not direct evidence showing the impact of subjective expectancies on sequential effects. By using a fixed sequence, the results of the reported experiment showed that the repetition effect was enhanced in participants who acquired complete knowledge of the order. Nevertheless, a similar cost-benefit pattern was observed in all participants and in all learning blocks. Therefore, results of the experiment suggest that sequential effects, including the cost-benefit pattern, are the consequence of automatic mechanisms which operate independently of (and simultaneously with) explicit knowledge of the sequence or other subjective expectancies.

Hierarchical Modeling of Sequential Behavioral Data: Examining Complex Association Patterns in Mediation Models

ERIC Educational Resources Information Center

Dagne, Getachew A.; Brown, C. Hendricks; Howe, George W.

2007-01-01

This article presents new methods for modeling the strength of association between multiple behaviors in a behavioral sequence, particularly those involving substantively important interaction patterns. Modeling and identifying such interaction patterns becomes more complex when behaviors are assigned to more than two categories, as is the case…

Enhancing business intelligence by means of suggestive reviews.

PubMed

Qazi, Atika; Raj, Ram Gopal; Tahir, Muhammad; Cambria, Erik; Syed, Karim Bux Shah

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

Enhancing Business Intelligence by Means of Suggestive Reviews

PubMed Central

Qazi, Atika

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. PMID:25054188

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.

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

Trends in overweight and physical activity among U.S. military personnel, 1995-1998.

PubMed

Lindquist, C H; Bray, R M

2001-01-01

The purpose of this study was to deter mine whether changes in physical activity patterns account for the increasing prevalence of obesity, utilizing a large, representative sample of male and female U.S. military personnel. Data from the 1995 and 1998 waves of the Department of Defense Survey of Health Related Behaviors among Military Personnel were utilized. Overweight was defined as body mass index > or =25. Respondents were classified as physically active if they reported > or =3 days/week of vigorous activity. Three sequential multivariate logistic regression models were analyzed separately for males and females with overweight regressed on year of study (1995 or 1998), demographic characteristics, and physical activity. Some 50% of military personnel in 1995 and 54% in 1998 were classified as overweight, representing a significant increase in overweight over the 3-year period for both males and females. Overweight military personnel were more likely to be male, older, African American or Hispanic, married, and enlisted personnel. Physical activity levels were high, with around 67% of the sample engaging in regular, vigorous physical activity. Although physical activity levels increased among male personnel between 1995 and 1998, there was not an independent association between physical activity and overweight, and changing physical activity patterns did not account for the increase in over weight from 1995 to 1998. The U.S. military is experiencing a trend toward increasing overweight that mirrors the pattern among the general population. The results of this study suggest that the rise in overweight among the military is not explained by a decrease in physical activity. Copyright 2000 American Health Foundation and Academic Press.

Leaching Properties of Naturally Occurring Heavy Metals from Soils

NASA Astrophysics Data System (ADS)

Zhang, M.; Hoshino, M.; Yoshikawa, M.; Hara, J.; Sugita, H.

2014-12-01

The major threats to human health from heavy metals are associated with exposure to arsenic, lead, cadmium, chromium, mercury, as well as some other elements. The effects of such heavy metals on human health have been extensively studied and reviewed by international organizations such as WHO. Due to their toxicity, heavy metal contaminations have been regulated by national environmental standards in many countries, and/or laws such as the Soil Contamination Countermeasures Act in Japan. Leaching of naturally occurring heavy metals from the soils, especially those around abandoned metal mines into surrounding water systems, either groundwater or surface water systems, is one of the major pathways of exposure. Therefore, understanding the leaching properties of toxic heavy metals from naturally polluted soils is of fundamentally importance for effectively managing abandoned metal mines, excavated rocks discharged from infrastructure constructions such as tunneling, and/or selecting a pertinent countermeasure against pollution when it is necessary. In this study, soil samples taken from the surroundings of abandoned metal mines in different regions in Japan were collected and analyzed. The samples contained multiple heavy metals such as lead, arsenic and chromium. Standard leaching test and sequential leaching test considering different forms of contaminants, such as trivalent and pentavalent arsenics, and trivalent and hexavalent chromiums, together with standard test for evaluating total concentration, X-ray Fluorescence Analysis (XRF), X-ray diffraction analysis (XRD) and Cation Exchange Capacity (CEC) tests were performed. In addition, sequential leaching tests were performed to evaluate long-term leaching properties of lead from representative samples. This presentation introduces the details of the above experimental study, discusses the relationships among leaching properties and chemical and mineral compositions, indicates the difficulties associated with remediation of naturally polluted sites, and emphasizes the importance of risk-based countermeasures against naturally occurring heavy metals. Keywords: Leaching properties, Control Factor, Naturally Occurring Heavy Metals, Lead, Arsenic, Chromium

Geochemical Study on an Abandoned Copper Smelting Plant Using Rare Earth Elements

NASA Astrophysics Data System (ADS)

Sun, S. H.

2017-12-01

The Shuei Nan Dong Copper Smelting Plant smelting is located on the northern coast of New Taipei City, Taiwan. The plant built in 1906 for but has been shut down since 1987. However, the watershed is continuing to discharge acid mine water into the sea; and, the acid mine drainage releases high amounts of sulfate, heavy metals without any treatment. In this study, the water samples were sequentially collected along the main channel and its tributaries in the watershed. The results of hydrochemical analysis show that the untreated inflow water can be characterized with low pH value of <3 and enriched sulfate, copper and arsenic. However, the water is much less contaminated in the upstream area until a major tributary converge. The results of principal component analysis (PCA) demonstrate that the first principal component (PC) can explain >80% of the total variance and almost all chemical components have high loadings in the PC. Therefore, the hydrochemical properties in the watershed are mainly dominated by the mixing process between main channel and the major tributary but the geochemical reactions during flow down the channel is insignificant. Rare earth elements (REE) are an excellent tracer, which can indicate sources of chemical components and geochemical reactions in water. The analysis results demonstrate two distinct REE patterns. The water with low REE can be characterized by prominent Eu positive anomaly and Ce negative anomaly, which may result from the alteration of Na-plagioclase in sandstone and oxidation reaction when contact with air, respectively. On the contrary, the water with high REE shows only minor Ce negative anomaly and insignificant Eu positive anomaly. In addition, there is an enrichment of middle REE in high-REE water, which is quite different with the REE pattern of pyrite. According to the Grawunder's study (2014), it corresponds to the complexation to sulphite during pyrite oxidation. It is worth noting that REE show no considerable fractionation along the channel and confirms the results from PCA. It can be derived that the water may not reached equilibrium condition. A simple aerated retention pool could dramatically reduce the pollutants due to coprecipitation of iron oxide and aluminum oxide.

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...

Speckle pattern sequential extraction metric for estimating the focus spot size on a remote diffuse target.

PubMed

Yu, Zhan; Li, Yuanyang; Liu, Lisheng; Guo, Jin; Wang, Tingfeng; Yang, Guoqing

2017-11-10

The speckle pattern (line by line) sequential extraction (SPSE) metric is proposed by the one-dimensional speckle intensity level crossing theory. Through the sequential extraction of received speckle information, the speckle metrics for estimating the variation of focusing spot size on a remote diffuse target are obtained. Based on the simulation, we will give some discussions about the SPSE metric range of application under the theoretical conditions, and the aperture size will affect the metric performance of the observation system. The results of the analyses are verified by the experiment. This method is applied to the detection of relative static target (speckled jitter frequency is less than the CCD sampling frequency). The SPSE metric can determine the variation of the focusing spot size over a long distance, moreover, the metric will estimate the spot size under some conditions. Therefore, the monitoring and the feedback of far-field spot will be implemented laser focusing system applications and help the system to optimize the focusing performance.

Detection, mapping and estimation of rate of spread of grass fires from southern African ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Wightman, J. M.

1973-01-01

Sequential band-6 imagery of the Zambesi Basin of southern Africa recorded substantial changes in burn patterns resulting from late dry season grass fires. One example from northern Botswana, indicates that a fire consumed approximately 70 square miles of grassland over a 24-hour period. Another example from western Zambia indicates increased fire activity over a 19-day period. Other examples clearly define the area of widespread grass fires in Angola, Botswana, Rhodesia and Zambia. From the fire patterns visible on the sequential portions of the imagery, and the time intervals involved, the rates of spread of the fires are estimated and compared with estimates derived from experimental burning plots in Zambia and Canada. It is concluded that sequential ERTS-1 imagery, of the quality studied, clearly provides the information needed to detect and map grass fires and to monitor their rates of spread in this region during the late dry season.

Geochemical assessments and classification of coal mine spoils for better understanding of potential salinity issues at closure.

PubMed

Park, Jin Hee; Li, Xiaofang; Edraki, Mansour; Baumgartl, Thomas; Kirsch, Bernie

2013-06-01

Coal mining wastes in the form of spoils, rejects and tailings deposited on a mine lease can cause various environmental issues including contamination by toxic metals, acid mine drainage and salinity. Dissolution of salt from saline mine spoil, in particular, during rainfall events may result in local or regional dispersion of salts through leaching or in the accumulation of dissolved salts in soil pore water and inhibition of plant growth. The salinity in coal mine environments is from the geogenic salt accumulations and weathering of spoils upon surface exposure. The salts are mainly sulfates and chlorides of calcium, magnesium and sodium. The objective of the research is to investigate and assess the source and mobility of salts and trace elements in various spoil types, thereby predicting the leaching behavior of the salts and trace elements from spoils which have similar geochemical properties. X-ray diffraction analysis, total digestion, sequential extraction and column experiments were conducted to achieve the objectives. Sodium and chloride concentrations best represented salinity of the spoils, which might originate from halite. Electrical conductivity, sodium and chloride concentrations in the leachate decreased sharply with increasing leaching cycles. Leaching of trace elements was not significant in the studied area. Geochemical classification of spoil/waste defined for rehabilitation purposes was useful to predict potential salinity, which corresponded with the classification from cluster analysis based on leaching data of major elements. Certain spoil groups showed high potential salinity by releasing high sodium and chloride concentrations. Therefore, the leaching characteristics of sites having saline susceptible spoils require monitoring, and suitable remediation technologies have to be applied.

Recovery of iron oxides from acid mine drainage and their application as adsorbent or catalyst.

PubMed

Flores, Rubia Gomes; Andersen, Silvia Layara Floriani; Maia, Leonardo Kenji Komay; José, Humberto Jorge; Moreira, Regina de Fatima Peralta Muniz

2012-11-30

Iron oxide particles recovered from acid mine drainage represent a potential low-cost feedstock to replace reagent-grade chemicals in the production of goethite, ferrihydrite or magnetite with relatively high purity. Also, the properties of iron oxides recovered from acid mine drainage mean that they can be exploited as catalysts and/or adsorbents to remove azo dyes from aqueous solutions. The main aim of this study was to recover iron oxides with relatively high purity from acid mine drainage to act as a catalyst in the oxidation of dye through a Fenton-like mechanism or as an adsorbent to remove dyes from an aqueous solution. Iron oxides (goethite) were recovered from acid mine drainage through a sequential precipitation method. Thermal treatment at temperatures higher than 300 °C produces hematite through a decrease in the BET area and an increase in the point of zero charge. In the absence of hydrogen peroxide, the solids adsorbed the textile dye Procion Red H-E7B according to the Langmuir model, and the maximum amount adsorbed decreased as the temperature of the thermal treatment increased. The decomposition kinetics of hydrogen peroxide is dependent on the H(2)O(2) concentration and iron oxides dosage, but the second-order rate constant normalized to the BET surface area is similar to that for different iron oxides tested in this and others studies. These results indicate that acid mine drainage could be used as a source material for the production of iron oxide catalysts/adsorbents, with comparable quality to those produced using analytical-grade reagents. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of EDTA decontamination on soils affected by mining activities and impact of treatment on the geochemical partition of metal contaminants.

PubMed

Xia, Wenbin; Gao, Hui; Wang, Xianhai; Zhou, Chunhua; Liu, Yunguo; Fan, Ting; Wang, Xin

2009-05-30

Two soil samples were collected at mining areas located in southern Hunan Province, China. EDTA extraction of Pb, Zn, Cu and Cd from these two tailing soils was studied using column leaching experiments. The redistributions of heavy metals (HMs) were determined using the modified BCR (Community Bureau of Reference) sequential extraction procedure, before and after EDTA extraction. The results indicated that EDTA was an effective extractant because of its strong chelating ability for various HMs. The proportions of Pb, Zn, Cu and Cd in the four fractions varied largely after EDTA extraction. The extraction efficiency of EDTA of the acid-extractable fraction (AEX) was significant in shallow soil column, while in deeper soil column, decrease of the extraction efficiency of reduced (RED), oxidizable (OX) and residual fractions (RES) was obtained, which was mainly due to the decrease of EDTA concentration.

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

Data Stream Mining

NASA Astrophysics Data System (ADS)

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

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

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

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.

The structure and evolution of galacto-detonation waves - Some analytic results in sequential star formation models of spiral galaxies

NASA Technical Reports Server (NTRS)

Cowie, L. L.; Rybicki, G. B.

1982-01-01

Waves of star formation in a uniform, differentially rotating disk galaxy are treated analytically as a propagating detonation wave front. It is shown, that if single solitary waves could be excited, they would evolve asymptotically to one of two stable spiral forms, each of which rotates with a fixed pattern speed. Simple numerical solutions confirm these results. However, the pattern of waves that develop naturally from an initially localized disturbance is more complex and dies out within a few rotation periods. These results suggest a conclusive observational test for deciding whether sequential star formation is an important determinant of spiral structure in some class of galaxies.

Sequential infiltration synthesis for advanced lithography

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

Darling, Seth B.; Elam, Jeffrey W.; Tseng, Yu-Chih

A plasma etch resist material modified by an inorganic protective component via sequential infiltration synthesis (SIS) and methods of preparing the modified resist material. The modified resist material is characterized by an improved resistance to a plasma etching or related process relative to the unmodified resist material, thereby allowing formation of patterned features into a substrate material, which may be high-aspect ratio features. The SIS process forms the protective component within the bulk resist material through a plurality of alternating exposures to gas phase precursors which infiltrate the resist material. The plasma etch resist material may be initially patterned usingmore » photolithography, electron-beam lithography or a block copolymer self-assembly process.« less

Sequential establishment of stripe patterns in an expanding cell population.

PubMed

Liu, Chenli; Fu, Xiongfei; Liu, Lizhong; Ren, Xiaojing; Chau, Carlos K L; Li, Sihong; Xiang, Lu; Zeng, Hualing; Chen, Guanhua; Tang, Lei-Han; Lenz, Peter; Cui, Xiaodong; Huang, Wei; Hwa, Terence; Huang, Jian-Dong

2011-10-14

Periodic stripe patterns are ubiquitous in living organisms, yet the underlying developmental processes are complex and difficult to disentangle. We describe a synthetic genetic circuit that couples cell density and motility. This system enabled programmed Escherichia coli cells to form periodic stripes of high and low cell densities sequentially and autonomously. Theoretical and experimental analyses reveal that the spatial structure arises from a recurrent aggregation process at the front of the continuously expanding cell population. The number of stripes formed could be tuned by modulating the basal expression of a single gene. The results establish motility control as a simple route to establishing recurrent structures without requiring an extrinsic pacemaker.

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.

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.

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.

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.

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Exploring the Learner's Knowledge Construction and Cognitive Patterns of Different Asynchronous Platforms: Comparison of an Online Discussion Forum and Facebook

ERIC Educational Resources Information Center

Hou, Huei-Tse; Wang, Shu-Ming; Lin, Peng-Chun; Chang, Kuo-En

2015-01-01

The primary purpose of this study is to explore the knowledge construction behaviour and cognitive patterns involved in students' online discussion using online forum and Facebook (FB). This study employed quantitative content analysis and lag sequential analysis to examine the content and behavioural patterns of 50 students from a private…

Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

ERIC Educational Resources Information Center

Sun, Jerry Chih-Yuan; Kuo, Cian-Yu; Hou, Huei-Tse; Lin, Yu-Yan

2017-01-01

The purposes of this study were to provide a game-based anti-phishing lesson to 110 elementary school students in Taiwan, explore their learning behavioral patterns, and investigate the effects of the flow states on their learning behavioral patterns and learning achievement. The study recorded behaviour logs, and applied a pre- and post-test on…

Evaluation of tests to assess the quality of mine-contaminated soils.

PubMed

Alvarenga, P; Palma, P; Gonçalves, A P; Fernandes, R M; de Varennes, A; Vallini, G; Duarte, E; Cunha-Queda, A C

2008-04-01

An acid metal-contaminated soil from the Aljustrel mining area (a pyrite mine located in SW Portugal in the Iberian Pyrite Belt) was subjected to chemical characterisation and total metal quantification (Cd, Cr, Cu, Ni, Pb and Zn). Water-soluble metals were determined and a sequential extraction procedure was used to investigate metal speciation. Two bioavailable metal fractions were determined: a mobile fraction and a mobilisable fraction. Soil ecotoxicity was studied using a battery of bioassays: plant growth test and seed germination with cress (Lepidium sativum L.), earthworm (Eisenia fetida) mortality, E. fetida avoidance behaviour, luminescent inhibition of Vibrio fischeri and Daphnia magna immobilisation. Although the total content of Cu, Zn and Pb in the soil was large (362, 245 and 1,250 mg/kg dry matter, respectively), these metals were mostly structurally bound (87% for Cu, 81% for Zn and 89% for Pb) and, therefore, scarcely bioavailable. Nonetheless, the D. magna immobilization test using soil leachate showed an EC50 (48 h) of 36.3% (v/v), and the luminescent inhibition of V. fischeri presented an EC20 (15 min) of 45.2% and an EC20 (30 min) of 10.7% (v/v), suggesting a considerable toxic effect. In the direct exposure bioassays, E. fetida avoided the mine soil at the highest concentrations (50%, 75% and 100% v/v). At the same soil concentrations, cress showed negligible growth. The results suggest the need to use a battery of toxicity tests, in conjunction with chemical methods, in order to assess the quality of mine-contaminated soils correctly.

A transcriptome analysis of two grapevine populations segregating for tendril phyllotaxy

USDA-ARS?s Scientific Manuscript database

The shoot structure of cultivated grapevine Vitis vinifera L. typically exhibits a 3-node modular repetitive pattern, two sequential leaf-opposed tendrils followed by a tendril-free node. In this study, we investigated the molecular basis of this pattern by characterizing differentially expressed ge...

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.

Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Wang, Cheng-Long; Yu, Hsiao-Hsuan; Wang, Yung-Chieh

2009-01-01

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

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.

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

Alcohol and the pancreas. II. Pancreatic morphology of advanced alcoholic pancreatitis.

PubMed

Noronha, M; Bordalo, O; Dreiling, D A

1981-08-01

The histopathology of advanced chronic alcoholic pancreatitis is dominated by cellular degeneration, atrophy and fibrosis. Sequential changes in the histopathology of alcoholic pancreatic disease has been defined and traced from initial injury to end-stage disease. These sequential histopathologies have been correlated with clinical syndrome and secretory patterns. The data are more consistent with a toxic-metabolic pathogenesis of alcoholic pancreatitis than the previous Big Duct and Small Duct hypotheses.

Potential contaminants at a dredged spoil placement site, Charles City County, Virginia, as revealed by sequential extraction

PubMed Central

Tang, Jianwu; Whittecar, G Richard; Johannesson, Karen H; Daniels, W Lee

2004-01-01

Backfills of dredged sediments onto a former sand and gravel mine site in Charles City County, VA may have the potential to contaminate local groundwater. To evaluate the mobility of trace elements and to identify the potential contaminants from the dredged sediments, a sequential extraction scheme was used to partition trace elements associated with the sediments from the local aquifer and the dredged sediments into five fractions: exchangeable, acidic, reducible, oxidizable, and residual phases. Sequential extractions indicate that, for most of the trace elements examined, the residual phases account for the largest proportion of the total concentrations, and their total extractable fractions are mainly from reducible and oxidizable phases. Only Cd, Pb, and Zn have an appreciable extractable proportion from the acidic phase in the filled dredged sediments. Our groundwater monitoring data suggest that the dredged sediments are mainly subject to a decrease in pH and a series of oxidation reactions, when exposed to the atmosphere. Because the trace elements released by carbonate dissolution and the oxidation (e.g., organic matter degradation, iron sulfide and, ammonia oxidation) are subsequently immobilized by sorption to iron, manganese, and aluminum oxides, no potential contaminants to local groundwater are expected by addition of the dredged sediments to this site.

Removal of arsenic and cadmium with sequential soil washing techniques using Na2EDTA, oxalic and phosphoric acid: Optimization conditions, removal effectiveness and ecological risks.

PubMed

Wei, Meng; Chen, Jiajun; Wang, Xingwei

2016-08-01

Testing of sequential soil washing in triplicate using typical chelating agent (Na2EDTA), organic acid (oxalic acid) and inorganic weak acid (phosphoric acid) was conducted to remediate soil contaminated by heavy metals close to a mining area. The aim of the testing was to improve removal efficiency and reduce mobility of heavy metals. The sequential extraction procedure and further speciation analysis of heavy metals demonstrated that the primary components of arsenic and cadmium in the soil were residual As (O-As) and exchangeable fraction, which accounted for 60% and 70% of total arsenic and cadmium, respectively. It was determined that soil washing agents and their washing order were critical to removal efficiencies of metal fractions, metal bioavailability and potential mobility due to different levels of dissolution of residual fractions and inter-transformation of metal fractions. The optimal soil washing option for arsenic and cadmium was identified as phosphoric-oxalic acid-Na2EDTA sequence (POE) based on the high removal efficiency (41.9% for arsenic and 89.6% for cadmium) and the minimal harmful effects of the mobility and bioavailability of the remaining heavy metals. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

The target-to-foils shift in simultaneous and sequential lineups.

PubMed

Clark, Steven E; Davey, Sherrie L

2005-04-01

A theoretical cornerstone in eyewitness identification research is the proposition that witnesses, in making decisions from standard simultaneous lineups, make relative judgments. The present research considers two sources of support for this proposal. An experiment by G. L. Wells (1993) showed that if the target is removed from a lineup, witnesses shift their responses to pick foils, rather than rejecting the lineups, a result we will term a target-to-foils shift. Additional empirical support is provided by results from sequential lineups which typically show higher accuracy than simultaneous lineups, presumably because of a decrease in the use of relative judgments in making identification decisions. The combination of these two lines of research suggests that the target-to-foils shift should be reduced in sequential lineups relative to simultaneous lineups. Results of two experiments showed an overall advantage for sequential lineups, but also showed a target-to-foils shift equal in size for simultaneous and sequential lineups. Additional analyses indicated that the target-to-foils shift in sequential lineups was moderated in part by an order effect and was produced with (Experiment 2) or without (Experiment 1) a shift in decision criterion. This complex pattern of results suggests that more work is needed to understand the processes which underlie decisions in simultaneous and sequential lineups.

Aging and sequential modulations of poorer strategy effects: An EEG study in arithmetic problem solving.

PubMed

Hinault, Thomas; Lemaire, Patrick; Phillips, Natalie

2016-01-01

This study investigated age-related differences in electrophysiological signatures of sequential modulations of poorer strategy effects. Sequential modulations of poorer strategy effects refer to decreased poorer strategy effects (i.e., poorer performance when the cued strategy is not the best) on current problem following poorer strategy problems compared to after better strategy problems. Analyses on electrophysiological (EEG) data revealed important age-related changes in time, frequency, and coherence of brain activities underlying sequential modulations of poorer strategy effects. More specifically, sequential modulations of poorer strategy effects were associated with earlier and later time windows (i.e., between 200- and 550 ms and between 850- and 1250 ms). Event-related potentials (ERPs) also revealed an earlier onset in older adults, together with more anterior and less lateralized activations. Furthermore, sequential modulations of poorer strategy effects were associated with theta and alpha frequencies in young adults while these modulations were found in delta frequency and theta inter-hemispheric coherence in older adults, consistent with qualitatively distinct patterns of brain activity. These findings have important implications to further our understanding of age-related differences and similarities in sequential modulations of cognitive control processes during arithmetic strategy execution. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Understanding User Behavioral Patterns in Open Knowledge Communities

ERIC Educational Resources Information Center

Yang, Xianmin; Song, Shuqiang; Zhao, Xinshuo; Yu, Shengquan

2018-01-01

Open knowledge communities (OKCs) have become popular in the era of knowledge economy. This study aimed to explore how users collaboratively create and share knowledge in OKCs. In particular, this research identified the behavior distribution and behavioral patterns of users by conducting frequency distribution and lag sequential analyses. Some…

How Temporal and Spatial Aspects of Presenting Visualizations Affect Learning about Locomotion Patterns

ERIC Educational Resources Information Center

Imhof, Birgit; Scheiter, Katharina; Edelmann, Jorg; Gerjets, Peter

2012-01-01

Two studies investigated the effectiveness of dynamic and static visualizations for a perceptual learning task (locomotion pattern classification). In Study 1, seventy-five students viewed either dynamic, static-sequential, or static-simultaneous visualizations. For tasks of intermediate difficulty, dynamic visualizations led to better…

A Computational Model of Event Segmentation from Perceptual Prediction

ERIC Educational Resources Information Center

Reynolds, Jeremy R.; Zacks, Jeffrey M.; Braver, Todd S.

2007-01-01

People tend to perceive ongoing continuous activity as series of discrete events. This partitioning of continuous activity may occur, in part, because events correspond to dynamic patterns that have recurred across different contexts. Recurring patterns may lead to reliable sequential dependencies in observers' experiences, which then can be used…

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...

Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Dong; Tse, Peter W.

2015-05-01

Slurry pumps are commonly used in oil-sand mining for pumping mixtures of abrasive liquids and solids. These operations cause constant wear of slurry pump impellers, which results in the breakdown of the slurry pumps. This paper develops a prognostic method for estimating remaining useful life of slurry pump impellers. First, a moving-average wear degradation index is proposed to assess the performance degradation of the slurry pump impeller. Secondly, the state space model of the proposed health index is constructed. A general sequential Monte Carlo method is employed to derive the parameters of the state space model. The remaining useful life of the slurry pump impeller is estimated by extrapolating the established state space model to a specified alert threshold. Data collected from an industrial oil sand pump were used to validate the developed method. The results show that the accuracy of the developed method improves as more data become available.

A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements

PubMed Central

2014-01-01

Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditionally been limited to sequentially controlling DOFs one at a time. However, able-bodied individuals use multiple DOFs simultaneously, and it may be beneficial to provide amputees the ability to perform simultaneous movements. In this study, four amputees who had undergone targeted motor reinnervation (TMR) surgery with previous training using myoelectric prostheses were configured to use three control strategies: 1) conventional amplitude-based myoelectric control, 2) sequential (one-DOF) pattern recognition control, 3) simultaneous pattern recognition control. Simultaneous pattern recognition was enabled by having amputees train each simultaneous movement as a separate motion class. For tasks that required control over just one DOF, sequential pattern recognition based control performed the best with the lowest average completion times, completion rates and length error. For tasks that required control over 2 DOFs, the simultaneous pattern recognition controller performed the best with the lowest average completion times, completion rates and length error compared to the other control strategies. In the two strategies in which users could employ simultaneous movements (conventional and simultaneous pattern recognition), amputees chose to use simultaneous movements 78% of the time with simultaneous pattern recognition and 64% of the time with conventional control for tasks that required two DOF motions to reach the target. These results suggest that when amputees are given the ability to control multiple DOFs simultaneously, they choose to perform tasks that utilize multiple DOFs with simultaneous movements. Additionally, they were able to perform these tasks with higher performance (faster speed, lower length error and higher completion rates) without losing substantial performance in 1 DOF tasks. PMID:24410948

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

Biochar Mechanisms of Heavy Metal Sorption and Potential Utility

NASA Astrophysics Data System (ADS)

Ippolito, J.

2015-12-01

Mining-affected lands are a global issue; in the USA alone there are an estimated 500,000 abandoned mines encompassing hundreds of thousands of hectares. Many of these sites generate acidic mine drainage that causes release of heavy metals, and subsequently degradation in environmental quality. Because of its potential liming characteristics, biochar may play a pivotal role as a soil amendment in future mine land reclamation. However, to date, most studies have focused on the use of biochar to sorb metals from solution. Previous studies suggest that metals are complexed by biochar surface function groups (leading to ion exchange, complexation), coordination with Pi electrons (C=C) of carbon, and precipitation of inorganic mineral phases. Several recent studies have focused on the use of biochar for amending mine land soils, showing that biochar can indeed reduce heavy metal lability, yet the mechanism(s) behind labile metal reduction have yet to be established. In a proof-of-concept study, we added lodgepole pine, tamarisk, and switchgrass biochar (0, 5, 10, 15% by weight; 500 oC) to four different western US mine land soils affected by various heavy metals (Cd, Cu, Mn, Pb, Zn). Extraction with 0.01M CaCl2 showed that increasing biochar application rate significantly decreased 'bioaccessible' metals in almost all instances. A concomitant increase in solution pH was observed, suggesting that metals may be rendered bio-inaccessible through precipitation as carbonate or (hydr)oxide phases, or sorbed onto mineral surfaces. However, this was only supposition and required further research. Thus, following the 0.01M CaCl2 extraction, biochar-soil mixtures were air-dried and metals were further extracted using the four-step BCR sequential removal procedure. Results from selective extraction suggest that, as compared to the controls, most metals in the biochar-amended mine land soils were associated with exchange sites, carbonate, and oxide phases. Biochar may play a pivotal role as a soil amendment in the future of mine land reclamation, although elevated pH levels should be maintained to prolong sequestration while lessening the possibility of metal resolubilization.

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

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes.

PubMed

Karacan, C Özgen; Olea, Ricardo A

2013-04-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

Hydrology, geomorphology, and flood profiles of Lemon Creek, Juneau, Alaska

USGS Publications Warehouse

Host, Randy H.; Neal, Edward G.

2005-01-01

Lemon Creek near Juneau, Alaska has a history of extensive gravel mining, which straightened and deepened the stream channel in the lower reaches of the study area. Gravel mining and channel excavation began in the 1940s and continued through the mid-1980s. Time sequential aerial photos and field investigations indicate that the channel morphology is reverting to pre-disturbance conditions through aggradation of sediment and re-establishment of braided channels, which may result in decreased channel conveyance and increased flooding potential. Time sequential surveys of selected channel cross sections were conducted in an attempt to determine rates of channel aggradation/degradation throughout three reaches of the study area. In order to assess flooding potential in the lower reaches of the study area the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System model was used to estimate the water-surface elevations for the 2-, 10-, 25-, 50-, and 100-year floods. A regionally based regression equation was used to estimate the magnitude of floods for the selected recurrence intervals. Forty-two cross sections were surveyed to define the hydraulic characteristics along a 1.7-mile reach of the stream. High-water marks from a peak flow of 1,820 cubic feet per second, or about a 5-year flood, were surveyed and used to calibrate the model throughout the study area. The stream channel at a bridge in the lower reach could not be simulated without violating assumptions of the model. A model without the lower bridge indicates flood potential is limited to a small area.

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

USGS Publications Warehouse

Karacan, C. Özgen; Olea, Ricardo A.

2013-01-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

PubMed Central

Karacan, C.Özgen; Olea, Ricardo A.

2015-01-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests. PMID:26190930

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.

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

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.

A Functional Measurement Study on Averaging Numerosity

ERIC Educational Resources Information Center

Tira, Michael D.; Tagliabue, Mariaelena; Vidotto, Giulio

2014-01-01

In two experiments, participants judged the average numerosity between two sequentially presented dot patterns to perform an approximate arithmetic task. In Experiment 1, the response was given on a 0-20 numerical scale (categorical scaling), and in Experiment 2, the response was given by the production of a dot pattern of the desired numerosity…

PATTERN RECOGNITION APPROACH TO MEDICAL DIAGNOSIS,

DTIC Science & Technology

A sequential method of pattern recognition was used to recognize hyperthyroidism in a sample of 2219 patients being treated at the Straub Clinic in...the most prominent class features are selected. Thus, the symptoms which best distinguish hyperthyroidism are extracted at every step and the number of tests required to reach a diagnosis is reduced. (Author)

Reconstructing the spatial pattern of trees from routine stand examination measurements

USGS Publications Warehouse

Hanus, M.L.; Hann, D.W.; Marshall, D.D.

1998-01-01

Reconstruction of the spatial pattern of trees is important for the accurate visual display of unmapped stands. The proposed process for generating the spatial pattern is a nonsimple sequential inhibition process, with the inhibition zone proportionate to the scaled maximum crown width of an open-grown tree of the same species and same diameter at breast height as the subject tree. The results of this coordinate generation procedure are compared with mapped stem data from nine natural stands of Douglas-fir at two ages by the use of a transformed Ripley's K(d) function. The results of this comparison indicate that the proposed method, based on complete tree lists, successfully replicated the spatial patterns of the trees in all nine stands at both ages and over the range of distances examined. On the basis of these findings and the procedure's ability to model effects through time, the nonsimple sequential inhibition process has been chosen to generate tree coordinates in the VIZ4ST computer program for displaying forest stand structure in naturally regenerated young Douglas-fir stands. For. Sci.

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.

ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems

PubMed Central

Expósito, Roberto R.

2018-01-01

Biclustering techniques are gaining attention in the analysis of large-scale datasets as they identify two-dimensional submatrices where both rows and columns are correlated. In this work we present ParBiBit, a parallel tool to accelerate the search of interesting biclusters on binary datasets, which are very popular on different fields such as genetics, marketing or text mining. It is based on the state-of-the-art sequential Java tool BiBit, which has been proved accurate by several studies, especially on scenarios that result on many large biclusters. ParBiBit uses the same methodology as BiBit (grouping the binary information into patterns) and provides the same results. Nevertheless, our tool significantly improves performance thanks to an efficient implementation based on C++11 that includes support for threads and MPI processes in order to exploit the compute capabilities of modern distributed-memory systems, which provide several multicore CPU nodes interconnected through a network. Our performance evaluation with 18 representative input datasets on two different eight-node systems shows that our tool is significantly faster than the original BiBit. Source code in C++ and MPI running on Linux systems as well as a reference manual are available at https://sourceforge.net/projects/parbibit/. PMID:29608567

ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems.

PubMed

González-Domínguez, Jorge; Expósito, Roberto R

2018-01-01

Biclustering techniques are gaining attention in the analysis of large-scale datasets as they identify two-dimensional submatrices where both rows and columns are correlated. In this work we present ParBiBit, a parallel tool to accelerate the search of interesting biclusters on binary datasets, which are very popular on different fields such as genetics, marketing or text mining. It is based on the state-of-the-art sequential Java tool BiBit, which has been proved accurate by several studies, especially on scenarios that result on many large biclusters. ParBiBit uses the same methodology as BiBit (grouping the binary information into patterns) and provides the same results. Nevertheless, our tool significantly improves performance thanks to an efficient implementation based on C++11 that includes support for threads and MPI processes in order to exploit the compute capabilities of modern distributed-memory systems, which provide several multicore CPU nodes interconnected through a network. Our performance evaluation with 18 representative input datasets on two different eight-node systems shows that our tool is significantly faster than the original BiBit. Source code in C++ and MPI running on Linux systems as well as a reference manual are available at https://sourceforge.net/projects/parbibit/.

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.

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…

Investigation of Rock Mass Stability Around the Tunnels in an Underground Mine in USA Using Three-Dimensional Numerical Modeling

NASA Astrophysics Data System (ADS)

Xing, Yan; Kulatilake, P. H. S. W.; Sandbak, L. A.

2018-02-01

The stability of the rock mass around the tunnels in an underground mine was investigated using the distinct element method. A three-dimensional model was developed based on the available geological, geotechnical, and mine construction information. It incorporates a complex lithological system, persistent and non-persistent faults, and a complex tunnel system including backfilled tunnels. The strain-softening constitutive model was applied for the rock masses. The rock mass properties were estimated using the Hoek-Brown equations based on the intact rock properties and the RMR values. The fault material behavior was modeled using the continuously yielding joint model. Sequential construction and rock supporting procedures were simulated based on the way they progressed in the mine. Stress analyses were performed to study the effect of the horizontal in situ stresses and the variability of rock mass properties on tunnel stability, and to evaluate the effectiveness of rock supports. The rock mass behavior was assessed using the stresses, failure zones, deformations around the tunnels, and the fault shear displacement vectors. The safety of rock supports was quantified using the bond shear and bolt tensile failures. Results show that the major fault and weak interlayer have distinct influences on the displacements and stresses around the tunnels. Comparison between the numerical modeling results and the field measurements indicated the cases with the average rock mass properties, and the K 0 values between 0.5 and 1.25 provide satisfactory agreement with the field measurements.

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

Difference in vascular patterns between transosseous-equivalent and transosseous rotator cuff repair.

PubMed

Urita, Atsushi; Funakoshi, Tadanao; Horie, Tatsunori; Nishida, Mutsumi; Iwasaki, Norimasa

2017-01-01

Vascularity is the important factor of biologic healing of the repaired tissue. The purpose of this study was to clarify sequential vascular patterns of repaired rotator cuff by suture techniques. We randomized 21 shoulders in 20 patients undergoing arthroscopic rotator cuff repair into 2 groups: transosseous-equivalent repair (TOE group, n = 10) and transosseous repair (TO group, n = 11). Blood flow in 4 regions inside the cuff (lateral articular, lateral bursal, medial articular, and medial bursal), in the knotless suture anchor in the TOE group, and in the bone tunnel in the TO group was measured using contrast-enhanced ultrasound at 1 month, 2 months, 3 months, and 6 months postoperatively. The sequential vascular pattern inside the repaired rotator cuff was different between groups. The blood flow in the lateral articular area at 1 month, 2 months, and 3 months (P = .002, .005, and .025) and that in the lateral bursal area at 2 months (P = .031) in the TO group were significantly greater than those in the TOE group postoperatively. Blood flow was significantly greater for the bone tunnels in the TO group than for the knotless suture anchor in the TOE group at 1 month and 2 months postoperatively (P = .041 and .009). This study clarified that the sequential vascular pattern inside the repaired rotator cuff depends on the suture technique used. Bone tunnels through the footprint may contribute to biologic healing by increasing blood flow in the repaired rotator cuff. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

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

Mothers as Teachers: Instruction and Control Patterns Observed in Interactions of Middle-Class Mothers with Trainable Mentally Retarded and Nonretarded Children. Final Report Number 7.32.

ERIC Educational Resources Information Center

Dolley, Diane Greenough

A study was conducted to compare teaching and control patterns used by mothers of 4- to 6-year-old trainable mentally retarded (TMR) children to patterns used by mothers of nonretarded children, and to evaluate an analysis strategy identifying sequential behavior chains from observational data. Literature was explored on three topics: cognitive…

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.

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.

Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation.

PubMed

Saito, Shota; Hirata, Yoshito; Sasahara, Kazutoshi; Suzuki, Hideyuki

2015-01-01

Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF). In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.

Copper, zinc and lead biogeochemistry in aquatic and land plants from the Iberian Pyrite Belt (Portugal) and north of Morocco mining areas.

PubMed

Durães, Nuno; Bobos, Iuliu; Ferreira da Silva, Eduardo; Dekayir, Abdelilah

2015-02-01

The ability of aquatic (Juncus effusus L., Scirpus holoschoenus L., Thypha latifolia L. and Juncus sp.) and land (Cistus ladanifer L., Erica andevalensis C.-R., Nerium oleander L., Isatis tinctoria L., Rosmarinus officinalis L., Cynodon dactylon L. and Hordeum murinum L.) plants from Portugal (Aljustrel, Lousal and São Domingos) and Morocco (Tighza and Zeida) mining areas to uptake, translocate and tolerate heavy metals (Cu, Zn and Pb) was evaluated. The soils (rhizosphere) of the first mining area are characterized by high acidity conditions (pH 2-5), whereas from the second area, by alkaline conditions (pH 7.0-8.5). Physicochemical parameters and mineralogy of the rhizosphere were determined from both areas. Chemical analysis of plants and the rhizosphere was carried out by inductively coupled plasma emission spectrometry. The sequential chemical extraction procedure was applied for rhizosphere samples collected from both mining areas. In the acid conditions, the aquatic plants show a high capacity for Zn bioaccumulation and translocation and less for Pb, reflecting the following metal mobility sequence: Zn > Cu > Pb. Kaolinite detected in the roots by infrared spectroscopy (IR) contributed to metal fixation (i.e. Cu), reducing its translocation to the aerial parts. Lead identified in the roots of land plants (e.g. E. andevalensis) was probably adsorbed by C-H functional groups identified by IR, being easily translocated to the aerial parts. It was found that aquatic plants are more efficient for phytostabilization than bioaccumulation. Lead is more bioavailable in the rhizosphere from Morocco mining areas due to scarcity of minerals with high adsorption ability, being absorbed and translocated by both aquatic and land plants.

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.

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.

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.

Nonlinear interferometry approach to photonic sequential logic

NASA Astrophysics Data System (ADS)

Mabuchi, Hideo

2011-10-01

Motivated by rapidly advancing capabilities for extensive nanoscale patterning of optical materials, I propose an approach to implementing photonic sequential logic that exploits circuit-scale phase coherence for efficient realizations of fundamental components such as a NAND-gate-with-fanout and a bistable latch. Kerr-nonlinear optical resonators are utilized in combination with interference effects to drive the binary logic. Quantum-optical input-output models are characterized numerically using design parameters that yield attojoule-scale energy separation between the latch states.

Random Boolean networks for autoassociative memory: Optimization and sequential learning

NASA Astrophysics Data System (ADS)

Sherrington, D.; Wong, K. Y. M.

Conventional neural networks are based on synaptic storage of information, even when the neural states are discrete and bounded. In general, the set of potential local operations is much greater. Here we discuss some aspects of the properties of networks of binary neurons with more general Boolean functions controlling the local dynamics. Two specific aspects are emphasised; (i) optimization in the presence of noise and (ii) a simple model for short-term memory exhibiting primacy and recency in the recall of sequentially taught patterns.

Dopamine reward prediction-error signalling: a two-component response

PubMed Central

Schultz, Wolfram

2017-01-01

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020

Assessing anthropogenic levels, speciation, and potential mobility of rare earth elements (REEs) in ex-tin mining area.

PubMed

Khan, Aysha Masood; Yusoff, Ismail; Bakar, Nor Kartini Abu; Bakar, Ahmad Farid Abu; Alias, Yatimah

2016-12-01

A study was carried out to determine the level of rare earth elements (REEs) in water and sediment samples from ex-mining lakes and River in Kinta Valley, Perak, Malaysia. Surface water and sediments from an ex-mining lake and Kinta River water samples were analyzed for REEs by inductively coupled plasma mass spectrometry. The total concentration of REEs in the ex-mining lake water samples and sediments were found to be 3685 mg/l and 14159 mg/kg, respectively, while the total concentration of REEs in Kinta River water sample was found to be 1224 mg/l. REEs in mining lake water were found to be within 2.42 mg/l (Tb) to 46.50 mg/l (Ce), while for the Kinta River, it was 1.33 mg/l (Ho) to 29.95 mg/l (Ce). Sediment samples were also found with REEs from 9.81 mg/kg (Ho) to 765.84 mg/kg (Ce). Ce showed the highest average concentrations for mining lake (3.88 to 49.08 mg/l) and Kinta River (4.44 to 33.15 mg/l) water samples, while the concentration of La was the highest (11.59 to 771.61 mg/kg) in the mining lake sediment. Lu was shown to have the highest enrichment of REEs in ex-mining lake sediments (107.3). Multivariate statistical analyses such as factor analysis and principal component analysis indicated that REEs were associated and controlled by mixed origin, with similar contributions from anthropogenic and geogenic sources. The speciation study of REEs in ex-tin mining sediments using a modified five-stage sequential extraction procedure indicated that yttrium (Y), gadolinium (Gd), and lanthanum (La) were obtained at higher percentages from the adsorbed/exchanged/carbonate fraction. The average potential mobility of the REEs was arranged in a descending order: Yb > Gd > Y = Dy > Pr > Er > Tm > Eu > Nd > Tb > Sc > Lu > Ce > La, implying that under favorable conditions, these REEs could be released and subsequently pollute the environment.

The Biogeochemistry of Indium, Gallium, and Germanium in Mine Wastes

NASA Astrophysics Data System (ADS)

White, S. J.; Schaider, L. A.; Shine, J. P.

2017-12-01

Indium (In), gallium (Ga), and germanium (Ge) are metals important in new energy technologies, and use of these metals is expanding dramatically. Humans are significantly impacting their natural cycling. Mining and smelting appear to be currently the largest sources of these metals to the environment, primarily because In, Ga, and Ge are byproducts of lead and zinc mining. The life cycle of these metals is poorly understood, including partitioning and speciation during mining processes, environmental behavior, and toxicity. The Tar Creek Superfund Site in Oklahoma, USA, is an abandoned Mississippi Valley-type lead and zinc mining area, containing sphalerite (ZnS) and galena (PbS). 30 major tailings piles remain in the area; elevated concentrations of lead (Pb), zinc (Zn), and cadmium (Cd) in these wastes have caused human health concerns. In order to better understand the biogeochemical cycling of In, Ga, and Ge associated with mining processes, we conducted geochemical and biological extractions of size-fractionated mine tailings from the Tar Creek site. Small tailings particles (<2.5 μm) contain higher concentrations of In, Ga, and Ge than large particles (>0.5 mm); a similar enrichment has been shown previously for Pb, Zn, and Cd. Ge is highly elevated in the mine wastes at this site; small particles contain up to 40x crustal concentrations. Ga and In are not significantly higher than crustal. (Crustal concentrations: Ge 1.4 mg/kg; Ga 14 mg/kg; In 100 mg/kg) While Pb, Zn, and Cd have been shown previously to be highly labile, and thus significantly re-worked from the original sulfide ore, sequential extractions suggest that In, Ga, and Ge are in less labile forms. In and Ga are liberated primarily from solutions that target semi-labile amorphous sulfides, Fe- and Mn-oxyhydroxides, and crystalline sulfide phases. By contrast, over 85% of the Ge in mine wastes from this site is bound in a residual mineral fraction (e.g. silicates) that is not liberated by a hot nitric acid leach. The bioaccessibility of In, Ga, and Ge also is significant - simulated gastric fluid extractions release 41-84% of each metal, suggesting that they do not reside in the ZnS or PbS phases. Future studies will further explore the cycling of Ga, Ge, and In at the Tar Creek site, including differences in speciation, mobility, and bioaccessibility of each.

Sequential and selective localized optical heating in water via on-chip dielectric nanopatterning.

PubMed

Morsy, Ahmed M; Biswas, Roshni; Povinelli, Michelle L

2017-07-24

We study the use of nanopatterned silicon membranes to obtain optically-induced heating in water. We show that by varying the detuning between an absorptive optical resonance of the patterned membrane and an illumination laser, both the magnitude and response time of the temperature rise can be controlled. This allows for either sequential or selective heating of different patterned areas on chip. We obtain a steady-state temperature of approximately 100 °C for a 805.5nm CW laser power density of 66 µW/μm 2 and observe microbubble formation. The ability to spatially and temporally control temperature on the microscale should enable the study of heat-induced effects in a variety of chemical and biological lab-on-chip applications.

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.

Spatiotemporal analysis of changes in lode mining claims around the McDermitt Caldera, northern Nevada and southern Oregon

USGS Publications Warehouse

Coyan, Joshua; Zientek, Michael L.; Mihalasky, Mark J.

2017-01-01

Resource managers and agencies involved with planning for future federal land needs are required to complete an assessment of and forecast for future land use every ten years. Predicting mining activities on federal lands is difficult as current regulations do not require disclosure of exploration results. In these cases, historic mining claims may serve as a useful proxy for determining where mining-related activities may occur. We assess the utility of using a space–time cube (STC) and associated analyses to evaluate and characterize mining claim activities around the McDermitt Caldera in northern Nevada and southern Oregon. The most significant advantage of arranging the mining claim data into a STC is the ability to visualize and compare the data, which allows scientists to better understand patterns and results. Additional analyses of the STC (i.e., Trend, Emerging Hot Spot, Hot Spot, and Cluster and Outlier Analyses) provide extra insights into the data and may aid in predicting future mining claim activities.

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.

Distribution of potentially toxic elements (PTEs) in tailings, soils, and plants around Gol-E-Gohar iron mine, a case study in Iran

PubMed Central

Soltani, Naghmeh; Keshavarzi, Behnam; Moore, Farid; Sorooshian, Armin; Ahmadi, Mohamad Reza

2017-01-01

This study investigated the concentration of potentially toxic elements (PTEs) including Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, V, and Zn in 102 soils (in the Near and Far areas of the mine), 7 tailings, and 60 plant samples (shoots and roots of Artemisia sieberi and Zygophylum species) collected at the Gol-E-Gohar iron ore mine in Iran. The elemental concentrations in tailings and soil samples (in Near and Far areas) varied between 7.4 and 35.8 mg kg−1 for As (with a mean of 25.39 mg kg−1 for tailings), 7.9 and 261.5 mg kg−1 (mean 189.83 mg kg−1 for tailings) for Co, 17.7 and 885.03 mg kg−1 (mean 472.77 mg kg−1 for tailings) for Cu, 12,500 and 400,000 mg kg−1 (mean 120,642.86 mg kg−1 for tailings) for Fe, and 28.1 and 278.1 mg kg−1 (mean 150.29 mg kg−1 for tailings) for Ni. A number of physicochemical parameters and pollution index for soils were determined around the mine. Sequential extractions of tailings and soil samples indicated that Fe, Cr, and Co were the least mobile and that Mn, Zn, Cu, and As were potentially available for plants uptake. Similar to soil, the concentration of Al, As, Co, Cr, Cu, Fe, Mn, Mo, Ni, and Zn in plant samples decreased with the distance from the mining/processing areas. Data on plants showed that metal concentrations in shoots usually exceeded those in roots and varied significantly between the two investigated species (Artemisia sieberi > Zygophylum). All the reported results suggest that the soil and plants near the iron ore mine are contaminated with PTEs and that they can be potentially dispersed in the environment via aerosol transport and deposition. PMID:28620857

Tube bundle system studies at Signal Peak Energy Bull Mountains #1 Mine

PubMed Central

Zipf, R.K.; Ochsner, R.; Krog, R.; Marchewka, W.; Valente, M.; Jensen, R.

2015-01-01

A tube bundle system (TBS) is a mechanical system for continuously drawing gas samples through tubes from multiple monitoring points located in an underground coal mine for analysis and display on the surface. The U.S. National Institute for Occupational Safety and Health (NIOSH), in collaboration with Signal Peak Energy (SPE), LLC, Bull Mountains No. 1 Mine, operated a TBS during mining of two bleederless, longwall panels. This paper describes the gas analysis data and its interpretation. As verified by the TBS, coal at the SPE mine tends to oxidize slowly. It was known that a reservoir of low-oxygen concentration atmosphere developed about 610 m (2,000 ft) behind the longwall face. A bleederless ventilation system facilitates formation of an inert atmosphere in this longwall gob and decreases the likelihood of spontaneous combustion. Connections of the mine atmosphere to the surface through subsidence cracks could allow airflow into the longwall gob, revive coal oxidation and increase spontaneous combustion risk. The atmospheric composition of the sealed areas was homogeneous, except in the immediate vicinity of suspected ingassing points. The TBS verified that gases within the partially sealed, bleederless longwall gob expanded into the longwall tailgate area when barometric pressure decreased. The concentration of carbon dioxide in the back return airflow at the longwall tailgate was observed to increase by a factor of three and possibly up to 10 times the typical background concentration of 0.5 to 1.0%, depending on the size of the longwall gob and the magnitude of barometric pressure decrease. TBS have the inherent disadvantage of slow response time due to travel time of the gas samples and sequential gas analyses. A TBS or similar continuous monitoring system could be beneficial in detecting and providing warning of potentially hazardous gas concentrations, if the slow response time of the system is always understood. PMID:26306075

Tube bundle system studies at Signal Peak Energy Bull Mountains #1 Mine.

PubMed

Zipf, R K; Ochsner, R; Krog, R; Marchewka, W; Valente, M; Jensen, R

2014-03-01

A tube bundle system (TBS) is a mechanical system for continuously drawing gas samples through tubes from multiple monitoring points located in an underground coal mine for analysis and display on the surface. The U.S. National Institute for Occupational Safety and Health (NIOSH), in collaboration with Signal Peak Energy (SPE), LLC, Bull Mountains No. 1 Mine, operated a TBS during mining of two bleederless, longwall panels. This paper describes the gas analysis data and its interpretation. As verified by the TBS, coal at the SPE mine tends to oxidize slowly. It was known that a reservoir of low-oxygen concentration atmosphere developed about 610 m (2,000 ft) behind the longwall face. A bleederless ventilation system facilitates formation of an inert atmosphere in this longwall gob and decreases the likelihood of spontaneous combustion. Connections of the mine atmosphere to the surface through subsidence cracks could allow airflow into the longwall gob, revive coal oxidation and increase spontaneous combustion risk. The atmospheric composition of the sealed areas was homogeneous, except in the immediate vicinity of suspected ingassing points. The TBS verified that gases within the partially sealed, bleederless longwall gob expanded into the longwall tailgate area when barometric pressure decreased. The concentration of carbon dioxide in the back return airflow at the longwall tailgate was observed to increase by a factor of three and possibly up to 10 times the typical background concentration of 0.5 to 1.0%, depending on the size of the longwall gob and the magnitude of barometric pressure decrease. TBS have the inherent disadvantage of slow response time due to travel time of the gas samples and sequential gas analyses. A TBS or similar continuous monitoring system could be beneficial in detecting and providing warning of potentially hazardous gas concentrations, if the slow response time of the system is always understood.

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.

Comprehensive waste characterization and organic pollution co-occurrence in a Hg and As mining and metallurgy brownfield.

PubMed

Gallego, J R; Esquinas, N; Rodríguez-Valdés, E; Menéndez-Aguado, J M; Sierra, C

2015-12-30

The abandonment of Hg-As mining and metallurgy sites, together with long-term weathering, can dramatically degrade the environment. In this work it is exemplified the complex legacy of contamination that afflicts Hg-As brownfields through the detailed study of a paradigmatic site. Firstly, an in-depth study of the former industrial process was performed to identify sources of different types of waste. Subsequently, the composition and reactivity of As- and Hg-rich wastes (calcines, As-rich soot, stupp, and flue dust) was analyzed by means of multielemental analysis, mineralogical characterization (X-ray diffraction, electronic, and optical microscopy, microbrobe), chemical speciation, and sequential extractions. As-rich soot in the form of arsenolite, a relatively mobile by-product of the pyrometallurgical process, and stupp, a residue originated in the former condensing system, were determined to be the main risk at the site. In addition, the screening of organic pollution was also aimed, as shown by the outcome of benzo(a) pyrene and other PAHs, and by the identification of unexpected Hg organo-compounds (phenylmercury propionate). The approach followed unravels evidence from waste from the mining and metallurgy industry that may be present in other similar sites, and identifies unexpected contaminants overlooked by conventional analyses. Copyright © 2015 Elsevier B.V. All rights reserved.

Mineralogical characteristics of sediments and heavy metal mobilization along a river watershed affected by acid mine drainage.

PubMed

Xie, Yingying; Lu, Guining; Yang, Chengfang; Qu, Lu; Chen, Meiqin; Guo, Chuling; Dang, Zhi

2018-01-01

Trace-element concentrations in acid mine drainage (AMD) are primarily controlled by the mineralogy at the sediment-water interface. Results are presented for a combined geochemical and mineralogical survey of Dabaoshan Mine, South China. Developed sequential extraction experiments with the analysis of the main mineralogical phases by semi-quantitative XRD, differential X-ray diffraction (DXRD) and scanning electron microscopy (SEM) were conducted to identify the quantitative relationship between iron minerals and heavy metals. Results showed that schwertmannite, jarosite, goethite and ferrihydrite were the dominant Fe-oxyhydroxide minerals which were detected alternately in the surface sediment with the increasing pH from 2.50 to 6.93 along the Hengshi River. Decreasing contents of schwertmannite ranging from 35 wt % to 6.5 wt % were detected along the Hengshi River, which was corresponding to the decreasing metal contents. The easily reducible fractions exert higher affinity of metals while compared with reducible and relatively stable minerals. A qualitative analysis of heavy metals extracted from the sediments indicated that the retention ability varied: Pb > Mn > Zn > As ≈ Cu > Cr > Cd ≈ Ni. Results in this study are avail for understanding the fate and transport of heavy metals associated with iron minerals and establishing the remediation strategies of AMD systems.

Mineralogical characteristics of sediments and heavy metal mobilization along a river watershed affected by acid mine drainage

PubMed Central

Xie, Yingying; Yang, Chengfang; Qu, Lu; Chen, Meiqin; Guo, Chuling; Dang, Zhi

2018-01-01

Trace-element concentrations in acid mine drainage (AMD) are primarily controlled by the mineralogy at the sediment-water interface. Results are presented for a combined geochemical and mineralogical survey of Dabaoshan Mine, South China. Developed sequential extraction experiments with the analysis of the main mineralogical phases by semi-quantitative XRD, differential X-ray diffraction (DXRD) and scanning electron microscopy (SEM) were conducted to identify the quantitative relationship between iron minerals and heavy metals. Results showed that schwertmannite, jarosite, goethite and ferrihydrite were the dominant Fe-oxyhydroxide minerals which were detected alternately in the surface sediment with the increasing pH from 2.50 to 6.93 along the Hengshi River. Decreasing contents of schwertmannite ranging from 35 wt % to 6.5 wt % were detected along the Hengshi River, which was corresponding to the decreasing metal contents. The easily reducible fractions exert higher affinity of metals while compared with reducible and relatively stable minerals. A qualitative analysis of heavy metals extracted from the sediments indicated that the retention ability varied: Pb > Mn > Zn > As ≈ Cu > Cr > Cd ≈ Ni. Results in this study are avail for understanding the fate and transport of heavy metals associated with iron minerals and establishing the remediation strategies of AMD systems. PMID:29304091

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

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu; Huang, Jyun-Hao

2013-08-01

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

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.

Perception of Randomness: On the Time of Streaks

ERIC Educational Resources Information Center

Sun, Yanlong; Wang, Hongbin

2010-01-01

People tend to think that streaks in random sequential events are rare and remarkable. When they actually encounter streaks, they tend to consider the underlying process as non-random. The present paper examines the time of pattern occurrences in sequences of Bernoulli trials, and shows that among all patterns of the same length, a streak is the…

Applying Learning Analytics to Explore the Effects of Motivation on Online Students' Reading Behavioral Patterns

ERIC Educational Resources Information Center

Sun, Jerry Chih-Yuan; Lin, Che-Tsun; Chou, Chien

2018-01-01

This study aims to apply a sequential analysis to explore the effect of learning motivation on online reading behavioral patterns. The study's participants consisted of 160 graduate students who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration…

Patterns of Teacher's Instructional Moves: What Makes Mathematical Instructional Practices Unique?

ERIC Educational Resources Information Center

Pinter, Holly Henderson

2013-01-01

The purpose of this study was to examine patterns in fourth-grade teachers' use of instructional moves in the implementation of standards-based mathematical teaching practices. Using a mixed methods sequential explanatory design, the study consisted of two phases: quantitative selection and qualitative analysis. The first phase of the study…

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…

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.

A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors.

PubMed

Le-Thi-Thu, Huong; Casanola-Martín, Gerardo M; Marrero-Ponce, Yovani; Rescigno, Antonio; Abad, Concepcion; Khan, Mahmud Tareq Hassan

2014-01-01

The tyrosinase is a bifunctional, copper-containing enzyme widely distributed in the phylogenetic tree. This enzyme is involved in the production of melanin and some other pigments in humans, animals and plants, including skin pigmentations in mammals, and browning process in plants and vegetables. Therefore, enzyme inhibitors has been under the attention of the scientist community, due to its broad applications in food, cosmetic, agricultural and medicinal fields, to avoid the undesirable effects of abnormal melanin overproduction. However, the research of novel chemical with antityrosinase activity demands the use of more efficient tools to speed up the tyrosinase inhibitors discovery process. This chapter is focused in the different components of a predictive modeling workflow for the identification and prioritization of potential new compounds with activity against the tyrosinase enzyme. In this case, two structure chemical libraries Spectrum Collection and Drugbank are used in this attempt to combine different virtual screening data mining techniques, in a sequential manner helping to avoid the usually expensive and time consuming traditional methods. Some of the sequential steps summarize here comprise the use of drug-likeness filters, similarity searching, classification and potency QSAR multiclassifier systems, modeling molecular interactions systems, and similarity/diversity analysis. Finally, the methodologies showed here provide a rational workflow for virtual screening hit analysis and selection as a promissory drug discovery strategy for use in target identification phase.

Transportation forecasting : analysis and quantitative methods

DOT National Transportation Integrated Search

1983-01-01

This Record contains the following papers: Development of Survey Instruments Suitable for Determining Non-Home Activity Patterns; Sequential, History-Dependent Approach to Trip-Chaining Behavior; Identifying Time and History Dependencies of Activity ...

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.

Sequential or parallel decomposed processing of two-digit numbers? Evidence from eye-tracking.

PubMed

Moeller, Korbinian; Fischer, Martin H; Nuerk, Hans-Christoph; Willmes, Klaus

2009-02-01

While reaction time data have shown that decomposed processing of two-digit numbers occurs, there is little evidence about how decomposed processing functions. Poltrock and Schwartz (1984) argued that multi-digit numbers are compared in a sequential digit-by-digit fashion starting at the leftmost digit pair. In contrast, Nuerk and Willmes (2005) favoured parallel processing of the digits constituting a number. These models (i.e., sequential decomposition, parallel decomposition) make different predictions regarding the fixation pattern in a two-digit number magnitude comparison task and can therefore be differentiated by eye fixation data. We tested these models by evaluating participants' eye fixation behaviour while selecting the larger of two numbers. The stimulus set consisted of within-decade comparisons (e.g., 53_57) and between-decade comparisons (e.g., 42_57). The between-decade comparisons were further divided into compatible and incompatible trials (cf. Nuerk, Weger, & Willmes, 2001) and trials with different decade and unit distances. The observed fixation pattern implies that the comparison of two-digit numbers is not executed by sequentially comparing decade and unit digits as proposed by Poltrock and Schwartz (1984) but rather in a decomposed but parallel fashion. Moreover, the present fixation data provide first evidence that digit processing in multi-digit numbers is not a pure bottom-up effect, but is also influenced by top-down factors. Finally, implications for multi-digit number processing beyond the range of two-digit numbers are discussed.

The effect of abandoned mining ponds on trace elements dynamics in the soil-plant system

NASA Astrophysics Data System (ADS)

Gabarrón, María; Faz, Ángel; Zornoza, Raúl; Acosta, Jose A.

2017-04-01

In semiarid climate regions lack of vegetation and dryer climate contribute to erosion of abandoned mining surface areas making them up important potential sources of metal pollution into the environment. The objectives of this study were to determine the influence of mine ponds in agriculture and forest soils, and identify the dynamic of metals in the soil-plant system for native plant species (Ballota hirsuta) and crop species (Hordeum vulgare) in two ancient mining districts: La Unión and Mazarrón. To achieve these objectives, wastes samples from mine ponds and soil samples (rhizosphere and non-rhizosphere soils) from natural and agricultural lands were collected. In addition, six plants (Ballota hirsuta) from natural area and 3 plants (Hordeum vulgare) from crops were collected. Physicochemical properties and total, water soluble and bioavailable metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) and arsenic were measured in waste/soil samples. The chemical speciation of metals in soil was estimated by a sequential extraction procedure. For plants analyses, each plant were divided in roots, stem and leaves and metal content measured by ICP-MS. Results indicated that mine, natural and agricultural soils were contaminated by As, Cd, Cu, Pb, and Zn. Chemical partitioning revealed higher mobility of metals in mine ponds than natural and agriculture soils while only Fe and As are completely bound to the soil matrix due to the mineralogical compositions of soils. The accumulation of metals in Ballota hirsuta in La Union decrease as Fe>As>Cr>Ni>Cu>Zn>Cd>Mn>Co>Pb while in Mazarrón did as As>Fe>Cr>Pb>Cu>Ni>Co>Mn>Zn>Cd. Ballota hirsuta showed high ability to bio-accumulate Cu, Cr, Fe, Ni, and As, transferring a large amount to edible parts without exceeding the toxicity limits for animals. Results for barley plants (Hordeum vulgare) showed the ability to absorb and accumulate As, Fe, Mn, Pb and Zn, although the transfer ability of As, Cd and Pb was lower. Although the behavior of metals reflects a root barrier effect, the amount of Pb in grain overreached the permissible limit in aliments.

Acid neutralizing capacity and leachate results for igneous rocks, with associated carbon contents of derived soils, Animas River AML site, Silverton, Colorado

USGS Publications Warehouse

Yager, Douglas B.; Stanton, Mark R.; Choate, LaDonna M.; Burchell,

2009-01-01

Mine planning efforts have historically overlooked the possible acid neutralizing capacity (ANC) that local igneous rocks can provide to help neutralize acidmine drainage. As a result, limestone has been traditionally hauled to mine sites for use in neutralizing acid drainage. Local igneous rocks, when used as part of mine life-cycle planning and acid mitigation strategy, may reduce the need to transport limestone to mine sites because these rocks can contain acid neutralizing minerals. Igneous hydrothermal events often introduce moderately altered mineral assemblages peripheral to more intensely altered rocks that host metal-bearing veins and ore bodies. These less altered rocks can contain ANC minerals (calcite-chlorite-epidote) and are referred to as a propylitic assemblage. In addition, the carbon contents of soils in areas of new mining or those areas undergoing restoration have been historically unknown. Soil organic carbon is an important constituent to characterize as a soil recovery benchmark that can be referred to during mine cycle planning and restoration.
This study addresses the mineralogy, ANC, and leachate chemistry of propylitic volcanic rocks that host polymetallic mineralization in the Animas River watershed near the historical Silverton, Colorado, mining area. Acid titration tests on volcanic rocks containing calcite (2 – 20 wt %) and chlorite (6 – 25 wt %), have ANC ranging from 4 – 146 kg/ton CaCO₃ equivalence. Results from a 6-month duration, kinetic reaction vessel test containing layered pyritic mine waste and underlying ANC volcanic rock (saturated with deionized water) indicate that acid generating mine waste (pH 2.4) has not overwhelmed the ANC of propylitic volcanic rocks (pH 5.8). Sequential leachate laboratory experiments evaluated the concentration of metals liberated during leaching. Leachate concentrations of Cu-Zn-As-Pb for ANC volcanic rock are one-to-three orders of magnitude lower when compared to leached solution from mine waste used in the kinetic reaction vessel test. This finding suggests that mine waste and not ANC rock may generate the majority of leachable metals in a field scenario.
The organic carbon content of naturally reclaimed soils derived from weathering of propylitically-altered andesite was determined in catchments where ANC studies were initiated. Soils were found to have total carbon concentrations (TOC) that exceed global average soil TOC abundances by as much as 1.5 – 5 times. These data support an environmental management system involving use of ANC rocks as part of life-cycle mine planning to reduce post-mine closure acid mitigation measures. Carbon contents of undisturbed soils in mined catchments can possibly be used to validate post-reclamation success and help quantify carbon sequestration for CO₂ emission offset trading as carbon markets mature.

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.

IgG and IgM anti-snRNP reactivity in sequentially obtained serum samples from patients with connective tissue diseases.

PubMed Central

Nyman, U; Lundberg, I; Hedfors, E; Wahren, M; Pettersson, I

1992-01-01

Sequentially obtained serum samples from 30 patients with connective tissue disease positive for antibody to ribonucleoprotein (RNP) were examined to determine the specificities of IgG and IgM antibodies to snRNP during the disease course using immunoblotting of nuclear extracts. The antibody patterns were correlated with disease activity. The patterns of antibody to snRNP of individual patients were mainly stable during the study but changes in levels of antibody to snRNP were seen corresponding to changes in clinical activity. These results indicate that increased reactivity of serum IgM antibodies against the B/B' proteins seems to precede a clinically evident exacerbation of disease whereas IgG antibody reactivity to the 70 K protein peaks at the time of a disease flare. Images PMID:1485812

Concerted vs. Sequential. Two Activation Patterns of Vast Arrays of Intracellular Ca2+ Channels in Muscle

PubMed Central

Zhou, Jinsong; Brum, Gustavo; González, Adom; Launikonis, Bradley S.; Stern, Michael D.; Ríos, Eduardo

2005-01-01

To signal cell responses, Ca2+ is released from storage through intracellular Ca2+ channels. Unlike most plasmalemmal channels, these are clustered in quasi-crystalline arrays, which should endow them with unique properties. Two distinct patterns of local activation of Ca2+ release were revealed in images of Ca2+ sparks in permeabilized cells of amphibian muscle. In the presence of sulfate, an anion that enters the SR and precipitates Ca2+, sparks became wider than in the conventional, glutamate-based solution. Some of these were “protoplatykurtic” (had a flat top from early on), suggesting an extensive array of channels that activate simultaneously. Under these conditions the rate of production of signal mass was roughly constant during the rise time of the spark and could be as high as 5 μm3 ms−1, consistent with a release current >50 pA since the beginning of the event. This pattern, called “concerted activation,” was observed also in rat muscle fibers. When sulfate was combined with a reduced cytosolic [Ca2+] (50 nM) these sparks coexisted (and interfered) with a sequential progression of channel opening, probably mediated by Ca2+-induced Ca2+ release (CICR). Sequential propagation, observed only in frogs, may require parajunctional channels, of RyR isoform β, which are absent in the rat. Concerted opening instead appears to be a property of RyR α in the amphibian and the homologous isoform 1 in the mammal. PMID:16186560

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

PubMed

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

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.

Sequential Multiplex Analyte Capturing for Phosphoprotein Profiling*

PubMed Central

Poetz, Oliver; Henzler, Tanja; Hartmann, Michael; Kazmaier, Cornelia; Templin, Markus F.; Herget, Thomas; Joos, Thomas O.

2010-01-01

Microarray-based sandwich immunoassays can simultaneously detect dozens of proteins. However, their use in quantifying large numbers of proteins is hampered by cross-reactivity and incompatibilities caused by the immunoassays themselves. Sequential multiplex analyte capturing addresses these problems by repeatedly probing the same sample with different sets of antibody-coated, magnetic suspension bead arrays. As a miniaturized immunoassay format, suspension bead array-based assays fulfill the criteria of the ambient analyte theory, and our experiments reveal that the analyte concentrations are not significantly changed. The value of sequential multiplex analyte capturing was demonstrated by probing tumor cell line lysates for the abundance of seven different receptor tyrosine kinases and their degree of phosphorylation and by measuring the complex phosphorylation pattern of the epidermal growth factor receptor in the same sample from the same cavity. PMID:20682761

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.

Decision Aids for Naval Air ASW

DTIC Science & Technology

1980-03-15

Algorithm for Zone Optimization Investigation) NADC Developing Sonobuoy Pattern for Air ASW Search DAISY (Decision Aiding Information System) Wharton...sion making behavior. 0 Artificial intelligence sequential pattern recognition algorithm for reconstructing the decision maker’s utility functions. 0...display presenting the uncertainty area of the target. 3.1.5 Algorithm for Zone Optimization Investigation (AZOI) -- Naval Air Development Center 0 A

From Monochrome to Technicolor: Simple Generic Approaches to Multicomponent Protein Nanopatterning Using Siloxanes with Photoremovable Protein-Resistant Protecting Groups

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

El Zubir, Osama; Xia, Sijing; Ducker, Robert E.

We show that sequential protein deposition is possible by photodeprotection of films formed from a tetraethylene-glycol functionalized nitrophenylethoxycarbonyl-protected aminopropyltriethoxysilane (NPEOC-APTES). Exposure to near-UV irradiation removes the protein-resistant protecting group, and allows protein adsorption onto the resulting aminated surface. The protein resistance was tested using proteins with fluorescent labels and microspectroscopy of two-component structures formed by micro- and nanopatterning and deposition of yellow and green fluorescent proteins (YFP/GFP). Nonspecific adsorption onto regions where the protecting group remained intact was negligible. Multiple component patterns were also formed by near-field methods. Because reading and writing can be decoupled in a near-field microscope, itmore » is possible to carry out sequential patterning steps at a single location involving different proteins. Up to four different proteins were formed into geometric patterns using near-field lithography. Interferometric lithography facilitates the organization of proteins over square cm areas. Two-component patterns consisting of 150 nm streptavidin dots formed within an orthogonal grid of bars of GFP at a period of ca. 500 nm could just be resolved by fluorescence microscopy.« less

From Monochrome to Technicolor: Simple Generic Approaches to Multicomponent Protein Nanopatterning Using Siloxanes with Photoremovable Protein-Resistant Protecting Groups

DOE PAGES

El Zubir, Osama; Xia, Sijing; Ducker, Robert E.; ...

2017-05-27

We show that sequential protein deposition is possible by photodeprotection of films formed from a tetraethylene-glycol functionalized nitrophenylethoxycarbonyl-protected aminopropyltriethoxysilane (NPEOC-APTES). Exposure to near-UV irradiation removes the protein-resistant protecting group, and allows protein adsorption onto the resulting aminated surface. The protein resistance was tested using proteins with fluorescent labels and microspectroscopy of two-component structures formed by micro- and nanopatterning and deposition of yellow and green fluorescent proteins (YFP/GFP). Nonspecific adsorption onto regions where the protecting group remained intact was negligible. Multiple component patterns were also formed by near-field methods. Because reading and writing can be decoupled in a near-field microscope, itmore » is possible to carry out sequential patterning steps at a single location involving different proteins. Up to four different proteins were formed into geometric patterns using near-field lithography. Interferometric lithography facilitates the organization of proteins over square cm areas. Two-component patterns consisting of 150 nm streptavidin dots formed within an orthogonal grid of bars of GFP at a period of ca. 500 nm could just be resolved by fluorescence microscopy.« less

Sequential dependencies in recall of sequences: filling in the blanks.

PubMed

Farrell, Simon; Hurlstone, Mark J; Lewandowsky, Stephan

2013-08-01

Sequential dependencies can provide valuable information about the processes supporting memory, particularly memory for serial order. Earlier analyses have suggested that anticipation errors-reporting items ahead of their correct position in the sequence-tend to be followed by recall of the displaced item, consistent with primacy gradient models of serial recall. However, a more recent analysis instead suggests that anticipation errors are followed by further anticipation errors, consistent with chaining models. We report analyses of 21 conditions from published serial recall data sets, in which we observed a systematic pattern whereby anticipations tended to be followed by the "filling in" of displaced items. We note that cases where a different pattern held tended to apply to recall of longer lists under serial learning conditions or to conditions where participants were free to skip over items. Although the different patterns that can be observed might imply a dissociation (e.g., between short- and long-term memory), we show that these different patterns are naturally predicted by Farrell's (Psychological Review 119:223-271, 2012) model of short-term and episodic memory and relate to whether or not spontaneously formed groups of items can be skipped over during recall.

[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

Arsenic geochemistry of alluvial sediments and pore waters affected by mine tailings along the Belle Fourche and Cheyenne River floodplains

USGS Publications Warehouse

Pfeifle, Bryce D.; Stamm, John F.; Stone, James J.

2018-01-01

Gold mining operations in the northern Black Hills of South Dakota resulted in the discharge of arsenopyrite-bearing mine tailings into Whitewood Creek from 1876 to 1977. Those tailings were transported further downstream along the Belle Fourche River, the Cheyenne River, and the Missouri River. An estimated 110 million metric tons of tailings remain stored in alluvial deposits of the Belle Fourche and Cheyenne Rivers. Pore-water dialysis samplers were deployed in the channel and backwaters of the Belle Fourche and Cheyenne Rivers to determine temporal and seasonal changes in the geochemistry of groundwater in alluvial sediments. Alluvial sediment adjacent to the dialysis samplers were cored for geochemical analysis. In comparison to US Environmental Protection Agency drinking water standards and reference concentrations of alluvial sediment not containing mine tailings, the Belle Fourche River sites had elevated concentrations of arsenic in pore water (2570 μg/L compared to 10 μg/L) and sediment (1010 ppm compared to < 34 ppm), respectively. Pore water arsenic concentration was affected by dissolution of iron oxyhydroxides under reducing conditions. Sequential extraction of iron and arsenic from sediment cores indicates that substantial quantities of soluble metals were present. Dissolution of arsenic sorbed to alluvial sediment particles appears to be affected by changing groundwater levels that cause shifts in redox conditions. Bioreductive processes did not appear to be a substantial transport pathway but could affect speciation of arsenic, especially at the Cheyenne River sampling sites where microbial activity was determined to be greater than at Belle Fourche sampling sites.

Speciation of mercury and mode of transport from placer gold mine tailings

USGS Publications Warehouse

Slowey, A.J.; Rytuba, J.J.; Brown, Gordon E.

2005-01-01

Historic placer gold mining in the Clear Creek tributary to the Sacramento River (Redding, CA) has highly impacted the hydrology and ecology of an important salmonid spawning stream. Restoration of the watershed utilized dredge tailings contaminated with mercury (Hg) introduced during gold mining, posing the possibility of persistent Hg release to the surrounding environment, including the San Francisco Bay Delta. Column experiments have been performed to evaluate the extent of Hg transport under chemical conditions potentially similar to those in river restoration projects utilizing dredge tailings such as at Clear Creek. Physicochemical perturbations, in the form of shifts in column influent ionic strength and the presence of a low molecular weight organic acid, were applied to coarse and fine sand placer tailings containing 109-194 and 69-90 ng of Hg/g, respectively. Significant concentrations of mercury, up to 16 ??g/L, leach from these sediments in dissolved and particle-associated forms. Sequential chemical extractions (SCE) of these tailings indicate that elemental Hg initially introduced during gold mining has been transformed to readily soluble species, such as mercury oxides and chlorides (3-4%), intermediately extractable phases that likely include (in)organic sorption complexes and amalgams (75-87%), and fractions of highly insoluble forms such as mercury sulfides (6-20%; e.g., cinnabar and metacinnabar). Extended X-ray absorption fine structure (EXAFS) spectroscopic analysis of colloids obtained from column effluent identified cinnabar particles as the dominant mobile mercury-bearing phase. The fraction of intermediately extractable Hg phases also likely includes mobile colloids to which Hg is adsorbed. ?? 2005 American Chemical Society.

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.

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

[Sequential prescriptions: Arguments for a change of therapeutic patterns in treatment resistant depressions].

PubMed

Allouche, G

2016-02-01

Among the therapeutic strategies in treatment of resistant depression, the use of sequential prescriptions is discussed here. A number of observations, initially quite isolated and few controlled studies, some large-scale, have been reported, which showed a definite therapeutic effect of certain requirements in sequential treatment of depression. The Sequenced Treatment Alternatives to Relieve Depression Study (STAR*D) is up to now the largest clinical trial exploring treatment strategies in non psychotic resistant depression in real-life conditions with an algorithm of sequential decision. The main conclusions of this study are the following: after two unsuccessful attempts, the chance of remission decreases considerably. A 12-months follow-up showed that the higher the use of the processing steps were high, the more common the relapses were during this period. The pharmacological differences between psychotropic did not cause clinically significant difference. The positive effect of lithium in combination with antidepressants has been known since the work of De Montigny. Antidepressants allow readjustment of physiological sequence involving different monoaminergic systems together. Studies with tricyclic antidepressant-thyroid hormone T3: in depression, decreased norepinephrine at the synaptic receptors believed to cause hypersensitivity of these receptors. Thyroid hormones modulate the activity of adrenergic receptors. There would be a balance of activity between alpha and beta-adrenergic receptors, depending on the bioavailability of thyroid hormones. ECT may in some cases promote pharmacological response in case of previous resistance, or be effective in preventing relapse. Cognitive therapy and antidepressant medications likely have an effect on different types of depression. We can consider the interest of cognitive therapy in a sequential pattern after effective treatment with an antidepressant effect for treatment of residual symptoms, preventing relapses and recurrences, in antidepressant maintenance. These data support the interest of therapeutic strategies based on evolutionary criteria. Sequential models inspired by statistical methods may incorporate the effects of a future treatment by measuring the current one. Copyright © 2015 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.

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.

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

Submicrometer Metallic Barcodes

NASA Astrophysics Data System (ADS)

Nicewarner-Peña, Sheila R.; Freeman, R. Griffith; Reiss, Brian D.; He, Lin; Peña, David J.; Walton, Ian D.; Cromer, Remy; Keating, Christine D.; Natan, Michael J.

2001-10-01

We synthesized multimetal microrods intrinsically encoded with submicrometer stripes. Complex striping patterns are readily prepared by sequential electrochemical deposition of metal ions into templates with uniformly sized pores. The differential reflectivity of adjacent stripes enables identification of the striping patterns by conventional light microscopy. This readout mechanism does not interfere with the use of fluorescence for detection of analytes bound to particles by affinity capture, as demonstrated by DNA and protein bioassays.

Integrated approach to assess the environmental impact of mining activities: estimation of the spatial distribution of soil contamination (Panasqueira mining area, Central Portugal).

PubMed

Candeias, Carla; Ávila, Paula F; Ferreira da Silva, Eduardo; Teixeira, João Paulo

2015-03-01

Through the years, mining and beneficiation processes in Panasqueira Sn-W mine (Central Portugal) produced large amounts of As-rich mine wastes laid up in huge tailings and open-air impoundments (Barroca Grande and Rio tailings) that are the main source of pollution in the surrounding area once they are exposed to the weathering conditions leading to the formation of acid mine drainage (AMD) and consequently to the contamination of the surrounding environments, particularly soils. The active mine started the exploration during the nineteenth century. This study aims to look at the extension of the soil pollution due to mining activities and tailing erosion by combining data on the degree of soil contamination that allows a better understanding of the dynamics inherent to leaching, transport, and accumulation of some potential toxic elements in soil and their environmental relevance. Soil samples were collected in the surrounding soils of the mine, were digested in aqua regia, and were analyzed for 36 elements by inductively coupled plasma mass spectrometry (ICP-MS). Selected results are that (a) an association of elements like Ag, As, Bi, Cd, Cu, W, and Zn strongly correlated and controlled by the local sulfide mineralization geochemical signature was revealed; (b) the global area discloses significant concentrations of As, Bi, Cd, and W linked to the exchangeable and acid-soluble bearing phases; and (c) wind promotes the mechanical dispersion of the rejected materials, from the milled waste rocks and the mineral processing plant, with subsequent deposition on soils and waters. Arsenic- and sulfide-related heavy metals (such as Cu and Cd) are associated to the fine materials that are transported in suspension by surface waters or associated to the acidic waters, draining these sites and contaminating the local soils. Part of this fraction, especially for As, Cd, and Cu, is temporally retained in solid phases by precipitation of soluble secondary minerals (through the precipitation of hydrated metal sulfates) in warm, dry periods, but such minerals are easily dissolved during rainy periods. Climate is an important instability factor, and the hot and dry summers and cold, rainy, and windy winters in this region are physical phenomena that enhance the good receptivity of these soils to retain some of the metals present in the primary and also the secondary mineralogy. Considering the obtained results from both the sequential chemical extraction and the environmental risk assessment according to the risk assessment code, Ag, Cd, Cu, and Zn are classified with very high risk while As is classified with medium risk.

Thinking in clinical nursing practice: a study of critical care nurses' thinking applying the think-aloud, protocol analysis method.

PubMed

Han, Kyung-Ja; Kim, Hesook Suzie; Kim, Mae-Ja; Hong, Kyung-Ja; Park, Sungae; Yun, Soon-Nyoung; Song, Misoon; Jung, Yoenyi; Kim, Haewon; Kim, Dong-Oak Debbie; Choi, Heejung; Kim, Kyungae

2007-06-01

The purpose of the paper is to discover the patterns and processes of decision-making in clinical nursing practice. A set of think-aloud data from five critical care nurses during 40 to 50 minutes of caregiving in intensive care units were obtained and analyzed by applying the procedures recommended by Ericsson and Simon for protocol analysis. Four thinking processes before acting were identified to constitute various sorts of thoughts in which the nurses were engaged during patient care: reviewing, validation, consideration, rationalization, and action. In addition, three patterns of sequential streaming of thinking (short, intermediate, long) were identified to reveal various ways the nurses dealt with clinical situations involving nursing tasks and responsibilities. This study specifies the initial categories of thoughts for each of the processes and various patterns with which these processes are sequentially combined, providing insights into the ways nurses think about problems and address their concerns. The findings suggest that the thinking in clinical practice involves more than focused decision-making and reasoning, and needs to be examined from a broader perspective.

Evidence for the temporal regulation of insect segmentation by a conserved sequence of transcription factors

PubMed Central

2018-01-01

ABSTRACT Long-germ insects, such as the fruit fly Drosophila melanogaster, pattern their segments simultaneously, whereas short-germ insects, such as the beetle Tribolium castaneum, pattern their segments sequentially, from anterior to posterior. Although the two modes of segmentation at first appear quite distinct, much of this difference might simply reflect developmental heterochrony. We now show here that, in both Drosophila and Tribolium, segment patterning occurs within a common framework of sequential Caudal, Dichaete and Odd-paired expression. In Drosophila, these transcription factors are expressed like simple timers within the blastoderm, whereas in Tribolium they form wavefronts that sweep from anterior to posterior across the germband. In Drosophila, all three are known to regulate pair-rule gene expression and influence the temporal progression of segmentation. We propose that these regulatory roles are conserved in short-germ embryos, and that therefore the changing expression profiles of these genes across insects provide a mechanistic explanation for observed differences in the timing of segmentation. In support of this hypothesis, we demonstrate that Odd-paired is essential for segmentation in Tribolium, contrary to previous reports. PMID:29724758

Sleep to the beat: A nap favours consolidation of timing.

PubMed

Verweij, Ilse M; Onuki, Yoshiyuki; Van Someren, Eus J W; Van der Werf, Ysbrand D

2016-06-01

Growing evidence suggests that sleep is important for procedural learning, but few studies have investigated the effect of sleep on the temporal aspects of motor skill learning. We assessed the effect of a 90-min day-time nap on learning a motor timing task, using 2 adaptations of a serial interception sequence learning (SISL) task. Forty-two right-handed participants performed the task before and after a 90-min period of sleep or wake. Electroencephalography (EEG) was recorded throughout. The motor task consisted of a sequential spatial pattern and was performed according to 2 different timing conditions, that is, either following a sequential or a random temporal pattern. The increase in accuracy was compared between groups using a mixed linear regression model. Within the sleep group, performance improvement was modeled based on sleep characteristics, including spindle- and slow-wave density. The sleep group, but not the wake group, showed improvement in the random temporal, but especially and significantly more strongly in the sequential temporal condition. None of the sleep characteristics predicted improvement on either general of the timing conditions. In conclusion, a daytime nap improves performance on a timing task. We show that performance on the task with a sequential timing sequence benefits more from sleep than motor timing. More important, the temporal sequence did not benefit initial learning, because differences arose only after an offline period and specifically when this period contained sleep. Sleep appears to aid in the extraction of regularities for optimal subsequent performance. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

In vivo detection of 13C isotopomer turnover in the human brain by sequential infusion of 13C labeled substrates

NASA Astrophysics Data System (ADS)

Li, Shizhe; Zhang, Yan; Ferraris Araneta, Maria; Xiang, Yun; Johnson, Christopher; Innis, Robert B.; Shen, Jun

2012-05-01

This study demonstrates the feasibility of simultaneously detecting human brain metabolites labeled by two substrates infused in a sequential order. In vivo 13C spectra of carboxylic/amide carbons were acquired only during the infusion of the second substrate. This approach allowed dynamic detection of 13C labeling from two substrates with considerably different labeling patterns. [2-13C]glucose and [U-13C6]glucose were used to generate singlet and doublet signals of the same carboxylic/amide carbon atom, respectively. Because of the large one-bond 13C-13C homonuclear J coupling between a carboxylic/amide carbon and an aliphatic carbon (˜50 Hz), the singlet and doublet signals of the same carboxylic/amide carbon were well distinguished. The results demonstrated that different 13C isotopomer patterns could be simultaneously and distinctly measured in vivo in a clinical setting at 3 T.

Prosody and alignment: a sequential perspective

NASA Astrophysics Data System (ADS)

Szczepek Reed, Beatrice

2010-12-01

In their analysis of a corpus of classroom interactions in an inner city high school, Roth and Tobin describe how teachers and students accomplish interactional alignment by prosodically matching each other's turns. Prosodic matching, and specific prosodic patterns are interpreted as signs of, and contributions to successful interactional outcomes and positive emotions. Lack of prosodic matching, and other specific prosodic patterns are interpreted as features of unsuccessful interactions, and negative emotions. This forum focuses on the article's analysis of the relation between interpersonal alignment, emotion and prosody. It argues that prosodic matching, and other prosodic linking practices, play a primarily sequential role, i.e. one that displays the way in which participants place and design their turns in relation to other participants' turns. Prosodic matching, rather than being a conversational action in itself, is argued to be an interactional practice (Schegloff 1997), which is not always employed for the accomplishment of `positive', or aligning actions.

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.

Bioleaching characteristics, influencing factors of Cu solubilization and survival of Herbaspirillum sp. GW103 in Cu contaminated mine soil.

PubMed

Govarthanan, Muthusamy; Lee, Gun-Woong; Park, Jung-Hee; Kim, Jae Su; Lim, Sung-Sik; Seo, Sang-Ki; Cho, Min; Myung, Hyun; Kamala-Kannan, Seralathan; Oh, Byung-Taek

2014-08-01

This study was aimed at assess the potential of diazotrophic bacteria, Herbaspirillum sp. GW103, for bioleaching of Cu in mine soil. The strain exhibited resistance to As (550mgL(-1)), Cu (350mgL(-1)), Zn (300mgL(-1)) and Pb (200mgL(-1)). The copper resistance was further confirmed by locating copA and copB genes. The survival of the isolate GW103 during bioleaching was analyzed using green fluorescent protein tagged GW103. Response surface methodology based Box-Behnken design was used to optimize the physical and chemical conditions for Cu bioleaching. Five significant variables (temperature, incubation time, CaCO3, coconut oil cake (COC), agitation rate) were selected for the optimization. Second-order polynomials were established to identify the relationship between Cu bioleaching and variables. The optimal conditions for maximum Cu bioleaching (66%) were 30°C, 60h of incubation with 1.75% of CaCO3 and 3% COC at 140rpm. The results of Cu sequential extraction studies indicated that the isolate GW103 leached Cu from ion-exchangeable, reducible, strong organic and residual fractions. Obtained results point out that the isolate GW103 could be used for bioleaching of Cu from mine soils. Copyright © 2014 Elsevier Ltd. All rights reserved.

Attenuation of dissolved metals in neutral mine drainage in the Zambian Copperbelt.

PubMed

Sracek, Ondra; Filip, Jan; Mihaljevič, Martin; Kříbek, Bohdan; Majer, Vladimír; Veselovský, František

2011-01-01

Behaviour of metals like Cu and Co was studied in nearly neutral (pH ≥ 6.4) mine drainage seepage in a stream downgradient of a tailing dam at Chambishi site in the Copperbelt of Zambia. They are attenuated by precipitation of ferruginous ochres that incorporate significant quantities of metals. Using chemical analysis, X-ray powder diffraction and Mössbauer spectroscopy, we show that the ochres are composed mostly of amorphous ferric hydroxide. Close to the seepage face, the total Fe content of ochres increases due to precipitation of amorphous ferric hydroxide, but total Fe in sediment decreases further downstream. The stream then flows through wetland (dambo) where the remaining fraction of metals is removed. During rainy periods, increased flow rate may result in re-suspension of ochres, increasing thus the mobility of metals. Major ions like sulphate are conservative at the start of the dry period (May), but gypsum may probably precipitate later at the end of the dry period. Sequential extractions of bulk sediments indicate that Mn behaves differently to Fe, with a trend of increasing Mn with distance from the tailing dam. There is much more Fe than Mn in residual (Aqua Regia) fraction, indicating that amorphous ferric hydroxides are transformed to more crystalline phases deeper in sediment. Environmental impact of mine drainage is relatively limited due to its neutral character.

The Domino Way to Heterocycles

PubMed Central

Padwa, Albert; Bur, Scott K.

2007-01-01

Sequential transformations enable the facile synthesis of complex target molecules from simple building blocks in a single preparative step. Their value is amplified if they also create multiple stereogenic centers. In the ongoing search for new domino processes, emphasis is usually placed on sequential reactions which occur cleanly and without forming by-products. As a prerequisite for an ideally proceeding one-pot sequential transformation, the reactivity pattern of all participating components has to be such that each building block gets involved in a reaction only when it is supposed to do so. The development of sequences that combine transformations of fundamentally different mechanisms broadens the scope of such procedures in synthetic chemistry. This mini review contains a representative sampling from the last 15 years on the kinds of reactions that have been sequenced into cascades to produce heterocyclic molecules. PMID:17940591

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).

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

Andrei, Mariana Lucia, E-mail: marianaluciaandrei@yahoo.com; Babes-Bolyai University, Environmental Science and Engineering Faculty, 30 Fantanele, 400294, Cluj-Napoca; Senila, Marin

The Cu and Pb partitioning in nonferrous mine tailings was investigated using the Tessier sequential extraction scheme. The contents of Cu and Pb found in the five operationally defined fractions were determined by inductively coupled plasma optical emission spectrometry. The results showed different partitioning patterns for Cu and Pb in the studied tailings. The total Cu and Pb contents were higher in tailings from Brazesti than in those from Saliste, while the Cu contents in the first two fractions considered as mobile were comparable and the content of mobile Pb was the highest in Brazesti tailings. In the tailings frommore » Saliste about 30% of Cu and 3% of Pb were found in exchangeable fraction, while in those from Brazesti no metals were found in the exchangeable fraction, but the percent of Cu and Pb found in the bound to carbonate fraction were high (20% and 26%, respectively). The highest Pb content was found in the residual fraction in Saliste tailings and in bound to Fe and Mn oxides fraction in Brazesti tailings, while the highest Cu content was found in the fraction bound to organic matter in Saliste tailings and in the residual fraction in Brazesti tailings. In case of tailings of Brazesti medium environmental risk was found both for Pb and Cu, while in case of Saliste tailings low risk for Pb and high risk for Cu were found.« less

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.

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.

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.

Transition play in team performance of volleyball: a log-linear analysis.

PubMed

Eom, H J; Schutz, R W

1992-09-01

The purpose of this study was to develop and test a method to analyze and evaluate sequential skill performances in a team sport. An on-line computerized system was developed to record and summarize the sequential skill performances in volleyball. Seventy-two sample games from the third Federation of International Volleyball Cup men's competition were videotaped and grouped into two categories according to the final team standing and game outcome. Log-linear procedures were used to investigate the nature and degree of the relationship in the first-order (pass-to-set, set-to-spike) and second-order (pass-to-spike) transition plays. Results showed that there was a significant dependency in both the first-order and second-order transition plays, indicating that the outcome of a skill performance is highly influenced by the quality of a preceding skill performance. In addition, the pattern of the transition plays was stable and consistent, regardless of the classification status: Game Outcome, Team Standing, or Transition Process. The methodology and subsequent results provide valuable aids for a thorough understanding of the characteristics of transition plays in volleyball. In addition, the concept of sequential performance analysis may serve as an example for sport scientists in investigating probabilistic patterns of motor performance.

Simultaneous Versus Sequential Presentation in Testing Recognition Memory for Faces.

PubMed

Finley, Jason R; Roediger, Henry L; Hughes, Andrea D; Wahlheim, Christopher N; Jacoby, Larry L

2015-01-01

Three experiments examined the issue of whether faces could be better recognized in a simul- taneous test format (2-alternative forced choice [2AFC]) or a sequential test format (yes-no). All experiments showed that when target faces were present in the test, the simultaneous procedure led to superior performance (area under the ROC curve), whether lures were high or low in similarity to the targets. However, when a target-absent condition was used in which no lures resembled the targets but the lures were similar to each other, the simultaneous procedure yielded higher false alarm rates (Experiments 2 and 3) and worse overall performance (Experi- ment 3). This pattern persisted even when we excluded responses that participants opted to withhold rather than volunteer. We conclude that for the basic recognition procedures used in these experiments, simultaneous presentation of alternatives (2AFC) generally leads to better discriminability than does sequential presentation (yes-no) when a target is among the alterna- tives. However, our results also show that the opposite can occur when there is no target among the alternatives. An important future step is to see whether these patterns extend to more realistic eyewitness lineup procedures. The pictures used in the experiment are available online at http://www.press.uillinois.edu/journals/ajp/media/testing_recognition/.

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.

Patterning of anteroposterior body axis displayed in the expression of Hox genes in sea cucumber Apostichopus japonicus.

PubMed

Kikuchi, Mani; Omori, Akihito; Kurokawa, Daisuke; Akasaka, Koji

2015-09-01

The presence of an anteroposterior body axis is a fundamental feature of bilateria. Within this group, echinoderms have secondarily evolved pentameral symmetric body plans. Although all echinoderms present bilaterally symmetric larval stages, they dramatically rearrange their body axis and develop a pentaradial body plan during metamorphosis. Therefore, the location of their anteroposterior body axis in adult forms remains a contentious issue. Unlike other echinoderms, sea cucumbers present an obvious anteroposterior axis not rearranged during metamorphosis, thus representing an interesting group to study their anteroposterior axis patterning. Hox genes are known to play a broadly conserved role in anteroposterior axis patterning in deuterostomes. Here, we report the expression patterns of Hox genes from early development to pentactula stage in sea cucumber. In early larval stages, five Hox genes (AjHox1, AjHox7, AjHox8, AjHox11/13a, and AjHox11/13b) were expressed sequentially along the archenteron, suggesting that the role of anteroposterior patterning of the Hox genes is conserved in bilateral larvae of echinoderms. In doliolaria and pentactula stages, eight Hox genes (AjHox1, AjHox5, AjHox7, AjHox8, AjHox9/10, AjHox11/13a, AjHox11/13b, and AjHox11/13c) were expressed sequentially along the digestive tract, following a similar expression pattern to that found in the visceral mesoderm of other bilateria. Unlike other echinoderms, pentameral expression patterns of AjHox genes were not observed in sea cucumber. Altogether, we concluded that AjHox genes are involved in the patterning of the digestive tract in both larvae and metamorphosis of sea cucumbers. In addition, the anteroposterior axis in sea cucumbers might be patterned like that of other bilateria.

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.

Mercury and gold concentrations of highly polluted environmental samples determined using prompt gamma-ray analysis and instrument neutron activation analysis

NASA Astrophysics Data System (ADS)

Osawa, Takahito; Hatsukawa, Yuichi; Appel, Peter W. U.; Matsue, Hideaki

2011-04-01

The authors have established a method of determining mercury and gold in severely polluted environmental samples using prompt gamma-ray analysis (PGA) and instrumental neutron activation analysis (INAA). Since large amounts of mercury are constantly being released into the environment by small-scale gold mining in many developing countries, the mercury concentration in tailings and water has to be determined to mitigate environmental pollution. Cold-vapor atomic absorption analysis, the most pervasive method of mercury analysis, is not suitable because tailings and water around mining facilities have extremely high mercury concentrations. On the other hand, PGA can determine high mercury concentrations in polluted samples as it has an appropriate level of sensitivity. Moreover, gold concentrations can be determined sequentially by using INAA after PGA. In conclusion, the analytical procedure established in this work using PGA and INAA is the best way to evaluate the degree of pollution and the tailing resource value. This method will significantly contribute to mitigating problems in the global environment.

Geochemical behavior, environmental availability, and reconstruction of historical trends of Cu, Pb, and Zn in sediment cores of the Cananéia-Iguape coastal system, Southeastern Brazil.

PubMed

Tramonte, Keila Modesto; Figueira, Rubens Cesar Lopes; Majer, Alessandra Pereira; de Lima Ferreira, Paulo Alves; Batista, Miriam Fernanda; Ribeiro, Andreza Portella; de Mahiques, Michel Michaelovitch

2018-02-01

The Cananéia-Iguape system is located in a coastal region of southeastern Brazil, recognized by UNESCO as an Atlantic Forest Biosphere Reserve. This system has suffered substantial environmental impacts due to the opening of an artificial channel and by past intensive mining activities. In this paper was performed the sequential chemical extraction of Cu, Pb, and Zn, on previously described sediment cores, and the statistical treatment of the data, allowing to estimate the remobilization geochemical behavior, the available content and the trend of accumulation between 1926 and 2008. The maximum available level (sum of all mobile fraction) were, in mgkg -1 , 18.74 for Cu, 177.55 for Pb and 123.03 for Zn. Considering its environmental availability, Pb remains a concern in the system. It was possible to recognize the anthropic contribution of Pb, being the mining activities considered the only potential source of this metal in the region. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification and assessment tools for structural motif discovery algorithms.

PubMed

Badr, Ghada; Al-Turaiki, Isra; Mathkour, Hassan

2013-01-01

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case. In this paper, we survey, classify, and compare different algorithms that solve the structural motif discovery problem, where the underlying sequences may be different. We highlight their strengths and weaknesses. We start by proposing a benchmark dataset and a measurement tool that can be used to evaluate different motif discovery approaches. Then, we proceed by proposing our experimental setup. Finally, results are obtained using the proposed benchmark to compare available tools. To the best of our knowledge, this is the first attempt to compare tools solely designed for structural motif discovery. Results show that the accuracy of discovered motifs is relatively low. The results also suggest a complementary behavior among tools where some tools perform well on simple structures, while other tools are better for complex structures. We have classified and evaluated the performance of available structural motif discovery tools. In addition, we have proposed a benchmark dataset with tools that can be used to evaluate newly developed tools.

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.

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.

Corrective Action Investigation Plan for Corrective Action Unit 97: Yucca Flat/Climax Mine, Nevada National Security Site, Nevada with ROTCs 1, 2, and 3 (Revision 0, September 2000)

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

Andrews, Robert; Marutzky, Sam

2000-09-01

This Corrective Action Investigation Plan contains the U.S. Department of Energy, Nevada Operations Office's (DOE/NV's) approach to collect the data necessary to evaluate Corrective Action Alternatives (CAAs) appropriate for the closure of Corrective Action Unit (CAU) 97 under the Federal Facility Agreement and Consent Order (FFACO). Corrective Action Unit 97, collectively known as the Yucca Flat/Climax Mine CAU, consists of 720 Corrective Action Sites (CASs). The Yucca Flat/Climax Mine CAU extends over several areas of the NTS and constitutes one of several areas used for underground nuclear testing in the past. The nuclear tests resulted in groundwater contamination in themore » vicinity as well as downgradient of the underground test areas. Based on site history, the Yucca Flat underground nuclear tests were conducted in alluvial, volcanic, and carbonate rocks; whereas, the Climax Mine tests were conducted in an igneous intrusion located in northern Yucca Flat. Particle-tracking simulations performed during the regional evaluation indicate that the local Climax Mine groundwater flow system merges into the much larger Yucca Flat groundwater flow systems during the 1,000-year time period of interest. Addressing these two areas jointly and simultaneously investigating them as a combined CAU has been determined the best way to proceed with corrective action investigation (CAI) activities. The purpose and scope of the CAI includes characterization activities and model development conducted in five major sequential steps designed to be consistent with FFACO Underground Test Area Project's strategy to predict the location of the contaminant boundary, develop and implement a corrective action, and close each CAU. The results of this field investigation will support a defensible evaluation of CAAs in the subsequent corrective action decision document.« less

Bisubstrate inhibition: Theory and application to N-acetyltransferases.

PubMed

Yu, Michael; Magalhães, Maria L B; Cook, Paul F; Blanchard, John S

2006-12-12

Bisubstrate inhibitors represent a potentially powerful group of compounds that have found significant therapeutic utility. Although these compounds have been synthesized and tested against a number of enzymes that catalyze sequential bireactant reactions, the detailed theory for predicting the expected patterns of inhibition against the two substrates for various bireactant kinetic mechanisms has, heretofore, not been presented. We have derived the rate equations for all likely sequential bireactant mechanisms and provide two examples in which bisubstrate inhibitors allow the kinetic mechanism to be determined. Bisubstrate inhibitor kinetics is a powerful diagnostic for the determination of kinetic mechanisms.

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.

Distribution of potentially toxic elements (PTEs) in tailings, soils, and plants around Gol-E-Gohar iron mine, a case study in Iran.

PubMed

Soltani, Naghmeh; Keshavarzi, Behnam; Moore, Farid; Sorooshian, Armin; Ahmadi, Mohamad Reza

2017-08-01

This study investigated the concentration of potentially toxic elements (PTEs) including Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, V, and Zn in 102 soils (in the Near and Far areas of the mine), 7 tailings, and 60 plant samples (shoots and roots of Artemisia sieberi and Zygophylum species) collected at the Gol-E-Gohar iron ore mine in Iran. The elemental concentrations in tailings and soil samples (in Near and Far areas) varied between 7.4 and 35.8 mg kg -1 for As (with a mean of 25.39 mg kg -1 for tailings), 7.9 and 261.5 mg kg -1 (mean 189.83 mg kg -1 for tailings) for Co, 17.7 and 885.03 mg kg -1 (mean 472.77 mg kg -1 for tailings) for Cu, 12,500 and 400,000 mg kg -1 (mean 120,642.86 mg kg -1 for tailings) for Fe, and 28.1 and 278.1 mg kg -1 (mean 150.29 mg kg -1 for tailings) for Ni. A number of physicochemical parameters and pollution index for soils were determined around the mine. Sequential extractions of tailings and soil samples indicated that Fe, Cr, and Co were the least mobile and that Mn, Zn, Cu, and As were potentially available for plants uptake. Similar to soil, the concentration of Al, As, Co, Cr, Cu, Fe, Mn, Mo, Ni, and Zn in plant samples decreased with the distance from the mining/processing areas. Data on plants showed that metal concentrations in shoots usually exceeded those in roots and varied significantly between the two investigated species (Artemisia sieberi > Zygophylum). All the reported results suggest that the soil and plants near the iron ore mine are contaminated with PTEs and that they can be potentially dispersed in the environment via aerosol transport and deposition.

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

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

PubMed

Kinilakodi, Harisha; Grayson, R Larry

2011-05-01

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

A methodology to leverage cross-sectional accelerometry to capture weather's influence in active living research.

PubMed

Katapally, Tarun R; Rainham, Daniel; Muhajarine, Nazeem

2016-06-27

While active living interventions focus on modifying urban design and built environment, weather variation, a phenomenon that perennially interacts with these environmental factors, is consistently underexplored. This study's objective is to develop a methodology to link weather data with existing cross-sectional accelerometry data in capturing weather variation. Saskatoon's neighbourhoods were classified into grid-pattern, fractured grid-pattern and curvilinear neighbourhoods. Thereafter, 137 Actical accelerometers were used to derive moderate to vigorous physical activity (MVPA) and sedentary behaviour (SB) data from 455 children in 25 sequential one-week cycles between April and June, 2010. This sequential deployment was necessary to overcome the difference in the ratio between the sample size and the number of accelerometers. A data linkage methodology was developed, where each accelerometry cycle was matched with localized (Saskatoon-specific) weather patterns derived from Environment Canada. Statistical analyses were conducted to depict the influence of urban design on MVPA and SB after factoring in localized weather patterns. Integration of cross-sectional accelerometry with localized weather patterns allowed the capture of weather variation during a single seasonal transition. Overall, during the transition from spring to summer in Saskatoon, MVPA increased and SB decreased during warmer days. After factoring in localized weather, a recurring observation was that children residing in fractured grid-pattern neighbourhoods accumulated significantly lower MVPA and higher SB. The proposed methodology could be utilized to link globally available cross-sectional accelerometry data with place-specific weather data to understand how built and social environmental factors interact with varying weather patterns in influencing active living.