Stream Deniable-Encryption Algorithms

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N.A. Moldovyan

2016-04-01

Full Text Available A method for stream deniable encryption of secret message is proposed, which is computationally indistinguishable from the probabilistic encryption of some fake message. The method uses generation of two key streams with some secure block cipher. One of the key streams is generated depending on the secret key and the other one is generated depending on the fake key. The key streams are mixed with the secret and fake data streams so that the output ciphertext looks like the ciphertext produced by some probabilistic encryption algorithm applied to the fake message, while using the fake key. When the receiver or/and sender of the ciphertext are coerced to open the encryption key and the source message, they open the fake key and the fake message. To disclose their lie the coercer should demonstrate possibility of the alternative decryption of the ciphertext, however this is a computationally hard problem.

STREAMFINDER I: A New Algorithm for detecting Stellar Streams

Science.gov (United States)

Malhan, Khyati; Ibata, Rodrigo A.

2018-04-01

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the ESA/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia dataset.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

Science.gov (United States)

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.

Riffle zoobenthos in streams receiving acid mine drainage

Energy Technology Data Exchange (ETDEWEB)

Koryak, M; Shapiro, M A; Sykora, J L

1972-01-01

The bottom fauna of a stream polluted by acid mine drainage, was studied, using the standard methods of sample collecting. In localities immediately influenced by mine drainage, where very low pH values and high acidities prevail, the effect of acid mine wastes on the ecology and composition of the benthic fauna is, in general, similar to the effect of organic pollution. In these areas we found high numbers of individuals comprised of a few species. In the zones of active neutralization, where iron hydroxides are deposited, species diversity slightly increases but the biomass is very low. The most numerous invertebrates in the stream sections exhibiting high acidity and low pH are midge larvae, especially Tendipes gr. riparius. The number of insect groups present increases steadily with progressive neutralization until crustacea (amphipoda) and oligochaeta appear, indicating considerable improvement in water quality. The supply of desirable benthic fish food (Tendipes ssp.) is very high in the parts of the stream where low pH, high acidity, and high ferrous iron concentrations prevail. Unfortunately, fish cannot survive under these conditions to utilize this abundant food supply. On the other hand, in the less acidic zones, where fish could possibly survive, the deposition of ferric iron drastically diminishes the total biomass of benthic organisms and therefore severely limits fish populations.

Impact of potash mining in streams: the Llobregat basin (northeast Spain as a case study

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Ruben Ladrera

2016-12-01

Full Text Available Potash mining is significantly increasing the salt concentration of rivers and streams due to lixiviates coming from the mine tailings. In the present study, we have focused on the middle Llobregat basin (northeast Spain, where an important potash mining activity exists from the beginning of the XX century. Up to 50 million tonnes of saline waste have been disposed in the area, mainly composed of sodium chloride. We assessed the ecological status of streams adjacent to the mines by studying different physicochemical and hydromorphological variables, as well as aquatic macroinvertebrates. We found extraordinary high values of salinity in the studied streams, reaching conductivities up to 132.4 mS/cm. Salt-polluted streams were characterized by a deterioration of the riparian vegetation and the fluvial habitat. Both macroinvertebrate richness and abundance decreased with increasing salinity. In the most polluted stream only two families of macroinvertebrates were found: Ephydridae and Ceratopogonidae. According to the biotic indices IBMWP and IMMi-T, none of the sites met the requirements of the Water Framework Directive (WFD; i.e., good ecological status. Overall, we can conclude that potash-mining activities have the potential to cause severe ecological damage to their surrounding streams. This is mainly related to an inadequate management of the mine tailings, leading to highly saline runoff and percolates entering surface waters. Thus, we urge water managers and policy makers to take action to prevent, detect and remediate salt pollution of rivers and streams in potash mining areas.

Randomized algorithms in automatic control and data mining

CERN Document Server

Granichin, Oleg; Toledano-Kitai, Dvora

2015-01-01

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

An Evolutionary Algorithm to Mine High-Utility Itemsets

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Jerry Chun-Wei Lin

2015-01-01

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

Mining algorithm for association rules in big data based on Hadoop

Science.gov (United States)

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

2018-04-01

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

Quantum algorithm for association rules mining

Science.gov (United States)

Yu, Chao-Hua; Gao, Fei; Wang, Qing-Le; Wen, Qiao-Yan

2016-10-01

Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by discovering the relationships between itemsets (sets of items). In this paper, we address ARM in the quantum settings and propose a quantum algorithm for the key part of ARM, finding frequent itemsets from the candidate itemsets and acquiring their supports. Specifically, for the case in which there are Mf(k ) frequent k -itemsets in the Mc(k ) candidate k -itemsets (Mf(k )≤Mc(k ) ), our algorithm can efficiently mine these frequent k -itemsets and estimate their supports by using parallel amplitude estimation and amplitude amplification with complexity O (k/√{Mc(k )Mf(k ) } ɛ ) , where ɛ is the error for estimating the supports. Compared with the classical counterpart, i.e., the classical sampling-based algorithm, whose complexity is O (k/Mc(k ) ɛ2) , our quantum algorithm quadratically improves the dependence on both ɛ and Mc(k ) in the best case when Mf(k )≪Mc(k ) and on ɛ alone in the worst case when Mf(k )≈Mc(k ) .

Contrast data mining concepts, algorithms, and applications

CERN Document Server

Dong, Guozhu

2012-01-01

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Science.gov (United States)

Gan, Wensheng; Zhang, Binbin

2015-01-01

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

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

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

The impact of episodic coal mine drainage pollution on benthic macroinvertebrates in streams in the Anthracite region of Pennsylvania

International Nuclear Information System (INIS)

MacCausland, A.; McTammany, M.E.

2007-01-01

Episodic coal mine drainage, caused by fluctuations in mine discharges relative to stream flow, has devastating effects on aquatic macroinvertebrate communities. Seven stream reaches in the Anthracite region of Pennsylvania were identified as chronically, episodically or not impaired by mine drainage, and sampled seasonally for 1 year to determine the effect of episodic mine drainage on macroinvertebrates. Specific conductance fluctuated seasonally in episodic sites; it was lower in winter when discharge increased and higher in summer when discharges decreased and mine drainage made up a larger proportion of stream flow. Although we hypothesized that episodic streams would have higher macroinvertebrate richness than chronic streams, comparisons showed no differences in richness between treatments. Episodic pollution may result from undersized or poorly maintained passive treatment systems; therefore, intensive macroinvertebrate monitoring may be needed to identify streams being affected by episodic mine drainage because macroinvertebrate richness may be sensitive to water quality fluctuations. - Episodic coal mine pollution decreases benthic macroinvertebrate richness and density

Effects of acid mine drainage on a headwater stream ecosystem in Colorado

International Nuclear Information System (INIS)

Niyogi, D.K.; Lewis, W.M. Jr.; McKnight, D.M.

1994-01-01

The ecological effects of acid mine drainage were investigated during the summer of 1993 on St. Kevin Gulch, a headwater stream near Leadville, Colorado. The stream currently receives acidic water from an abandoned mine. The pH downstream of the mine is between 3.5 and 4.5, and several metals exceed concentrations toxic to aquatic organisms. Zinc is present at especially high concentrations (1 to 10 mg/L) Furthermore, the stream bottom is covered with a thick layer of iron hydroxide precipitates. Effects on stream biota have been dramatic. Aquatic flora in the affected reach is limited to a green filamentous alga, Ulothrix subtilissima. Macroinvertebrate densities are significantly lower in the affected reach (mean = 99 indiv/m 2 ; SD = 88 indiv/M 2 ) compared to an upstream (pristine) reference reach (mean = 1,735 indiv/m 2 ; SD = 652 indiv/M 2 ). Functional processes were also studied in the stream. Net primary production (NPP) was measured during midday with recirculating chambers. Production was significantly lower in the affected reach (mean NPP 13.3 MgO 2 hr -1 m -2 ; SD = 87 MgO 2 hr -1 m -2 ) than the upstream reference reach (NPP = 64.1 MgO 2 hr -1 m -2 ; SD = 27.7 MgO 2 hr -1 m -2 ). Decomposition, measured with litter bags, was also lower in the affected reach than the upstream site. In 1994, St. Kevin Gulch is scheduled to undergo remediation that will treat the acidic water from the mine. Further studies on this stream will provide information on the recovery processes in lotic ecosystems

Minería de datos sobre streams de redes sociales, una herramienta al servicio de la Bibliotecología = Data Mining Streams of Social Networks, A Tool to Improve The Library Services

Directory of Open Access Journals (Sweden)

Sonia Jaramillo Valbuena

2015-12-01

, Facebook, RSS feeds and blogs, generate a large amount of unstructured data streams. They can be used to the problem of mining topic-specific influence, graph mining, opinion mining and recommender systems, thus achieving that libraries can obtain maximum benefit from the use of Information and Communication Technologies. From the perspective of data stream mining, the processing of these streams poses significant challenges. The algorithms must be adapted to problems such as: high arrival rate, memory requirements without restrictions, diverse sources of data and concept-drift. In this work, we explore the current state-of-the-art solutions of data stream mining originating from social networks, specifically, Facebook and Twitter. We present a review of the most representative algorithms and how they contribute to knowledge discovery in the area of librarianship. We conclude by presenting some of the problems that are the subject of active research.

A Mining Algorithm for Extracting Decision Process Data Models

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Cristina-Claudia DOLEAN

2011-01-01

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

Stream-sediment geochemistry in mining-impacted streams: Prichard, Eagle, and Beaver creeks, northern Coeur d'Alene Mining District, northern Idaho

Science.gov (United States)

Box, Stephen E.; Wallis, John C.; Briggs, Paul H.; Brown, Zoe Ann

2005-01-01

This report presents the results of one aspect of an integrated watershed-characterization study that was undertaken to assess the impacts of historical mining and milling of silver-lead-zinc ores on water and sediment composition and on aquatic biota in streams draining the northern part of the Coeur d?Alene Mining District in northern Idaho. We present the results of chemical analyses of 62 samples of streambed sediment, 19 samples of suspended sediment, 23 samples of streambank soil, and 29 samples of mine- and mill-related artificial- fill material collected from the drainages of Prichard, Eagle, and Beaver Creeks, all tributaries to the North Fork of the Coeur d?Alene River. All samples were sieved into three grain-size fractions (Beaver Creek drainages has resulted in enrichments of lead, zinc, mercury, arsenic, cadmium, silver, copper, cobalt, and, to a lesser extent, iron and manganese in streambed sediment. Using samples collected from the relatively unimpacted West Fork of Eagle Creek as representative of background compositions, streambed sediment in the vicinity of the mines and millsites has Pb and Zn contents of 20 to 100 times background values, decreasing to 2 to 5 times background values at the mouth of the each stream, 15 to 20 km downstream. Lesser enrichments (<10 times background values) of mercury and arsenic also are generally associated with, and decrease downstream from, historical silver-lead-zinc mining in the drainages. However, enrichments of arsenic and, to a lesser extent, mercury also are areally associated with the lode gold deposits along Prichard Creek near Murray, which were not studied here. Metal contents in samples of unfractionated suspended sediment collected during a high-flow event in April 2000 are generally similar to, but slightly higher than, those in the fine (<0.063- mm grain size) fraction of streambed sediment from the same sampling site. Although metal enrichment in streambed sediment typically begins adjacent to

Streaming Algorithms for Line Simplification

DEFF Research Database (Denmark)

Abam, Mohammad; de Berg, Mark; Hachenberger, Peter

2010-01-01

this problem in a streaming setting, where we only have a limited amount of storage, so that we cannot store all the points. We analyze the competitive ratio of our algorithms, allowing resource augmentation: we let our algorithm maintain a simplification with 2k (internal) points and compare the error of our...... simplification to the error of the optimal simplification with k points. We obtain the algorithms with O(1) competitive ratio for three cases: convex paths, where the error is measured using the Hausdorff distance (or Fréchet distance), xy-monotone paths, where the error is measured using the Hausdorff distance...... (or Fréchet distance), and general paths, where the error is measured using the Fréchet distance. In the first case the algorithm needs O(k) additional storage, and in the latter two cases the algorithm needs O(k 2) additional storage....

A partition enhanced mining algorithm for distributed association rule mining systems

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A.O. Ogunde

2015-11-01

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

Image Encryption Using a Lightweight Stream Encryption Algorithm

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Saeed Bahrami

2012-01-01

Full Text Available Security of the multimedia data including image and video is one of the basic requirements for the telecommunications and computer networks. In this paper, we consider a simple and lightweight stream encryption algorithm for image encryption, and a series of tests are performed to confirm suitability of the described encryption algorithm. These tests include visual test, histogram analysis, information entropy, encryption quality, correlation analysis, differential analysis, and performance analysis. Based on this analysis, it can be concluded that the present algorithm in comparison to A5/1 and W7 stream ciphers has the same security level, is better in terms of the speed of performance, and is used for real-time applications.

Continuity-Aware Scheduling Algorithm for Scalable Video Streaming

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Atinat Palawan

2016-05-01

Full Text Available The consumer demand for retrieving and delivering visual content through consumer electronic devices has increased rapidly in recent years. The quality of video in packet networks is susceptible to certain traffic characteristics: average bandwidth availability, loss, delay and delay variation (jitter. This paper presents a scheduling algorithm that modifies the stream of scalable video to combat jitter. The algorithm provides unequal look-ahead by safeguarding the base layer (without the need for overhead of the scalable video. The results of the experiments show that our scheduling algorithm reduces the number of frames with a violated deadline and significantly improves the continuity of the video stream without compromising the average Y Peek Signal-to-Noise Ratio (PSNR.

On finding similar items in a stream of transactions

DEFF Research Database (Denmark)

Campagna, Andrea; Pagh, Rasmus

2010-01-01

While there has been a lot of work on finding frequent itemsets in transaction data streams, none of these solve the problem of finding similar pairs according to standard similarity measures. This paper is a first attempt at dealing with this, arguably more important, problem. We start out with ...... in random order, and show that surprisingly, not only is small-space similarity mining possible for the most common similarity measures, but the mining accuracy {\\em improves\\/} with the length of the stream for any fixed support threshold....... with a negative result that also explains the lack of theoretical upper bounds on the space usage of data mining algorithms for finding frequent itemsets: Any algorithm that (even only approximately and with a chance of error) finds the most frequent $k$-itemset must use space $\\Omega...

URL Mining Using Agglomerative Clustering Algorithm

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Chinmay R. Deshmukh

2015-02-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Rieuwerts, J.S., E-mail: jrieuwerts@plymouth.ac.uk [School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth PL4 8AA (United Kingdom); Mighanetara, K.; Braungardt, C.B. [School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth PL4 8AA (United Kingdom); Rollinson, G.K. [Camborne School of Mines, CEMPS, University of Exeter, Tremough Campus, Penryn, Cornwall TR10 9EZ (United Kingdom); Pirrie, D. [Helford Geoscience LLP, Menallack Farm, Treverva, Penryn, Cornwall TR10 9BP (United Kingdom); Azizi, F. [School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth PL4 8AA (United Kingdom)

2014-02-01

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{sup 5} mg kg{sup −1} As and concentrations in stream sediments of up to 2.5 × 10{sup 4} mg kg{sup −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. - Highlights: • Stream sediments in a former mining area remain polluted with up to 25 g As per kg. • The main arsenic mineral in adjacent mine wastes appears to be scorodite. • Low solubility scorodite was inversely correlated with potentially mobile As. • Combining

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

International Nuclear Information System (INIS)

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

2014-01-01

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 mg kg −1 As and concentrations in stream sediments of up to 2.5 × 10 4 mg kg −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. - Highlights: • Stream sediments in a former mining area remain polluted with up to 25 g As per kg. • The main arsenic mineral in adjacent mine wastes appears to be scorodite. • Low solubility scorodite was inversely correlated with potentially mobile As. • Combining mineralogical and

Recommending Learning Activities in Social Network Using Data Mining Algorithms

Science.gov (United States)

Mahnane, Lamia

2017-01-01

In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…

Incremental temporal pattern mining using efficient batch-free stream clustering

NARCIS (Netherlands)

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

2017-01-01

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

pubmed.mineR: An R package with text-mining algorithms to ...

Indian Academy of Sciences (India)

2015-09-29

Sep 29, 2015 ... using text-mining algorithms for biomedical research pur- poses. ... studies are described to illustrate some potential uses of ... This is the most applied task. ... other alphabets (for example, Greek alphabets) and hyphens.

pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.

Science.gov (United States)

Rani, Jyoti; Shah, A B Rauf; Ramachandran, Srinivasan

2015-10-01

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.

Comparison of Firefly algorithm and Artificial Immune System algorithm for lot streaming in -machine flow shop scheduling

Directory of Open Access Journals (Sweden)

G. Vijay Chakaravarthy

2012-11-01

Full Text Available Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan. The objective of this paper is to minimize the makespan and total flow time of -job, -machine lot streaming problem in a flow shop with equal and variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve flow shop problems with lot streaming. In this research, Firefly Algorithm (FA and Artificial Immune System (AIS algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results shows that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.

Zips : mining compressing sequential patterns in streams

NARCIS (Netherlands)

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

2013-01-01

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

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

Science.gov (United States)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Arsenic transport in groundwater, surface water, and the hyporheic zone of a mine-influenced stream-aquifer system

OpenAIRE

Brown, Brendan

2005-01-01

We investigated the transport of dissolved arsenic in groundwater, surface water and the hyporheic zone in a stream-aquifer system influenced by an abandoned arsenopyrite mine. Mine tailing piles consisting of a host of arsenic-bearing minerals including arsenopyrite and scorodite remain adjacent to the stream and represent a continuous source of arsenic. Arsenic loads from the stream, springs, and groundwater were quantified at the study reach on nine dates from January to August 2005 and ...

Restoration as mitigation: analysis of stream mitigation for coal mining impacts in southern Appalachia.

Science.gov (United States)

Palmer, Margaret A; Hondula, Kelly L

2014-09-16

Compensatory mitigation is commonly used to replace aquatic natural resources being lost or degraded but little is known about the success of stream mitigation. This article presents a synthesis of information about 434 stream mitigation projects from 117 permits for surface mining in Appalachia. Data from annual monitoring reports indicate that the ratio of lengths of stream impacted to lengths of stream mitigation projects were <1 for many projects, and most mitigation was implemented on perennial streams while most impacts were to ephemeral and intermittent streams. Regulatory requirements for assessing project outcome were minimal; visual assessments were the most common and 97% of the projects reported suboptimal or marginal habitat even after 5 years of monitoring. Less than a third of the projects provided biotic or chemical data; most of these were impaired with biotic indices below state standards and stream conductivity exceeding federal water quality criteria. Levels of selenium known to impair aquatic life were reported in 7 of the 11 projects that provided Se data. Overall, the data show that mitigation efforts being implemented in southern Appalachia for coal mining are not meeting the objectives of the Clean Water Act to replace lost or degraded streams ecosystems and their functions.

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

Science.gov (United States)

Abdous, M'hammed; He, Wu

2011-01-01

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

Towards an evaluation framework for process mining algorithms

NARCIS (Netherlands)

Rozinat, A.; Alves De Medeiros, A.K.; Günther, C.W.; Weijters, A.J.M.M.; Aalst, van der W.M.P.

2007-01-01

Although there has been a lot of progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we outline elements of an evaluation framework that is intended

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

Science.gov (United States)

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Mindy Yeager Armstead

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

Effects of coal mining, forestry, and road construction on southern Appalachian stream invertebrates and habitats.

Science.gov (United States)

Gangloff, Michael M; Perkins, Michael; Blum, Peter W; Walker, Craig

2015-03-01

Coal has been extracted via surface and sub-surface mining for decades throughout the Appalachian Mountains. New interest in ridge-top mining has raised concerns about possible waterway impacts. We examined effects of forestry, mining, and road construction-based disturbance on physico-chemistry and macroinvertebrate communities in east-central Tennessee headwater streams. Although 11 of 30 sites failed Tennessee's biocriteria scoring system, invertebrate richness was moderately high and we did not find significant differences in any water chemistry or habitat parameters between sites with passing and failing scores. However, conductivity and dissolved solid concentrations appeared elevated in the majority of study streams. Principal components (PCs) analysis indicated that six PCs accounted for ~77 % of among-site habitat variability. One PC associated with dissolved oxygen and specific conductance explained the second highest proportion of among-site variability after catchment area. Specific conductance was not correlated with catchment area but was strongly correlated with mining activity. Composition and success of multivariate models using habitat PCs to predict macroinvertebrate metrics was highly variable. PC scores associated with water chemistry and substrate composition were most frequently included in significant models. These results suggest that impacts of historical and current coal mining remain a source of water quality and macroinvertebrate community impairment in this region, but effects are subtle. Our results suggest that surface mining may have chronic and system-wide effects on habitat conditions and invertebrate communities in Cumberland Plateau streams.

Effective application of improved profit-mining algorithm for the interday trading model.

Science.gov (United States)

Hsieh, Yu-Lung; Yang, Don-Lin; Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

Directory of Open Access Journals (Sweden)

Yu-Lung Hsieh

2014-01-01

Full Text Available Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Clustering big data streams : recent challenges and contributions

NARCIS (Netherlands)

Hassani, M.; Seidl, T.

Traditional clustering algorithms merely considered static data. Today's various applications and research issues in big data mining have however to deal with continuous, possibly infinite streams of data, arriving at high velocity. Web traffic data, surveillance data, sensor measurements and stock

The ClusTree : indexing micro-clusters for anytime stream mining

DEFF Research Database (Denmark)

Kranen, Philipp; Assent, Ira; Baldauf, Corinna

2011-01-01

-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter-free algorithm that automatically adapts...... introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Additionally we present solutions to handle very fast streams through aggregation mechanisms and propose novel descent strategies that improve the clustering result on slower streams as long as time permits...

Disinfectant properties of acid mine drainage: its effects on enteric bacteria in a sewage-contaminated stream

Energy Technology Data Exchange (ETDEWEB)

Keating, S.T.; Celements, C.M.; Ostrowski, D.; Hanlon, T. [St. Francis College, Loretto, PA (United States). Dept. of Biology

1996-09-01

Studies conducted in a Cambria County, Pennsylvania, acid mine drainage stream suggest that mine drainage rapidly reduces in situ populations of fecal bacteria associated with inputs of untreated sewage. The density of lactose-fermenting bacteria, mostly coliform species from sewage, declined 1000-fold over a distance of less than 100 m following the input of high acid (pH 3.5 to 4.0), high ferrous iron (45 mg/l) acid mine drainage. Enterobacteriaceae were isolated from the stream, identified, and tested for tolerance to acid mine drainage by exposing cells to drainage for 10 minutes at 0 or 37{degree}C. Populations of all tested isolates were reduced by this treatment, but some isolates were significantly less affected than others. Thus, while mine drainage may act as a disinfectant, it may not reduce all populations of disease-causing intestinal bacteria at an equal, rapid rate.

Effect of Strip Mining on Water Quality in Small Streams in Eastern Kentucky, 1967-1975

Science.gov (United States)

Kenneth L. Dyer; Willie R. Curtis

1977-01-01

Eight years of streamflow data are analyzed to show the effects of strip mining on chemical quality of water in six first-order streams in Breathitt County, Kentucky. All these watersheds were unmined in August, 1967, but five have since been strip mined. The accumulated data from this case history study indicate that strip mining causes large increases in the...

Fingerprinting two metal contaminants in streams with Cu isotopes near the Dexing Mine, China

Energy Technology Data Exchange (ETDEWEB)

Song, Shiming [Chinese Geological Survey, Nanjing Center, Nanjing (China); Mathur, Ryan, E-mail: mathurr@juniata.edu [Department of Geology, Juniata College, Huntingdon, PA (United States); Ruiz, Joaquin [Department of Geosciences, University of Arizona, Tucson, AZ (United States); Chen, Dandan [Chinese Geological Survey, Nanjing Center, Nanjing (China); Allin, Nicholas [Department of Geology, Juniata College, Huntingdon, PA (United States); Guo, Kunyi; Kang, Wenkai [Chinese Geological Survey, Nanjing Center, Nanjing (China)

2016-02-15

Transition metal isotope signatures are becoming useful for fingerprinting sources in surface waters. This study explored the use of Cu isotope values to trace dissolved metal contaminants in stream water throughout a watershed affected by mining by-products of the Dexing Mine, the largest porphyry Cu operation in Asia. Cu isotope values of stream water were compared to potential mineral sources of Cu in the mining operation, and to proximity to the known Cu sources. The first mineral source, chalcopyrite, CuFeS{sub 2} has a ‘tight’ cluster of Cu isotope values (− 0.15‰ to + 1.65‰; + 0.37 ± 0.6‰, 1σ, n = 10), and the second mineral source, pyrite (FeS{sub 2}), has a much larger range of Cu isotope values (− 4‰ to + 11.9‰; 2.7 ± 4.3‰, 1σ, n = 16). Dissolved Cu isotope values of stream water indicated metal derived from either chalcopyrite or pyrite. Above known Cu mineralization, stream waters are approximately + 1.5‰ greater than the average chalcopyrite and are interpreted as derived from weathering of chalcopyrite. In contrast, dissolved Cu isotope values in stream water emanating from tailings piles had Cu isotope values similar to or greater than pyrite (>+6‰, a common mineral in the tailings). These values are interpreted as sourced from the tailings, even in solutions that possess significantly lower concentrations of Cu (< 0.05 ppm). Elevated Cu isotope values were also found in two soil and two tailings samples (δ{sup 65}Cu ranging between + 2 to + 5‰). These data point to the mineral pyrite in tailings as the mineral source for the elevated Cu isotope values. Therefore, Cu isotope values of waters emanating from a clearly contaminated drainage possess different Cu isotope values, permitting the discrimination of Cu derived from chalcopyrite and pyrite in solution. Data demonstrate the utility of Cu isotopic values in waters, minerals, and soils to fingerprint metallic contamination for environmental problems. - Highlights: �

Research on Health State Perception Algorithm of Mining Equipment Based on Frequency Closeness

Directory of Open Access Journals (Sweden)

Gang Wang

2014-06-01

Full Text Available The health state perception of mining equipment is intended to have an online real- time knowledge and analysis of the running conditions of large mining equipments. Due to its unknown failure mode, a challenge was raised to the traditional fault diagnosis of mining equipments. A health state perception algorithm of mining equipment was introduced in this paper, and through continuous sampling of the machine vibration data, the time-series data set was set up; subsequently, the mode set based on the frequency closeness was constructed by the d neighborhood method combined with the TSDM algorithm, thus the forecast method on the basis of the dual mode set was eventually formed. In the calculation of the frequency closeness, the Goertzel algorithm was introduced to effectively decrease the computation amount. It was indicated through the simulation test on the vibration data of the drum shaft base that the health state of the device could be effectively distinguished. The algorithm has been successfully applied to equipment monitoring in the Huoer Xinhe Coal Mine of Shanxi Coal Imp&Exp. Group Co., Ltd.

Study on the Method of Association Rules Mining Based on Genetic Algorithm and Application in Analysis of Seawater Samples

Directory of Open Access Journals (Sweden)

Qiuhong Sun

2014-04-01

Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.

A fast density-based clustering algorithm for real-time Internet of Things stream.

Science.gov (United States)

Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Depauperate macroinvertebrates in a mine affected stream: Clean water may be the key to recovery

International Nuclear Information System (INIS)

Battaglia, M.; Hose, G.C.; Turak, E.; Warden, B.

2005-01-01

Acid mine drainage (AMD) is frequently linked with changes in macroinvertebrate assemblages, but the relative contribution of water and sediment to toxicity is equivocal. We have shown that the macroinvertebrate fauna of Neubecks Ck, a mine impacted stream in New South Wales, Australia, was much poorer than in two reference streams. Multivariate RELATE analyses indicated that the patterns in the biological data were more strongly correlated with the concentrations of common metals in the surface water than the pore water of these streams. From this we hypothesised that the water was more toxic to the biota than the sediment and we tested this hypothesis with a sediment transplant experiment. Sediment from Neubecks Ck that was placed in reference streams retained high concentrations of metals throughout the experiment, yet supported a macroinvertebrate assemblage similar to that in the reference streams. Sediment from the reference streams that was placed in Neubecks Ck supported few, if any, animals. This indicates that water in Neubecks Ck is toxic to biota, but that sediment is able to support aquatic biota in clean water. Therefore, remediation should focus on improving water quality rather than sediment quality. - Macroinvertebrates colonise contaminated sediment in clean water

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

2017-11-01

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

Developing and Implementing the Data Mining Algorithms in RAVEN

International Nuclear Information System (INIS)

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea; Rabiti, Cristian

2015-01-01

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.

Developing and Implementing the Data Mining Algorithms in RAVEN

Energy Technology Data Exchange (ETDEWEB)

Sen, Ramazan Sonat [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.

Opportunities for membrane technologies in the treatment of mining and mineral process streams and effluents

International Nuclear Information System (INIS)

Awadalla, F.T.; Kumar, A.

1994-01-01

The membrane separation technologies of microfiltration, ultrafiltration, nanofiltration, and reverse osmosis are suitable for treating many dilute streams and effluents generated in mining and mineral processing. Membrane technologies are capable of treating these dilute streams in order to produce clean permeate water for recycle and a concentrate that can potentially be used for valuable metals recovery. Membrane technologies can be utilized alone, or in combination with other techniques as a polishing step, in these separation processes. A review of potential applications of membranes for the treatment of different process streams and effluents for water recycling and pollution control is given here. Although membranes may not be optimum in all applications, these technologies are recognized in the mining sector for the many potential advantages they can provide. 59 refs

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

Science.gov (United States)

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

2014-10-01

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

On the Suitability of Genetic-Based Algorithms for Data Mining

NARCIS (Netherlands)

Choenni, R.S.

1998-01-01

Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search

Examining microbial community response to a strong chemical gradient: the effects of surface coal mining on stream bacteria

Science.gov (United States)

Bier, R.; Lindberg, T. T.; Wang, S.; Ellis, J. C.; Di Giulio, R. T.; Bernhardt, E. S.

2012-12-01

Surface coal mining is the dominant form of land cover change in northern and central Appalachia. In this process, shallow coal seams are exposed by removing overlying rock with explosives. The resulting fragmented carbonate rock and coal residues are disposed of in stream valleys. These valley fills generate alkaline mine drainage (AlkMD), dramatically increasing alkalinity, ionic strength, substrate supply (esp. SO42-), and trace element (Mn, Li, Se, U) concentrations in downstream rivers as well as significant losses of sensitive fish and macroinvertebrate species. In prior work within the Mud River, which drains the largest surface mine complex in Appalachia, we found that concentrations of AlkMD increase proportionally with the extent of upstream mining. Here we ask "How do stream microbial communities change along this strong chemical gradient?" We collected surface water and benthic biofilms from 25 stream reaches throughout the Mud River spanning the full range of surface mining impacts, with 0-96% of the contributing watershed area converted to surface coal mines. Microbial communities were collected from biofilms grown on a common substrate (red maple veneers) that were incubated in each stream reach for four months prior to collection in April, 2011. 16S rRNA genes from microbial communities at each study site were examined using 454 sequencing and compared with a generalized UniFrac distance matrix (674 sequence eveness) that was used in statistical analyses. Water chemistry at the sites was sampled monthly from July 2010 to December 2010 and again in April 2011. In April, surface water concentrations of SO42-, Ca2+, Mg2+, and Se2- increased linearly with the extent of upstream mining (all regressions R2 >0.43; pPERMANOVA; p=0.029). Bacterial diversity (OTU richness defined at 3% sequence difference) peaked at intermediate conductivities (600 μS cm-1). Environmental data that correlated significantly with the ordination axes were a variety of surface

Symmetric Stream Cipher using Triple Transposition Key Method and Base64 Algorithm for Security Improvement

Science.gov (United States)

Nurdiyanto, Heri; Rahim, Robbi; Wulan, Nur

2017-12-01

Symmetric type cryptography algorithm is known many weaknesses in encryption process compared with asymmetric type algorithm, symmetric stream cipher are algorithm that works on XOR process between plaintext and key, to improve the security of symmetric stream cipher algorithm done improvisation by using Triple Transposition Key which developed from Transposition Cipher and also use Base64 algorithm for encryption ending process, and from experiment the ciphertext that produced good enough and very random.

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

Science.gov (United States)

Ying Wah, Teh

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753

Recovery of a mining-damaged stream ecosystem

Directory of Open Access Journals (Sweden)

Christopher A. Mebane

2015-03-01

Full Text Available Abstract This paper presents a 30+ year record of changes in benthic macroinvertebrate communities and fish populations associated with improving water quality in mining-influenced streams. Panther Creek, a tributary to the Salmon River in central Idaho, USA suffered intensive damage from mining and milling operations at the Blackbird Mine that released copper (Cu, arsenic (As, and cobalt (Co into tributaries. From the 1960s through the 1980s, no fish and few aquatic invertebrates could be found in 40 km of mine-affected reaches of Panther Creek downstream of the metals contaminated tributaries, Blackbird and Big Deer Creeks. Efforts to restore water quality began in 1995, and by 2002 Cu levels had been reduced by about 90%, with incremental declines since. Rainbow Trout (Oncorhynchus mykiss were early colonizers, quickly expanding their range as areas became habitable when Cu concentrations dropped below about 3X the U.S. Environmental Protection Agency’s biotic ligand model (BLM based chronic aquatic life criterion. Anadromous Chinook Salmon (O. tshawytscha and steelhead (O. mykiss have also reoccupied Panther Creek. Full recovery of salmonid populations occurred within about 12-years after the onset of restoration efforts and about 4-years after the Cu chronic criteria had mostly been met, with recovery interpreted as similarity in densities, biomass, year class strength, and condition factors between reference sites and mining-influenced sites. Shorthead Sculpin (Cottus confusus were slower than salmonids to disperse and colonize. While benthic macroinvertebrate biomass has increased, species richness has plateaued at about 70 to 90% of reference despite the Cu criterion having been met for several years. Different invertebrate taxa had distinctly different recovery trajectories. Among the slowest taxa to recover were Ephemerella, Cinygmula and Rhithrogena mayflies, Enchytraeidae oligochaetes, and Heterlimnius aquatic beetles. Potential

A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays

Directory of Open Access Journals (Sweden)

Kittipong Hiriotappa

2017-01-01

Full Text Available Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm.

Exploration of diffuse and discrete sources of acid mine drainage to a headwater mountain stream in Colorado, USA

Science.gov (United States)

Johnston, Allison; Runkel, Robert L.; Navarre-Sitchler, Alexis; Singha, Kamini

2017-01-01

We investigated the impact of acid mine drainage (AMD) contamination from the Minnesota Mine, an inactive gold and silver mine, on Lion Creek, a headwater mountain stream near Empire, Colorado. The objective was to map the sources of AMD contamination, including discrete sources visible at the surface and diffuse inputs that were not readily apparent. This was achieved using geochemical sampling, in-stream and in-seep fluid electrical conductivity (EC) logging, and electrical resistivity imaging (ERI) of the subsurface. The low pH of the AMD-impacted water correlated to high fluid EC values that served as a target for the ERI. From ERI, we identified two likely sources of diffuse contamination entering the stream: (1) the subsurface extent of two seepage faces visible on the surface, and (2) rainfall runoff washing salts deposited on the streambank and in a tailings pile on the east bank of Lion Creek. Additionally, rainfall leaching through the tailings pile is a potential diffuse source of contamination if the subsurface beneath the tailings pile is hydraulically connected with the stream. In-stream fluid EC was lowest when stream discharge was highest in early summer and then increased throughout the summer as stream discharge decreased, indicating that the concentration of dissolved solids in the stream is largely controlled by mixing of groundwater and snowmelt. Total dissolved solids (TDS) load is greatest in early summer and displays a large diel signal. Identification of diffuse sources and variability in TDS load through time should allow for more targeted remediation options.

A backtracking algorithm for the stream AND-parallel execution of logic programs

Energy Technology Data Exchange (ETDEWEB)

Somogyi, Z.; Ramamohanarao, K.; Vaghani, J. (Univ. of Melbourne, Parkville (Australia))

1988-06-01

The authors present the first backtracking algorithm for stream AND-parallel logic programs. It relies on compile-time knowledge of the data flow graph of each clause to let it figure out efficiently which goals to kill or restart when a goal fails. This crucial information, which they derive from mode declarations, was not available at compile-time in any previous stream AND-parallel system. They show that modes can increase the precision of the backtracking algorithm, though their algorithm allows this precision to be traded off against overhead on a procedure-by-procedure and call-by-call basis. The modes also allow their algorithm to handle efficiently programs that manipulate partially instantiated data structures and an important class of programs with circular dependency graphs. On code that does not need backtracking, the efficiency of their algorithm approaches that of the committed-choice languages; on code that does need backtracking its overhead is comparable to that of the independent AND-parallel backtracking algorithms.

HYBRID CHRIPTOGRAPHY STREAM CIPHER AND RSA ALGORITHM WITH DIGITAL SIGNATURE AS A KEY

Directory of Open Access Journals (Sweden)

Grace Lamudur Arta Sihombing

2017-03-01

Full Text Available Confidentiality of data is very important in communication. Many cyber crimes that exploit security holes for entry and manipulation. To ensure the security and confidentiality of the data, required a certain technique to encrypt data or information called cryptography. It is one of the components that can not be ignored in building security. And this research aimed to analyze the hybrid cryptography with symmetric key by using a stream cipher algorithm and asymmetric key by using RSA (Rivest Shamir Adleman algorithm. The advantages of hybrid cryptography is the speed in processing data using a symmetric algorithm and easy transfer of key using asymmetric algorithm. This can increase the speed of transaction processing data. Stream Cipher Algorithm using the image digital signature as a keys, that will be secured by the RSA algorithm. So, the key for encryption and decryption are different. Blum Blum Shub methods used to generate keys for the value p, q on the RSA algorithm. It will be very difficult for a cryptanalyst to break the key. Analysis of hybrid cryptography stream cipher and RSA algorithms with digital signatures as a key, indicates that the size of the encrypted file is equal to the size of the plaintext, not to be larger or smaller so that the time required for encryption and decryption process is relatively fast.

An efficient reversible privacy-preserving data mining technology over data streams.

Science.gov (United States)

Lin, Chen-Yi; Kao, Yuan-Hung; Lee, Wei-Bin; Chen, Rong-Chang

2016-01-01

With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices.

Influence of acid mine drainage on microbial communities in stream and groundwater samples at Guryong Mine, South Korea

Science.gov (United States)

Kim, Jaisoo; Koo, So-Yeon; Kim, Ji-Young; Lee, Eun-Hee; Lee, Sang-Don; Ko, Kyung-Seok; Ko, Dong-Chan; Cho, Kyung-Suk

2009-10-01

The effects of acid mine drainage (AMD) in a stream and groundwater near an abandoned copper mine were characterized by physicochemical properties, bacterial community structure using denaturing gel gradient electrophoresis (DGGE), and microbial activity/diversity using Ecoplate technique. Based on DGGE fingerprints, the eubacterial community structures grouped into the stream water (GRS1, GRS2 and GRS3) and groundwater samples (GW1 and GW2), apparently based on differences in water temperature and the concentrations of dissolved oxygen, nitrate and sulfate. The most highly AMD-contaminated sample (GRS1) had additional α-Proteobacteria whereas the groundwater samples included additional β-Proteobacteria, suggesting the development of populations resistant to AMD toxicity under aerobic and anaerobic conditions, respectively. Community level physiological activities on the 31 Ecoplate substrates suggested that the activities decreased with increasing concentrations of sulfate and heavy metals derived from AMD. The Shannon index showed that microbial diversity was greatest in GRS2, and lowest in GRS1, and was probably related to the level of AMD.

New Splitting Criteria for Decision Trees in Stationary Data Streams.

Science.gov (United States)

Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

2018-06-01

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

A real time sorting algorithm to time sort any deterministic time disordered data stream

Science.gov (United States)

Saini, J.; Mandal, S.; Chakrabarti, A.; Chattopadhyay, S.

2017-12-01

In new generation high intensity high energy physics experiments, millions of free streaming high rate data sources are to be readout. Free streaming data with associated time-stamp can only be controlled by thresholds as there is no trigger information available for the readout. Therefore, these readouts are prone to collect large amount of noise and unwanted data. For this reason, these experiments can have output data rate of several orders of magnitude higher than the useful signal data rate. It is therefore necessary to perform online processing of the data to extract useful information from the full data set. Without trigger information, pre-processing on the free streaming data can only be done with time based correlation among the data set. Multiple data sources have different path delays and bandwidth utilizations and therefore the unsorted merged data requires significant computational efforts for real time manifestation of sorting before analysis. Present work reports a new high speed scalable data stream sorting algorithm with its architectural design, verified through Field programmable Gate Array (FPGA) based hardware simulation. Realistic time based simulated data likely to be collected in an high energy physics experiment have been used to study the performance of the algorithm. The proposed algorithm uses parallel read-write blocks with added memory management and zero suppression features to make it efficient for high rate data-streams. This algorithm is best suited for online data streams with deterministic time disorder/unsorting on FPGA like hardware.

An Optimization Routing Algorithm for Green Communication in Underground Mines

Directory of Open Access Journals (Sweden)

Heng Xu

2018-06-01

Full Text Available With the long-term dependence of humans on ore-based energy, underground mines are utilized around the world, and underground mining is often dangerous. Therefore, many underground mines have established networks that manage and acquire information from sensor nodes deployed on miners and in other places. Since the power supplies of many mobile sensor nodes are batteries, green communication is an effective approach of reducing the energy consumption of a network and extending its longevity. To reduce the energy consumption of networks, all factors that negatively influence the lifetime should be considered. The degree constraint minimum spanning tree (DCMST is introduced in this study to consider all the heterogeneous factors and assign weights for the next step of the evaluation. Then, a genetic algorithm (GA is introduced to cluster sensor nodes in the network and balance energy consumption according to several heterogeneous factors and routing paths from DCMST. Based on a comparison of the simulation results, the optimization routing algorithm proposed in this study for use in green communication in underground mines can effectively reduce the network energy consumption and extend the lifetimes of networks.

Data Mining at NASA: From Theory to Applications

Science.gov (United States)

Srivastava, Ashok N.

2009-01-01

This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

Practical mine ventilation optimization based on genetic algorithms for free splitting networks

Energy Technology Data Exchange (ETDEWEB)

Acuna, E.; Maynard, R.; Hall, S. [Laurentian Univ., Sudbury, ON (Canada). Mirarco Mining Innovation; Hardcastle, S.G.; Li, G. [Natural Resources Canada, Sudbury, ON (Canada). CANMET Mining and Mineral Sciences Laboratories; Lowndes, I.S. [Nottingham Univ., Nottingham (United Kingdom). Process and Environmental Research Division; Tonnos, A. [Bestech, Sudbury, ON (Canada)

2010-07-01

The method used to optimize the design and operation of mine ventilation has generally been based on case studies and expert knowledge. It has yet to benefit from optimization techniques used and proven in other fields of engineering. Currently, optimization of mine ventilation systems is a manual based decision process performed by an experienced mine ventilation specialist assisted by commercial ventilation distribution solvers. These analysis tools are widely used in the mining industry to evaluate the practical and economic viability of alternative ventilation system configurations. The scenario which is usually selected is the one that reports the lowest energy consumption while delivering the required airflow distribution. Since most commercial solvers do not have an integrated optimization algorithm network, the process of generating a series of potential ventilation solutions using the conventional iterative design strategy can be time consuming. For that reason, a genetic algorithm (GA) optimization routine was developed in combination with a ventilation solver to determine the potential optimal solutions of a primary mine ventilation system based on a free splitting network. The optimization method was used in a small size mine ventilation network. The technique was shown to have the capacity to generate good feasible solutions and improve upon the manual results obtained by mine ventilation specialists. 9 refs., 7 tabs., 3 figs.

Rare itemsets mining algorithm based on RP-Tree and spark framework

Science.gov (United States)

Liu, Sainan; Pan, Haoan

2018-05-01

For the issues of the rare itemsets mining in big data, this paper proposed a rare itemsets mining algorithm based on RP-Tree and Spark framework. Firstly, it arranged the data vertically according to the transaction identifier, in order to solve the defects of scan the entire data set, the vertical datasets are divided into frequent vertical datasets and rare vertical datasets. Then, it adopted the RP-Tree algorithm to construct the frequent pattern tree that contains rare items and generate rare 1-itemsets. After that, it calculated the support of the itemsets by scanning the two vertical data sets, finally, it used the iterative process to generate rare itemsets. The experimental show that the algorithm can effectively excavate rare itemsets and have great superiority in execution time.

Roles of Benthic Algae in the Structure, Function, and Assessment of Stream Ecosystems Affected by Acid Mine Drainage

Science.gov (United States)

Tens of thousands of stream kilometers around the world are degraded by a legacy of environmental impacts and acid mine drainage (AMD) caused by abandoned underground and surface mines, piles of discarded coal wastes, and tailings. Increased acidity, high concentrations of metals...

Frequent Pairs in Data Streams: Exploiting Parallelism and Skew

DEFF Research Database (Denmark)

Campagna, Andrea; Kutzkow, Konstantin; Pagh, Rasmus

2011-01-01

We introduce the Pair Streaming Engine (PairSE) that detects frequent pairs in a data stream of transactions. Our algorithm finds the most frequent pairs with high probability, and gives tight bounds on their frequency. It is particularly space efficient for skewed distribution of pair supports...... items mining in data streams. We show how to efficiently scale these approaches to handle large transactions. We report experimental results showcasing precision and recall of our method. In particular, we find that often our method achieves excellent precision, returning identical upper and lower...... bounds on the supports of the most frequent pairs....

Transmission Algorithm with QoS Considerations for a Sustainable MPEG Streaming Service

Directory of Open Access Journals (Sweden)

Sang-Hyong Kim

2017-03-01

Full Text Available With the proliferation of heterogeneous networks, there is a need to provide multimedia stream services in a sustainable manner. It is especially critical to maintain the Quality of Service (QoS standards. Existing multimedia streaming services have been studied to guarantee QoS on the receiving side. QoS has not been ensured due to the fact that the loss of streaming data to be transmitted has not been considered in network conditions. With an algorithm that considers the QoS and can reduce the overhead of the network, it will be possible to reduce the transmission error and wastage of communication network resources. In this paper, we propose a scheme that improves the reliability of multimedia transmissions by using an adaptive algorithm that switches between UDP (User Datagram Protocol and TCP (Transmission Control Protocol based on the size of the data. In addition, we present a method that retransmits essential portions of the multimedia data, thus improving transmission efficiency. We simulate an MPEG (Moving Picture Experts Group stream service and evaluate the performance of the proposed adaptive MPEG stream service.

Mining the National Career Assessment Examination Result Using Clustering Algorithm

Science.gov (United States)

Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

2018-03-01

Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

Using internal evaluation measures to validate the quality of diverse stream clustering algorithms

NARCIS (Netherlands)

Hassani, M.; Seidl, T.

2017-01-01

Measuring the quality of a clustering algorithm has shown to be as important as the algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for an input data. Streaming input data have many features that make them much more challenging than static ones. They

Vascular riffle flora of Appalachian streams: the ecology and effects of acid mine drainage on Justificia americana (L. ) Vahl

Energy Technology Data Exchange (ETDEWEB)

Koryak, M.; Reilly, R.J.

1984-06-01

Justicia americana is a stout-based colonial plant, abundant in most of the larger, low to moderate gradient streams of the upper Ohio River basin. The distribution of J. americana is related to acid drainage from bituminous coal mining operations in the upper Ohio River drainage basin. Possible fluvial and biological consequences of the colonization or absence of Justicia are considered. Luxuriant growths were noted on gravel bars and riffles of larger, unpolluted streams in the basin. Acid mine drainage severely depresses the growth of the plant, leaving gravel shoals and riffles in the acid streams either barren or dominated by other emergent species. Particular among these new species is Elecocharis acicularis. The elimination of J. americana from suitable habitat adversely affects channel morphology, substrate composition, general aesthetic quality and aquatic stream life in the region. 16 references, 2 figures, 3 tables.

Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining

Directory of Open Access Journals (Sweden)

P. Kalaivani

2015-01-01

Full Text Available With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

A hybrid heuristic algorithm for the open-pit-mining operational planning problem.

OpenAIRE

Souza, Marcone Jamilson Freitas; Coelho, Igor Machado; Ribas, Sabir; Santos, Haroldo Gambini; Merschmann, Luiz Henrique de Campos

2010-01-01

This paper deals with the Open-Pit-Mining Operational Planning problem with dynamic truck allocation. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to the literature, this problem is NPhard, so a heuristic strategy is justified. We present a hybrid algorithm that combines characteristics of two metaheuristics: Greedy Randomized Adaptive Search Procedures and General Varia...

MINING ON CAR DATABASE EMPLOYING LEARNING AND CLUSTERING ALGORITHMS

OpenAIRE

Muhammad Rukunuddin Ghalib; Shivam Vohra; Sunish Vohra; Akash Juneja

2013-01-01

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the known learning algorithms used are Naïve Bayesian (NB) and SMO (Self-Minimal-Optimisation) .Thus the following two learning algorithms are used on a Car review database and thus a model is hence created which predicts the characteristic of a review comment after getting trained. It was found that model successfully predicted correctly about the review comm...

Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.

Energy Technology Data Exchange (ETDEWEB)

Matulef, Kevin Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewer resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.

Data Stream Mining

Science.gov (United States)

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

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

Assessing mercury exposure and effects to American dippers in headwater streams near mining sites.

Science.gov (United States)

Henny, Charles J; Kaiser, James L; Packard, Heidi A; Grove, Robert A; Taft, Michael R

2005-10-01

To evaluate mercury (Hg) exposure and possible adverse effects of Hg on American dipper (Cinclus mexicanus) reproduction, we collected eggs and nestling feathers and the larval/nymph form of three Orders of aquatic macroinvertebrates (Ephemeroptera, Plecoptera and Trichoptera = EPT) important in their diet from three major headwater tributaries of the upper Willamette River, Oregon in 2002. The Coast Fork Willamette River is contaminated with Hg due to historical cinnabar (HgS) mining at the Black Butte Mine; the Row River is affected by past gold-mining operations located within the Bohemia Mining District, where Hg was used in the amalgamation process to recover gold; and the Middle Fork Willamette River is the reference area with no known mining. Methyl mercury (MeHg) concentrations (geometric mean) in composite EPT larvae (111.9 ng/g dry weight [dw] or 19.8 ng/g wet weight [ww]), dipper eggs (38.5 ng/g ww) and nestling feathers (1158 ng/g ww) collected from the Coast Fork Willamette were significantly higher than MeHg concentrations in EPT and dipper samples from other streams. Total mercury (THg) concentrations in surface sediments along the same Hg-impacted streams were investigated by others in 1999 (Row River tributaries) and 2002 (Coast Fork). The reported sediment THg concentrations paralleled our biological findings. Dipper breeding territories at higher elevations had fewer second clutches; however, dipper reproductive success along all streams (including the lower elevation and most Hg-contaminated Coast Fork), was judged excellent compared to other studies reviewed. Furthermore, MeHg concentrations in EPT samples from this study were well below dietary concentrations in other aquatic bird species, such as loons and ducks, reported to cause Hg-related reproductive problems. Our data suggest that either dipper feathers or EPT composites used to project MeHg concentrations in dipper feathers (with biomagnification factor of 10-20x) may be used, but with

Parallel field line and stream line tracing algorithms for space physics applications

Science.gov (United States)

Toth, G.; de Zeeuw, D.; Monostori, G.

2004-05-01

Field line and stream line tracing is required in various space physics applications, such as the coupling of the global magnetosphere and inner magnetosphere models, the coupling of the solar energetic particle and heliosphere models, or the modeling of comets, where the multispecies chemical equations are solved along stream lines of a steady state solution obtained with single fluid MHD model. Tracing a vector field is an inherently serial process, which is difficult to parallelize. This is especially true when the data corresponding to the vector field is distributed over a large number of processors. We designed algorithms for the various applications, which scale well to a large number of processors. In the first algorithm the computational domain is divided into blocks. Each block is on a single processor. The algorithm folows the vector field inside the blocks, and calculates a mapping of the block surfaces. The blocks communicate the values at the coinciding surfaces, and the results are interpolated. Finally all block surfaces are defined and values inside the blocks are obtained. In the second algorithm all processors start integrating along the vector field inside the accessible volume. When the field line leaves the local subdomain, the position and other information is stored in a buffer. Periodically the processors exchange the buffers, and continue integration of the field lines until they reach a boundary. At that point the results are sent back to the originating processor. Efficiency is achieved by a careful phasing of computation and communication. In the third algorithm the results of a steady state simulation are stored on a hard drive. The vector field is contained in blocks. All processors read in all the grid and vector field data and the stream lines are integrated in parallel. If a stream line enters a block, which has already been integrated, the results can be interpolated. By a clever ordering of the blocks the execution speed can be

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

Science.gov (United States)

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Selected Metals in Sediments and Streams in the Oklahoma Part of the Tri-State Mining District, 2000-2006

Science.gov (United States)

Andrews, William J.; Becker, Mark F.; Mashburn, Shana L.; Smith, S. Jerrod

2009-01-01

The abandoned Tri-State mining district includes 1,188 square miles in northeastern Oklahoma, southeastern Kansas, and southwestern Missouri. The most productive part of the Tri-State mining district was the 40-square mile part in Oklahoma, commonly referred to as 'the Picher mining district' in north-central Ottawa County, Oklahoma. The Oklahoma part of the Tri-State mining district was a primary producing area of lead and zinc in the United States during the first half of the 20th century. Sulfide minerals of cadmium, iron, lead, and zinc that remained in flooded underground mine workings and in mine tailings on the land surface oxidized and dissolved with time, forming a variety of oxide, hydroxide, and hydroxycarbonate metallic minerals on the land surface and in streams that drain the district. Metals in water and sediments in streams draining the mining district can potentially impair the habitat and health of many forms of aquatic and terrestrial life. Lakebed, streambed and floodplain sediments and/or stream water were sampled at 30 sites in the Oklahoma part of the Tri-State mining district by the U.S. Geological Survey and the Oklahoma Department of Environmental Quality from 2000 to 2006 in cooperation with the U.S. Environmental Protection Agency, and the Quapaw and Seneca-Cayuga Tribes of Oklahoma. Aluminum and iron concentrations of several thousand milligrams per kilogram were measured in sediments collected from the upstream end of Grand Lake O' the Cherokees. Manganese and zinc concentrations in those sediments were several hundred milligrams per kilogram. Lead and cadmium concentrations in those sediments were about 10 percent and 0.1 percent of zinc concentrations, respectively. Sediment cores collected in a transect across the floodplain of Tar Creek near Miami, Oklahoma, in 2004 had similar or greater concentrations of those metals than sediment cores collected at the upstream end of Grand Lake O' the Cherokees. The greatest concentrations of

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang

2012-01-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng

2012-09-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users\\' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users\\' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

Effects of remediation on the bacterial community of an acid mine drainage impacted stream.

Science.gov (United States)

Ghosh, Suchismita; Moitra, Moumita; Woolverton, Christopher J; Leff, Laura G

2012-11-01

Acid mine drainage (AMD) represents a global threat to water resources, and as such, remediation of AMD-impacted streams is a common practice. During this study, we examined bacterial community structure and environmental conditions in a low-order AMD-impacted stream before, during, and after remediation. Bacterial community structure was examined via polymerase chain reaction amplification of 16S rRNA genes followed by denaturing gradient gel electrophoresis. Also, bacterial abundance and physicochemical data (including metal concentrations) were collected and relationships to bacterial community structure were determined using BIO-ENV analysis. Remediation of the study stream altered environmental conditions, including pH and concentrations of some metals, and consequently, the bacterial community changed. However, remediation did not necessarily restore the stream to conditions found in the unimpacted reference stream; for example, bacterial abundances and concentrations of some elements, such as sulfur, magnesium, and manganese, were different in the remediated stream than in the reference stream. BIO-ENV analysis revealed that changes in pH and iron concentration, associated with remediation, primarily explained temporal alterations in bacterial community structure. Although the sites sampled in the remediated stream were in relatively close proximity to each other, spatial variation in community composition suggests that differences in local environmental conditions may have large impacts on the microbial assemblage.

An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining

OpenAIRE

Yogendra Kumar Jain,; Vinod Kumar Yadav,; Geetika S. Panday

2011-01-01

The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Association analysis is a powerful toolfor discovering relationships which are hidden in large database. Association rules hiding algorithms get strong and efficient performance for protecting confidential and cru...

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

CERN Document Server

Pal, Sankar K

2004-01-01

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

Aquatic insect deversity and biomass in a stream marginally polluted by acid strip mine drainage

Energy Technology Data Exchange (ETDEWEB)

Tomkiewicz, S.M. Jr.; Dunson, W.A.

1977-01-01

Upper Three Runs receives a point source of acid mine drainage from a small acid feeder stream and the pH of the main stream falls from above 6 to about 4.5. Over the 1.2 km study section below the introduction of acid drainage, the pH rises to 5.0. This moderate degree of mine acid pollution has severely affected aquatic insect populations. The acid feeder itself (pH near 3.2) was inhabited only by a chironomid, a megalopteran (Sialis), and the caddisfly Ptilostomis. Biomass was very low (140 mg dry weight/m/sup 2/). The drainage of the acid feeder into the stream caused a drop in the Shannon-Weiner diversity index from 3.10 to 1.95, and a drop in biomass from 6.5 g/m/sup 2/ to 2.2 g/m/sup 2/. At the two stations further downstream, the diversity index remained relatively constant and the biomass leveled off at about 1.2 g/m/sup 2/. The number of taxa declined steadily from 30 at the control station to 13 at the lowest site. Populations of Coleoptera, Ephemeroptera and Trichoptera showed little or no recovery as the acid pollution ameliorated slightly. Representatives of the orders Diptera and Plecoptera (especially Nemoura) showed a decided recovery and increase in numbers near pH 5.0. If fish were able to survive in acid mine polluted waters of pH's between 4.5 and 5.0 they should find sufficient insect food for maintenance of a limited population.

An imperialist competitive algorithm for solving the production scheduling problem in open pit mine

Directory of Open Access Journals (Sweden)

Mojtaba Mokhtarian Asl

2016-06-01

Full Text Available Production scheduling (planning of an open-pit mine is the procedure during which the rock blocks are assigned to different production periods in a way that the highest net present value of the project achieved subject to operational constraints. The paper introduces a new and computationally less expensive meta-heuristic technique known as imperialist competitive algorithm (ICA for long-term production planning of open pit mines. The proposed algorithm modifies the original rules of the assimilation process. The ICA performance for different levels of the control factors has been studied and the results are presented. The result showed that ICA could be efficiently applied on mine production planning problem.

Soil Erosion from Agriculture and Mining: A Threat to Tropical Stream Ecosystems

Directory of Open Access Journals (Sweden)

Jan H. Mol

2013-09-01

Full Text Available In tropical countries soil erosion is often increased due to high erodibility of geologically old and weathered soils; intensive rainfall; inappropriate soil management; removal of forest vegetation cover; and mining activities. Stream ecosystems draining agricultural or mining areas are often severely impacted by the high loads of eroded material entering the stream channel; increasing turbidity; covering instream habitat and affecting the riparian zone; and thereby modifying habitat and food web structures. The biodiversity is severely threatened by these negative effects as the aquatic and riparian fauna and flora are not adapted to cope with excessive rates of erosion and sedimentation. Eroded material may also be polluted by pesticides or heavy metals that have an aggravating effect on functions and ecosystem services. Loss of superficial material and deepening of erosion gullies impoverish the nutrient and carbon contents of the soils; and lower the water tables; causing a “lose-lose” situation for agricultural productivity and environmental integrity. Several examples show how to interrupt this vicious cycle by integrated catchment management and by combining “green” and “hard” engineering for habitat restoration. In this review; we summarize current findings on this issue from tropical countries with a focus on case studies from Suriname and Brazil.

Export of detritus and invertebrate from headwater streams: linking mountaintop removal and valley fill coal mining to downstream receiving waters

Science.gov (United States)

Mountaintop removal and valley fill (MTR/VF) coal mining has resulted in large scale alteration of the topography, reduced forest productivity, and burial of headwater streams in the U.S. Central Appalachians. Although MTR/VF coal mining has occurred for several decades and the ...

On the Organization of Parallel Operation of Some Algorithms for Finding the Shortest Path on a Graph on a Computer System with Multiple Instruction Stream and Single Data Stream

Directory of Open Access Journals (Sweden)

V. E. Podol'skii

2015-01-01

Full Text Available The paper considers the implementing Bellman-Ford and Lee algorithms to find the shortest graph path on a computer system with multiple instruction stream and single data stream (MISD. The MISD computer is a computer that executes commands of arithmetic-logical processing (on the CPU and commands of structures processing (on the structures processor in parallel on a single data stream. Transformation of sequential programs into the MISD programs is a labor intensity process because it requires a stream of the arithmetic-logical processing to be manually separated from that of the structures processing. Algorithms based on the processing of data structures (e.g., algorithms on graphs show high performance on a MISD computer. Bellman-Ford and Lee algorithms for finding the shortest path on a graph are representatives of these algorithms. They are applied to robotics for automatic planning of the robot movement in-situ. Modification of Bellman-Ford and Lee algorithms for finding the shortest graph path in coprocessor MISD mode and the parallel MISD modification of these algorithms were first obtained in this article. Thus, this article continues a series of studies on the transformation of sequential algorithms into MISD ones (Dijkstra and Ford-Fulkerson 's algorithms and has a pronouncedly applied nature. The article also presents the analysis results of Bellman-Ford and Lee algorithms in MISD mode. The paper formulates the basic trends of a technique for parallelization of algorithms into arithmetic-logical processing stream and structures processing stream. Among the key areas for future research, development of the mathematical approach to provide a subsequently formalized and automated process of parallelizing sequential algorithms between the CPU and structures processor is highlighted. Among the mathematical models that can be used in future studies there are graph models of algorithms (e.g., dependency graph of a program. Due to the high

Development of energy-saving technologies providing comfortable microclimate conditions for mining

Directory of Open Access Journals (Sweden)

Б. П. Казаков

2017-03-01

Full Text Available The paper contains analysis of natural and technogenic factors influencing properties of mine atmosphere, defining level of mining safety and probability of emergencies. Main trends in development of energy-saving technologies providing comfortable microclimate conditions are highlighted. A complex of methods and mathematical models has been developed to carry out aerologic and thermophysical calculations. Main ways of improvement for existing calculation methods of stationary and non-stationary air distribution have been defined: use of ejection draught sources to organize recirculation ventilation; accounting of depression losses at working intersections; inertance impact of  air streams and mined-out spaces for modeling transitory emergency scenarios. Based on the calculation algorithm of airflow rate distribution in the mine network, processing method has been developed for the results of air-depressive surveys under conditions of data shortage. Processes of dust transfer have been modeled in view of its coagulation and settlement, as well as interaction with water drops in case of wet dust prevention. A method to calculate intensity of water evaporation and condensation has been suggested, which allows to forecast time, duration and quantity of precipitation and its migration inside the mine during winter season. Solving the problem of heat exchange between mine airflow and timbering of the ventilation shaft in a conjugation formulation permits to estimate depression value of natural draught and conditions of convective balance between air streams. Normalization of microclimatic parameters for mine atmosphere is forecasted for the use of heat-exchange units either heating or cooling and dehumidifying ventilation air. Algorithms are presented that permit to minimize ventilation energy demands at the stages of mine design and exploitation.

A study of the use of seeded ultrafiltration for the treatment of Thorium-uranium mining waste streams

International Nuclear Information System (INIS)

El-Sourougy, M.R.; Hooper, E.W.

1994-01-01

The use of seeded ultrafiltration for the treatment of radioactive waste streams arising from the nuclear industry has demonstrated its high potential as an efficient process for the removal of radionuclides present in the radwaste streams. The experimental data on simulated mining streams has given indications on the suitability of this technique for the treatment of mining waste streams. The results also show that the proper choice of absorbers can reduce not only the radioactivity level but also remove most of the products of both the thorium and uranium decay series. Decontamination factor (DF) for the system using manganese dioxide (MnO 2 ) are only slightly affected by the preparation method. On the contrary, the DF achieved using titanium hydroxide (HTiO) absorber was found to be dependent on the preparation method. The experimental data shows that total activity levels can be reduced to below detection limit (3E-3Bq/ml). The extent of decontamination of thorium containing waste streams was found to be dependent on the absorber used; in the order Diuranate > HTiO > Fe(OH) 3 > MnO 2 . The use of HTiO reduced the decay product activity of almost all the thorium daughters to nearly background levels. A DF of the order of 300 can easily be achieved using diuranate floc

Classification of Internet banking customers using data mining algorithms

Directory of Open Access Journals (Sweden)

Reza Radfar

2014-03-01

Full Text Available Classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. In this article we have presented an appropriate model to classify customers who use internet banking service. The model is developed based on CRISP-DM standard and we have used real data of Sina bank’s Internet bank. In compare to other decision trees, ours is based on both optimization and accuracy factors that recognizes new potential internet banking customers using a three level classification, which is low/medium and high. This is a practical, documentary-based research. Mining customer rules enables managers to make policies based on found out patterns in order to have a better perception of what customers really desire.

Background Traffic-Based Retransmission Algorithm for Multimedia Streaming Transfer over Concurrent Multipaths

Directory of Open Access Journals (Sweden)

Yuanlong Cao

2012-01-01

Full Text Available The content-rich multimedia streaming will be the most attractive services in the next-generation networks. With function of distribute data across multipath end-to-end paths based on SCTP's multihoming feature, concurrent multipath transfer SCTP (CMT-SCTP has been regarded as the most promising technology for the efficient multimedia streaming transmission. However, the current researches on CMT-SCTP mainly focus on the algorithms related to the data delivery performance while they seldom consider the background traffic factors. Actually, background traffic of realistic network environments has an important impact on the performance of CMT-SCTP. In this paper, we firstly investigate the effect of background traffic on the performance of CMT-SCTP based on a close realistic simulation topology with reasonable background traffic in NS2, and then based on the localness nature of background flow, a further improved retransmission algorithm, named RTX_CSI, is proposed to reach more benefits in terms of average throughput and achieve high users' experience of quality for multimedia streaming services.

An Application of Data Mining Algorithms for Shipbuilding Cost Estimation

NARCIS (Netherlands)

Kaluzny, B.L.; Barbici, S.; Berg, G.; Chiomento, R.; Derpanis,D.; Jonsson, U.; Shaw, R.H.A.D.; Smit, M.C.; Ramaroson, F.

2011-01-01

This article presents a novel application of known data mining algorithms to the problem of estimating the cost of ship development and construction. The work is a product of North Atlantic Treaty Organization Research and Technology Organization Systems Analysis and Studies 076 Task Group “NATO

Geochemical Characterization of Mine Waste, Mine Drainage, and Stream Sediments at the Pike Hill Copper Mine Superfund Site, Orange County, Vermont

Science.gov (United States)

Piatak, Nadine M.; Seal, Robert R.; Hammarstrom, Jane M.; Kiah, Richard G.; Deacon, Jeffrey R.; Adams, Monique; Anthony, Michael W.; Briggs, Paul H.; Jackson, John C.

2006-01-01

The Pike Hill Copper Mine Superfund Site in the Vermont copper belt consists of the abandoned Smith, Eureka, and Union mines, all of which exploited Besshi-type massive sulfide deposits. The site was listed on the U.S. Environmental Protection Agency (USEPA) National Priorities List in 2004 due to aquatic ecosystem impacts. This study was intended to be a precursor to a formal remedial investigation by the USEPA, and it focused on the characterization of mine waste, mine drainage, and stream sediments. A related study investigated the effects of the mine drainage on downstream surface waters. The potential for mine waste and drainage to have an adverse impact on aquatic ecosystems, on drinking- water supplies, and to human health was assessed on the basis of mineralogy, chemical concentrations, acid generation, and potential for metals to be leached from mine waste and soils. The results were compared to those from analyses of other Vermont copper belt Superfund sites, the Elizabeth Mine and Ely Copper Mine, to evaluate if the waste material at the Pike Hill Copper Mine was sufficiently similar to that of the other mine sites that USEPA can streamline the evaluation of remediation technologies. Mine-waste samples consisted of oxidized and unoxidized sulfidic ore and waste rock, and flotation-mill tailings. These samples contained as much as 16 weight percent sulfides that included chalcopyrite, pyrite, pyrrhotite, and sphalerite. During oxidation, sulfides weather and may release potentially toxic trace elements and may produce acid. In addition, soluble efflorescent sulfate salts were identified at the mines; during rain events, the dissolution of these salts contributes acid and metals to receiving waters. Mine waste contained concentrations of cadmium, copper, and iron that exceeded USEPA Preliminary Remediation Goals. The concentrations of selenium in mine waste were higher than the average composition of eastern United States soils. Most mine waste was

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

International Nuclear Information System (INIS)

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

2006-01-01

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

An Approximate L p Difference Algorithm for Massive Data Streams

Directory of Open Access Journals (Sweden)

Jessica H. Fong

2001-12-01

Full Text Available Several recent papers have shown how to approximate the difference ∑ i |a i-b i | or ∑|a i-b i | 2 between two functions, when the function values a i and b i are given in a data stream, and their order is chosen by an adversary. These algorithms use little space (much less than would be needed to store the entire stream and little time to process each item in the stream. They approximate with small relative error. Using different techniques, we show how to approximate the L p-difference ∑ i |a i-b i | p for any rational-valued p∈(0,2], with comparable efficiency and error. We also show how to approximate ∑ i |a i-b i | p for larger values of p but with a worse error guarantee. Our results fill in gaps left by recent work, by providing an algorithm that is precisely tunable for the application at hand. These results can be used to assess the difference between two chronologically or physically separated massive data sets, making one quick pass over each data set, without buffering the data or requiring the data source to pause. For example, one can use our techniques to judge whether the traffic on two remote network routers are similar without requiring either router to transmit a copy of its traffic. A web search engine could use such algorithms to construct a library of small ``sketches,'' one for each distinct page on the web; one can approximate the extent to which new web pages duplicate old ones by comparing the sketches of the web pages. Such techniques will become increasingly important as the enormous scale, distributional nature, and one-pass processing requirements of data sets become more commonplace.

rEMM: Extensible Markov Model for Data Stream Clustering in R

Directory of Open Access Journals (Sweden)

Michael Hahsler

2010-10-01

Full Text Available Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the R extension package rEMM which implements EMM and we discuss some examples and applications.

Novel mining methods

CSIR Research Space (South Africa)

Monchusi, B

2012-10-01

Full Text Available stream_source_info Monchusi_2012.pdf.txt stream_content_type text/plain stream_size 1953 Content-Encoding ISO-8859-1 stream_name Monchusi_2012.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Novel Mining Methods 4th... 2012 Slide 12 CSIR mine safety platform AR Drone Differential time-of-flight beacon Sampling ? CSIR 2012 Slide 13 Reef Laser-Induced Breakdown Spectroscopy (LIBS) head Scan X-Y Laser/Spectrometer/Computer Rock Breaking ? CSIR 2012 Slide...

Data mining with SPSS modeler theory, exercises and solutions

CERN Document Server

Wendler, Tilo

2016-01-01

Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.

Quality and mutagenicity of water and sediment of the streams impacted by the former uranium mine area Olší-Drahonín (Czech Republic).

Science.gov (United States)

Hudcová, H; Badurová, J; Rozkošný, M; Sova, J; Funková, R; Svobodová, J

2013-02-01

The water quality research performed in the years 2003-2010 demonstrated an impact of the mine water pumped from the closed Olší uranium mine and discharged from the mine water treatment plant (MWTP) and groundwater from springs in the area on the water quality of the Hadůvka stream. The water ecosystems of the lower part of the Hadůvka stream are impacted mainly by water originated from the springs located in the stream valley and drained syenit subsoil, naturally rich in uranium. Those inflows caused a very high concentration of uranium measured in the water of the stream, which exceeds the given limit value. No negative impact on the water ecosystems of the receiving Bobrůvka River was found. This reduction of impact is caused by five times higher average daily flow rate of the Bobrůvka River in comparison with the Hadůvka stream, which results in a sufficient dilution of pollution from the Hadůvka. Copyright © 2012 Elsevier Ltd. All rights reserved.

Quality and mutagenicity of water and sediment of the streams impacted by the former uranium mine area Olší–Drahonín (Czech Republic)

International Nuclear Information System (INIS)

Hudcová, H.; Badurová, J.; Rozkošný, M.; Sova, J.; Funková, R.; Svobodová, J.

2013-01-01

The water quality research performed in the years 2003–2010 demonstrated an impact of the mine water pumped from the closed Olší uranium mine and discharged from the mine water treatment plant (MWTP) and groundwater from springs in the area on the water quality of the Hadůvka stream. The water ecosystems of the lower part of the Hadůvka stream are impacted mainly by water originated from the springs located in the stream valley and drained syenit subsoil, naturally rich in uranium. Those inflows caused a very high concentration of uranium measured in the water of the stream, which exceeds the given limit value. No negative impact on the water ecosystems of the receiving Bobrůvka River was found. This reduction of impact is caused by five times higher average daily flow rate of the Bobrůvka River in comparison with the Hadůvka stream, which results in a sufficient dilution of pollution from the Hadůvka. - Highlights: ► No significant impact of former uranium mining in the Olší mine area on the water ecosystems. ► The water ecosystems impacted mainly by natural sources of uranium. ► The occurrence of mutagenic compounds in the surface water was found using Ames fluctuated test. ► The mutagenicity was repeatedly detected in sediments. ► None of the samples showed cytotoxic effects in tests with S. typhimurium or P. phosphoreum organisms.

Comparison analysis for classification algorithm in data mining and the study of model use

Science.gov (United States)

Chen, Junde; Zhang, Defu

2018-04-01

As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

Adsorption of copper, cadmium and zinc on suspended sediments in a stream contaminated by acid mine drainage: The effect of seasonal changes in dissolved organic carbon

International Nuclear Information System (INIS)

Macalady, D.L.; Ranville, J.F.; Smith, K.S.; Daniel, S.R.

1991-01-01

The release of metal-rich, acidic waters from abandoned mining operations is a major problem in Colorado and throughout the Western United States. In Colorado, over 600 km of stream reach are estimated to be affected by such releases (Wentz, 1974). The metals released adversely affect stream biota, including fish. It is therefore important to understand the chemical processes which influence metal transport in these waters. The report details studies of the role of suspended sediments with respect to the transport of several important trace metals in a stream impacted by acid mine drainage. The role of streambed sediments was studied in the same system as part of an earlier project (Acid Mine Drainage: streambed sorption of copper, cadmium and zinc, PB--93-118263)

Attribute Index and Uniform Design Based Multiobjective Association Rule Mining with Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Jie Zhang

2013-01-01

Full Text Available In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm.

Science.gov (United States)

Zhang, Jie; Wang, Yuping; Feng, Junhong

2013-01-01

In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

Rare Moss-Built Microterraces in a High-Altitude, Acid Mine Drainage-Polluted Stream (Cordillera Negra, Peru)

NARCIS (Netherlands)

Sevink, J.; Verstraten, J.M.; Kooijman, A.M.; Loayza-Muro, R.A.; Hoitinga, L.; Palomino, E.J.; Jansen, B.

2015-01-01

The Rio Santiago in the Cordillera Negra of Peru is severely contaminated by acid mine drainage in its headwaters. In a strongly acid stream, at about 3800 m above sea level (masl), microterraces were found with terrace walls built up of dead moss, with encrustations and interstitial fine, creamy

Reacting to different types of concept drift: the Accuracy Updated Ensemble algorithm.

Science.gov (United States)

Brzezinski, Dariusz; Stefanowski, Jerzy

2014-01-01

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.

Hydrogeochemical characteristics of streams with and without acid mine drainage impacts: A paired catchment study in karst geology, SW China

Science.gov (United States)

Sun, Jing; Tang, Changyuan; Wu, Pan; Strosnider, William H. J.; Han, Zhiwei

2013-11-01

A paired catchment study was used to assess karst hydrogeochemistry of two streams.Chemistry of streams with and without acid mine drainage (AMD) was very different.The observation was supported by PHREEQC modeling of equilibrium conditions.Ionic fluxes of AMD-impacted water were higher than that of non-AMD-impacted water.The higher ionic fluxes were predominantly controlled by the oxidation of pyrite.

Potential toxic elements in stream sediments, soils and waters in an abandoned radium mine (central Portugal).

Science.gov (United States)

Antunes, I M H R; Neiva, A M R; Albuquerque, M T D; Carvalho, P C S; Santos, A C T; Cunha, Pedro P

2018-02-01

The Alto da Várzea radium mine (AV) exploited ore and U-bearing minerals, such as autunite and torbernite. The mine was exploited underground from 1911 to 1922, closed in 1946 without restoration, and actually a commercial area is deployed. Stream sediments, soils and water samples were collected between 2008 and 2009. Stream sediments are mainly contaminated in As, Th, U and W, which is related to the AV radium mine. The PTEs, As, Co, Cr, Sr, Th, U, W, Zn, and electrical conductivity reached the highest values in soils collected inside the mine influence. Soils are contaminated with As and U and must not be used for any purpose. Most waters have pH values ranging from 4.3 to 6.8 and are poorly mineralized (EC = 41-186 µS/cm; TDS = 33-172 mg/L). Groundwater contains the highest Cu, Cr and Pb contents. Arsenic occurs predominantly as H 2 (AsO 4 ) - and H(AsO 4 ) 2- . Waters are saturated in goethite, haematite and some of them also in lepidocrocite and ferrihydrite, which adsorbs As (V). Lead is divalent in waters collected during the warm season, being mobile in these waters. Thorium occurs mainly as Th(OH) 3 (CO 3 ) - , Th(OH) 2 (CO 3 ) and Th(OH) 2 (CO 3 ) 2 2- , which increase water Th contents. Uranium occurs predominantly as UO 2 CO 3 , but CaUO 2 (CO 3 ) 3 2- and CaUO 2 (CO 3 ) 3 also occur, decreasing its mobility in water. The waters are contaminated in NO 2 - , Mn, Cu, As, Pb and U and must not be used for human consumption and in agricultural activities. The water contamination is mainly associated with the old radium mine and human activities. A restoration of the mining area with PTE monitoring is necessary to avoid a public hazard.

A New Algorithm for Cartographic Simplification of Streams and Lakes Using Deviation Angles and Error Bands

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Türkay Gökgöz

2015-10-01

Full Text Available Multi-representation databases (MRDBs are used in several geographical information system applications for different purposes. MRDBs are mainly obtained through model and cartographic generalizations. Simplification is the essential operator of cartographic generalization, and streams and lakes are essential features in hydrography. In this study, a new algorithm was developed for the simplification of streams and lakes. In this algorithm, deviation angles and error bands are used to determine the characteristic vertices and the planimetric accuracy of the features, respectively. The algorithm was tested using a high-resolution national hydrography dataset of Pomme de Terre, a sub-basin in the USA. To assess the performance of the new algorithm, the Bend Simplify and Douglas-Peucker algorithms, the medium-resolution hydrography dataset of the sub-basin, and Töpfer’s radical law were used. For quantitative analysis, the vertex numbers, the lengths, and the sinuosity values were computed. Consequently, it was shown that the new algorithm was able to meet the main requirements (i.e., accuracy, legibility and aesthetics, and storage.

Review of samples of tailings, soils and stream sediment adjacent to and downstream from the Ruth Mine, Inyo County, California

Science.gov (United States)

Rytuba, James J.; Kim, Christopher S.; Goldstein, Daniel N.

2011-01-01

The Ruth Mine and mill are located in the western Mojave Desert in Inyo County, California (fig. 1). The mill processed gold-silver (Au-Ag) ores mined from the Ruth Au-Ag deposit, which is adjacent to the mill site. The Ruth Au-Ag deposit is hosted in Mesozoic intrusive rocks and is similar to other Au-Ag deposits in the western Mojave Desert that are associated with Miocene volcanic centers that formed on a basement of Mesozoic granitic rocks (Bateman, 1907; Gardner, 1954; Rytuba, 1996). The volcanic rocks consist of silicic domes and associated flows, pyroclastic rocks, and subvolcanic intrusions (fig. 2) that were emplaced into Mesozoic silicic intrusive rocks (Troxel and Morton, 1962). The Ruth Mine is on Federal land managed by the U.S. Bureau of Land Management (BLM). Tailings from the mine have been eroded and transported downstream into Homewood Canyon and then into Searles Valley (figs. 3, 4, 5, and 6). The BLM provided recreational facilities at the mine site for day-use hikers and restored and maintained the original mine buildings in collaboration with local citizen groups for use by visitors (fig. 7). The BLM requested that the U.S. Geological Survey (USGS), in collaboration with Chapman University, measure arsenic (As) and other geochemical constituents in soils and tailings at the mine site and in stream sediments downstream from the mine in Homewood Canyon and in Searles Valley (fig. 3). The request was made because initial sampling of the site by BLM staff indicated high concentrations of As in tailings and soils adjacent to the Ruth Mine. This report summarizes data obtained from field sampling of mine tailings and soils adjacent to the Ruth Mine and stream sediments downstream from the mine on June 7, 2009. Our results permit a preliminary assessment of the sources of As and associated chemical constituents that could potentially impact humans and biota.

Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm

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Xiong Yan

2016-01-01

Full Text Available In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP. In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.

On-Board Mining in the Sensor Web

Science.gov (United States)

Tanner, S.; Conover, H.; Graves, S.; Ramachandran, R.; Rushing, J.

2004-12-01

On-board data mining can contribute to many research and engineering applications, including natural hazard detection and prediction, intelligent sensor control, and the generation of customized data products for direct distribution to users. The ability to mine sensor data in real time can also be a critical component of autonomous operations, supporting deep space missions, unmanned aerial and ground-based vehicles (UAVs, UGVs), and a wide range of sensor meshes, webs and grids. On-board processing is expected to play a significant role in the next generation of NASA, Homeland Security, Department of Defense and civilian programs, providing for greater flexibility and versatility in measurements of physical systems. In addition, the use of UAV and UGV systems is increasing in military, emergency response and industrial applications. As research into the autonomy of these vehicles progresses, especially in fleet or web configurations, the applicability of on-board data mining is expected to increase significantly. Data mining in real time on board sensor platforms presents unique challenges. Most notably, the data to be mined is a continuous stream, rather than a fixed store such as a database. This means that the data mining algorithms must be modified to make only a single pass through the data. In addition, the on-board environment requires real time processing with limited computing resources, thus the algorithms must use fixed and relatively small amounts of processing time and memory. The University of Alabama in Huntsville is developing an innovative processing framework for the on-board data and information environment. The Environment for On-Board Processing (EVE) and the Adaptive On-board Data Processing (AODP) projects serve as proofs-of-concept of advanced information systems for remote sensing platforms. The EVE real-time processing infrastructure will upload, schedule and control the execution of processing plans on board remote sensors. These plans

Occurrence, distribution, and volume of metals-contaminated sediment of selected streams draining the Tri-State Mining District, Missouri, Oklahoma, and Kansas, 2011–12

Science.gov (United States)

Smith, D. Charlie

2016-12-14

Lead and zinc were mined in the Tri-State Mining District (TSMD) of southwest Missouri, northeast Oklahoma, and southeast Kansas for more than 100 years. The effects of mining on the landscape are still evident, nearly 50 years after the last mine ceased operation. The legacies of mining are the mine waste and discharge of groundwater from underground mines. The mine-waste piles and underground mines are continuous sources of trace metals (primarily lead, zinc, and cadmium) to the streams that drain the TSMD. Many previous studies characterized the horizontal extent of mine-waste contamination in streams but little information exists on the depth of mine-waste contamination in these streams. Characterizing the vertical extent of contamination is difficult because of the large amount of coarse-grained material, ranging from coarse gravel to boulders, within channel sediment. The U.S. Geological Survey, in cooperation with U.S. Fish and Wildlife service, collected channel-sediment samples at depth for subsequent analyses that would allow attainment of the following goals: (1) determination of the relation between concentration and depth for lead, zinc and cadmium in channel sediments and flood-plain sediments, and (2) determination of the volume of gravel-bar sediment from the surface to the maximum depth with concentrations of these metals that exceeded sediment-quality guidelines. For the purpose of this report, volume of gravel-bar sediment is considered to be distributed in two forms, gravel bars and the wetted channel, and this study focused on gravel bars. Concentrations of lead, zinc, and cadmium in samples were compared to the consensus probable effects concentration (CPEC) and Tri-State Mining District specific probable effects concentration (TPEC) sediment-quality guidelines.During the study, more than 700 sediment samples were collected from borings at multiple sites, including gravel bars and flood plains, along Center Creek, Turkey Creek, Shoal Creek

Geochemistry of acid mine drainage from a coal mining area and processes controlling metal attenuation in stream waters, southern Brazil

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VERIDIANA P. CAMPANER

2014-06-01

Full Text Available Acid drainage influence on the water and sediment quality was investigated in a coal mining area (southern Brazil. Mine drainage showed pH between 3.2 and 4.6 and elevated concentrations of sulfate, As and metals, of which, Fe, Mn and Zn exceeded the limits for the emission of effluents stated in the Brazilian legislation. Arsenic also exceeded the limit, but only slightly. Groundwater monitoring wells from active mines and tailings piles showed pH interval and chemical concentrations similar to those of mine drainage. However, the river and ground water samples of municipal public water supplies revealed a pH range from 7.2 to 7.5 and low chemical concentrations, although Cd concentration slightly exceeded the limit adopted by Brazilian legislation for groundwater. In general, surface waters showed large pH range (6 to 10.8, and changes caused by acid drainage in the chemical composition of these waters were not very significant. Locally, acid drainage seemed to have dissolved carbonate rocks present in the local stratigraphic sequence, attenuating the dispersion of metals and As. Stream sediments presented anomalies of these elements, which were strongly dependent on the proximity of tailings piles and abandoned mines. We found that precipitation processes in sediments and the dilution of dissolved phases were responsible for the attenuation of the concentrations of the metals and As in the acid drainage and river water mixing zone. In general, a larger influence of mining activities on the chemical composition of the surface waters and sediments was observed when enrichment factors in relation to regional background levels were used.

Geochemistry of acid mine drainage from a coal mining area and processes controlling metal attenuation in stream waters, southern Brazil.

Science.gov (United States)

Campaner, Veridiana P; Luiz-Silva, Wanilson; Machado, Wilson

2014-05-14

Acid drainage influence on the water and sediment quality was investigated in a coal mining area (southern Brazil). Mine drainage showed pH between 3.2 and 4.6 and elevated concentrations of sulfate, As and metals, of which, Fe, Mn and Zn exceeded the limits for the emission of effluents stated in the Brazilian legislation. Arsenic also exceeded the limit, but only slightly. Groundwater monitoring wells from active mines and tailings piles showed pH interval and chemical concentrations similar to those of mine drainage. However, the river and ground water samples of municipal public water supplies revealed a pH range from 7.2 to 7.5 and low chemical concentrations, although Cd concentration slightly exceeded the limit adopted by Brazilian legislation for groundwater. In general, surface waters showed large pH range (6 to 10.8), and changes caused by acid drainage in the chemical composition of these waters were not very significant. Locally, acid drainage seemed to have dissolved carbonate rocks present in the local stratigraphic sequence, attenuating the dispersion of metals and As. Stream sediments presented anomalies of these elements, which were strongly dependent on the proximity of tailings piles and abandoned mines. We found that precipitation processes in sediments and the dilution of dissolved phases were responsible for the attenuation of the concentrations of the metals and As in the acid drainage and river water mixing zone. In general, a larger influence of mining activities on the chemical composition of the surface waters and sediments was observed when enrichment factors in relation to regional background levels were used.

Benthic Communities of Low-Order Streams Affected by Acid Mine Drainages: A Case Study from Central Europe

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Marek Svitok

2014-05-01

Full Text Available Only little attention has been paid to the impact of acid mine drainages (AMD on aquatic ecosystems in Central Europe. In this study, we investigate the physico-chemical properties of low-order streams and the response of benthic invertebrates to AMD pollution in the Banská Štiavnica mining region (Slovakia. The studied streams showed typical signs of mine drainage pollution: higher conductivity, elevated iron, aluminum, zinc and copper loads and accumulations of ferric precipitates. Electric conductivity correlated strongly with most of the investigated elements (weighted mean absolute correlation = 0.95 and, therefore, can be recommended as a good proxy indicator for rapid AMD pollution assessments. The diversity and composition of invertebrate assemblages was related to water chemistry. Taxa richness decreased significantly along an AMD-intensity gradient. While moderately affected sites supported relatively rich assemblages, the harshest environmental conditions (pH < 2.5 were typical for the presence of a limited number of very tolerant taxa, such as Oligochaeta and some Diptera (Limnophyes, Forcipomyiinae. The trophic guild structure correlated significantly with AMD chemistry, whereby predators completely disappeared under the most severe AMD conditions. We also provide a brief review of the AMD literature and outline the needs for future detailed studies involving functional descriptors of the impact of AMD on aquatic ecosystems.

A Survey on Accessing Data over Cloud Environment using Data mining Algorithms

OpenAIRE

B.Prasanalakshmi; A.Selvaraj

2015-01-01

In today's world to access the large set of data is more complex, because the data may be structured and unstructured like in the form of text, images, videos, etc., it cannot be controlled from the internet users this is known as Big data. Useful data can be accessed through extracting from big data with the help of data mining algorithms. Data mining is a technique for determine the patterns; classify the data, clustering from the large set of data. In this paper we will discuss how large s...

Assessment of corn and banana leaves as potential standardized substrates for leaf decomposition in streams affected by mountaintop removal coal mining, West Virginia, USA

Science.gov (United States)

Mountaintop removal and valley filling is a method of coal mining that buries Central Appalachian headwater streams. A 2007 federal court ruling highlighted the need for measurement of both ecosystem structure and function when assessing streams for mitigaton. Rapid functional as...

Stream water quality in coal mined areas of the lower Cheat River Basin, West Virginia and Pennsylvania, during low-flow conditions, July 1997

Science.gov (United States)

Williams, Donald R.; Clark, Mary E.; Brown, Juliane B.

1999-01-01

IntroductionThe Cheat River Basin is in the Allegheny Plateau and Allegheny Mountain Sections of the Appalachian Plateau Physiographic Province (Fenneman, 1946) and is almost entirely within the state of West Virginia. The Cheat River drains an area of 1,422 square miles in Randolph, Tucker, Preston, and Monongalia Counties in West Virginia and Fayette County in Pennsylvania. From its headwaters in Randolph County, W.Va., the Cheat River flows 157 miles north to the Pennsylvania state line, where it enters the Monongahela River. The Cheat River drainage comprises approximately 19 percent of the total Monongahela River Basin. The Cheat River and streams within the Cheat River Basin are characterized by steep gradients, rock channels, and high flow velocities that have created a thriving white-water rafting industry for the area. The headwaters of the Cheat River contain some of the most pristine and aesthetic streams in West Virginia. The attraction to the area, particularly the lower part of the Cheat River Basin (the lower 412 square miles of the basin), has been suppressed because of poor water quality. The economy of the Lower Cheat River Basin has been dominated by coal mining over many decades. As a result, many abandoned deep and surface mines discharge untreated acid mine drainage (AMD), which degrades water quality, into the Cheat River and many of its tributary streams. Approximately 60 regulated mine-related discharges (West Virginia Department of Environmental Protection, 1996) and 185 abandoned mine sites (U.S. Office of Surface Mining, 1998) discharge treated and untreated AMD into the Cheat River and its tributaries.The West Virginia Department of Environmental Protection (WVDEP) Office of Abandoned Mine Lands and Reclamation (AML&R) has recently completed several AMD reclamation projects throughout the Cheat River Basin that have collectively improved the mainstem water quality. The AML&R office is currently involved in acquiring grant funds and

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

Science.gov (United States)

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

Formulations and algorithms for problems on rock mass and support deformation during mining

Science.gov (United States)

Seryakov, VM

2018-03-01

The analysis of problem formulations to calculate stress-strain state of mine support and surrounding rocks mass in rock mechanics shows that such formulations incompletely describe the mechanical features of joint deformation in the rock mass–support system. The present paper proposes an algorithm to take into account the actual conditions of rock mass and support interaction and the algorithm implementation method to ensure efficient calculation of stresses in rocks and support.

Trace metals of an acid mine drainage stream using a chemical model (WATEQ) and sediment analysis

International Nuclear Information System (INIS)

West, K.A.; Wilson, T.P.

1992-01-01

The high metal contents common to the discharge of acid-mine drainage (AMD) from mines and mine spoils is an environmental concern to both government and industry. This paper reports the results of investigation of the behavior of metals in an AMD system at a former surface coal mine in Tuscarawas County, Oh. AMD discharges from seeps travels, in respective order through a laminar flow stream; a Typha-dominated wetland; a turbulent flow stream; and a sediment retention pond. Dissolved metals (Fe, Mn, Zn, Cr, Cd, Cu, and Al) major and minor components, and other parameters (pH, dissolved oxygen and Eh) were measured in the AMD water at each sample location. A chemical mineral equilibrium model (WATEQ) was used to predict the minerals which should precipitate at each site. Results suggest that the seeps are supersaturated and should be precipitating hematite, goethite and magnetite (iron oxides), and siderite (iron carbonate), whereas water of the other downstream sites were at or below equilibrium conditions for these minerals. The hydrogeochemistry of the AMD was further studied using sequential chemical attacks on the precipitate sediment surface coatings, in order to determine metal concentrations in the exchangeable, carbonate, Fe-Mn oxyhydroxide, and oxidizable fractions. The carbonate and exchangeable fractions of the precipitate are dominated by Ca and Fe, as well as Mg in the carbonate fraction. The Fe-Mn oxyhydroxide fraction contained Fe, Al, Mn, Mg, and trace metals, and also contained the greatest concentration of total elements in the system. The Fe-Mn oxyhydroxide is therefore, the major sink for metals of this AMD system. The decrease in the concentration of metals in the sediment precipitates in the downstream locations, is consistent with WATEQ and water analysis results

Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

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Senol Celik

Full Text Available ABSTRACT The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth and testicular (testicular length, scrotal length, and scrotal circumference measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1 and with interaction (MARS_2 terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

The role of remediation, natural alkalinity sources and physical stream parameters in stream recovery.

Science.gov (United States)

Kruse, Natalie A; DeRose, Lisa; Korenowsky, Rebekah; Bowman, Jennifer R; Lopez, Dina; Johnson, Kelly; Rankin, Edward

2013-10-15

Acid mine drainage (AMD) negatively impacts not only stream chemistry, but also aquatic biology. The ultimate goal of AMD treatment is restoration of the biological community, but that goal is rarely explicit in treatment system design. Hewett Fork in Raccoon Creek Watershed, Ohio, has been impacted by historic coal mining and has been treated with a calcium oxide doser in the headwaters of the watershed since 2004. All of the acidic inputs are isolated to a 1.5 km stretch of stream in the headwaters of the Hewett Fork watershed. The macroinvertebrate and fish communities have begun to recover and it is possible to distinguish three zones downstream of the doser: an impaired zone, a transition zone and a recovered zone. Alkalinity from both the doser and natural sources and physical stream parameters play a role in stream restoration. In Hewett Fork, natural alkaline additions downstream are higher than those from the doser. Both, alkaline additions and stream velocity drive sediment and metal deposition. Metal deposition occurs in several patterns; aluminum tends to deposit in regions of low stream velocity, while iron tends to deposit once sufficient alkalinity is added to the system downstream of mining inputs. The majority of metal deposition occurs upstream of the recovered zone. Both the physical stream parameters and natural alkalinity sources influence biological recovery in treated AMD streams and should be considered in remediation plans. Copyright © 2013 Elsevier Ltd. All rights reserved.

Data mining theories, algorithms, and examples

CERN Document Server

Ye, Nong

2013-01-01

AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal

75 FR 22723 - Stream Protection Rule; Environmental Impact Statement

Science.gov (United States)

2010-04-30

..., 784, 816, and 817 RIN 1029-AC63 Stream Protection Rule; Environmental Impact Statement AGENCY: Office... streams from the adverse impacts of surface coal mining operations. We are requesting comments for the... mining activities may be conducted in or near perennial or intermittent streams. That rule, which this...

Geochemical and Pb isotopic evidence for sources and dispersal of metal contamination in stream sediments from the mining and smelting district of Pribram, Czech Republic

International Nuclear Information System (INIS)

Ettler, Vojtech; Mihaljevic, Martin; Sebek, Ondrej; Molek, Michael; Grygar, Tomas; Zeman, Josef

2006-01-01

Stream sediments from the mining and smelting district of Pribram, Czech Republic, were studied to determine the degree, sources and dispersal of metal contamination using a combination of bulk metal and mineralogical determinations, sequential extractions and Pb isotopic analyses. The highest metal concentrations were found 3-4 km downstream from the main polymetallic mining site (9800 mg Pb kg -1 , 26 039 mg Zn kg -1 , 316.4 mg Cd kg -1 , 256.9 mg Cu kg -1 ). The calculated enrichment factors (EFs) confirmed the extreme degree of contamination by Pb, Zn and Cd (EF > 40). Lead, Zn and Cd are bound mainly to Fe oxides and hydroxides. In the most contaminated samples Pb is also present as Pb carbonates and litharge (PbO). Lead isotopic analysis indicates that the predominant source of stream sediment contamination is historic Pb-Ag mining and primary Pb smelting ( 206 Pb/ 207 Pb = 1.16), while the role of secondary smelting (car battery processing) is negligible. - Pb isotopes properly complete traditional investigations of metal sources and dispersal in contaminated stream sediments

Evaluation of Metal Toxicity in Streams Affected by Abandoned Mine Lands, Upper Animas River Watershed, Colorado

Science.gov (United States)

Besser, John M.; Allert, Ann L.; Hardesty, Douglas K.; Ingersoll, Christopher G.; May, Thomas W.; Wang, Ning; Leib, Kenneth J.

2001-01-01

Acid drainage from abandoned mines and from naturally-acidic rocks and soil in the upper Animas River watershed of Colorado generates elevated concentrations of acidity and dissolved metals in stream waters and deposition of metal-contaminated particulates in streambed sediments, resulting in both toxicity and habitat degradation for stream biota. High concentrations of iron (Fe), aluminum (Al), zinc (Zn), copper (Cu), cadmium (Cd), and lead (Pb) occur in acid streams draining headwaters of the upper Animas River watershed, and high concentrations of some metals, especially Zn, persist in circumneutral reaches of the Animas River and Mineral Creek, downstream of mixing zones of acid tributaries. Seasonal variation of metal concentrations is reflected in variation in toxicity of stream water. Loadings of dissolved metals to the upper Animas River and tributaries are greatest during summer, during periods of high stream discharge from snowmelt and monsoonal rains, but adverse effects on stream biota may be greater during winter low-flow periods, when stream flows are dominated by inputs of groundwater and contain greatest concentrations of dissolved metals. Fine stream-bed sediments of the upper Animas River watershed also contain elevated concentrations of potentially toxic metals. Greatest sediment metal concentrations occur in the Animas River upstream from Silverton, where there are extensive deposits of mine and mill tailings, and in mixing zones in the Animas River and lower Mineral Creek, where precipitates of Fe and Al oxides also contain high concentrations of other metals. This report summarizes the findings of a series of toxicity studies in streams of the upper Animas River watershed, conducted on-site and in the laboratory between 1998 and 2000. The objectives of these studies were: (1) to determine the relative toxicity of stream water and fine stream-bed sediments to fish and invertebrates; (2) to determine the seasonal range of toxicity in stream

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

Science.gov (United States)

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 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.

Application of an empiric Bayesian data mining algorithm to reports of pancreatitis associated with atypical antipsychotics.

Science.gov (United States)

Hauben, Manfred

2004-09-01

To compare the results from one frequently cited data mining algorithm with those from a study, which was published in a peer-reviewed journal, that examined the association of pancreatitis with selected atypical antipsychotics observed by traditional rule-based methods of signal detection. Retrospective pharmacovigilance study. The widely studied data mining algorithm known as the Multi-item Gamma Poisson Shrinker (MGPS) was applied to adverse-event reports from the United States Food and Drug Administration's Adverse Event Reporting System database through the first quarter of 2003 for clozapine, olanzapine, and risperidone to determine if a significant signal of pancreatitis would have been generated by this method in advance of their review or the addition of these events to the respective product labels. Data mining was performed by using nine preferred terms relevant to drug-induced pancreatitis from the Medical Dictionary for Regulatory Activities (MedDRA). Results from a previous study on the antipsychotics were reviewed and analyzed. Physicians' Desk References (PDRs) starting from 1994 were manually reviewed to determine the first year that pancreatitis was listed as an adverse event in the product label for each antipsychotic. This information was used as a surrogate marker of the timing of initial signal detection by traditional criteria. Pancreatitis was listed as an adverse event in a PDR for all three atypical antipsychotics. Despite the presence of up to 88 reports/drug-event combination in the Food and Drug Administration's Adverse Event Reporting System database, the MGPS failed to generate a signal of disproportional reporting of pancreatitis associated with the three antipsychotics despite the signaling of these drug-event combinations by traditional rule-based methods, as reflected in product labeling and/or the literature. These discordant findings illustrate key principles in the application of data mining algorithms to drug safety

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

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Yang Ou

2014-01-01

Full Text Available This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Ecology of endangered damselfly Coenagrion ornatum in post-mining streams in relation to their restoration

OpenAIRE

TICHÁNEK, Filip

2016-01-01

The thesis explores various aspects of ecology of endangered damselfly Coenagrion ornatum, the specialists for lowland headwaters, in post-mining streams of Radovesicka spoil. The first part of thesis is manuscript which has been already submitted in Journal of Insect Conservation. In the first part, we focused on population estimate of the local population using capture-recapture method, and explored its habitat requirements across life stages and spatial scales. In the next part, I assess m...

Hydrogeochemical assessment of mine-impacted water and sediment of iron ore mining

Science.gov (United States)

Nur Atirah Affandi, Fatin; Kusin, Faradiella Mohd; Aqilah Sulong, Nur; Madzin, Zafira

2018-04-01

This study was carried out to evaluate the hydrogeochemical behaviour of mine-impacted water and sediment of a former iron ore mining area. Sampling of mine water and sediment were carried out at selected locations within the mine including the former mining ponds, mine tailings and the nearby stream. The water samples were analysed for their hydrochemical facies, major and trace elements including heavy metals. The water in the mining ponds and the mine tailings was characterised as highly acidic (pH 2.54-3.07), but has near-neutral pH in the nearby stream. Results indicated that Fe and Mn in water have exceeded the recommended guidelines values and was also supported by the results of geochemical modelling. The results also indicated that sediments in the mining area were contaminated with Cd and As as shown by the potential ecological risk index values. The total risk index of heavy metals in the sediment were ranked in the order of Cd>As>Pb>Cu>Zn>Cr. Overall, the extent of potential ecological risks of the mining area were categorised as having low to moderate ecological risk.

Acid mine drainage arising from gold mining activity in Johannesburg, South Africa and environs

International Nuclear Information System (INIS)

Naicker, K.; Cukrowska, E.; McCarthy, T.S.

2003-01-01

Ground water within the mining district is heavily contaminated and acidified. - The Witwatersrand region of South Africa is famous for its gold production and a major conurbation, centred on Johannesburg, has developed as a result of mining activity. A study was undertaken of surface and ground water in a drainage system in this area. Soils were also analysed from a site within the mining district. This study revealed that the ground water within the mining district is heavily contaminated and acidified as a result of oxidation of pyrite (FeS 2 ) contained within mine tailings dumps, and has elevated concentrations of heavy metals. Where the water table is close to surface, the upper 20 cm of soil profiles are severely contaminated by heavy metals due to capillary rise and evaporation of the ground water. The polluted ground water is discharging into streams in the area and contributes up to 20% of stream discharge, causing a lowering of pH of the stream water. Much of the metal load is precipitated in the stream: Fe and Mn precipitate as a consequence of oxidation, while other heavy metals are being removed by co-precipitation. The oxidation of iron has created a redox buffer which controls the pH of the stream water. The rate of oxidation and of dilution is slow and the deleterious effect of the addition of contaminated water persists for more than 10 km beyond the source

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper

Science.gov (United States)

Luo, Gang

2017-01-01

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic. PMID:29177022

Mining the multigroup-discrete ordinates algorithm for high quality solutions

International Nuclear Information System (INIS)

Ganapol, B.D.; Kornreich, D.E.

2005-01-01

A novel approach to the numerical solution of the neutron transport equation via the discrete ordinates (SN) method is presented. The new technique is referred to as 'mining' low order (SN) numerical solutions to obtain high order accuracy. The new numerical method, called the Multigroup Converged SN (MGCSN) algorithm, is a combination of several sequence accelerators: Romberg and Wynn-epsilon. The extreme accuracy obtained by the method is demonstrated through self consistency and comparison to the independent semi-analytical benchmark BLUE. (authors)

Methylmercury and dissolved organic carbon relationships in a wetland-rich watershed impacted by elevated sulfate from mining

International Nuclear Information System (INIS)

Berndt, Michael E.; Bavin, Travis K.

2012-01-01

Methylmercury (MeHg), dissolved organic carbon (DOC), and sulfate (SO 4 = ) relationships were investigated in the mining-influenced St. Louis River watershed in northeast Minnesota. Fewer wetlands and higher SO 4 = in the mining region lead to generally lower availability and solubility of DOC in mining streams compared to non-mining streams. MeHg concentrations, however, are similarly low in mining and non-mining streams during low flow periods, implying that the extra DOC found in non-mining streams carries little MeHg with it during these periods. High water levels elevated MeHg concentrations in both stream types owing to release from wetlands of DOC species that contain MeHg and remain relatively soluble in streams with elevated ionic strength. In-river methylation appeared to be a negligible component of the MeHg budget for the St. Louis River during this study as MeHg and DOC concentrations were intermediate to those observed in its mining-influenced and wetland-dominated tributaries. - Highlights: ► St. Louis River tributaries were sampled for MeHg, SO 4 = , and DOC. ► Mine land tributaries had elevated SO 4 = and low DOC compared to other streams. ► MeHg concentration ranges overlapped for mining and non-mining streams. ► MeHg is carried by a DOC component found in both types of streams. ► Mining streams lack the low-MeHg DOC type common in non-mining streams. - Methylmercury concentrations in mining and non-mining streams are controlled by quantity and quality of DOC.

In-stream chemical neutralization: A whole watershed approach to mitigating acid mine drainage

International Nuclear Information System (INIS)

Britt, D.L.

1994-01-01

The North Branch of the Potomac River is adversely affected by acid mine drainage (AMD) throughout its entire length. As an alternative to mine-mouth treatment methods an in-stream AMD-neutralization demonstration program for an approximately 25-mile segment of the North Branch of the Potomac River was designed and implemented. This river segment was ranked as the highest priority site in Maryland for a demonstration project owing to its combination of very poor water quality and excellent potential for supporting a recreational sport fishery in the absence of toxic metal and acid loadings. A whole-watershed approach employing Scandinavian doser technologies and calcium carbonate neutralizing agents is the basis for the North Branch Potomac River demonstration project. The project involves four phases: feasibility (1), design (2), implementation (3), and monitoring (4). This watershed approach to mitigating AMD is expected to restore circumneutrial water quality and to promote desirable fishery resources throughout the mainstem and selected tributaries of the North Branch of the Potomac River Upstream of Jennings Randolph Dam. This paper summarizes Phases 1--3 of the demonstration project

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

Science.gov (United States)

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 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.

A genetic algorithm approach to recognition and data mining

Energy Technology Data Exchange (ETDEWEB)

Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

1996-12-31

We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

The current environmental impact of base-metal mining at the Tui Mine, Te Aroha, New Zealand

International Nuclear Information System (INIS)

Sabti, H.; Hossain, M.M.; Brooks, R.R.; Stewart, R.B.

2000-01-01

The current environmental impact of base metal mining at the Tui Mine, Te Aroha and gold mining near Waihi, was investigated by analysis of local waters, stream sediments and aquatic vegetation. X-ray diffraction analysis of heavy metal fractions in stream sediments showed the presence of pyrite in the upper reaches of the Tunakohoia and Tui streams that drain the mineralised reefs and Tui tailings dam. Relatively immobile lead (galena) was retained close to the source, whereas copper and zinc minerals were more mobile and distributed further downstream from the areas of mineralisation. Gold was determined in sediments from the Ohinemuri and Waitekauri Rivers along with other heavy metals derived from sulphide mineralisation at Waihi and Waitekauri. Analysis of waters from the Tui and Tunakohoia streams showed concentrations of arsenic, cadmium, lead and zinc above recommended levels for potable water in the upper parts of these waterways. The discharge of these streams into the Waihou River (sampled upstream from Te Aroha and downstream past Paeroa) did not have any significant effect on heavy-metal concentrations in this river. Aquatic macrophytes sampled in the Waihou, Ohinemuri and Waitekauri Rivers had very high heavy-metal concentrations compared with the ambient water and should be considered as potentially useful for assessing the impact of low-metal fluxes into the waters. Gold was detected in aquatic marcophytes from streams draining both the Martha Mine at Waihi and the Golden Cross Mine at Waitekauri and indicated the possibility of prospecting for gold by analysis of these plants. (author). 17 refs., 3 figs., 6 tabs

Examine the criteria for establishing the small span small pillar concept as a safe mining method in deep mines,Volume 1 of 1.

CSIR Research Space (South Africa)

De Frey, FSA

2002-01-01

Full Text Available with conventional breast panel 38 4.4 Critical evaluation of results 39 4.5 A recommended alternative method 41 4.6 Comparison criteria used as for other layouts 45 4.7 Conclusions and future research needs 47 5 MINE ECONOMICS 48 5.1 Mine economic criteria 48... stream_source_info GAP822.pdf.txt stream_content_type text/plain stream_size 176631 Content-Encoding UTF-8 stream_name GAP822.pdf.txt Content-Type text/plain; charset=UTF-8 Safety in Mines Research Advisory Committee...

Abandoned mine drainage in the Swatara Creek Basin, southern anthracite coalfield, Pennsylvania, USA: 1. stream quality trends coinciding with the return of fish

Science.gov (United States)

Cravotta, Charles A.; Brightbill, Robin A.; Langland, Michael J.

2010-01-01

Acidic mine drainage (AMD) from legacy anthracite mines has contaminated Swatara Creek in eastern Pennsylvania. Intermittently collected base-flow data for 1959–1986 indicate that fish were absent immediately downstream from the mined area where pH ranged from 3.5 to 7.2 and concentrations of sulfate, dissolved iron, and dissolved aluminum were as high as 250, 2.0, and 4.7 mg/L, respectively. However, in the 1990s, fish returned to upper Swatara Creek, coinciding with the implementation of AMD treatment (limestone drains, limestone diversion wells, limestone sand, constructed wetlands) in the watershed. During 1996–2006, as many as 25 species of fish were identified in the reach downstream from the mined area, with base-flow pH from 5.8 to 7.6 and concentrations of sulfate, dissolved iron, and dissolved aluminum as high as 120, 1.2, and 0.43 mg/L, respectively. Several of the fish taxa are intolerant of pollution and low pH, such as river chub (Nocomis icropogon) and longnose dace (Rhinichthys cataractae). Cold-water species such as brook trout (Salvelinus fontinalis) and warm-water species such as rock bass (Ambloplites rupestris) varied in predominance depending on stream flow and stream temperature. Storm flow data for 1996–2007 indicated pH, alkalinity, and sulfate concentrations decreased as the stream flow and associated storm-runoff component increased, whereas iron and other metal concentrations were poorly correlated with stream flow because of hysteresis effects (greater metal concentrations during rising stage than falling stage). Prior to 1999, pH\\5.0 was recorded during several storm events; however, since the implementation of AMD treatments, pH has been maintained near neutral. Flow-adjusted trends for1997–2006 indicated significant increases in calcium; decreases in hydrogen ion, dissolved aluminum, dissolved and total manganese, and total iron; and no change in sulfate or dissolved iron in Swatara Creek immediately downstream from the

An algorithm of opinion leaders mining based on signed network

Science.gov (United States)

Cao, Linlin; Zheng, Mingchun; Zhang, Yuanyuan; Zhang, Fuming

2018-04-01

With the rapid development of mobile Internet, user gradually become the leader of social media, the abruptly rise of new media has changed the traditional information's dissemination pattern and regularity. There is new era significance of opinion leaders, gatekeepers in the classical theory of mass communication, and it has further expansion and extension to a certain extent. In the existing mining of opinion leaders, it is mainly from the research of network structure and user behavior without considering an important attribute: whether the user has a real impact. In this paper, we take the symbolic network as the research tool, by giving symbol which correspondingly represents support or oppose to the link about point of view relationship between users and combining traditional algorithms of mining with symbolism which can describe the change of view between users, we will get the opinion leader who has real impact on users, then the result is more accurate and effective.

In situ studies with Asian clams (Carbacula fluminea) detect acid mine drainage and nutrient inputs in low-order streams

International Nuclear Information System (INIS)

Soucek, D. J.; Schmidt, T. S.; Cherry, D. S.

2001-01-01

This study evaluates the correlation between transplanted Asiatic clam and indigenous community responses to acid mine drainage and nutrient loading in first-to-third-order streams, by comparing the toxicological endpoints of clam survival and growth with benthic macro-invertebrate community indices as community responses to both acid mine drainage and nutrient loading. Clam survival was found to be positively correlated with water column pH and negatively correlated with conductivity and metal concentrations. There was also a positive correlation with the relative abundance of the macro-invertebrate Ephemeroptera, the most sensitive taxonomic group, to acid mine drainage in this watershed. No correlation was found between clam growth and acid mine drainage inputs, but there was evidence of positive correlation with nitrate concentrations and the relative abundance of collector-filterer functional feeding groups. These results suggest that clam growth is related to nutrient levels and accurately reflect benthic macro-invertebrate responses to nutrient loading. 28 refs., 5 tabs., 1 fig

Occurrence and transport of selected constituents in streams near the Stibnite mining area, Central Idaho, 2012–14

Science.gov (United States)

Etheridge, Alexandra B.

2015-12-07

Mining of stibnite (antimony sulfide), tungsten, gold, silver, and mercury near the town of Stibnite in central Idaho has left a legacy of trace element contamination in local streams. Water-quality and streamflow monitoring data from a network of five streamflow-gaging stations were used to estimate trace-element and suspended-sediment loads and flow-weighted concentrations in the Stibnite mining area between 2012 and 2014. Measured concentrations of arsenic exceeded human health-based water-quality criteria at each streamflow-gaging station, except for Meadow Creek (site 2), which was selected to represent background conditions in the study area. Measured concentrations of antimony exceeded human health-based water-quality criteria at sites 3, 4, and 5.

Calibration of Mine Ventilation Network Models Using the Non-Linear Optimization Algorithm

Directory of Open Access Journals (Sweden)

Guang Xu

2017-12-01

Full Text Available Effective ventilation planning is vital to underground mining. To ensure stable operation of the ventilation system and to avoid airflow disorder, mine ventilation network (MVN models have been widely used in simulating and optimizing the mine ventilation system. However, one of the challenges for MVN model simulation is that the simulated airflow distribution results do not match the measured data. To solve this problem, a simple and effective calibration method is proposed based on the non-linear optimization algorithm. The calibrated model not only makes simulated airflow distribution results in accordance with the on-site measured data, but also controls the errors of other parameters within a minimum range. The proposed method was then applied to calibrate an MVN model in a real case, which is built based on ventilation survey results and Ventsim software. Finally, airflow simulation experiments are carried out respectively using data before and after calibration, whose results were compared and analyzed. This showed that the simulated airflows in the calibrated model agreed much better to the ventilation survey data, which verifies the effectiveness of calibrating method.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm.

Science.gov (United States)

Luck, Margaux; Schmitt, Caroline; Talbi, Neila; Gouya, Laurent; Caradeuc, Cédric; Puy, Hervé; Bertho, Gildas; Pallet, Nicolas

2018-01-01

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease. In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses. Urine 1 H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained. Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients. These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

Determination of total arsenic in streams and sediments from Obuasi gold mines

International Nuclear Information System (INIS)

Serfor Armah, Yaw

1994-03-01

In this work streams and sediments of Obuasi, a major gold mining town in Ghana were analysed. In addition to the total arsenic the parameters determined included the levels of Pe, Al, Nn and Au and nutrients. Leaching of arsenic from the sediment was also carried out to ascertain the rate at which As will be removed from the sediment to acceptable levels. Results indicate that in spite of the newly installed Arsenic Recovery Plant (ARP) which is able to remove about 90% of the arsenic dusts, the streams in the area remain heavily polluted with arsenic. In the water Total Arsenic values range between 0.13 - 20.00ppm. The sediments are also polluted to a depth of at least 30cm with values ranging from 15.38 - 50.00ppm. Contrary to expectations, the gold concentration in both the water and sediment are too low and may not be suitable for exploration. The leaching results show that very little amount of arsenic was leached from the sediments. Even after 20 weeks of continuous leaching less than 1% of As had been leached. This was attributed to the ability of arsenic to form sparingly soluble compounds with Fe, Al, Mn etc in the sediment environment. (au)

Stream and floodplain restoration in a riparian ecosystem disturbed by placer mining

Science.gov (United States)

Karle, Kenneth F.; Densmore, Roseann V.

1994-01-01

Techniques for the hydrologic restoration of placer-mined streams and floodplains were developed in Denali National Park and Preserve Alaska, USA. The hydrologic study focused on a design of stream and floodplain geometry using hydraulic capacity and shear stress equations. Slope and sinuosity values were based on regional relationships. Design requirements include a channel capacity for a 1.5-year (bankfull) discharge and a floodplain capacity for a 1.5- to 100-year discharge. Concern for potential damage to the project from annual flooding before natural revegetation occurs led to development of alder (Alnus crispa) brush bars to dissipate floodwater energy and encourage sediment deposition. The brush bars, constructed of alder bundles tied together and anchored laterally adjacent to the channel, were installed on the floodplain in several configurations to test their effectiveness. A moderate flood near the end of the two-year construction phase of the project provided data on channel design, stability, floodplain erosion, and brush bar effectiveness. The brush bars provided substantial protection, but unconsolidated bank material and a lack of bed armour for a new channel segment led to some bank erosion, slope changes and an increase in sinuosity in several reaches of the study area.

A study of the Bienstock-Zuckerberg algorithm, Applications in Mining and Resource Constrained Project Scheduling

OpenAIRE

Muñoz, Gonzalo; Espinoza, Daniel; Goycoolea, Marcos; Moreno, Eduardo; Queyranne, Maurice; Rivera, Orlando

2016-01-01

We study a Lagrangian decomposition algorithm recently proposed by Dan Bienstock and Mark Zuckerberg for solving the LP relaxation of a class of open pit mine project scheduling problems. In this study we show that the Bienstock-Zuckerberg (BZ) algorithm can be used to solve LP relaxations corresponding to a much broader class of scheduling problems, including the well-known Resource Constrained Project Scheduling Problem (RCPSP), and multi-modal variants of the RCPSP that consider batch proc...

Application of Data Mining Algorithm to Recipient of Motorcycle Installment

Directory of Open Access Journals (Sweden)

Harry Dhika

2015-12-01

Full Text Available The study was conducted in the subsidiaries that provide services of finance related to the purchase of a motorcycle on credit. At the time of applying, consumers enter their personal data. Based on the personal data, it will be known whether the consumer credit data is approved or rejected. From 224 consumer data obtained, it is known that the number of consumers whose applications are approved is 87% or about 217 consumers and consumers whose application is rejected is 16% or as much as 6 consumers. Acceptance of motorcycle financing on credit by using the method of applying the algorithm through CRIS-P DM is the industry standard in the processing of data mining. The algorithm used in the decision making is the algorithm C4.5. The results obtained previously, the level of accuracy is measured with the Confusion Matrix and Receiver Operating characteristic (ROC. Evaluation of the Confusion Matrix is intended to seek the value of accuracy, precision value, and the value of recall data. While the Receiver Operating Characteristic (ROC is used to find data tables and comparison Area Under Curve (AUC.

Assessing Lost Ecosystem Service Benefits Due to Mining-Induced Stream Degradation in the Appalachian Region: Economic Approaches to Valuing Recreational Fishing Impacts

Science.gov (United States)

Sport fishing is a popular activity for Appalachian residents and visitors. The region’s coldwater streams support a strong regional outdoor tourism industry. We examined the influence of surface coal mining, in the context of other stressors, on freshwater sport fishing in...

Seasonal factors controlling mineral precipitation in the acid mine drainage at Donghae coal mine, Korea

International Nuclear Information System (INIS)

Kim, J.J.; Kim, S.J.

2004-01-01

Monitoring over a 12 month period in the Sanae creek flow in acid mine drainage, Donghae coal mine area, demonstrates that the concentrations of dissolved metals and sulphate are highest during autumn when water flow in the creek is at its lowest. The highest pH values of the stream were measured in April and May, whereas the lowest pH was recorded in October. The Fe concentration of stream water rapidly decreased downstream due to the precipitation of Fe oxyhydroxide and/or oxyhydroxysulfate phases in the stream. Mineral precipitates in the creek in the Donghae mine area show various colours such as brownish yellow (Munsell colour 9.5 YR hues), reddish brown (Munsell colour 3.5 YR hues) and white depending on seasons and distance from the pollution source in the creek. Such phenomena are attributed to the variation in pH and chemical composition of stream water caused by seasonal factors. The measured pH ranges in stream water of the brownish yellow, white and reddish brown precipitates are pH 3.2-4.5, 4.5-6.0 and 5.3-6.9, respectively

StreamQRE: Modular Specification and Efficient Evaluation of Quantitative Queries over Streaming Data.

Science.gov (United States)

Mamouras, Konstantinos; Raghothaman, Mukund; Alur, Rajeev; Ives, Zachary G; Khanna, Sanjeev

2017-06-01

Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.

Stream sediment detailed geochemical survey for Date Creek Basin, Arizona

International Nuclear Information System (INIS)

Butz, T.R.; Tieman, D.J.; Grimes, J.G.; Bard, C.S.; Helgerson, R.N.; Pritz, P.M.; Wolf, D.A.

1981-01-01

The purpose of the Date Creek Supplement is to characterize the chemistry of sediment samples representing stream basins in which the Anderson Mine (and related prospects) occur. Once characterized, the chemistry is then used to delineate other areas within the Date Creek Basin where stream sediment chemistry resembles that of the Anderson Mine area. This supplementary report examines more closely the data from sediment samples taken in 239 stream basins collected over a total area of approximately 900 km 2 (350 mi 2 ). Cluster and discriminant analyses are used to characterize the geochemistry of the stream sediment samples collected in the Date Creek Basin. Cluster and discriminant analysis plots are used to delineate areas having a potential for uranium mineralization similar to that of the Anderson Mine

Data mining methods

CERN Document Server

Chattamvelli, Rajan

2015-01-01

DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of datamining in a web field including banking, e-commerce, medicine, engineering and management. This book starts byintroducing data and information, basic data type, data category and applications of data mining. The second chapterbriefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statisticsare discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an indepthand useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabularmethod for decision tree building is discussed. The chapter on association rules discusses popular algorithms andcompares various algorithms in summary table form. An interesting application of genetic algorithm is introduced inthe next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived...

An overview of data mining algorithms in drug induced toxicity prediction.

Science.gov (United States)

Omer, Ankur; Singh, Poonam; Yadav, N K; Singh, R K

2014-04-01

The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

RStorm: Developing and Testing Streaming Algorithms in R

NARCIS (Netherlands)

Kaptein, M.C.

2014-01-01

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams.

RStorm : Developing and testing streaming algorithms in R

NARCIS (Netherlands)

Kaptein, M.C.

2014-01-01

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams.

Effects of acid mine drainage on dissolved inorganic carbon and stable carbon isotopes in receiving streams

International Nuclear Information System (INIS)

Fonyuy, Ernest W.; Atekwana, Eliot A.

2008-01-01

Dissolved inorganic carbon (DIC) constitutes a significant fraction of a stream's carbon budget, yet the role of acid mine drainage (AMD) in DIC dynamics in receiving streams remains poorly understood. The objective of this study was to evaluate spatial and temporal effects of AMD and its chemical evolution on DIC and stable isotope ratio of DIC (δ 13 C DIC ) in receiving streams. We examined spatial and seasonal variations in physical and chemical parameters, DIC, and δ 13 C DIC in a stream receiving AMD. In addition, we mixed different proportions of AMD and tap water in a laboratory experiment to investigate AMD dilution and variable bicarbonate concentrations to simulate downstream and seasonal hydrologic conditions in the stream. Field and laboratory samples showed variable pH, overall decreases in Fe 2+ , alkalinity, and DIC, and variable increase in δ 13 C DIC . We attribute the decrease in alkalinity, DIC loss, and enrichment of 13 C of DIC in stream water to protons produced from oxidation of Fe 2+ followed by Fe 3+ hydrolysis and precipitation of Fe(OH) 3(s) . The extent of DIC decrease and 13 C enrichment of DIC was related to the amount of HCO 3 - dehydrated by protons. The laboratory experiment showed that lower 13 C enrichment occurred in unmixed AMD (2.7 per mille ) when the amount of protons produced was in excess of HCO 3 - or in tap water (3.2 per mille ) where no protons were produced from Fe 3+ hydrolysis for HCO 3 - dehydration. The 13 C enrichment increased and was highest for AMD-tap water mixture (8.0 per mille ) where Fe 2+ was proportional to HCO 3 - concentration. Thus, the variable downstream and seasonal 13 C enrichment in stream water was due in part to: (1) variations in the volume of stream water initially mixed with AMD and (2) to HCO 3 - input from groundwater and seepage in the downstream direction. Protons produced during the chemical evolution of AMD caused seasonal losses of 50 to >98% of stream water DIC. This loss of DIC

Geochemistry of stream sediments, water and U-Th radiation anomaly around Neyshabour Fyrouzeh mine and its environmental impact on people living nearby villages

International Nuclear Information System (INIS)

Karimpour, M. H.; Malekzadeh Shafaroudi, A.

2013-01-01

Fyrouzeh mine is located about 55 km northwest of Neyshabour in the Province of Khorasan Razavi. The exposed rocks are mainly volcanic and intrusive with intermediate composition and all of them are altered. This mine is the first type of IOCG recognized in Iran with Cu-Au-LREE-U. Besides Cu-Au-U, this area shows As, Mo, Zn and Th anomalies. Geochemical evaluation of stream sediment with regard to environmental concern revealed high Cu anomalies. Rocks show high uranium anomalies (up to 35 ppm) higher than the standard values (1 ppm). Airborne radiometric maps show high U and Th anomalies in a broad area. Ag, Hg and Mn show anomalies within the stream sediments. Cu, Pb, Zn, Ag, Ni, Mn, Sb, Hg, and U content of both drinking and agricultural water are fortunately within the range of standard, only two samples have higher As content (more than 10 ppb). High level of U-Th radiation and contamination of stream sediment with respect to Cu, Hg, Ag, Mn and agricultural water to As are important environmental issues and people health therefore they need to be study.

Personal continuous route pattern mining

Institute of Scientific and Technical Information of China (English)

Qian YE; Ling CHEN; Gen-cai CHEN

2009-01-01

In the daily life, people often repeat regular routes in certain periods. In this paper, a mining system is developed to find the continuous route patterns of personal past trips. In order to count the diversity of personal moving status, the mining system employs the adaptive GPS data recording and five data filters to guarantee the clean trips data. The mining system uses a client/server architecture to protect personal privacy and to reduce the computational load. The server conducts the main mining procedure but with insufficient information to recover real personal routes. In order to improve the scalability of sequential pattern mining, a novel pattern mining algorithm, continuous route pattern mining (CRPM), is proposed. This algorithm can tolerate the different disturbances in real routes and extract the frequent patterns. Experimental results based on nine persons' trips show that CRPM can extract more than two times longer route patterns than the traditional route pattern mining algorithms.

Fractionation of chemical elements including the REEs and 226Ra in stream contaminated with coal-mine effluent

International Nuclear Information System (INIS)

Centeno, L.M.; Faure, G.; Lee, G.; Talnagi, J.

2004-01-01

Water draining from abandoned open-pit coal mines in southeastern Ohio typically has a low pH and high concentrations of Fe, Al and Mn, as well as of trace metals (Pb, Cu, Zn, Ni, Co, etc.) and of the rare earth elements (REEs). The cations of different elements are sorbed selectively by Fe and Al hydroxide precipitates which form with increasing pH. As a result, the trace elements are separated from each other when the hydroxide precipitates are deposited in the channel of a flowing stream. Therefore, the low-energy environment of a stream contaminated by mine effluent is a favorable site for the chemical fractionation of the REEs and of other groups of elements with similar chemical properties. The interpretation of chemical analyses of water collected along a 30-km-stretch of Rush Creek near the town of New Lexington, Perry County, Ohio, indicates that the abundances of the REEs in the water appear to change downstream when they are normalized to the REE concentrations of the mine effluent. In addition, the Ce/La ratios (and those of all REEs) in the water decrease consistently downstream. The evidence indicates that the REEs which remain in solution are enriched La and Ce because the other REEs are sorbed more efficiently. The solid Fe(OH) 3 precipitates in the channel of Rush Creek upstream of New Lexington also contain radioactive 226 Ra that was sorbed from the water. This isotope of Ra is a decay product of 238 U which occurs in the Middle Pennsylvanian (Upper Carboniferous) coal and in the associated shale of southeastern Ohio. The activity of 226 Ra of the Fe(OH) 3 precipitates increases with rising pH, but then declines farther downstream as the concentration of Ra remaining in the water decreases

Heavy metal contamination in stream water and sediments of gold ...

African Journals Online (AJOL)

This study assessed the seasonal variation in heavy metal contamination of stream water and sediments in the gold mining area of Atakunmosa West local Government, Osun State, Nigeria. Twelve villages of prominence in illegal gold mining were selected for the study covering dry and wet seasons of 2012. Stream water ...

Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

Science.gov (United States)

Tataw, Oben Moses

2013-01-01

Interdisciplinary research in computer science requires the development of computational techniques for practical application in different domains. This usually requires careful integration of different areas of technical expertise. This dissertation presents image and time series analysis algorithms, with practical interdisciplinary applications…

Origin of acid mine drainage in Enugu

International Nuclear Information System (INIS)

Uma, K.O.

1992-01-01

Mine flooding is a serious problem in the Enugu Coal Mines and has led to the abandonment of two of the four mines. About 1800 m 3 of water is pumped out daily from the mines into the nearby streams. The source of this enormous volume of water has been established based on the hydrodynamics and hydrology of the area. Two prolific aquifers - an unconfined and a confined system - overlie the mines, but the mine water is derived principally from the unconfined aquifer. The pathway of flow is, provided by the numerous fractures connecting the two aquifers and the mine tunnel. The major hydrochemical activity resulting in pollution of the mine water occurs within the sumps in the floor of the longwalls. These sumps act as oxidation chambers where groundwater from the fractures mixes and subsequently reacts with sulfur-rich solutes released by coal mining. Contrary to general belief, the mine drainage has not seriously degraded the chemistry of receiving streams. The pH and electric conductivity, representing, the dissolved ions, were increased less than 10% of the values in the unaffected region

Multivariate spatial condition mapping using subtractive fuzzy cluster means.

Science.gov (United States)

Sabit, Hakilo; Al-Anbuky, Adnan

2014-10-13

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

Reactive solute transport in acidic streams

Science.gov (United States)

Broshears, R.E.

1996-01-01

Spatial and temporal profiles of Ph and concentrations of toxic metals in streams affected by acid mine drainage are the result of the interplay of physical and biogeochemical processes. This paper describes a reactive solute transport model that provides a physically and thermodynamically quantitative interpretation of these profiles. The model combines a transport module that includes advection-dispersion and transient storage with a geochemical speciation module based on MINTEQA2. Input to the model includes stream hydrologic properties derived from tracer-dilution experiments, headwater and lateral inflow concentrations analyzed in field samples, and a thermodynamic database. Simulations reproduced the general features of steady-state patterns of observed pH and concentrations of aluminum and sulfate in St. Kevin Gulch, an acid mine drainage stream near Leadville, Colorado. These patterns were altered temporarily by injection of sodium carbonate into the stream. A transient simulation reproduced the observed effects of the base injection.

Survival of mayfly larvae under mine acid conditions

Energy Technology Data Exchange (ETDEWEB)

Napier, S. Jr.; Hummon, W.D.

1976-01-01

Mayfly larvae were abundant and diverse in riffle zones of three control streams in southeastern Ohio. But none were found in such zones of three streams having current or past histories of mine acid pollution, despite vegetative recovery of reclaimed land bordering two of the streams. Laboratory studies showed stepwise increases in non-predatory mortality of mayfly larvae with increased mine acidity. Dragonfly larvae predation on mayfly larvae was constant at pH 8.1 to 4.1, but decreased at pH 3.1 despite tolerance of dragonfly larvae to low pH conditions. Extensive acid mine pollution thus may threaten aquatic biota through removal of food sources or reduced feeding rates as well as through direct mortality.

Stream Clustering of Growing Objects

Science.gov (United States)

Siddiqui, Zaigham Faraz; Spiliopoulou, Myra

We study incremental clustering of objects that grow and accumulate over time. The objects come from a multi-table stream e.g. streams of Customer and Transaction. As the Transactions stream accumulates, the Customers’ profiles grow. First, we use an incremental propositionalisation to convert the multi-table stream into a single-table stream upon which we apply clustering. For this purpose, we develop an online version of K-Means algorithm that can handle these swelling objects and any new objects that arrive. The algorithm also monitors the quality of the model and performs re-clustering when it deteriorates. We evaluate our method on the PKDD Challenge 1999 dataset.

Selenium and other trace elements in aquatic insects in coal mine-affected streams in the Rocky Mountains of Alberta, Canada

Energy Technology Data Exchange (ETDEWEB)

Wayland, M.; Crosley, R. [Environmental Canada, Saskatoon, SK (Canada)

2006-05-15

We determined levels of Se, As, Cd, Pb, and Zn in aquatic insects at coal mine-impacted and reference sites in streams in the Rocky Mountain foothills of west central Alberta from 2001-2003. Selenium levels were greater at coal mine-impacted sites than at reference sites in caddisflies but not in mayflies or stoneflies. Arsenic levels were greater at coal mine-impacted sites than at reference sites in caddisflies and stoneflies but not in mayflies. Zn levels were higher at coal mine-impacted sites than at reference sites in all three groups of insects. At coal mine-impacted sites, Se levels in mayflies and caddisflies were greater than those in stoneflies while at reference sites mayflies contained greater concentrations of Se than either caddisflies or stoneflies. Arsenic levels in mayflies were greater than those in caddisflies at reference and coal mine-impacted sites and were greater than those in stoneflies at reference sites. At both types of sites Cd differed amongst insect taxa in the order of mayflies < caddisflies < stoneflies. The same was true of Zn at coal mine-affected sites. At reference sites, stoneflies had greater concentrations of Zn than both mayflies and caddisflies. At both types of sites, Pb levels were greater in mayflies and caddisflies than they were in stoneflies. Of the five trace elements considered in this study, only Se was sufficiently elevated in aquatic invertebrates to be of potential concern for consumers such as fish and aquatic birds. Such was the case at both coal mine-impacted and reference sites.

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

Science.gov (United States)

2014-01-01

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

VALUING ACID MINE DRAINAGE REMEDIATION OF IMPAIRED WATERWAYS IN WEST VIRGINIA: A HEDONIC MODELING APPROACH

Science.gov (United States)

States with active and abandoned mines face large private and public costs to remediate damage to streams and rivers from acid mine drainage (AMD), the metal rich runoff flowing primarily from abandoned mines and surface deposits of mine waste. AMD can lower stream and river pH ...

Online feature selection with streaming features.

Science.gov (United States)

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

The Stream-Catchment (StreamCat) and Lake-Catchment ...

Science.gov (United States)

Background/Question/MethodsLake and stream conditions respond to both natural and human-related landscape features. Characterizing these features within contributing areas (i.e., delineated watersheds) of streams and lakes could improve our understanding of how biological conditions vary spatially and improve the use, management, and restoration of these aquatic resources. However, the specialized geospatial techniques required to define and characterize stream and lake watersheds has limited their widespread use in both scientific and management efforts at large spatial scales. We developed the StreamCat and LakeCat Datasets to model, predict, and map the probable biological conditions of streams and lakes across the conterminous US (CONUS). Both StreamCat and LakeCat contain watershed-level characterizations of several hundred natural (e.g., soils, geology, climate, and land cover) and anthropogenic (e.g., urbanization, agriculture, mining, and forest management) landscape features for ca. 2.6 million stream segments and 376,000 lakes across the CONUS, respectively. These datasets can be paired with field samples to provide independent variables for modeling and other analyses. We paired 1,380 stream and 1,073 lake samples from the USEPAs National Aquatic Resource Surveys with StreamCat and LakeCat and used random forest (RF) to model and then map an invertebrate condition index and chlorophyll a concentration, respectively. Results/ConclusionsThe invertebrate

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

Science.gov (United States)

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

2016-03-01

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

Parallel sorting algorithms

CERN Document Server

Akl, Selim G

1985-01-01

Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the

Relationships among exceedences of metals criteria, the results of ambient bioassays, and community metrics in mining-impacted streams.

Science.gov (United States)

Griffith, Michael B; Lazorchak, James M; Herlihy, Alan T

2004-07-01

If bioassessments are to help diagnose the specific environmental stressors affecting streams, a better understanding is needed of the relationships between community metrics and ambient criteria or ambient bioassays. However, this relationship is not simple, because metrics assess responses at the community level of biological organization, while ambient criteria and ambient bioassays assess or are based on responses at the individual level. For metals, the relationship is further complicated by the influence of other chemical variables, such as hardness, on their bioavailability and toxicity. In 1993 and 1994, U.S. Environmental Protection Agency (U.S. EPA) conducted a Regional Environmental Monitoring and Assessment Program (REMAP) survey on wadeable streams in Colorado's (USA) Southern Rockies Ecoregion. In this ecoregion, mining over the past century has resulted in metals contamination of streams. The surveys collected data on fish and macroinvertebrate assemblages, physical habitat, and sediment and water chemistry and toxicity. These data provide a framework for assessing diagnostic community metrics for specific environmental stressors. We characterized streams as metals-affected based on exceedence of hardness-adjusted criteria for cadmium, copper, lead, and zinc in water; on water toxicity tests (48-h Pimephales promelas and Ceriodaphnia dubia survival); on exceedence of sediment threshold effect levels (TELs); or on sediment toxicity tests (7-d Hyalella azteca survival and growth). Macroinvertebrate and fish metrics were compared among affected and unaffected sites to identify metrics sensitive to metals. Several macroinvertebrate metrics, particularly richness metrics, were less in affected streams, while other metrics were not. This is a function of the sensitivity of the individual metrics to metals effects. Fish metrics were less sensitive to metals because of the low diversity of fish in these streams.

Productivity of Stream Definitions

NARCIS (Netherlands)

Endrullis, Jörg; Grabmayer, Clemens; Hendriks, Dimitri; Isihara, Ariya; Klop, Jan

2007-01-01

We give an algorithm for deciding productivity of a large and natural class of recursive stream definitions. A stream definition is called ‘productive’ if it can be evaluated continuously in such a way that a uniquely determined stream is obtained as the limit. Whereas productivity is undecidable

Productivity of stream definitions

NARCIS (Netherlands)

Endrullis, J.; Grabmayer, C.A.; Hendriks, D.; Isihara, A.; Klop, J.W.

2008-01-01

We give an algorithm for deciding productivity of a large and natural class of recursive stream definitions. A stream definition is called ‘productive’ if it can be evaluated continually in such a way that a uniquely determined stream in constructor normal form is obtained as the limit. Whereas

A Novel Image Stream Cipher Based On Dynamic Substitution

OpenAIRE

Elsharkawi, A.; El-Sagheer, R. M.; Akah, H.; Taha, H.

2016-01-01

Recently, many chaos-based stream cipher algorithms have been developed. Traditional chaos stream cipher is based on XORing a generated secure random number sequence based on chaotic maps (e.g. logistic map, Bernoulli Map, Tent Map etc.) with the original image to get the encrypted image, This type of stream cipher seems to be vulnerable to chosen plaintext attacks. This paper introduces a new stream cipher algorithm based on dynamic substitution box. The new algorithm uses one substitution b...

Interactive collision detection for deformable models using streaming AABBs.

Science.gov (United States)

Zhang, Xinyu; Kim, Young J

2007-01-01

We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At runtime, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30 approximately 100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB

Event metadata records as a testbed for scalable data mining

International Nuclear Information System (INIS)

Gemmeren, P van; Malon, D

2010-01-01

At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.

Overview of mine drainage geochemistry at historical mines, Humboldt River basin and adjacent mining areas, Nevada. Chapter E.

Science.gov (United States)

Nash, J. Thomas; Stillings, Lisa L.

2004-01-01

Reconnaissance hydrogeochemical studies of the Humboldt River basin and adjacent areas of northern Nevada have identified local sources of acidic waters generated by historical mine workings and mine waste. The mine-related acidic waters are rare and generally flow less than a kilometer before being neutralized by natural processes. Where waters have a pH of less than about 3, particularly in the presence of sulfide minerals, the waters take on high to extremely high concentrations of many potentially toxic metals. The processes that create these acidic, metal-rich waters in Nevada are the same as for other parts of the world, but the scale of transport and the fate of metals are much more localized because of the ubiquitous presence of caliche soils. Acid mine drainage is rare in historical mining districts of northern Nevada, and the volume of drainage rarely exceeds about 20 gpm. My findings are in close agreement with those of Price and others (1995) who estimated that less than 0.05 percent of inactive and abandoned mines in Nevada are likely to be a concern for acid mine drainage. Most historical mining districts have no draining mines. Only in two districts (Hilltop and National) does water affected by mining flow into streams of significant size and length (more than 8 km). Water quality in even the worst cases is naturally attenuated to meet water-quality standards within about 1 km of the source. Only a few historical mines release acidic water with elevated metal concentrations to small streams that reach the Humboldt River, and these contaminants and are not detectable in the Humboldt. These reconnaissance studies offer encouraging evidence that abandoned mines in Nevada create only minimal and local water-quality problems. Natural attenuation processes are sufficient to compensate for these relatively small sources of contamination. These results may provide useful analogs for future mining in the Humboldt River basin, but attention must be given to

Evaluation of the water quality related to the acid mine drainage of an abandoned mercury mine (Alaşehir, Turkey).

Science.gov (United States)

Gemici, Unsal

2008-12-01

Mobility of metals in water, mine wastes, and stream sediments around the abandoned Alaşehir mercury mine was investigated to evaluate the environmental effects around the area. Mine waters are dominantly acidic with pH values of 2.55 in arid season and 2.70 in wet season and are sulfate rich. Acidity is caused mainly by the oxidation of sulfide minerals. Pyrite is the main acid-producing mineral in the Alaşehir area. Of the major ions, SO(4) shows a notable increase reaching 3981 mg/l, which exceeds the WHO (WHO guidelines for drinking water quality, vol. 2. Health criteria and other supporting information, 1993) and TS (Sular-Içme ve kullanma sulari. Ankara: Türk Standartlari Enstitüsü, 1997) drinking water standard of 250 mg/L. Mine waters have As, Fe, Mn, Ni, and Al with concentrations higher than drinking water standards. Hg concentrations of adit water samples and surface waters draining the mine area are between 0.25 and 0.274 microg/L and are below the WHO (WHO guidelines for drinking water quality, vol. 2. Health criteria and other supporting information, 1993) drinking water standard of 1.0 microg/L. However, the concentrations are above the 0.012 microg/L standard (EPA, Water quality standards. Establishment of numeric criteria for priority toxic pollutants, states' compliance, final rule. Fed. Reg., 40 CFR, Part 131, 57/246, 60847-60916, 1992) used to protect aquatic life. Stream sediment samples have abnormally high values of especially Hg, As, Ni, and Cr metals. Geoaccumulation (Igeo) and pollution index (PI) values are significantly high and denote heavy contamination in stream sediments. The stream sediments derived from the mining area with the surface waters are potentially hazardous to the environment adjacent to the abandoned Hg mine and are in need of remediation.

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

Science.gov (United States)

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

1978-01-01

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

A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material

Energy Technology Data Exchange (ETDEWEB)

Yu, S.W.; Ding, C.; Zhu, K.J. [China University of Geoscience, Wuhan (China)

2011-08-15

In the open vehicle routing problem (OVRP), the objective is to minimize the number of vehicles and the total distance (or time) traveled. This study primarily focuses on solving an open vehicle routing problem (OVRP) by applying a novel hybrid genetic algorithm and the Tabu search (GA-TS), which combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. Firstly, the proposed algorithm uses natural number coding according to the customer demands and the captivity of the vehicle for globe optimization. Secondly, individuals of population do TS local search with a certain degree of probability, namely, do the local routing optimization of all customer sites belong to one vehicle. The mechanism not only improves the ability of global optimization, but also ensures the speed of operation. The algorithm was used in Zhengzhou Coal Mine and Power Supply Co., Ltd.'s transport vehicle routing optimization.

Tracing disturbance impacts on water quantity and quality through a stream network

Science.gov (United States)

Ross, Matthew; Nippgen, Fabian; McGlynn, Brian; Bernhardt, Emily

2017-04-01

By dismantling and redistributing 100s of meters of bedrock to mine coal from the surface, mountaintop mining with valley fills has dramatically changed catchment hydrology and biogeochemistry over more than 5,000 km2 in Central Appalachia. Throughout this expansive coal region, mining operators deposit tens of millions of m3 of crushed bedrock into headwater valleys, creating valley fills, which have substantial subsurface water storage potential. Streams draining mines have reduced peakflows, elevated baseflows, and lower event runoff ratios on average. The water stored in and percolating through valley fills drives the dissolution and oxidation of pyrite into sulfuric acid which reacts with carbonate-rich materials to rapidly weather out a suite of elements including Ca2+, Mg2+, K+, SO42-, HCO3-, and the pollutant Selenium. Together these ions increase the average specific conductance of mined streams from 60 to 1,500 µS/cm, 25-times higher than unmined streams, exporting 45-times more total dissolved solids. Together, the increased catchment storage, consequent elevated baseflow, and elevated weathering rates from mining have the potential to lower water quality throughout river networks in Central Appalachia, especially during the summer low flow period. To better understand the water quality impacts of mining at the river network scale, we used the paired catchment approach. Working in the Mud River, West Virginia, we instrumented a 4th order catchment 35 km2, that was 46% mined. Within the large catchment we instrumented 8 additional 1st-3rd order sub-catchments that varied in catchment size, mining cover, mine size, and mine age. At each site we measured stream discharge and specific conductance (SC). Using SC as a trace for mining we did simple hydrograph separations at our largest catchments, partitioning the hydrograph between mined and unmined water. Our results suggest that on an annual scale, mine water contributes a disproportionate percentage of

VALUING ACID MINE DRAINAGE REMEDIATION IN WEST VIRGINIA: A HEDONIC MODELING APPROACH

Science.gov (United States)

States with active and abandoned mines face large private and public costs to remediate damage to streams and rivers from acid mine drainage (AMD). Appalachian states have an especially large number of contaminated streams and rivers, and the USGS places AMD as the primary source...

Red River Stream Improvement Final Design Nez Perce National Forest.

Energy Technology Data Exchange (ETDEWEB)

Watershed Consulting, LLC

2007-03-15

This report details the final stream improvement design along the reach of Red River between the bridge below Dawson Creek, upstream for approximately 2 miles, Idaho County, Idaho. Geomorphic mapping, hydrologic profiles and cross-sections were presented along with existing fish habitat maps in the conceptual design report. This information is used to develop a stream improvement design intended to improve aquatic habitat and restore riparian health in the reach. The area was placer mined using large bucket dredges between 1938 and 1957. This activity removed most of the riparian vegetation in the stream corridor and obliterated the channel bed and banks. The reach was also cut-off from most valley margin tributaries. In the 50 years since large-scale dredging ceased, the channel has been re-established and parts of the riparian zone have grown in. However, the recruitment of large woody debris to the stream has been extremely low and overhead cover is poor. Pool habitat makes up more than 37% of the reach, and habitat diversity is much better than the project reach on Crooked River. There is little large woody debris in the stream to provide cover for spawning and juvenile rearing, because the majority of the woody debris does not span a significant part of the channel, but is mainly on the side slopes of the stream. Most of the riparian zone has very little soil or subsoil left after the mining and so now consists primarily of unconsolidated cobble tailings or heavily compacted gravel tailings. Knapweed and lodgepole pine are the most successful colonizers of these post mining landforms. Tributary fans which add complexity to many other streams in the region, have been isolated from the main reach due to placer mining and road building.

Impact of acid mine drainage from mining exploitations on the Margajita River basin and the Hatillo reservoir (Dominican Republic)

International Nuclear Information System (INIS)

Grandia, F.; Salas, J.; Arcos, D.; Archambault, A.; Cottard, F.

2009-01-01

Mining of the Pueblo Viejo high-sulphidation epithermal deposit (Dominican Republic) leads to environmental impact due to the formation of acid mine drainage associated with the oxidative dissolution of sulphides and sulpho salts. In addition to the very low pH, the acid waters are capable of transporting away from the mining areas high concentrations of metals and metalloids in solution. In the present work, a geochemical study of sediments deposited in the Hatillo reservoir is carried out. This reservoir is fed by the Margajita and Yuna streams which transport leachates from the Pueblo Viejo and Falcondo-Bonao (Cr-Ni) mining areas, respectively. The results show that these sediments have very high concentrations of Fe, Al and sulphate, along with significant amounts of As, Zn and Te, which are of especial environmental concern. The main contributor to this metal discharge into the reservoir is the Margajita stream, whereas the Yuna stream does not transport significant amounts of metals in solution due to its neutral pH, although it is likely that metals such as Mn, Cr, Ni and Co can be mobilised as a particulate. (Author) 5 refs.

Use of Natural and Applied Tracers to Guide Targeted Remediation Efforts in an Acid Mine Drainage System, Colorado Rockies, USA

OpenAIRE

Cowie, Rory; Williams, Mark; Wireman, Mike; Runkel, Robert

2014-01-01

Stream water quality in areas of the western United States continues to be degraded by acid mine drainage (AMD), a legacy of hard-rock mining. The Rico-Argentine Mine in southwestern Colorado consists of complex multiple-level mine workings connected to a drainage tunnel discharging AMD to passive treatment ponds that discharge to the Dolores River. The mine workings are excavated into the hillslope on either side of a tributary stream with workings passing directly under the stream channel. ...

Data mining in agriculture

CERN Document Server

Mucherino, Antonio; Pardalos, Panos M

2009-01-01

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009.

A genetic algorithm approach for open-pit mine production scheduling

Directory of Open Access Journals (Sweden)

Aref Alipour

2017-06-01

Full Text Available In an Open-Pit Production Scheduling (OPPS problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D copper orebody model. The orebody is featured as two-dimensional (2D array of blocks. Likewise, counterpart 2D GA array was used to represent the OPPS problem’s solution space. Thereupon, the fitness function is defined according to the OPPS problem’s objective function to assess the solution domain. Also, new normalization method was used for the handling of block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficiently in solving OPPS problem.

Mercury pollution in Wuchuan mercury mining area, Guizhou, Southwestern China: the impacts from large scale and artisanal mercury mining.

Science.gov (United States)

Li, Ping; Feng, Xinbin; Qiu, Guangle; Shang, Lihai; Wang, Shaofeng

2012-07-01

To evaluate the environmental impacts from large scale mercury mining (LSMM) and artisanal mercury mining (AMM), total mercury (THg) and methyl mercury (MeHg) were determined in mine waste, ambient air, stream water and soil samples collected from Wuchuan mercury (Hg) mining area, Guizhou, Southwestern China. Mine wastes from both LSMM and AMM contained high THg concentrations, which are important Hg contamination sources to the local environment. Total gaseous mercury (TGM) concentrations in the ambient air near AMM furnaces were highly elevated, which indicated that AMM retorting is a major source of Hg emission. THg concentrations in the stream water varied from 43 to 2100 ng/L, where the elevated values were mainly found in the vicinity of AMM and mine waste heaps of LSMM. Surface soils were seriously contaminated with Hg, and land using types and organic matter played an important role in accumulation and transportation of Hg in soil. The results indicated heavy Hg contaminations in the study area, which were resulted from both LSMM and AMM. The areas impacted by LSMM were concentrated in the historical mining and smelting facilities, while Hg pollution resulted from AMM can be distributed anywhere in the Hg mining area. Copyright © 2011 Elsevier Ltd. All rights reserved.

Stable Carbon Isotope Characterization of CO2 Loss in Acid Mine Drainage Impacted Stream Water: Observations from a Laboratory Experiment

Science.gov (United States)

Ali, H. N.; Atekwana, E. A.

2007-05-01

Water from an acid mine drainage spring, ground water from a mine tailings pile, stream water and tap water were acidified to simulate acid mine drainage (AMD) contamination. The objective was to determine how acidification of stream water by AMD affected DIC loss and carbon isotope fraction. Two 20 L HDP containers (reactors) containing samples from each source were left un-acidified and allowed to evolve under ambient conditions for several weeks in the laboratory and two others were acidified. Acidification was carried out progressively with sulfuric acid to pH <3. For acidified samples, one reactor was acidified open to the atmosphere and the other closed from contact with atmosphere and CO2(g) was collected under vacuum. The un-acidified samples did not show significant alkalinity and DIC loss, and the 13C of DIC was enriched with time. The acidified samples showed decrease in alkalinity and DIC and increase in the 13C of DIC and CO2(g) with progressive acidification. The enrichment of 13C of DIC for un-acidified samples was due to exchange with atmospheric CO2. On the other hand, the 13C enrichment in the acidified samples was due to fractionation during dehydration of HCO3- and diffusive loss of CO2(g) from the aqueous phase. The actual values measured depended on the amount of CO2 lost from the aqueous phase during acidification. Samples with greater CO2 loss (closed acidification) had greater 13C enrichment. Beyond the HCO3- titration end point, the δ13C of DIC and CO2(g) was similar and nearly constant. The result of this study suggests that AMD effects on DIC can be modeled as a first order kinetic reaction and the isotope enrichment modeled using Rayleigh distillation.

Mining the IPTV Channel Change Event Stream to Discover Insight and Detect Ads

Directory of Open Access Journals (Sweden)

Matej Kren

2016-01-01

Full Text Available IPTV has been widely deployed throughout the world, bringing significant advantages to users in terms of the channel offering, video on demand, and interactive applications. One aspect that has been often neglected is the ability of precise and unobtrusive telemetry. TV set-top boxes that are deployed in modern IPTV systems can be thought of as capable sensor nodes that collect vast amounts of data, representing both the user activity and the quality of service delivered by the system itself. In this paper we focus on the user-generated events and analyze how the data stream of channel change events received from the entire IPTV network can be mined to obtain insight about the content. We demonstrate that it is possible to predict the occurrence of TV ads with high probability and show that the approach could be extended to model the user behavior and classify the viewership in multiple dimensions.

Low-complexity transcoding algorithm from H.264/AVC to SVC using data mining

Science.gov (United States)

Garrido-Cantos, Rosario; De Cock, Jan; Martínez, Jose Luis; Van Leuven, Sebastian; Cuenca, Pedro; Garrido, Antonio

2013-12-01

Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a low-complexity algorithm to convert an H.264/AVC bitstream without scalability to scalable bitstreams with temporal scalability in baseline and main profiles by accelerating the mode decision task of the scalable video coding encoding stage using machine learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.

A survey of temporal data mining

Indian Academy of Sciences (India)

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams ...

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

Science.gov (United States)

Sridhar, S

2013-09-01

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

AN EFFECTIVE RECOMMENDATIONS BY DIFFUSION ALGORITHM FOR WEB GRAPH MINING

Directory of Open Access Journals (Sweden)

S. Vasukipriya

2013-04-01

Full Text Available The information on the World Wide Web grows in an explosive rate. Societies are relying more on the Web for their miscellaneous needs of information. Recommendation systems are active information filtering systems that attempt to present the information items like movies, music, images, books recommendations, tags recommendations, query suggestions, etc., to the users. Various kinds of data bases are used for the recommendations; fundamentally these data bases can be molded in the form of many types of graphs. Aiming at provided that a general framework on effective DR (Recommendations by Diffusion algorithm for web graphs mining. First introduce a novel graph diffusion model based on heat diffusion. This method can be applied to both undirected graphs and directed graphs. Then it shows how to convert different Web data sources into correct graphs in our models.

Surface-water and groundwater interactions in an extensively mined watershed, upper Schuylkill River, Pennsylvania, USA

Science.gov (United States)

Cravotta,, Charles A.; Goode, Daniel J.; Bartles, Michael D.; Risser, Dennis W.; Galeone, Daniel G.

2014-01-01

Streams crossing underground coal mines may lose flow, while abandoned mine drainage (AMD) restores flow downstream. During 2005-12, discharge from the Pine Knot Mine Tunnel, the largest AMD source in the upper Schuylkill River Basin, had near-neutral pH and elevated concentrations of iron, manganese, and sulfate. Discharge from the tunnel responded rapidly to recharge but exhibited a prolonged recession compared to nearby streams, consistent with rapid infiltration and slow release of groundwater from the mine. Downstream of the AMD, dissolved iron was attenuated by oxidation and precipitation while dissolved CO2 degassed and pH increased. During high-flow conditions, the AMD and downstream waters exhibited decreased pH, iron, and sulfate with increased acidity that were modeled by mixing net-alkaline AMD with recharge or runoff having low ionic strength and low pH. Attenuation of dissolved iron within the river was least effective during high-flow conditions because of decreased transport time coupled with inhibitory effects of low pH on oxidation kinetics. A numerical model of groundwater flow was calibrated using groundwater levels in the Pine Knot Mine and discharge data for the Pine Knot Mine Tunnel and the West Branch Schuylkill River during a snowmelt event in January 2012. Although the calibrated model indicated substantial recharge to the mine complex took place away from streams, simulation of rapid changes in mine pool level and tunnel discharge during a high flow event in May 2012 required a source of direct recharge to the Pine Knot Mine. Such recharge produced small changes in mine pool level and rapid changes in tunnel flow rate because of extensive unsaturated storage capacity and high transmissivity within the mine complex. Thus, elimination of stream leakage could have a small effect on the annual discharge from the tunnel, but a large effect on peak discharge and associated water quality in streams.

CAMS: OLAPing Multidimensional Data Streams Efficiently

Science.gov (United States)

Cuzzocrea, Alfredo

In the context of data stream research, taming the multidimensionality of real-life data streams in order to efficiently support OLAP analysis/mining tasks is a critical challenge. Inspired by this fundamental motivation, in this paper we introduce CAMS (C ube-based A cquisition model for M ultidimensional S treams), a model for efficiently OLAPing multidimensional data streams. CAMS combines a set of data stream processing methodologies, namely (i) the OLAP dimension flattening process, which allows us to obtain dimensionality reduction of multidimensional data streams, and (ii) the OLAP stream aggregation scheme, which aggregates data stream readings according to an OLAP-hierarchy-based membership approach. We complete our analytical contribution by means of experimental assessment and analysis of both the efficiency and the scalability of OLAPing capabilities of CAMS on synthetic multidimensional data streams. Both analytical and experimental results clearly connote CAMS as an enabling component for next-generation Data Stream Management Systems.

Mining Product Data Models: A Case Study

Directory of Open Access Journals (Sweden)

Cristina-Claudia DOLEAN

2014-01-01

Full Text Available This paper presents two case studies used to prove the validity of some data-flow mining algorithms. We proposed the data-flow mining algorithms because most part of mining algorithms focuses on the control-flow perspective. First case study uses event logs generated by an ERP system (Navision after we set several trackers on the data elements needed in the process analyzed; while the second case study uses the event logs generated by YAWL system. We offered a general solution of data-flow model extraction from different data sources. In order to apply the data-flow mining algorithms the event logs must comply a certain format (using InputOutput extension. But to respect this format, a set of conversion tools is needed. We depicted the conversion tools used and how we got the data-flow models. Moreover, the data-flow model is compared to the control-flow model.

Methylation of Hg downstream from the Bonanza Hg mine, Oregon

Science.gov (United States)

Gray, John E.; Hines, Mark E.; Krabbenhoft, David P.; Thoms, Bryn

2012-01-01

Speciation of Hg and conversion to methyl-Hg were evaluated in stream sediment, stream water, and aquatic snails collected downstream from the Bonanza Hg mine, Oregon. Total production from the Bonanza mine was >1360t of Hg, during mining from the late 1800s to 1960, ranking it as an intermediate sized Hg mine on an international scale. The primary objective of this study was to evaluate the distribution, transport, and methylation of Hg downstream from a Hg mine in a coastal temperate climatic zone. Data shown here for methyl-Hg, a neurotoxin hazardous to humans, are the first reported for sediment and water from this area. Stream sediment collected from Foster Creek flowing downstream from the Bonanza mine contained elevated Hg concentrations that ranged from 590 to 71,000ng/g, all of which (except the most distal sample) exceeded the probable effect concentration (PEC) of 1060ng/g, the Hg concentration above which harmful effects are likely to be observed in sediment-dwelling organisms. Concentrations of methyl-Hg in stream sediment collected from Foster Creek varied from 11 to 62ng/g and were highly elevated compared to regional baseline concentrations (0.11-0.82ng/g) established in this study. Methyl-Hg concentrations in stream sediment collected in this study showed a significant correlation with total organic C (TOC, R2=0.62), generally indicating increased methyl-Hg formation with increasing TOC in sediment. Isotopic-tracer methods indicated that several samples of Foster Creek sediment exhibited high rates of Hg-methylation. Concentrations of Hg in water collected downstream from the mine varied from 17 to 270ng/L and were also elevated compared to baselines, but all were below the 770ng/L Hg standard recommended by the USEPA to protect against chronic effects to aquatic wildlife. Concentrations of methyl-Hg in the water collected from Foster Creek ranged from 0.17 to 1.8ng/L, which were elevated compared to regional baseline sites upstream and downstream

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm.

Science.gov (United States)

Tchagang, Alain B; Phan, Sieu; Famili, Fazel; Shearer, Heather; Fobert, Pierre; Huang, Yi; Zou, Jitao; Huang, Daiqing; Cutler, Adrian; Liu, Ziying; Pan, Youlian

2012-04-04

Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

Evolving temporal association rules with genetic algorithms

OpenAIRE

Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian A.

2010-01-01

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of...

Use of NTRIP for Optimizing the Decoding Algorithm for Real-Time Data Streams

Directory of Open Access Journals (Sweden)

Zhanke He

2014-10-01

Full Text Available As a network transmission protocol, Networked Transport of RTCM via Internet Protocol (NTRIP is widely used in GPS and Global Orbiting Navigational Satellite System (GLONASS Augmentation systems, such as Continuous Operational Reference System (CORS, Wide Area Augmentation System (WAAS and Satellite Based Augmentation Systems (SBAS. With the deployment of BeiDou Navigation Satellite system(BDS to serve the Asia-Pacific region, there are increasing needs for ground monitoring of the BeiDou Navigation Satellite system and the development of the high-precision real-time BeiDou products. This paper aims to optimize the decoding algorithm of NTRIP Client data streams and the user authentication strategies of the NTRIP Caster based on NTRIP. The proposed method greatly enhances the handling efficiency and significantly reduces the data transmission delay compared with the Federal Agency for Cartography and Geodesy (BKG NTRIP. Meanwhile, a transcoding method is proposed to facilitate the data transformation from the BINary EXchange (BINEX format to the RTCM format. The transformation scheme thus solves the problem of handing real-time data streams from Trimble receivers in the BeiDou Navigation Satellite System indigenously developed by China.

Use of NTRIP for optimizing the decoding algorithm for real-time data streams.

Science.gov (United States)

He, Zhanke; Tang, Wenda; Yang, Xuhai; Wang, Liming; Liu, Jihua

2014-10-10

As a network transmission protocol, Networked Transport of RTCM via Internet Protocol (NTRIP) is widely used in GPS and Global Orbiting Navigational Satellite System (GLONASS) Augmentation systems, such as Continuous Operational Reference System (CORS), Wide Area Augmentation System (WAAS) and Satellite Based Augmentation Systems (SBAS). With the deployment of BeiDou Navigation Satellite system(BDS) to serve the Asia-Pacific region, there are increasing needs for ground monitoring of the BeiDou Navigation Satellite system and the development of the high-precision real-time BeiDou products. This paper aims to optimize the decoding algorithm of NTRIP Client data streams and the user authentication strategies of the NTRIP Caster based on NTRIP. The proposed method greatly enhances the handling efficiency and significantly reduces the data transmission delay compared with the Federal Agency for Cartography and Geodesy (BKG) NTRIP. Meanwhile, a transcoding method is proposed to facilitate the data transformation from the BINary EXchange (BINEX) format to the RTCM format. The transformation scheme thus solves the problem of handing real-time data streams from Trimble receivers in the BeiDou Navigation Satellite System indigenously developed by China.

The role of scientists in acid mine drainage policy response in Gauteng, South Africa: Presentation

CSIR Research Space (South Africa)

Funke, Nicola S

2012-10-01

Full Text Available . This government response came after considerable publicity in the media and threats of legal action by NGOs. • Complexity: historical link between government and mines, lack of inter-departmental coordination, scientific uncertainty, many actors involved... stream_source_info Funke3_2012.pdf.txt stream_content_type text/plain stream_size 6180 Content-Encoding UTF-8 stream_name Funke3_2012.pdf.txt Content-Type text/plain; charset=UTF-8 The role of scientists in Acid Mine...

Physico-chemical processes in acid mine drainage in coal mining, south Brazil

Energy Technology Data Exchange (ETDEWEB)

Campaner, Veridiana Polvani; Luiz-silva, Wanilson. [Universidade Estadual de Campinas, Campinas (Brazil)

2009-07-01

Acid mine drainage generated from coal mine showed a pH of 3.2, high concentrations of SO{sub 4}{sup 2-}, Al, Fe, Mn, Zn and minor As, Cd, Co, Cr, Cu, Ni and Pb. The major reduction in the concentration occurred for Al, As, Cr, Fe and Pb after the treatment with CaO. The evolution of these acid waters within the tributary stream showed decreasing concentration for all soluble constituents, except Al. This natural attenuation was controlled by pH (6.4 to 10.8) as a result of concurrent mixing with tributary stream and reaction with local bedrock that contains limestone. Aluminum increasing concentration during this evolution seems to be related to an input of Al-enriched waters due to the leaching of silicate minerals in alkaline conditions. 47 refs., 3 figs., 3 tabs.

A Neural-Network Clustering-Based Algorithm for Privacy Preserving Data Mining

Science.gov (United States)

Tsiafoulis, S.; Zorkadis, V. C.; Karras, D. A.

The increasing use of fast and efficient data mining algorithms in huge collections of personal data, facilitated through the exponential growth of technology, in particular in the field of electronic data storage media and processing power, has raised serious ethical, philosophical and legal issues related to privacy protection. To cope with these concerns, several privacy preserving methodologies have been proposed, classified in two categories, methodologies that aim at protecting the sensitive data and those that aim at protecting the mining results. In our work, we focus on sensitive data protection and compare existing techniques according to their anonymity degree achieved, the information loss suffered and their performance characteristics. The ℓ-diversity principle is combined with k-anonymity concepts, so that background information can not be exploited to successfully attack the privacy of data subjects data refer to. Based on Kohonen Self Organizing Feature Maps (SOMs), we firstly organize data sets in subspaces according to their information theoretical distance to each other, then create the most relevant classes paying special attention to rare sensitive attribute values, and finally generalize attribute values to the minimum extend required so that both the data disclosure probability and the information loss are possibly kept negligible. Furthermore, we propose information theoretical measures for assessing the anonymity degree achieved and empirical tests to demonstrate it.

Stream Processing Using Grammars and Regular Expressions

DEFF Research Database (Denmark)

Rasmussen, Ulrik Terp

disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs...... as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present...... Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle...

Detecting the effects of coal mining, acid rain, and natural gas extraction in Appalachian basin streams in Pennsylvania (USA) through analysis of barium and sulfate concentrations.

Science.gov (United States)

Niu, Xianzeng; Wendt, Anna; Li, Zhenhui; Agarwal, Amal; Xue, Lingzhou; Gonzales, Matthew; Brantley, Susan L

2018-04-01

To understand how extraction of different energy sources impacts water resources requires assessment of how water chemistry has changed in comparison with the background values of pristine streams. With such understanding, we can develop better water quality standards and ecological interpretations. However, determination of pristine background chemistry is difficult in areas with heavy human impact. To learn to do this, we compiled a master dataset of sulfate and barium concentrations ([SO 4 ], [Ba]) in Pennsylvania (PA, USA) streams from publically available sources. These elements were chosen because they can represent contamination related to oil/gas and coal, respectively. We applied changepoint analysis (i.e., likelihood ratio test) to identify pristine streams, which we defined as streams with a low variability in concentrations as measured over years. From these pristine streams, we estimated the baseline concentrations for major bedrock types in PA. Overall, we found that 48,471 data values are available for [SO 4 ] from 1904 to 2014 and 3243 data for [Ba] from 1963 to 2014. Statewide [SO 4 ] baseline was estimated to be 15.8 ± 9.6 mg/L, but values range from 12.4 to 26.7 mg/L for different bedrock types. The statewide [Ba] baseline is 27.7 ± 10.6 µg/L and values range from 25.8 to 38.7 µg/L. Results show that most increases in [SO 4 ] from the baseline occurred in areas with intensive coal mining activities, confirming previous studies. Sulfate inputs from acid rain were also documented. Slight increases in [Ba] since 2007 and higher [Ba] in areas with higher densities of gas wells when compared to other areas could document impacts from shale gas development, the prevalence of basin brines, or decreases in acid rain and its coupled effects on [Ba] related to barite solubility. The largest impacts on PA stream [Ba] and [SO 4 ] are related to releases from coal mining or burning rather than oil and gas development.

Online Nonparametric Bayesian Activity Mining and Analysis From Surveillance Video.

Science.gov (United States)

Bastani, Vahid; Marcenaro, Lucio; Regazzoni, Carlo S

2016-05-01

A method for online incremental mining of activity patterns from the surveillance video stream is presented in this paper. The framework consists of a learning block in which Dirichlet process mixture model is employed for the incremental clustering of trajectories. Stochastic trajectory pattern models are formed using the Gaussian process regression of the corresponding flow functions. Moreover, a sequential Monte Carlo method based on Rao-Blackwellized particle filter is proposed for tracking and online classification as well as the detection of abnormality during the observation of an object. Experimental results on real surveillance video data are provided to show the performance of the proposed algorithm in different tasks of trajectory clustering, classification, and abnormality detection.

Impacts of groundwater metal loads from bedrock fractures on water quality of a mountain stream.

Science.gov (United States)

Caruso, Brian S; Dawson, Helen E

2009-06-01

Acid mine drainage and metal loads from hardrock mines to surface waters is a significant problem in the western USA and many parts of the world. Mines often occur in mountain environments with fractured bedrock aquifers that serve as pathways for metals transport to streams. This study evaluates impacts from current and potential future groundwater metal (Cd, Cu, and Zn) loads from fractures underlying the Gilt Edge Mine, South Dakota, on concentrations in Strawberry Creek using existing flow and water quality data and simple mixing/dilution mass balance models. Results showed that metal loads from bedrock fractures to the creek currently contribute water quality is achieved upstream in Strawberry Creek, fracture metal loads would be water quality standards exceedances once groundwater with elevated metals concentrations in the aquifer matrix migrates to the fractures and discharges to the stream. Potential future metal loads from an upstream fracture would contribute a small proportion of the total load relative to current loads in the stream. Cd has the highest stream concentrations relative to standards. Even if all stream water was treated to remove 90% of the Cd, the standard would still not be achieved. At a fracture farther downstream, the Cd standard can only be met if the upstream water is treated achieving a 90% reduction in Cd concentrations and the median stream flow is maintained.

Mining

Directory of Open Access Journals (Sweden)

Khairullah Khan

2014-09-01

Full Text Available Opinion mining is an interesting area of research because of its applications in various fields. Collecting opinions of people about products and about social and political events and problems through the Web is becoming increasingly popular every day. The opinions of users are helpful for the public and for stakeholders when making certain decisions. Opinion mining is a way to retrieve information through search engines, Web blogs and social networks. Because of the huge number of reviews in the form of unstructured text, it is impossible to summarize the information manually. Accordingly, efficient computational methods are needed for mining and summarizing the reviews from corpuses and Web documents. This study presents a systematic literature survey regarding the computational techniques, models and algorithms for mining opinion components from unstructured reviews.

Data Mining and Machine Learning in Astronomy

Science.gov (United States)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Contamination of water and soil by the Erdenet copper-molybdenum mine in Mongolia

Science.gov (United States)

Battogtokh, B.; Lee, J.; Woo, N. C.; Nyamjav, A.

2013-12-01

As one of the largest copper-molybdenum (Cu-Mo) mines in the world, the Erdenet Mine in Mongolia has been active since 1978, and is expected to continue operations for at least another 30 years. In this study, the potential impacts of mining activities on the soil and water environments have been evaluated. Water samples showed high concentrations of sulfate, calcium, magnesium, Mo, and arsenic, and high pH values in the order of high to low as follows: tailing water > Khangal River > groundwater. Statistical analysis and the δ2H and δ18O values of water samples indicate that the tailing water directly affects the stream water and indirectly affects groundwater through recharge processes. Soil and stream sediments are highly contaminated with Cu and Mo, which are major elements of ore minerals. Based on the contamination factor (CF), the pollution load index (PLI), and the degree of contamination (Cd), soil appears to be less contaminated than stream sediments. The soil particle size is similar to that of tailing materials, but stream sediments have much coarser particles, implying that the materials have different origins. Contamination levels in stream sediments display a tendency to decrease with distance from the mine, but no such changes are found in soil. Consequently, soil contamination by metals is attributable to wind-blown dusts from the tailing materials, and stream sediment contamination is caused by discharges from uncontained subgrade ore stock materials. Considering the evident impact on the soil and water environment, and the human health risk from the Erdenet Mine, measures to mitigate its environmental impact should be taken immediately including source control, the establishment of a systematic and continuous monitoring system, and a comprehensive risk assessment. Sampling locations around the Erdenet Mine

The precipitation of indium at elevated pH in a stream influenced by acid mine drainage

Science.gov (United States)

White, Sarah Jane O.; Hussain, Fatima A.; Hemond, Harold F.; Sacco, Sarah A.; Shine, James P.; Runkel, Robert L.; Walton-Day, Katherine; Kimball, Briant A.

2017-01-01

Indium is an increasingly important metal in semiconductors and electronics and has uses in important energy technologies such as photovoltaic cells and light-emitting diodes (LEDs). One significant flux of indium to the environment is from lead, zinc, copper, and tin mining and smelting, but little is known about its aqueous behavior after it is mobilized. In this study, we use Mineral Creek, a headwater stream in southwestern Colorado severely affected by heavy metal contamination as a result of acid mine drainage, as a natural laboratory to study the aqueous behavior of indium. At the existing pH of ~ 3, indium concentrations are 6–29 μg/L (10,000 × those found in natural rivers), and are completely filterable through a 0.45 μm filter. During a pH modification experiment, the pH of the system was raised to > 8, and > 99% of the indium became associated with the suspended solid phase (i.e. does not pass through a 0.45 μm filter). To determine the mechanism of removal of indium from the filterable and likely primarily dissolved phase, we conducted laboratory experiments to determine an upper bound for a sorption constant to iron oxides, and used this, along with other published thermodynamic constants, to model the partitioning of indium in Mineral Creek. Modeling results suggest that the removal of indium from the filterable phase is consistent with precipitation of indium hydroxide from a dissolved phase. This work demonstrates that nonferrous mining processes can be a significant source of indium to the environment, and provides critical information about the aqueous behavior of indium.

Preliminary Results: Release Of Metals From Acid-Mine Drainage Contaminated Streambed Sediments Under Anaerobic Conditions

Science.gov (United States)

Many miles of streams in the western U.S. are contaminated with acid-mine drainage (AMD) from abandoned metal mines. Treatment of these streams may include removal of the existing sediments, with subsequent burial (e.g., in a repository). Burial of previously aerobic sediments ma...

AMIDST: Analysis of MassIve Data STreams

DEFF Research Database (Denmark)

Masegosa, Andres; Martinez, Ana Maria; Borchani, Hanen

2015-01-01

The Analysis of MassIve Data STreams (AMIDST) Java toolbox provides a collection of scalable and parallel algorithms for inference and learning of hybrid Bayesian networks from data streams. The toolbox, available at http://amidst.github.io/toolbox/ under the Apache Software License version 2.......0, also efficiently leverages existing functionalities and algorithms by interfacing to software tools such as HUGIN and MOA....

Long-term dispersal of heavy metals in a catchment affected by historic lead and zinc mining

Energy Technology Data Exchange (ETDEWEB)

Ciszewski, Dariusz; Kubsik, Urszula; Aleksander-Kwaterczak, Urszula [AGH Univ. of Science and Technology, Krakow (Poland)

2012-10-15

The Matylda catchment, in southern Poland, was polluted by the discharge of mine waters from a lead and zinc mine that inundated parts of a valley floor and caused the accumulation of metal-polluted sediments. After a partial reclamation of the mine site in the early 1980s, polluted sediments continue to accumulate on downstream floodplains and in fishponds. The aim of this study was to reconstruct the changes in metal dispersal during 100 years of mining and during the 40-year post-mining period and to propose a strategy for pollution mitigation in the area. Analyses of Cu, Cd, Pb, Zn, Mn, Ca, Mg and Fe concentrations, speciation of heavy metals and mineralogical analyses were undertaken on overbank sediment cores and in stream sediments. Concentrations of the same elements and macro-ions soluble in stream waters were also determined. Concentrations of Zn, Cd and Pb in the sediment profiles vary between 40,000 and 55,000, 300 and 600 and 30,000 and 50,000 mg kg{sup -1}, respectively. Changes of metal concentrations and the stratigraphy of sediments from the floodplains, stream channels and fishponds suggest rapid changes of metal loads migrating downstream during both the mining and post-mining periods. Since the time of mine closure, fine-grained, mine-derived sediments (ca. 12 cm thick) have been the main source of pollution of post-mining sediments and surface waters. Closure of the mine was followed by a relatively short period of rapid redistribution of sediment-associated heavy metals in the stream channel. Since the 1980s, the floodplain and fishponds have received a constant supply of metals. It contrasts with the slow sediment accretion rate and a rapid decrease of metal concentrations in floodplain pools due to dilution by decomposed leaf litter. A fivefold increase of Cd content in waters over the 4.6 km reach of the Matylda stream indicates continuous leaching of this element from the contaminated valley floor. Unsuccessful mine site rehabilitation is

StreamMap: Smooth Dynamic Visualization of High-Density Streaming Points.

Science.gov (United States)

Li, Chenhui; Baciu, George; Han, Yu

2018-03-01

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Zhang, Xiangliang

2016-01-01

Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali

2016-11-08

Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

Mining Upgrades to Reduce Pollution

Science.gov (United States)

Settlement with Southern Coal Corporation and 26 affiliates requires the companies to comprehensively upgrade their coal mining and processing operations to prevent polluted wastewater from threatening rivers and streams and communities across Appalachia.

Quality of water and sediment in streams affected by historical mining, and quality of Mine Tailings, in the Rio Grande/Rio Bravo Basin, Big Bend Area of the United States and Mexico, August 2002

Science.gov (United States)

Lambert, Rebecca B.; Kolbe, Christine M.; Belzer, Wayne

2008-01-01

The U.S. Geological Survey, in cooperation with the International Boundary and Water Commission - U.S. and Mexican Sections, the National Park Service, the Texas Commission on Environmental Quality, the Secretaria de Medio Ambiente y Recursos Naturales in Mexico, the Area de Proteccion de Flora y Fauna Canon de Santa Elena in Mexico, and the Area de Proteccion de Flora y Fauna Maderas del Carmen in Mexico, collected samples of stream water, streambed sediment, and mine tailings during August 2002 for a study to determine whether trace elements from abandoned mines in the area in and around Big Bend National Park have affected the water and sediment quality in the Rio Grande/Rio Bravo Basin of the United States and Mexico. Samples were collected from eight sites on the main stem of the Rio Grande/Rio Bravo, four Rio Grande/Rio Bravo tributary sites downstream from abandoned mines or mine-tailing sites, and 11 mine-tailing sites. Mines in the area were operated to produce fluorite, germanium, iron, lead, mercury, silver, and zinc during the late 1800s through at least the late 1970s. Moderate (relatively neutral) pHs in stream-water samples collected at the 12 Rio Grande/Rio Bravo main-stem and tributary sites indicate that water is well mixed, diluted, and buffered with respect to the solubility of trace elements. The highest sulfate concentrations were in water samples from tributaries draining the Terlingua mining district. Only the sample from the Rough Run Draw site exceeded the Texas Surface Water Quality Standards general-use protection criterion for sulfate. All chloride and dissolved solids concentrations in water samples were less than the general-use protection criteria. Aluminum, copper, mercury, nickel, selenium, and zinc were detected in all water samples for which each element was analyzed. Cadmium, chromium, and lead were detected in samples less frequently, and silver was not detected in any of the samples. None of the sample concentrations of

Web Mining and Social Networking

DEFF Research Database (Denmark)

Xu, Guandong; Zhang, Yanchun; Li, Lin

This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web ...... sense of individuals or communities. The volume will benefit both academic and industry communities interested in the techniques and applications of web search, web data management, web mining and web knowledge discovery, as well as web community and social network analysis.......This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web...... mining, and the issue of how to incorporate web mining into web personalization and recommendation systems are also reviewed. Additionally, the volume explores web community mining and analysis to find the structural, organizational and temporal developments of web communities and reveal the societal...

A node linkage approach for sequential pattern mining.

Directory of Open Access Journals (Sweden)

Osvaldo Navarro

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

Environmental risk assessment of lead-zinc mining: a case study of Adudu metallogenic province, middle Benue Trough, Nigeria.

Science.gov (United States)

Igwe, Ogbonnaya; Una, Chuku Okoro; Abu, Ezekiel; Adepehin, Ekundayo Joseph

2017-09-07

Assessment of the impacts of lead-zinc mining in Adudu-Imon metallogenic province was carried out. Reconnaissance and detailed field studies were done. Lithologies, stream sediments, farmland soils, mine tailings, artificial pond water, stream water, well water, and borehole water were collected and subjected to atomic absorption spectrometry (AAS) and X-ray fluorescence (XRF) analyses. Geochemical maps were generated using ArcGIS 10.1. Significant contamination with cadmium (Cd), iron (Fe), and lead (Pb) was recorded in the collected water samples. Virtually all collected soil samples were observed to be highly contaminated when compared with the European Union environmental policy standard. The discharge of mining effluents through farmlands to the Bakebu stream, which drains the area, further exposes the dwellers of this environment to the accumulation of potentially harmful metals (PHMs) in their bodies through the consumption of food crops, aquatic animals, and domestic uses of the water collected from the stream channels. The study revealed non-conformity of past mining operations in the Adudu-Imon province to existing mining laws in Nigeria. Inhabitants of this region should stop farming in the vicinity of the mines, fishing from the Bakebu stream channels should be discouraged, and domestic use of the water should be condemned, even as concerned government agencies put necessary mercenaries in place to ensure conformity of miners to standard mining regulations in Nigeria.

Effects of sulphuric acid pollution on the biology of streams in the Transvaal, South Africa

Energy Technology Data Exchange (ETDEWEB)

Harrison, A D

1958-01-01

Strongly acid effluents or drainage waters are produced during gold and coal mining activities in the Transvaal. Sulphuric acid is produced during oxidation of pyrites exposed by mining operations and much of it finds its way into streams and creates serious pollution problems. The object of this paper is to give a short account of the effects of this acid pollution on the biology of these streams. The first streams considered are the Klip and Klipspruit near their confluence at Olifantsvlei, near Johannesburg. These were studied during a two-year investigation of the area. Both receive acid pollution from gold mine dumps and slimes dams, the seepages from which have pH values as low as 2.3. Both streams run over dolomite formations so the acid is gradually neutralised but highly mineralised, permanently hard water results. The Klip and the Klipspruit join in the middle of a y-shaped, swampy area, each stream coming down one of the upper arms of the y. A sampling station was set up on each where it runs slowly through the swamp just before confluence.

Distributed genetic process mining

NARCIS (Netherlands)

Bratosin, C.C.; Sidorova, N.; Aalst, van der W.M.P.

2010-01-01

Process mining aims at discovering process models from data logs in order to offer insight into the real use of information systems. Most of the existing process mining algorithms fail to discover complex constructs or have problems dealing with noise and infrequent behavior. The genetic process

The online performance estimation framework: heterogeneous ensemble learning for data streams

NARCIS (Netherlands)

van Rijn, J.N.; Holmes, G.; Pfahringer, B.; Vanschoren, J.

2018-01-01

Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An

Data Stream Clustering With Affinity Propagation

KAUST Repository

Zhang, Xiangliang

2014-07-09

Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.

Data Stream Clustering With Affinity Propagation

KAUST Repository

Zhang, Xiangliang; Furtlehner, Cyril; Germain-Renaud, Cecile; Sebag, Michele

2014-01-01

Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.

Preliminary Results: Release Of Metals From Acid-Mine Drainage Contaminated Streambed Sediments Under Anaerobic Conditions (Presentation)

Science.gov (United States)

Many miles of streams in the western U.S. are contaminated with acid-mine drainage (AMD) from abandoned metal mines. Treatment of these streams may include removal of the existing sediments, with subsequent burial (e.g., in a repository). Burial of previously aerobic sediments ma...

VALUING ACID MINE DRAINAGE REMEDIATION IN WEST VIRGINIA: A HEDONIC MODELING APPROACH INCORPORATING GEOGRAPHIC INFORMATION SYSTEMS

Science.gov (United States)

States with active and abandoned mines face large private and public costs to remediate damage to streams and rivers from acid mine drainage (AMD). Appalachian states have an especially large number of contaminated streams and rivers, and the USGS places AMD as the primary source...

Uranium mining in Australia: dreams--and reality

International Nuclear Information System (INIS)

Anon.

1977-01-01

By the early 1980's if the current mining projects described are allowed to go on stream, Australia will be able to produce at least 10 900 tons of U$sub 3$O$sub 8$ annually from ores whose grade ranges from a low of 0.150% to a high of 2.300%. The Jabiluka Project of uranium mining is described, and plans for other mines are discussed in Queensland, South and Western Australia. 2 refs

Anthropogenic and natural sources of acidity and metals and their influence on the structure of stream food webs

International Nuclear Information System (INIS)

Hogsden, Kristy L.; Harding, Jon S.

2012-01-01

We compared food web structure in 20 streams with either anthropogenic or natural sources of acidity and metals or circumneutral water chemistry in New Zealand. Community and diet analysis indicated that mining streams receiving anthropogenic inputs of acidic and metal-rich drainage had much simpler food webs (fewer species, shorter food chains, less links) than those in naturally acidic, naturally high metal, and circumneutral streams. Food webs of naturally high metal streams were structurally similar to those in mining streams, lacking fish predators and having few species. Whereas, webs in naturally acidic streams differed very little from those in circumneutral streams due to strong similarities in community composition and diets of secondary and top consumers. The combined negative effects of acidity and metals on stream food webs are clear. However, elevated metal concentrations, regardless of source, appear to play a more important role than acidity in driving food web structure. - Highlights: ► Food webs in acid mine drainage impacted streams are small and extremely simplified. ► Conductivity explained differences in food web properties between streams. ► Number of links and web size accounted for much dissimilarity between food webs. ► Food web structure was comparable in naturally acidic and circumneutral streams. - Food web structure differs in streams with anthropogenic and natural sources of acidity and metals.

Mine waters: Acidic to circumneutral

Science.gov (United States)

Nordstrom, D. Kirk

2011-01-01

Acid mine waters, often containing toxic concentrations of Fe, Al, Cu, Zn, Cd, Pb, Ni, Co, and Cr, can be produced from the mining of coal and metallic deposits. Values of pH for acid mine waters can range from –3.5 to 5, but even circumneutral (pH ≈ 7) mine waters can have high concentrations of As, Sb, Mo, U, and F. When mine waters are discharged into streams, lakes, and the oceans, serious degradation of water quality and injury to aquatic life can ensue, especially when tailings impoundments break suddenly. The main acid-producing process is the exposure of pyrite to air and water, which promotes oxidative dissolution, a reaction catalyzed by microbes. Current and future mining should plan for the prevention and remediation of these contaminant discharges by the application of hydrogeochemical principles and available technologies, which might include remining and recycling of waste materials.

Hydric soils and the relationship to plant diversity within reclaimed stream channels in semi-arid environments

International Nuclear Information System (INIS)

Schladweiler, B.K.; Rexroat, S.; Benson, S.

1999-01-01

Wetlands are especially important in semi-arid environments, such as the Powder River Basin of northeastern Wyoming, where water is a limiting factor for living organisms. Within this coal mining region of northeastern Wyoming, jurisdictional wetlands are mapped according to the US Army Corps of Engineers 1987 delineation procedure. Within the coal mining region of northeastern Wyoming, little or no full-scale mitigation or reconstruction attempts of jurisdictional wetland areas have been made until recently. Based on the importance of wetlands in a semi-arid environment and lack of information on existing or reconstructed areas, the specific objectives of the 1998 fieldwork were: (1) To define the pre-disturbance ecological state of hydric soils within jurisdictional sections of stream channels on two coal permit areas in northeastern Wyoming, and (2) To determine the effect that hydric soil parameters have on plant community distribution and composition within the two coal permit areas. Undisturbed sections of stream channels and disturbed sections of reconstructed or modified stream channels at the Rawhide Mine and Buckskin Mine, located north of Gillette, Wyoming, were selected for the study. Soils field and laboratory information and field vegetation cover were collected during 1998 within native stream channels and disturbed stream channels that had been reclaimed at each mine. Soils laboratory information is currently preliminary and included pH, electrical conductivity and sodium adsorption ratio. Results and statistical comparisons between soils and vegetation data will be presented

An environmental evaluation of the effects of the Eldor Mines

International Nuclear Information System (INIS)

1985-11-01

In 1968, Gulf Minerals Ltd. discovered uranium mineralization around a small body of water referred to as Rabbit Lake. Subsequent drilling at the site discovered sufficient uranium to justify development, and by 1974 the mining and milling processes were in operation and routine monitoring of the release of effluents to Wollaston Lake began, and has been carried out since. Collins, Pow and Hidden Bays of Wollaston Lake are adjacent to the uranium mining operations and the latter two have received drainage and treated process waters. In addition to the Umpherville River, 13 smaller streams of various sizes enter Hidden Bay. Of these, the small stream draining Park's Lake is affected, to a small degree, by the mining operation, while the second stream draining the tailings management area is affected to a significant degree by the facility. With the exception of those discharges to the Pow Bay watershed, virtually all of the mine water, process water and site runoff that may be potentially contaminated by the mining and milling operation is discharged after treatment and routine monitoring to Hidden Bay, the location of Hidden Bay Lodge which is the closest permanent population to the mine site. For these reasons, data from Hidden Bay were reviewed in detail. Both monitoring data and present and future site plans would suggest that if any environmental concerns were to raise related to water discharges, Hidden Bay would be the first place where they would become apparent

A Distributed Flocking Approach for Information Stream Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2006-01-01

Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection because they normally require a large amount of computation resource and long time to get accurate result. It is very difficult to cluster a dynamic changed text information streams on an individual computer. Our early research has resulted in a dynamic reactive flock clustering algorithm which can continually refine the clustering result and quickly react to the change of document contents. This character makes the algorithm suitable for cluster analyzing dynamic changed document information, such as text information stream. Because of the decentralized character of this algorithm, a distributed approach is a very natural way to increase the clustering speed of the algorithm. In this paper, we present a distributed multi-agent flocking approach for the text information stream clustering and discuss the decentralized architectures and communication schemes for load balance and status information synchronization in this approach.

Temporal Segmentation of MPEG Video Streams

Directory of Open Access Journals (Sweden)

Janko Calic

2002-06-01

Full Text Available Many algorithms for temporal video partitioning rely on the analysis of uncompressed video features. Since the information relevant to the partitioning process can be extracted directly from the MPEG compressed stream, higher efficiency can be achieved utilizing information from the MPEG compressed domain. This paper introduces a real-time algorithm for scene change detection that analyses the statistics of the macroblock features extracted directly from the MPEG stream. A method for extraction of the continuous frame difference that transforms the 3D video stream into a 1D curve is presented. This transform is then further employed to extract temporal units within the analysed video sequence. Results of computer simulations are reported.

Association rule extraction from XML stream data for wireless sensor networks.

Science.gov (United States)

Paik, Juryon; Nam, Junghyun; Kim, Ung Mo; Won, Dongho

2014-07-18

With the advances of wireless sensor networks, they yield massive volumes of disparate, dynamic and geographically-distributed and heterogeneous data. The data mining community has attempted to extract knowledge from the huge amount of data that they generate. However, previous mining work in WSNs has focused on supporting simple relational data structures, like one table per network, while there is a need for more complex data structures. This deficiency motivates XML, which is the current de facto format for the data exchange and modeling of a wide variety of data sources over the web, to be used in WSNs in order to encourage the interchangeability of heterogeneous types of sensors and systems. However, mining XML data for WSNs has two challenging issues: one is the endless data flow; and the other is the complex tree structure. In this paper, we present several new definitions and techniques related to association rule mining over XML data streams in WSNs. To the best of our knowledge, this work provides the first approach to mining XML stream data that generates frequent tree items without any redundancy.

77 FR 55430 - Arkansas Regulatory Program and Abandoned Mine Land Reclamation Plan

Science.gov (United States)

2012-09-10

... of its regulatory program and abandoned mine land reclamation plan, make grammatical changes, correct... portions of its regulatory program and abandoned mine land reclamation plan, make grammatical changes... Streams. PART 785--REQUIREMENTS FOR PERMITS FOR SPECIAL CATEGORIES OF MINING 785.13, 785.14, 785.15...

Methylmercury degradation and exposure pathways in streams and wetlands impacted by historical mining

Energy Technology Data Exchange (ETDEWEB)

Donovan, Patrick M., E-mail: pmdon@umich.edu [University of Michigan, Department of Earth and Environmental Sciences, 1100 N., University Ave., Ann Arbor, MI 48109 (United States); Blum, Joel D. [University of Michigan, Department of Earth and Environmental Sciences, 1100 N., University Ave., Ann Arbor, MI 48109 (United States); Singer, Michael Bliss [University of St Andrews, Department of Earth and Environmental Sciences, North St., St. Andrews, KY16 9AL (United Kingdom); Earth Research Institute, University of California Santa Barbara, Santa Barbara, CA, 91306 (United States); Marvin-DiPasquale, Mark [U.S. Geological Survey, Menlo Park, CA (United States); Tsui, Martin T.K. [Department of Biology, University of North Carolina at Greensboro, Greensboro, NC 27402 (United States)

2016-10-15

Monomethyl mercury (MMHg) and total mercury (THg) concentrations and Hg stable isotope ratios (δ{sup 202}Hg and Δ{sup 199}Hg) were measured in sediment and aquatic organisms from Cache Creek (California Coast Range) and Yolo Bypass (Sacramento Valley). Cache Creek sediment had a large range in THg (87 to 3870 ng/g) and δ{sup 202}Hg (− 1.69 to − 0.20‰) reflecting the heterogeneity of Hg mining sources in sediment. The δ{sup 202}Hg of Yolo Bypass wetland sediment suggests a mixture of high and low THg sediment sources. Relationships between %MMHg (the percent ratio of MMHg to THg) and Hg isotope values (δ{sup 202}Hg and Δ{sup 199}Hg) in fish and macroinvertebrates were used to identify and estimate the isotopic composition of MMHg. Deviation from linear relationships was found between %MMHg and Hg isotope values, which is indicative of the bioaccumulation of isotopically distinct pools of MMHg. The isotopic composition of pre-photodegraded MMHg (i.e., subtracting fractionation from photochemical reactions) was estimated and contrasting relationships were observed between the estimated δ{sup 202}Hg of pre-photodegraded MMHg and sediment IHg. Cache Creek had mass dependent fractionation (MDF; δ{sup 202}Hg) of at least − 0.4‰ whereas Yolo Bypass had MDF of + 0.2 to + 0.5‰. This result supports the hypothesis that Hg isotope fractionation between IHg and MMHg observed in rivers (− MDF) is unique compared to + MDF observed in non-flowing water environments such as wetlands, lakes, and the coastal ocean. - Highlights: • Mercury isotope ratios were measured in sediment and biota from Central California. • The isotopic composition of MMHg was estimated in streams and wetlands. • Mercury isotopes suggest multiple exposure pathways in these habitats. • Mass dependent fractionation between IHg and MMHg is different in streams.

An optimization framework for process discovery algorithms

NARCIS (Netherlands)

Weijters, A.J.M.M.; Stahlbock, R.

2011-01-01

Today there are many process mining techniques that, based on an event log, allow for the automatic induction of a process model. The process mining algorithms that are able to deal with incomplete event logs, exceptions, and noise typically have many parameters to tune the algorithm. Therefore, the

Software tool for data mining and its applications

Science.gov (United States)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

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

Instream sand and gravel mining: Environmental issues and regulatory process in the United States

Science.gov (United States)

Meador, M.R.; Layher, A.O.

1998-01-01

Sand and gravel are widely used throughout the U.S. construction industry, but their extraction can significantly affect the physical, chemical, and biological characteristics of mined streams. Fisheries biologists often find themselves involved in the complex environmental and regulatory issues related to instream sand and gravel mining. This paper provides an overview of information presented in a symposium held at the 1997 midyear meeting of the Southern Division of the American Fisheries Society in San Antonio, Texas, to discuss environmental issues and regulatory procedures related to instream mining. Conclusions from the symposium suggest that complex physicochemical and biotic responses to disturbance such as channel incision and alteration of riparian vegetation ultimately determine the effects of instream mining. An understanding of geomorphic processes can provide insight into the effects of mining operations on stream function, and multidisciplinary empirical studies are needed to determine the relative effects of mining versus other natural and human-induced stream alterations. Mining regulations often result in a confusing regulatory process complicated, for example, by the role of the U.S. Army Corps of Engineers, which has undergone numerous changes and remains unclear. Dialogue among scientists, miners, and regulators can provide an important first step toward developing a plan that integrates biology and politics to protect aquatic resources.

Graph Mining Meets the Semantic Web

Energy Technology Data Exchange (ETDEWEB)

Lee, Sangkeun (Matt) [ORNL; Sukumar, Sreenivas R [ORNL; Lim, Seung-Hwan [ORNL

2015-01-01

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

Environmental control technology for mining, milling, and refining thorium

International Nuclear Information System (INIS)

Weakley, S.A.; Blahnik, D.E.; Young, J.K.; Bloomster, C.H.

1980-02-01

The purpose of this report is to evaluate, in terms of cost and effectiveness, the various environmental control technologies that would be used to control the radioactive wastes generated in the mining, milling, and refining of thorium from domestic resources. The technologies, in order to be considered for study, had to reduce the radioactivity in the waste streams to meet Atomic Energy Commission (10 CFR 20) standards for natural thorium's maximum permissible concentration (MPC) in air and water. Further regulatory standards or licensing requirements, either federal, state, or local, were not examined. The availability and cost of producing thorium from domestic resources is addressed in a companion volume. The objectives of this study were: (1) to identify the major waste streams generated during the mining, milling, and refining of reactor-grade thorium oxide from domestic resources; and (2) to determine the cost and levels of control of existing and advanced environmental control technologies for these waste streams. Six potential domestic deposits of thorium oxide, in addition to stockpiled thorium sludges, are discussed in this report. A summary of the location and characteristics of the potential domestic thorium resources and the mining, milling, and refining processes that will be needed to produce reactor-grade thorium oxide is presented in Section 2. The wastes from existing and potential domestic thorium oxide mines, mills, and refineries are identified in Section 3. Section 3 also presents the state-of-the-art technology and the costs associated with controlling the wastes from the mines, mills, and refineries. In Section 4, the available environmental control technologies for mines, mills, and refineries are assessed. Section 5 presents the cost and effectiveness estimates for the various environmental control technologies applicable to the mine, mill, and refinery for each domestic resource

An approach to quantify sources, seasonal change, and biogeochemical processes affecting metal loading in streams: Facilitating decisions for remediation of mine drainage

Science.gov (United States)

Kimball, B.A.; Runkel, R.L.; Walton-Day, K.

2010-01-01

Historical mining has left complex problems in catchments throughout the world. Land managers are faced with making cost-effective plans to remediate mine influences. Remediation plans are facilitated by spatial mass-loading profiles that indicate the locations of metal mass-loading, seasonal changes, and the extent of biogeochemical processes. Field-scale experiments during both low- and high-flow conditions and time-series data over diel cycles illustrate how this can be accomplished. A low-flow experiment provided spatially detailed loading profiles to indicate where loading occurred. For example, SO42 - was principally derived from sources upstream from the study reach, but three principal locations also were important for SO42 - loading within the reach. During high-flow conditions, Lagrangian sampling provided data to interpret seasonal changes and indicated locations where snowmelt runoff flushed metals to the stream. Comparison of metal concentrations between the low- and high-flow experiments indicated substantial increases in metal loading at high flow, but little change in metal concentrations, showing that toxicity at the most downstream sampling site was not substantially greater during snowmelt runoff. During high-flow conditions, a detailed temporal sampling at fixed sites indicated that Zn concentration more than doubled during the diel cycle. Monitoring programs must account for diel variation to provide meaningful results. Mass-loading studies during different flow conditions and detailed time-series over diel cycles provide useful scientific support for stream management decisions.

Big data mining analysis method based on cloud computing

Science.gov (United States)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

Algorithm for Compressing Time-Series Data

Science.gov (United States)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

STRIP: stream learning of influence probabilities

DEFF Research Database (Denmark)

Kutzkov, Konstantin

2013-01-01

cascades, and developing applications such as viral marketing. Motivated by modern microblogging platforms, such as twitter, in this paper we study the problem of learning influence probabilities in a data-stream scenario, in which the network topology is relatively stable and the challenge of a learning...... algorithm is to keep up with a continuous stream of tweets using a small amount of time and memory. Our contribution is a number of randomized approximation algorithms, categorized according to the available space (superlinear, linear, and sublinear in the number of nodes n) and according to dierent models...

Report on the laboratory examination of the effect of fungal invasion on the durability of untreated mine support timbers

CSIR Research Space (South Africa)

Hall, PJ

1967-02-01

Full Text Available stream_source_info RR 12_67 The laboratory examination of the effect of fungal invasion on the durability of untreated mine support timber COMRO 1967.pdf.txt stream_content_type text/plain stream_size 8 Content-Encoding ISO-8859...-1 stream_name RR 12_67 The laboratory examination of the effect of fungal invasion on the durability of untreated mine support timber COMRO 1967.pdf.txt Content-Type text/plain; charset=ISO-8859-1 ...

Web Mining and Social Networking

CERN Document Server

Xu, Guandong; Li, Lin

2011-01-01

This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web mining, and the issue of how to incorporate web mining into web personalization and recommendation systems are also reviewed. Additionally, the volume explores web community mining and analysis to find the structural, organizational and temporal developments of web communities and reveal the societal s

Process for integrating surface drainage constraints on mine planning

Energy Technology Data Exchange (ETDEWEB)

Sawatsky, L.F; Ade, F.L.; McDonald, D.M.; Pullman, B.J. [Golder Associates Ltd., Calgary, AB (Canada)

2009-07-01

Surface drainage for mine closures must be considered during all phases of mine planning and design in order to minimize environmental impacts and reduce costs. This paper discussed methods of integrating mine drainage criteria and associated mine planning constraints into the mine planning process. Drainage constraints included stream diversions; fish compensation channels; collection receptacles for the re-use of process water; separation of closed circuit water from fresh water; and the provision of storage ponds. The geomorphic approach replicated the ability of natural channels to respond to local and regional changes in hydrology as well as channel disturbances from extreme flood events, sedimentation, debris, ice jams, and beaver activity. The approach was designed to enable a sustainable system and provide conveyance capacity for extreme floods without spillage to adjacent watersheds. Channel dimensions, bank and bed materials, sediment loads, bed material supplies and the hydrologic conditions of the analogue stream were considered. Hydrologic analyses were conducted to determine design flood flow. Channel routes, valley slopes, sinuosity, width, and depth were established. It was concluded that by incorporating the geomorphic technique, mine operators and designers can construct self-sustaining drainage systems that require little or no maintenance in the long-term. 7 refs.

Association Rule Extraction from XML Stream Data for Wireless Sensor Networks

Science.gov (United States)

Paik, Juryon; Nam, Junghyun; Kim, Ung Mo; Won, Dongho

2014-01-01

With the advances of wireless sensor networks, they yield massive volumes of disparate, dynamic and geographically-distributed and heterogeneous data. The data mining community has attempted to extract knowledge from the huge amount of data that they generate. However, previous mining work in WSNs has focused on supporting simple relational data structures, like one table per network, while there is a need for more complex data structures. This deficiency motivates XML, which is the current de facto format for the data exchange and modeling of a wide variety of data sources over the web, to be used in WSNs in order to encourage the interchangeability of heterogeneous types of sensors and systems. However, mining XML data for WSNs has two challenging issues: one is the endless data flow; and the other is the complex tree structure. In this paper, we present several new definitions and techniques related to association rule mining over XML data streams in WSNs. To the best of our knowledge, this work provides the first approach to mining XML stream data that generates frequent tree items without any redundancy. PMID:25046017

High utility-itemset mining and privacy-preserving utility mining

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2016-03-01

Full Text Available In recent decades, high-utility itemset mining (HUIM has emerging a critical research topic since the quantity and profit factors are both concerned to mine the high-utility itemsets (HUIs. Generally, data mining is commonly used to discover interesting and useful knowledge from massive data. It may, however, lead to privacy threats if private or secure information (e.g., HUIs are published in the public place or misused. In this paper, we focus on the issues of HUIM and privacy-preserving utility mining (PPUM, and present two evolutionary algorithms to respectively mine HUIs and hide the sensitive high-utility itemsets in PPUM. Extensive experiments showed that the two proposed models for the applications of HUIM and PPUM can not only generate the high quality profitable itemsets according to the user-specified minimum utility threshold, but also enable the capability of privacy preserving for private or secure information (e.g., HUIs in real-word applications.

Algorithms for Academic Search and Recommendation Systems

DEFF Research Database (Denmark)

Amolochitis, Emmanouil

2014-01-01

are part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. In the third part of the work we present the design of a quantitative association rule mining algorithm. The introduced mining algorithm processes......In this work we present novel algorithms for academic search, recommendation and association rules mining. In the first part of the work we introduce a novel hierarchical heuristic scheme for re-ranking academic publications. The scheme is based on the hierarchical combination of a custom...... implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. On the second part we describe the design of hybrid recommender ensemble (user, item and content based). The newly introduced algorithms...

Tracing the sources of stream sediments by Pb isotopes and trace elements

International Nuclear Information System (INIS)

Kyung-Seok Ko; Jae Gon Kim; Kyoochul Ha; Kil Yong Lee

2012-01-01

The objective of this research is to trace the sources of stream sediments in a small watershed influenced by anthropogenic and lithogenic origins identified by the spatial distributions and temporal variations of stream sediments using geochemical interpretation of the stable and radiogenic isotopes, major components, and heavy metals data and principal component analysis. To know the effects of both present and past mining, the stream sediments were sampled at the stream tributaries and sediment coring work. The spatial distributions of heavy metals clearly showed the effects of Cu and Pb-Zn mineralization zones at the site. Anthropogenic Pb was elevated at the downstream area by the stream sediments due to an active quarry. The results of principal components analysis also represent the effects of the stream sediments origins, including anthropogenic wastes and the active quarry and lithogenic sediment. Anomalous Cu, indicating the effect of past Guryong mining, was identified at the deep core sediments of 1.80-5.05 m depth. The influence of active quarry was shown in the recently deposited sediments of 210 Pb and stable Pb and Sr isotopes. This study suggests that the chemical studies using radiogenic and stable isotopes and heavy metals and multivariate statistical method are useful tools to discriminate the sources of stream sediments with different origins. (author)

Towards automatic parameter tuning of stream processing systems

KAUST Repository

Bilal, Muhammad

2017-09-27

Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.

VALUING ACID MINE DRAINAGE REMEDIATION IN WEST VIRGINIA: BENEFIT TRANSFER WITH PREFERENCE CALIBRATION

Science.gov (United States)

Several thousand kilometers of West Virginia streams are degraded by acid mine drainage (AMD), and the estimates for cleanup range in the billions of dollars. Not enough money is available to restore all the affected streams, so some way to prioritize those streams is needed. Ben...

Abandoned Smolník mine (Slovakia – a catchment area affected by mining activities

Directory of Open Access Journals (Sweden)

Lintnerová, Otília

2008-06-01

Full Text Available Smolník is a historical Cu-mining area that was exploited from the 14th century to 1990. The Smolník mine was definitively closed and flooded in 1990–1994. Acid mine drainage discharging from the flooded mine (pH = 3.83, Fe = 542 mg/l, SO42– = 3642 mg/l, Cu = 1880 µg/l, Zn = 9599 µg/l, As = 108 mg/l acidified and contaminated the Smolník Creek water, which transported pollution into the Hnilec River catchment. The Smolník mine waste area has been used as a model area to document pollution of waters, stream sediments, and soils by metals and other toxic elements. Major goals of this complex study were to document creek water transport of the main pollutants (Fe, sulphates, Cu, Al, As, etc. in the form of suspended solids, to investigate elements mobility in common mine waste (rock and processing waste heaps and tailing impoundment and in the soil on the basis of neutralization and leach experiments. Different methodologies and techniques for sampling and chemical and mineralogical characterization of samples were used and checked to evaluate environmental risk of this abandoned mine area.

Mining of high utility-probability sequential patterns from uncertain databases.

Directory of Open Access Journals (Sweden)

Binbin Zhang

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

A novel procedure on next generation sequencing data analysis using text mining algorithm.

Science.gov (United States)

Zhao, Weizhong; Chen, James J; Perkins, Roger; Wang, Yuping; Liu, Zhichao; Hong, Huixiao; Tong, Weida; Zou, Wen

2016-05-13

Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

Text Clustering Algorithm Based on Random Cluster Core

Directory of Open Access Journals (Sweden)

Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Open Pit Water Control Safety A Case Of Nchanga Open Pit Mine Zambia

Directory of Open Access Journals (Sweden)

Silwamba C

2015-08-01

Full Text Available Abstract Mining in Chingola Zambia started underground in 1931 and was catastrophically flooded and closed. The present Nchanga Underground Mine NUG started in 1937. The Nchanga Open Pit NOP mine started in 1955 situated to the west of NUG and partially overlying it. Open pit water control safety operations in the Nchanga-Chingola area have successfully enabled the safe extraction of millions of tonnes of copper ore annually over the past 60 years from NUG mining as well as the NOP. At the start Nchanga mining license surface already had NUG and many watershed divides with the Nchanga and Chingola streams being the main streams feeding into Zambias second largest river Kafue river and 42 of the year was characterised by heavy rains ranging between 800mm to 1300mm per annum. In this paper the presence of very significant amounts of seasonal rain and subsurface water in the mining area was identified as both a curse and a blessing. An excess in seasonal rain and subsurface water would disrupt both open pit and underground mining operations. In order for NOP to be operated successfully stable and free from flooding coping water management tactics were adopted from 1955 to 2015 including 1. Underground mine pump chamber pumping system 2. Piezometer instrumented boreholes 3. Underground mine 1500-ft sub-haulage east borehole dewatering beneath the open pit 4. Nchanga and Chingola stream diversionary tunnel and open drains 5. Nchanga stream causeway and embankment dam in the Matero School Golf Club area 6. Pit perimeter borehole pumping 7. Outer and inner pit perimeter drains and bund walls 8. In-pit ramp side drains 9. In-pit sub-horizontal borehole geo-drains and water and 10. Pit bottom sump pumps. Application of grout curtains along the Vistula River Poland was noted as a possibility in the right circumstances although it had never been used at Nchanga Open Pit. An additional conclusion was that forward health safety and environmental end

Setup of a testing environment for mission planning in mining

NARCIS (Netherlands)

Groenen, J.P.J.; Steinbuch, M.

2013-01-01

Mission planning algorithms for surface mining applications are difficult to test as a result of the large scale tasks. To validate these algorithms, a scaled setup is created where the mining excavator is mimicked by an industrial robot. This report discusses the development of a software

Applied data mining

CERN Document Server

Xu, Guandong

2013-01-01

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

pubmed. mineR: An R package with text-mining algorithms to ...

Indian Academy of Sciences (India)

2016-08-26

Aug 26, 2016 ... Three case studies are presented, namely, `Evolving role of diabetes educators', `Cancer risk assessment' and `Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus ...

Anthropogenic and natural sources of acidity and metals and their influence on the structure of stream food webs.

Science.gov (United States)

Hogsden, Kristy L; Harding, Jon S

2012-03-01

We compared food web structure in 20 streams with either anthropogenic or natural sources of acidity and metals or circumneutral water chemistry in New Zealand. Community and diet analysis indicated that mining streams receiving anthropogenic inputs of acidic and metal-rich drainage had much simpler food webs (fewer species, shorter food chains, less links) than those in naturally acidic, naturally high metal, and circumneutral streams. Food webs of naturally high metal streams were structurally similar to those in mining streams, lacking fish predators and having few species. Whereas, webs in naturally acidic streams differed very little from those in circumneutral streams due to strong similarities in community composition and diets of secondary and top consumers. The combined negative effects of acidity and metals on stream food webs are clear. However, elevated metal concentrations, regardless of source, appear to play a more important role than acidity in driving food web structure. Copyright © 2011 Elsevier Ltd. All rights reserved.

Finding Frequent Closed Itemsets in Sliding Window in Linear Time

Science.gov (United States)

Chen, Junbo; Zhou, Bo; Chen, Lu; Wang, Xinyu; Ding, Yiqun

One of the most well-studied problems in data mining is computing the collection of frequent itemsets in large transactional databases. Since the introduction of the famous Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among such algorithms, the approach of mining closed itemsets has raised much interest in data mining community. The algorithms taking this approach include TITANIC [8], CLOSET+[6], DCI-Closed [4], FCI-Stream [3], GC-Tree [15], TGC-Tree [16] etc. Among these algorithms, FCI-Stream, GC-Tree and TGC-Tree are online algorithms work under sliding window environments. By the performance evaluation in [16], GC-Tree [15] is the fastest one. In this paper, an improved algorithm based on GC-Tree is proposed, the computational complexity of which is proved to be a linear combination of the average transaction size and the average closed itemset size. The algorithm is based on the essential theorem presented in Sect. 4.2. Empirically, the new algorithm is several orders of magnitude faster than the state of art algorithm, GC-Tree.

Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources

Science.gov (United States)

Domagalski, Joseph L.; Majewski, Michael S.; Alpers, Charles N.; Eckley, Chris S.; Eagles-Smith, Collin A.; Schenk, Liam N.; Wherry, Susan

2016-01-01

Annual stream loads of mercury (Hg) and inputs of wet and dry atmospheric Hg deposition to the landscape were investigated in watersheds of the Western United States and the Canadian-Alaskan Arctic. Mercury concentration and discharge data from flow gauging stations were used to compute annual mass loads with regression models. Measured wet and modeled dry deposition were compared to annual stream loads to compute ratios of Hg stream load to total Hg atmospheric deposition. Watershed land uses or cover included mining, undeveloped, urbanized, and mixed. Of 27 watersheds that were investigated, 15 had some degree of mining, either of Hg or precious metals (gold or silver), where Hg was used in the amalgamation process. Stream loads in excess of annual Hg atmospheric deposition (ratio > 1) were observed in watersheds containing Hg mines and in relatively small and medium-sized watersheds with gold or silver mines, however, larger watersheds containing gold or silver mines, some of which also contain large dams that trap sediment, were sometimes associated with lower load ratios (watersheds with natural vegetation tended to have low ratios of stream load to Hg deposition (watersheds (Mackenzie and Yukon Rivers) had a relatively elevated ratio of stream load to atmospheric deposition (0.27 and 0.74), possibly because of melting glaciers or permafrost releasing previously stored Hg to the streams. Overall, our research highlights the important role of watershed characteristics in determining whether a landscape is a net source of Hg or a net sink of atmospheric Hg.

Physics Mining of Multi-Source Data Sets

Science.gov (United States)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

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

Knowledge discovery from data streams

CERN Document Server

Gama, Joao

2010-01-01

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams.The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks,

Declarative Process Mining for DCR Graphs

DEFF Research Database (Denmark)

Debois, Søren; Hildebrandt, Thomas T.; Laursen, Paw Høvsgaard

2017-01-01

We investigate process mining for the declarative Dynamic Condition Response (DCR) graphs process modelling language. We contribute (a) a process mining algorithm for DCR graphs, (b) a proposal for a set of metrics quantifying output model quality, and (c) a preliminary example-based comparison...

Text Mining Applications and Theory

CERN Document Server

Berry, Michael W

2010-01-01

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning

Influence of the old mining loads on the contamination of streams, flows in the Water-work Reservoir “Ružín I” in 2004 year by the selected elements

Directory of Open Access Journals (Sweden)

Tomislav Špaldon

2005-11-01

Full Text Available This article presents results of the research concentrated on the content of selected elements, mostly heavy metals, in samples of stream waters and stream deposits from selected profiles of streams in the drainage basins of the Hnilec and Hornád river, which flow in the water-work Reservoir “Ružín I”. The sampling was carried out from the winter to the summer months, 2004. The major part of the drainage basins of these two rivers is located in the territory of the central Spiš, which is well-known from the historic times until these days by its intensive mining, mineral processing and metallurgical activities. The wastes generated by such activities are sources of metals, which penetrate into the surface waters and consequently into the stream deposits. From the point of view of the transfer and the transformation of these metal elements, their monitoring deserves a continuous attention

High Performance Data mining by Genetic Neural Network

Directory of Open Access Journals (Sweden)

Dadmehr Rahbari

2013-10-01

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

Macroinvertebrate assemblages in agricultural, mining, and urban tropical streams: implications for conservation and management.

Science.gov (United States)

Mwedzi, Tongayi; Bere, Taurai; Mangadze, Tinotenda

2016-06-01

The study evaluated the response of macroinvertebrate assemblages to changes in water quality in different land-use settings in Manyame catchment, Zimbabwe. Four land-use categories were identified: forested commercial farming, communal farming, Great Dyke mining (GDM) and urban areas. Macroinvertebrate community structure and physicochemical variables data were collected in two seasons from 41 sites following standard methods. Although not environmentally threatening, urban and GDM areas were characterised by higher conductivity, total dissolved solids, salinity, magnesium and hardness. Chlorides, total phosphates, total nitrogen, calcium, potassium and sodium were significantly highest in urban sites whilst dissolved oxygen (DO) was significantly higher in the forested commercial faming and GDM sites. Macroinvertebrate communities followed the observed changes in water quality. Macroinvertebrates in urban sites indicated severe pollution (e.g. Chironomidae) whilst those in forested commercial farming sites and GDM sites indicated relatively clean water (e.g. Notonemouridae). Forested watersheds together with good farm management practices are important in mitigating impacts of urbanisation and agriculture. Strategies that reduce oxygen-depleting substances must be devised to protect the health of Zimbabwean streams. The study affirms the wider applicability of the South African Scoring System in different land uses.

Methylmercury degradation and exposure pathways in streams and wetlands impacted by historical mining

International Nuclear Information System (INIS)

Donovan, Patrick M.; Blum, Joel D.; Singer, Michael Bliss; Marvin-DiPasquale, Mark; Tsui, Martin T.K.

2016-01-01

Monomethyl mercury (MMHg) and total mercury (THg) concentrations and Hg stable isotope ratios (δ"2"0"2Hg and Δ"1"9"9Hg) were measured in sediment and aquatic organisms from Cache Creek (California Coast Range) and Yolo Bypass (Sacramento Valley). Cache Creek sediment had a large range in THg (87 to 3870 ng/g) and δ"2"0"2Hg (− 1.69 to − 0.20‰) reflecting the heterogeneity of Hg mining sources in sediment. The δ"2"0"2Hg of Yolo Bypass wetland sediment suggests a mixture of high and low THg sediment sources. Relationships between %MMHg (the percent ratio of MMHg to THg) and Hg isotope values (δ"2"0"2Hg and Δ"1"9"9Hg) in fish and macroinvertebrates were used to identify and estimate the isotopic composition of MMHg. Deviation from linear relationships was found between %MMHg and Hg isotope values, which is indicative of the bioaccumulation of isotopically distinct pools of MMHg. The isotopic composition of pre-photodegraded MMHg (i.e., subtracting fractionation from photochemical reactions) was estimated and contrasting relationships were observed between the estimated δ"2"0"2Hg of pre-photodegraded MMHg and sediment IHg. Cache Creek had mass dependent fractionation (MDF; δ"2"0"2Hg) of at least − 0.4‰ whereas Yolo Bypass had MDF of + 0.2 to + 0.5‰. This result supports the hypothesis that Hg isotope fractionation between IHg and MMHg observed in rivers (− MDF) is unique compared to + MDF observed in non-flowing water environments such as wetlands, lakes, and the coastal ocean. - Highlights: • Mercury isotope ratios were measured in sediment and biota from Central California. • The isotopic composition of MMHg was estimated in streams and wetlands. • Mercury isotopes suggest multiple exposure pathways in these habitats. • Mass dependent fractionation between IHg and MMHg is different in streams.

Research of Improved Apriori Algorithm Based on Itemset Array

Directory of Open Access Journals (Sweden)

Naili Liu

2013-06-01

Full Text Available Mining frequent item sets is a major key process in data mining research. Apriori and many improved algorithms are lowly efficient because they need scan database many times and storage transaction ID in memory, so time and space overhead is very high. Especially, they are lower efficient when they process large scale database. The main task of the improved algorithm is to reduce time and space overhead for mining frequent item sets. Because, it scans database only once to generate binary item set array, it adopts binary instead of transaction ID when it storages transaction flag, it adopts logic AND operation to judge whether an item set is frequent item set. Moreover, the improved algorithm is more suitable for large scale database. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

Hydrogeology, water chemistry, and subsidence of underground coal mines at Huntsville, Missouri, July 1987 to December 1988. Water Resources Investigation

International Nuclear Information System (INIS)

Blevins, D.W.; Ziegler, A.C.

1992-01-01

Underground coal mining in and near Huntsville, in Randolph County in north-central Missouri, began soon after 1831. Mining in the Huntsville area was at its peak during 1903 and continued until 1966 when the last underground mine was closed and the economically recoverable coals under Huntsville had been mostly, if not completely, removed. The now abandoned mines are of concern to the public and to various State and Federal agencies for two reasons: (1) mine drainage acidifies streams and leaves large, soft, dangerous deposits of iron oxyhydroxides at mine springs and on streambeds (data on file at the Missouri Department of Natural Resources, Land Reclamation Commission), and (2) collapse of mine cavities sometimes causes surface subsidence resulting in property damage or personal injury. To address these concerns, the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, in 1987 initiated a study to: determine the location of mine springs, the seasonal variation of stream-water chemistry, and the effects of underground-mine water on flow and water quality of nearby ground water and receiving streams; and identify areas susceptible to surface subsidence because of mine collapse. The purpose of the report is to present the findings and data collected for the study

Preservation procedures for arsenic speciation in a stream affected by acid mine drainage in southwestern Spain

Energy Technology Data Exchange (ETDEWEB)

Sanchez-Rodas, Daniel; Oliveira, Vanesa; Gomez-Ariza, Jose Luis [University of Huelva, Department of Chemistry and Materials Science, Faculty of Experimental Sciences, Huelva (Spain); Sarmiento, Aguasanta M.; Nieto, Jose Miguel [University of Huelva, Department of Geology, Faculty of Experimental Sciences, Huelva (Spain)

2006-04-15

A preservation study has been performed for arsenic speciation in surface freshwaters affected by acid mine drainage (AMD), a pollution source characterized by low pH and high metallic content. Two sample preservation procedures described in the literature were attempted using opaque glass containers and refrigeration: i) addition of 0.25 mol L{sup -1} EDTA to the samples, which maintained the stability of the arsenic species for 3 h; and ii) in situ sample clean-up with a cationic exchange resin, in order to reduce the metallic load, which resulted in a partial co-adsorption of arsenic onto Fe precipitates. A new proposed method was also tried: sample acidification with 6 mol L{sup -1} HCl followed by in situ clean-up with a cationic exchange resin, which allowed a longer preservation time of at least 48 h. The proposed method was successfully applied to water samples with high arsenic content, taken from the Aguas Agrias Stream (Odiel River Basin, SW Spain), which is severely affected by AMD that originates at the nearby polymetallic sulfide mine of Tharsis. The speciation results obtained by liquid chromatography-hydride generation-atomic fluorescence spectrometry (HPLC-HG-AFS) indicated that during the summer the main arsenic species was As(V) at the hundred {mu}g L{sup -1} level, followed by DMA (dimethyl arsenic) and As(III) below the ten {mu}g L{sup -1} level. In winter, As(V) and As(III) increased at least fivefold, whereas the DMA was not detected. (orig.)

Water-quality trends for a stream draining the Southern Anthracite Field, Pennsylvania

Science.gov (United States)

Cravotta, C.A.; Bilger, Michael D.

2001-01-01

Stream flow, chemical and biological data for the northern part of Swatara Creek, which drains a 112 km2 area in the Southern Anthracite Field of eastern Pennsylvania, indicate progressive improvement in water quality since 1959, after which most mines in the watershed had been flooded. Drainage from the flooded mines contributes substantially to base flow in Swatara Creek. Beginning in 1995, a variety of treatment systems and surface reclamation were implemented at some of the abandoned mines. At Ravine, Pa., immediately downstream of the mined area, median SO4 concentration declined from about 150 mg l-1 in 1959 to 75 mg l-1 in 1999 while pH increased from acidic to near-neutral values (medians: c. pH 4 before 1975; c. pH 6 after 1975). Fish populations rebounded from non-existent during 1959-1990 to 21 species identified in 1999. Nevertheless, recent monitoring indicates (1) episodic acidification and elevated concentrations and transport of Fe, Al, Mn, and trace metals during storm flow; (2) elevated concentrations of Fe, Mn, Co, Cu, Pb, Ni, and Zn in streambed sediments relative to unmined areas and to toxicity guidelines for aquatic invertebrates and fish; and (3) elevated concentrations of metals in fish tissue, notably Zn. The metals are ubiquitous in the fine fraction (mining-affected tributaries and the main stem of Swatara Creek. As a result of scour and transport of streambed deposits, concentrations of suspended solids and total metals in the water column are correlated, and those for storm flow typically exceed base flow. Nevertheless, the metals concentrations are poorly correlated with stream flow because concentrations of suspended solids and total metals typically peak prior to peak stream stage. In contrast, SO4, specific conductance and pH are inversely correlated with stream flow as a result of dilution of poorly buffered stream water with weakly acidic storm runoff derived mainly from low-pH rainfall. Declines in pH to values approaching 5

EMiT: a process mining tool

NARCIS (Netherlands)

Dongen, van B.F.; Aalst, van der W.M.P.; Cortadella, J.; Reisig, W.

2004-01-01

Process mining offers a way to distill process models from event logs originating from transactional systems in logistics, banking, e-business, health-care, etc. The algorithms used for process mining are complex and in practise large logs are needed to derive a high-quality process model. To

Uranium recovery from mine water

International Nuclear Information System (INIS)

Sarkar, K.M.

1984-01-01

In many plant trials it has been proven that very small amounts (10 to 20 ppm) of uranium dissolved in mine water can be effectively recovered by the use of ion exchange resins and this uranium recovery has many advantages. In this paper an economic analysis at different levels of uranium contamination and at different market prices of uranium are described. For this study an operating mine-mill complex with a sulphuric acid leach circuit, followed by solvent extraction (SX) process, is considered, where contaminated mine water is available in excess of process requirements. It is further assumed that the sulphuric acid eluant containing uranium would be mixed with the mill pregnant liquor stream that proceeds to the SX plant for final uranium recovery

A direct mining approach to efficient constrained graph pattern discovery

DEFF Research Database (Denmark)

Zhu, Feida; Zhang, Zequn; Qu, Qiang

2013-01-01

Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-spe...

Grade Distribution Modeling within the Bauxite Seams of the Wachangping Mine, China, Using a Multi-Step Interpolation Algorithm

Directory of Open Access Journals (Sweden)

Shaofeng Wang

2017-05-01

Full Text Available Mineral reserve estimation and mining design depend on a precise modeling of the mineralized deposit. A multi-step interpolation algorithm, including 1D biharmonic spline estimator for interpolating floor altitudes, 2D nearest neighbor, linear, natural neighbor, cubic, biharmonic spline, inverse distance weighted, simple kriging, and ordinary kriging interpolations for grade distribution on the two vertical sections at roadways, and 3D linear interpolation for grade distribution between sections, was proposed to build a 3D grade distribution model of the mineralized seam in a longwall mining panel with a U-shaped layout having two roadways at both sides. Compared to field data from exploratory boreholes, this multi-step interpolation using a natural neighbor method shows an optimal stability and a minimal difference between interpolation and field data. Using this method, the 97,576 m3 of bauxite, in which the mass fraction of Al2O3 (Wa and the mass ratio of Al2O3 to SiO2 (Wa/s are 61.68% and 27.72, respectively, was delimited from the 189,260 m3 mineralized deposit in the 1102 longwall mining panel in the Wachangping mine, Southwest China. The mean absolute errors, the root mean squared errors and the relative standard deviations of errors between interpolated data and exploratory grade data at six boreholes are 2.544, 2.674, and 32.37% of Wa; and 1.761, 1.974, and 67.37% of Wa/s, respectively. The proposed method can be used for characterizing the grade distribution in a mineralized seam between two roadways at both sides of a longwall mining panel.

Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

Science.gov (United States)

Al-Saggaf, Yeslam; Islam, Md Zahidul

2015-08-01

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

Heavy metal pollution associated with an abandoned lead-zinc mine in the Kirki region, NE Greece.

Science.gov (United States)

Nikolaidis, Christos; Zafiriadis, Ilias; Mathioudakis, Vasileios; Constantinidis, Theodore

2010-09-01

The "Agios Philippos" mine in the Kirki region (NE Greece) has been abandoned in 1998 after half a century of ore exploration without a reclamation or remediation plan. This article aims at elucidating the potential environmental risks associated with this site by quantifying pollution in tailing basins, stream waters, stream sediments and agricultural fields. Concentrations of heavy metals in the abandoned mine tailings reached 12,567 mg/kg for Pb, 22,292 mg/kg for Zn, 174 mg/kg for Cd and 241 mg/kg for As. The geoaccumulation index and enrichment factor for these metals were indicative of extremely high contamination (I(geo) > 5) and extremely high enrichment (EF > 40), respectively. Stream waters in the proximity of the mine had an acidic pH equal to 5.96 and a high sulfate content (SO(4)(-2) = 545.5 mg/L), whereas concentrations of Mn, Zn and Cd reached 2,399 microg/L, 7,681 microg/L and 11.2 microg/L. High I(geo) and EF values for Cd, Zn and As in stream sediments indicates that surface water pollution has a historic background, which is typically associated with acid mine drainage. Agricultural fields in the proximity of the mine exhibited high I(geo) and EF values, which were in decreasing order Cd > Pb > Zn > As. These findings urge for an immediate remediation action of the afflicted area.

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

Directory of Open Access Journals (Sweden)

Lixiong Xu

2017-01-01

Full Text Available As one of the most effective function mining algorithms, Gene Expression Programming (GEP algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.

Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources

Science.gov (United States)

Domagalski, Joseph L.; Majewski, Michael S.; Alpers, Charles N.; Eckley, Chris S.; Eagles-Smith, Collin A.; Schenk, Liam N.; Wherry, Susan

2016-01-01

Annual stream loads of mercury (Hg) and inputs of wet and dry atmospheric Hg deposition to the landscape were investigated in watersheds of the Western United States and the Canadian-Alaskan Arctic. Mercury concentration and discharge data from flow gauging stations were used to compute annual mass loads with regression models. Measured wet and modeled dry deposition were compared to annual stream loads to compute ratios of Hg stream load to total Hg atmospheric deposition. Watershed land uses or cover included mining, undeveloped, urbanized, and mixed. Of 27 watersheds that were investigated, 15 had some degree of mining, either of Hg or precious metals (gold or silver), where Hg was used in the amalgamation process. Stream loads in excess of annual Hg atmospheric deposition (ratio > 1) were observed in watersheds containing Hg mines and in relatively small and medium-sized watersheds with gold or silver mines, however, larger watersheds containing gold or silver mines, some of which also contain large dams that trap sediment, were sometimes associated with lower load ratios (< 0.2). In the non-Arctic regions, watersheds with natural vegetation tended to have low ratios of stream load to Hg deposition (< 0.1), whereas urbanized areas had higher ratios (0.34–1.0) because of impervious surfaces. This indicated that, in ecosystems with natural vegetation, Hg is retained in the soil and may be transported subsequently to streams as a result of erosion or in association with dissolved organic carbon. Arctic watersheds (Mackenzie and Yukon Rivers) had a relatively elevated ratio of stream load to atmospheric deposition (0.27 and 0.74), possibly because of melting glaciers or permafrost releasing previously stored Hg to the streams. Overall, our research highlights the important role of watershed characteristics in determining whether a landscape is a net source of Hg or a net sink of atmospheric Hg.

Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method

Science.gov (United States)

Piotrowski, Adam P.; Napiorkowski, Jaroslaw J.

2018-06-01

A number of physical or data-driven models have been proposed to evaluate stream water temperatures based on hydrological and meteorological observations. However, physical models require a large amount of information that is frequently unavailable, while data-based models ignore the physical processes. Recently the air2stream model has been proposed as an intermediate alternative that is based on physical heat budget processes, but it is so simplified that the model may be applied like data-driven ones. However, the price for simplicity is the need to calibrate eight parameters that, although have some physical meaning, cannot be measured or evaluated a priori. As a result, applicability and performance of the air2stream model for a particular stream relies on the efficiency of the calibration method. The original air2stream model uses an inefficient 20-year old approach called Particle Swarm Optimization with inertia weight. This study aims at finding an effective and robust calibration method for the air2stream model. Twelve different optimization algorithms are examined on six different streams from northern USA (states of Washington, Oregon and New York), Poland and Switzerland, located in both high mountains, hilly and lowland areas. It is found that the performance of the air2stream model depends significantly on the calibration method. Two algorithms lead to the best results for each considered stream. The air2stream model, calibrated with the chosen optimization methods, performs favorably against classical streamwater temperature models. The MATLAB code of the air2stream model and the chosen calibration procedure (CoBiDE) are available as Supplementary Material on the Journal of Hydrology web page.

A study into the treatability of ochreous mine water discharges

Energy Technology Data Exchange (ETDEWEB)

Clark, C J; Crawshaw, D H

1979-01-01

The oxidation of ferrous salts in solution from waste-water discharges from 3 abandoned and flooded mines near Bromley, Lancs, (UK) has since 1968 caused discoloration in the Calder River. Deposition and dilution decreases the ochreous effect, but the iron oxide is harmful to the benthos by producing a low dissolved-oxygen environment. The Calder River is only a Class 4 river below the confluence with the stream which carried the mine waters, and pilot-plant studies and field trials are described to determine the feasibility of full- scale treatment of the stream waters, resulting in the recommendation of lagoon treatment followed by neutralization.

STREAM2016: Streaming Requirements, Experience, Applications and Middleware Workshop

Energy Technology Data Exchange (ETDEWEB)

Fox, Geoffrey [Indiana Univ., Bloomington, IN (United States); Jha, Shantenu [Rutgers Univ., New Brunswick, NJ (United States); Ramakrishnan, Lavanya [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-10-01

discusses four research directions driven by current and future application requirements reflecting the areas identified as important by STREAM2016. These include (i) Algorithms, (ii) Programming Models, Languages and Runtime Systems (iii) Human-in-the-loop and Steering in Scientific Workflow and (iv) Facilities.

Use of natural and applied tracers to guide targeted remediation efforts in an acid mine drainage system, Colorado Rockies, USA

Science.gov (United States)

Cowie, Rory; Williams, Mark W.; Wireman, Mike; Runkel, Robert L.

2014-01-01

Stream water quality in areas of the western United States continues to be degraded by acid mine drainage (AMD), a legacy of hard-rock mining. The Rico-Argentine Mine in southwestern Colorado consists of complex multiple-level mine workings connected to a drainage tunnel discharging AMD to passive treatment ponds that discharge to the Dolores River. The mine workings are excavated into the hillslope on either side of a tributary stream with workings passing directly under the stream channel. There is a need to define hydrologic connections between surface water, groundwater, and mine workings to understand the source of both water and contaminants in the drainage tunnel discharge. Source identification will allow targeted remediation strategies to be developed. To identify hydrologic connections we employed a combination of natural and applied tracers including isotopes, ionic tracers, and fluorescent dyes. Stable water isotopes (δ18O/δD) show a well-mixed hydrological system, while tritium levels in mine waters indicate a fast flow-through system with mean residence times of years not decades or longer. Addition of multiple independent tracers indicated that water is traveling through mine workings with minimal obstructions. The results from a simultaneous salt and dye tracer application demonstrated that both tracer types can be successfully used in acidic mine water conditions.

LHCb trigger streams optimization

Science.gov (United States)

Derkach, D.; Kazeev, N.; Neychev, R.; Panin, A.; Trofimov, I.; Ustyuzhanin, A.; Vesterinen, M.

2017-10-01

The LHCb experiment stores around 1011 collision events per year. A typical physics analysis deals with a final sample of up to 107 events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar lines together and balancing the stream sizes it is possible to reduce the overhead. We present a method for finding an optimal stream composition. The method is applied to a part of the LHCb data (Turbo stream) on the stage where it is prepared for user physics analysis. This results in an expected improvement of 15% in the speed of user analysis jobs, and will be applied on data to be recorded in 2017.

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

DEFF Research Database (Denmark)

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés

2017-01-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One...... Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear...... regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate...

A fast calculating two-stream-like multiple scattering algorithm that captures azimuthal and elevation variations

Science.gov (United States)

Fiorino, Steven T.; Elmore, Brannon; Schmidt, Jaclyn; Matchefts, Elizabeth; Burley, Jarred L.

2016-05-01

Properly accounting for multiple scattering effects can have important implications for remote sensing and possibly directed energy applications. For example, increasing path radiance can affect signal noise. This study describes the implementation of a fast-calculating two-stream-like multiple scattering algorithm that captures azimuthal and elevation variations into the Laser Environmental Effects Definition and Reference (LEEDR) atmospheric characterization and radiative transfer code. The multiple scattering algorithm fully solves for molecular, aerosol, cloud, and precipitation single-scatter layer effects with a Mie algorithm at every calculation point/layer rather than an interpolated value from a pre-calculated look-up-table. This top-down cumulative diffusivity method first considers the incident solar radiance contribution to a given layer accounting for solid angle and elevation, and it then measures the contribution of diffused energy from previous layers based on the transmission of the current level to produce a cumulative radiance that is reflected from a surface and measured at the aperture at the observer. Then a unique set of asymmetry and backscattering phase function parameter calculations are made which account for the radiance loss due to the molecular and aerosol constituent reflectivity within a level and allows for a more accurate characterization of diffuse layers that contribute to multiple scattered radiances in inhomogeneous atmospheres. The code logic is valid for spectral bands between 200 nm and radio wavelengths, and the accuracy is demonstrated by comparing the results from LEEDR to observed sky radiance data.

Towards an increase of flash flood geomorphic effects due to gravel mining and ground subsidence in Nogalte stream (Murcia, SE Spain

Directory of Open Access Journals (Sweden)

J. A. Ortega-Becerril

2016-10-01

Full Text Available Transition from endorheic alluvial fan environments to well-channelized fluvial systems in natural conditions may occur in response to base-level fluctuations. However, human-induced changes in semi-arid regions can also be responsible for similar unforeseen modifications. Our results confirm that in-channel gravel mining and aquifer overexploitation over the last 50 years in the case study area have changed the natural stability of the Nogalte stream and, as a result, its geomorphic parameters including channel depth and longitudinal profile have begun to adapt to the new situation. Using interferometric synthetic aperture radar (InSAR data we obtain maximum values for ground subsidence in the Upper Guadalentín Basin of  ∼ 10 cm yr−1 for the period 2003–2010. In this context of a lowered base level, the river is changing its natural flood model to a more powerful one. A comparison of the 1973 flood event, the most dramatic flood event ever recorded in the area, with the 2012 event, where there was a similar discharge but a sediment load deficit, reveals greater changes and a new flooding pattern and extension. In-channel gravel mining may be responsible for significant local changes in channel incision and profile. This, together with the collateral effects of aquifer overexploitation, can favour increased river velocity and stream power, which intensify the consequences of the flooding. The results obtained here clearly demonstrate an existing transition from the former alluvial pattern to a confined fluvial trend, which may become more pronounced in the future due to the time lag between the drop in aquifer level and ground subsidence, and introduce a new scenario to be taken into consideration in future natural hazard planning in this area.

Water-quality trends for a stream draining the Southern Anthracite Field, Pennsylvania

Science.gov (United States)

Cravotta, C.A.; Bilger, Michael D.

2001-01-01

Stream flow, chemical and biological data for the northern part of Swatara Creek, which drains a 112 km2 area in the Southern Anthracite Field of eastern Pennsylvania, indicate progressive improvement in water quality since 1959, after which most mines in the watershed had been flooded. Drainage from the flooded mines contributes substantially to base flow in Swatara Creek. Beginning in 1995, a variety of treatment systems and surface reclamation were implemented at some of the abandoned mines. At Ravine, Pa., immediately downstream of the mined area, median SO4 concentration declined from about 150 mg l-1 in 1959 to 75 mg l-1 in 1999 while pH increased from acidic to near-neutral values (medians: c. pH 4 before 1975; c. pH 6 after 1975). Fish populations rebounded from non-existent during 1959-1990 to 21 species identified in 1999. Nevertheless, recent monitoring indicates (1) episodic acidification and elevated concentrations and transport of Fe, Al, Mn, and trace metals during storm flow; (2) elevated concentrations of Fe, Mn, Co, Cu, Pb, Ni, and Zn in streambed sediments relative to unmined areas and to toxicity guidelines for aquatic invertebrates and fish; and (3) elevated concentrations of metals in fish tissue, notably Zn. The metals are ubiquitous in the fine fraction (water column are correlated, and those for storm flow typically exceed base flow. Nevertheless, the metals concentrations are poorly correlated with stream flow because concentrations of suspended solids and total metals typically peak prior to peak stream stage. In contrast, SO4, specific conductance and pH are inversely correlated with stream flow as a result of dilution of poorly buffered stream water with weakly acidic storm runoff derived mainly from low-pH rainfall. Declines in pH to values approaching 5.0 during storm flow events or declines in redox potential during burial of sediment could result in the remobilization of metals associated with suspended solids and streambed deposits.

Data Stream Classification Based on the Gamma Classifier

Directory of Open Access Journals (Sweden)

Abril Valeria Uriarte-Arcia

2015-01-01

Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Computer-aided system for fire fighting in an underground mine

Energy Technology Data Exchange (ETDEWEB)

Rosiek, F; Sikora, M; Urbanski, J [Politechnika Wroclawska (Poland). Instytut Gornictwa

1989-01-01

Discusses structure of an algorithm for computer-aided planning of fire fighting and rescue in an underground coal mine. The algorithm developed by the Mining Institute of the Wroclaw Technical University consists of ten options: regulations on fire fighting, fire alarm for miners working underground (rescue ways, fire zones etc.), information system for mine management, movements of fire fighting teams, distribution of fire fighting equipment, assessment of explosion hazards of fire gases, fire gas temperature control of blower operation, detection of endogenous fires, ventilation control. 2 refs.

Towards automatic parameter tuning of stream processing systems

KAUST Repository

Bilal, Muhammad; Canini, Marco

2017-01-01

for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing

Set-Oriented Mining for Association Rules in Relational Databases

NARCIS (Netherlands)

Houtsma, M.A.W.; Houtsma, M.A.W.; Swami, A.

1995-01-01

Describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss the optimization of these

Human impacts to mountain streams

Science.gov (United States)

Wohl, Ellen

2006-09-01

Mountain streams are here defined as channel networks within mountainous regions of the world. This definition encompasses tremendous diversity of physical and biological conditions, as well as history of land use. Human effects on mountain streams may result from activities undertaken within the stream channel that directly alter channel geometry, the dynamics of water and sediment movement, contaminants in the stream, or aquatic and riparian communities. Examples include channelization, construction of grade-control structures or check dams, removal of beavers, and placer mining. Human effects can also result from activities within the watershed that indirectly affect streams by altering the movement of water, sediment, and contaminants into the channel. Deforestation, cropping, grazing, land drainage, and urbanization are among the land uses that indirectly alter stream processes. An overview of the relative intensity of human impacts to mountain streams is provided by a table summarizing human effects on each of the major mountainous regions with respect to five categories: flow regulation, biotic integrity, water pollution, channel alteration, and land use. This table indicates that very few mountains have streams not at least moderately affected by land use. The least affected mountainous regions are those at very high or very low latitudes, although our scientific ignorance of conditions in low-latitude mountains in particular means that streams in these mountains might be more altered than is widely recognized. Four case studies from northern Sweden (arctic region), Colorado Front Range (semiarid temperate region), Swiss Alps (humid temperate region), and Papua New Guinea (humid tropics) are also used to explore in detail the history and effects on rivers of human activities in mountainous regions. The overview and case studies indicate that mountain streams must be managed with particular attention to upstream/downstream connections, hillslope

Layout Study and Application of Mobile App Recommendation Approach Based On Spark Streaming Framework

Science.gov (United States)

Wang, H. T.; Chen, T. T.; Yan, C.; Pan, H.

2018-05-01

For App recommended areas of mobile phone software, made while using conduct App application recommended combined weighted Slope One algorithm collaborative filtering algorithm items based on further improvement of the traditional collaborative filtering algorithm in cold start, data matrix sparseness and other issues, will recommend Spark stasis parallel algorithm platform, the introduction of real-time streaming streaming real-time computing framework to improve real-time software applications recommended.

IoT Stream Processing and Analytics in The Fog

OpenAIRE

Yang, Shusen

2017-01-01

The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many Fog applications such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the Cloud, but have not been comprehensively investigated in the context of Fog architecture. In this article, we present the general models and architecture of Fog data streaming, ...

Climatic zonation and land suitability determination for saffron in Khorasan-Razavi province using data mining algorithms

Directory of Open Access Journals (Sweden)

mehdi Bashiri

2017-12-01

Full Text Available Yield prediction for agricultural crops plays an important role in export-import planning, purchase guarantees, pricing, secure profits and increasing in agricultural productivity. Crop yield is affected by several parameters especially climate. In this study, the saffron yield in the Khorasan-Razavi province was evaluated by different classification algorithms including artificial neural networks, regression models, local linear trees, decision trees, discriminant analysis, random forest, support vector machine and nearest neighbor analysis. These algorithms analyzed data for 20 years (1989-2009 including 11 climatological parameters. The results showed that a few numbers of climatological parameters affect the saffron yield. The minimum, mean and maximum of temperature, had the highest positive correlations and the relative humidity of 6.5h, sunny hours, relative humidity of 18.5h, evaporation, relative humidity of 12.5h and absolute humidity had the highest negative correlations with saffron cultivation areas, respectively. In addition, in classification of saffron cultivation areas, the discriminant analysis and support vector machine had higher accuracies. The correlation between saffron cultivation area and saffron yield values was relatively high (r=0.38. The nearest neighbor analysis had the best prediction accuracy for classification of cultivation areas. For this algorithm the coefficients of determination were 1 and 0.944 for training and testing stages, respectively. However, the algorithms accuracy for prediction of crop yield from climatological parameters was low (the average coefficients of determination equal to 0.48 and 0.05 for training and testing stages. The best algorithm i.e. nearest neighbor analysis had coefficients of determination equal to 1 and 0.177 for saffron yield prediction. Results showed that, using climatological parameters and data mining algorithms can classify cultivation areas. By this way it is possible

Geochemical maps of stream sediments in central Colorado, from New Mexico to Wyoming

Science.gov (United States)

Eppinger, Robert G.; Giles, Stuart A.; Klein, Terry L.

2015-01-01

The U.S. Geological Survey has completed a series of geologic, mineral resource, and environmental assessment studies in the Rocky Mountains of central Colorado, from Leadville eastward to the range front and from New Mexico to the Wyoming border. Regional stream-sediment geochemical maps, useful for assessing mineral resources and environmental effects of historical mining activities, were produced as part of the study. The data portrayed in this 56-parameter portfolio of landscape geochemical maps serve as a geochemical baseline for the region, indicate element abundances characteristic of various lithologic terranes, and identify gross anthropogenic effects of historical mining. However, although reanalyzed in this study by modern, sensitive methods, the majority of the stream-sediment samples were collected in the 1970s. Thus, metal concentrations portrayed in these maps represent stream-sediment geochemistry at the time of collection.

Injection of FGD Grout to Abate Acid Mine Drainage in Underground Coal Mines

Energy Technology Data Exchange (ETDEWEB)

Mafi, S.; Damian, M.T.; Senita, R.E.; Jewitt, W.C.; Bair, S.; Chin, Y.C.; Whitlatch, E.; Traina, S.; Wolfe, W.

1997-07-01

Acid Mine Drainage (AMD) from abandoned underground coal mines in Ohio is a concern for both residents and regulatory agencies. Effluent from these mines is typically characterized by low pH and high iron and sulfate concentrations and may contaminate local drinking-water supplies and streams. The objective of this project is to demonstrate the technical feasibility of injecting cementitious alkaline materials, such as Flue Gas Desulfurization (FGD) material to mitigate current adverse environmental impacts associated with AMD in a small, abandoned deep mine in Coshocton County Ohio. The Flue Gas Desulfurization material will be provided from American Electric Power`s (AEP) Conesville Plant. It will be injected as a grout mix that will use Fixated Flue Gas Desulfurization material and water. The subject site for this study is located on the border of Coshocton and Muskingum Counties, Ohio, approximately 1.5 miles south-southwest of the town of Wills Creek. The study will be performed at an underground mine designated as Mm-127 in the Ohio Department of Natural Resources register, also known as the Roberts-Dawson Mine. The mine operated in the mid-1950s, during which approximately 2 million cubic feet of coal was removed. Effluent discharging from the abandoned mine entrances has low pH in the range of 2.8-3.0 that drains directly into Wills Creek Lake. The mine covers approximately 14.6 acres. It is estimated that 26,000 tons of FGD material will be provided from AEP`s Conesville Power Plant located approximately 3 miles northwest of the subject site.

Injection of FGD Grout to Abate Acid Mine Drainage in Underground Coal Mines

International Nuclear Information System (INIS)

Mafi, S.; Damian, M.T.; Senita, R.E.; Jewitt, W.C.; Bair, S.; Chin, Y.C.; Whitlatch, E.; Traina, S.; Wolfe, W.

1997-07-01

Acid Mine Drainage (AMD) from abandoned underground coal mines in Ohio is a concern for both residents and regulatory agencies. Effluent from these mines is typically characterized by low pH and high iron and sulfate concentrations and may contaminate local drinking-water supplies and streams. The objective of this project is to demonstrate the technical feasibility of injecting cementitious alkaline materials, such as Flue Gas Desulfurization (FGD) material to mitigate current adverse environmental impacts associated with AMD in a small, abandoned deep mine in Coshocton County Ohio. The Flue Gas Desulfurization material will be provided from American Electric Power's (AEP) Conesville Plant. It will be injected as a grout mix that will use Fixated Flue Gas Desulfurization material and water. The subject site for this study is located on the border of Coshocton and Muskingum Counties, Ohio, approximately 1.5 miles south-southwest of the town of Wills Creek. The study will be performed at an underground mine designated as Mm-127 in the Ohio Department of Natural Resources register, also known as the Roberts-Dawson Mine. The mine operated in the mid-1950s, during which approximately 2 million cubic feet of coal was removed. Effluent discharging from the abandoned mine entrances has low pH in the range of 2.8-3.0 that drains directly into Wills Creek Lake. The mine covers approximately 14.6 acres. It is estimated that 26,000 tons of FGD material will be provided from AEP's Conesville Power Plant located approximately 3 miles northwest of the subject site

Stream Kriging: Incremental and recursive ordinary Kriging over spatiotemporal data streams

Science.gov (United States)

Zhong, Xu; Kealy, Allison; Duckham, Matt

2016-05-01

Ordinary Kriging is widely used for geospatial interpolation and estimation. Due to the O (n3) time complexity of solving the system of linear equations, ordinary Kriging for a large set of source points is computationally intensive. Conducting real-time Kriging interpolation over continuously varying spatiotemporal data streams can therefore be especially challenging. This paper develops and tests two new strategies for improving the performance of an ordinary Kriging interpolator adapted to a stream-processing environment. These strategies rely on the expectation that, over time, source data points will frequently refer to the same spatial locations (for example, where static sensor nodes are generating repeated observations of a dynamic field). First, an incremental strategy improves efficiency in cases where a relatively small proportion of previously processed spatial locations are absent from the source points at any given iteration. Second, a recursive strategy improves efficiency in cases where there is substantial set overlap between the sets of spatial locations of source points at the current and previous iterations. These two strategies are evaluated in terms of their computational efficiency in comparison to ordinary Kriging algorithm. The results show that these two strategies can reduce the time taken to perform the interpolation by up to 90%, and approach average-case time complexity of O (n2) when most but not all source points refer to the same locations over time. By combining the approaches developed in this paper with existing heuristic ordinary Kriging algorithms, the conclusions indicate how further efficiency gains could potentially be accrued. The work ultimately contributes to the development of online ordinary Kriging interpolation algorithms, capable of real-time spatial interpolation with large streaming data sets.

21 Recipes for Mining Twitter

CERN Document Server

Russell, Matthew

2011-01-01

Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to: Use OAuth to access Twitter dataCreate and analyze graphs of retweet relationshipsUse the streaming API to harvest tweets in realtimeHarvest and analyze friends and followers

When Everything Changes: Mountaintop Mining Effects on Watershed Hydrology

Science.gov (United States)

Nippgen, F.; Ross, M. R.; McGlynn, B. L.; Bernhardt, E. S.

2015-12-01

Mountaintop removal coal mining (MTM) in the Central Appalachians has expanded over the last 40 years to cover ~7% of this mountainous landscape. MTM operations remove mountaintops and ridges with explosives and machinery to access underlying coal seams. Much of this crushed rock overburden is subsequently deposited into nearby valleys, creating valley fills that often bury headwater streams. In contrast to other disturbances such as forest clear-cutting, perturbations from MTM can extend hundreds of meters deep into the critical zone and completely reshape landscapes. Despite the expansiveness and intensity of the disturbance, MTM has only recently begun to receive focused attention from the hydrologic community and the effect of MTM on the hydrology of impacted watersheds is still not well understood. We are using a two-pronged approach consisting of GIS analysis to quantify spoil volumes and landscape change, together with empirical analysis and modeling of rainfall and runoff data collected in two sets of paired watersheds. We seek to investigate how MTM affects basic hydrologic metrics, including storm peakflows, runoff response times, baseflow, statistics of flow duration curves, and longer-term water balances. Each pair consists of a mined and an unmined watershed; the first set contains headwater streams (size ~100ha), the second set consists of 3rd order streams, draining ~3500ha. Mining covers ~ 95% of the headwater watershed, and 40% of the 3rd-order watershed. Initial GIS analysis indicates that the overburden moved during the mining process could be up to three times greater than previously estimated. Storm runoff peaks in the mined watersheds were muted as compared to the unmined watersheds and runoff ratios were reduced by up to 75% during both wet and dry antecedent conditions. The natural reference watersheds were highly responsive while the additional storage in the mined watersheds led to decreased peak flows during storms and enhanced baseflow

Heavy metal pollution in soils of abandoned mining areas (SE, Spain)

Science.gov (United States)

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

2009-04-01

Elevated levels of heavy metals can be found in and around disused metalliferous mines due to discharge and dispersion of mine wastes into nearby agricultural soils, food crops and stream systems. Heavy metals contained in the residues from mining and metallurgical operations are often dispersed by wind and/or water after their disposal. These areas have severe erosion problems caused by wind and water runoff in which soil and mine spoil texture, landscape topography and regional and microclimate play an important role. The present study was carried out in the Cabezo Rajao (La Uni

How sulfate-rich mine drainage affected aquatic ecosystem degradation in northeastern China, and potential ecological risk.

Science.gov (United States)

Zhao, Qian; Guo, Fen; Zhang, Yuan; Ma, Shuqin; Jia, Xiaobo; Meng, Wei

2017-12-31

Mining activity is an increasingly important stressor for freshwater ecosystems. However, the mechanism on how sulfate-rich mine drainage affects freshwater ecosystems is largely unknown, and its potential ecological risk has not been assessed so far. During 2009-2016, water and macroinvertebrate samples from 405 sample sites were collected along the mine drainage gradient from circum-neutral to alkaline waters in Hun-Tai River, Northeastern China. Results of linear regressions showed that sulfate-rich mine drainage was significantly positively correlated with the constituents typically derived from rock weathering (Ca 2+ , Mg 2+ and HCO 3 - +CO 3 2- ); the diversity of intolerant stream macroinvertebrates exhibited a steep decline along the gradient of sulfate-rich mine drainage. Meanwhile, stressor-response relationships between sulfate-rich mine drainage and macroinvertebrate communities were explored by two complementary statistical approaches in tandem (Threshold Indicator Taxa Analysis and the field-based method developed by USEPA). Results revealed that once stream sulfate concentrations in mine drainage exceeded 35mg/L, significant decline in the abundance of intolerant macroinvertebrate taxa occurred. An assessment of ecological risk posed by sulfate-rich mine drainage was conducted based on a tiered approach consisting of simple deterministic method (Hazard Quotient, HQ) to probabilistic method (Joint Probability Curve, JPC). Results indicated that sulfate-rich mine drainage posed a potential risk, and 64.62-84.88% of surface waters in Hun-Tai River exist serious risk while 5% threshold (HC 05 ) and 1% threshold (HC 01 ) were set up to protect macroinvertebrates, respectively. This study provided us a better understanding on the impacts of sulfate-rich mine drainage on freshwater ecosystems, and it would be helpful for future catchment management to protect streams from mining activity. Copyright © 2017. Published by Elsevier B.V.

Stream chemistry in the eastern United States. 2. Current sources of acidity in acidic and low acid-neutralizing-capacity streams

International Nuclear Information System (INIS)

Herlihy, A.T.; Kaufmann, P.R.; Mitch, M.E.

1991-01-01

The authors examined anion composition in National Stream Survey (NSS) data in order to evaluate the most probable sources of current acidity in acidic and low acid neutralizing capacity (ANC) streams in the eastern United States. Acidic streams that had almost no organic influence (less than 10% of total anions) and sulfate and nitrate concentrations indicative of evaporative concentration of atmospheric deposition were classified as acidic due to acidic deposition. These acidic streams were located in small forested watersheds in the Mid-Atlantic Highlands (an estimated 1950 km of stream length) and in the Mid-Atlantic Coastal Plain (1250 km). Acidic streams affected primarily by acidic deposition but also influenced by naturally occurring organic anions accounted for another 1180 km of acidic stream length and were located in the New Jersey Pine Barrens, plateau tops in the Mid-Atlantic and Southeast Highlands, and the Florida Panhandle. The total length of streams acidic due to acid mine drainage in the NSS (4590 km) was about the same as the total length of acidic streams likely affected by acidic deposition (4380 km). Acidic streams whose acid anion composition was dominated by organics were located in Florida and the Mid-Atlantic Coastal Plain. In Florida, most of the acidic streams were organic dominated, whereas about half of the streams in the Mid-Atlantic Coastal Plain were organic dominated. Organic-dominated acidic streams were not observed in the Mid-Atlantic and Southeast Highlands

Event Streams Clustering Using Machine Learning Techniques

Directory of Open Access Journals (Sweden)

Hanen Bouali

2015-10-01

Full Text Available Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track the underlying event streams changes, we opt for the continuously data of the sliding window which increases the time window based on changes over historical data. In this paper, to have the ability to access to historical data without requiring any significant storage or multiple passes over the data. In this paper, we propose a new algorithm for clustering multiple data streams using incremental support vector machine and data representative points’ technique. The algorithm uses a sliding window model for the most recent clustering results and data representative points to model the old data clustering results. Our experimental results on electromyography signal show a better clustering than other present in the literature

Mining Research on Vibration Signal Association Rules of Quayside Container Crane Hoisting Motor Based on Apriori Algorithm

Science.gov (United States)

Yang, Chencheng; Tang, Gang; Hu, Xiong

2017-07-01

Shore-hoisting motor in the daily work will produce a large number of vibration signal data,in order to analyze the correlation among the data and discover the fault and potential safety hazard of the motor, the data are discretized first, and then Apriori algorithm are used to mine the strong association rules among the data. The results show that the relationship between day 1 and day 16 is the most closely related, which can guide the staff to analyze the work of these two days of motor to find and solve the problem of fault and safety.

Data mining, mining data : energy consumption modelling

Energy Technology Data Exchange (ETDEWEB)

Dessureault, S. [Arizona Univ., Tucson, AZ (United States)

2007-09-15

Most modern mining operations are accumulating large amounts of data on production and business processes. Data, however, provides value only if it can be translated into information that appropriate users can utilize. This paper emphasized that a new technological focus should emerge, notably how to concentrate data into information; analyze information sufficiently to become knowledge; and, act on that knowledge. Researchers at the Mining Information Systems and Operations Management (MISOM) laboratory at the University of Arizona have created a method to transform data into action. The data-to-action approach was exercised in the development of an energy consumption model (ECM), in partnership with a major US-based copper mining company, 2 software companies, and the MISOM laboratory. The approach begins by integrating several key data sources using data warehousing techniques, and increasing the existing level of integration and data cleaning. An online analytical processing (OLAP) cube was also created to investigate the data and identify a subset of several million records. Data mining algorithms were applied using the information that was isolated by the OLAP cube. The data mining results showed that traditional cost drivers of energy consumption are poor predictors. A comparison was made between traditional methods of predicting energy consumption and the prediction formed using data mining. Traditionally, in the mines for which data were available, monthly averages of tons and distance are used to predict diesel fuel consumption. However, this article showed that new information technology can be used to incorporate many more variables into the budgeting process, resulting in more accurate predictions. The ECM helped mine planners improve the prediction of energy use through more data integration, measure development, and workflow analysis. 5 refs., 11 figs.

Radionuclides in sheep grazing near old uranium mines

Energy Technology Data Exchange (ETDEWEB)

Carvalho, Fernando P.; Oliveira, Joao M.; Malta, M. [Instituto Superior Tecnico/Campus Tecnologico e Nuclear/ (IST/CTN), Universidade de Lisboa, Estrada Nacional 10 - ao km 139,7, - 2695-066 Bobadela LRS (Portugal); Lemos, M.E. [Servicos de Alimentacao e Veterinaria da Regiao Centro, Bairro Na Sra dos Remedios, 6300 Guarda (Portugal); Vala, H.; Esteves, F. [Escola Superior Agraria de Viseu, Quinta da Alagoa, Estrada de Nelas, Ranhados,3500-606 Viseu (Portugal)

2014-07-01

During the past century extensive uranium mining took place in Portugal for radium and uranium production. Many uranium deposits were mined as open pits and after ore extraction and transportation to milling facilities, mining wastes were left on site. One uranium ore mining site, Boco Mine, was extracted in the 1960's and 70's and mining waste and open pits were left uncovered and non-remediated since closure of uranium mining activities. During the nineties a quarry for sand extraction was operated in the same site and water from a local stream was extensively used in sand sieving. Downstream the mine areas, agriculture soils along the water course are currently used for cattle grazing. Water from this stream, and water wells, soil, pasture and sheep meat were analyzed for radionuclides of the uranium series. The U- series radionuclide {sup 226}Ra was generally the highest in concentrations especially in soil, pasture, and in internal organs of sheep. Ra-226 concentrations averaged 1093±96 Bq/kg (dry weight) in soil, 43±3 Bq/kg (dw) in pasture, and 0.76±0.41 Bq/kg (dw) in muscle tissue of sheep grown there. Other sheep internal organs displayed much higher {sup 226}Ra concentrations, such as the brain and kidneys with 7.7±2.3 Bq/kg (dw) and 28±29 Bq/kg (dw), respectively. Results of tissue sample analysis for sheep grown in a comparison area were 2 to 11 times lower, depending on the tissue. Absorbed radiation doses for internal organs of sheep were computed and may exceed 20 mSv/y in the kidney. Although elevated, this absorbed radiation dose still is below the threshold for biological effects on mammals. Nevertheless, enhanced environmental radioactive contamination mainly due to radium was observed in the area of influence of this legacy uranium mine and there is potential food chain transfer for humans (authors)

Data Mining Web Services for Science Data Repositories

Science.gov (United States)

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

2006-12-01

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

Support-Less Association Rule Mining Using Tuple Count Cube

OpenAIRE

Qin Ding; William Perrizo

2007-01-01

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association...

Lead mobilisation in the hyporheic zone and river bank sediments of a contaminated stream. Contribution to diffuse pollution

Energy Technology Data Exchange (ETDEWEB)

Palumbo-Roe, Barbara; Wragg, Joanna; Banks, Vanessa J. [British Geological Survey, Keyworth Nottingham (United Kingdom)

2012-12-15

Purpose: Past metal mining has left a legacy of highly contaminated sediments representing a significant diffuse source of contamination to water bodies in the UK and worldwide. This paper presents the results of an integrated approach used to define the role of sediments in contributing to the dissolved lead (Pb) loading to surface water in a mining-impacted catchment. Materials and methods: The Rookhope Burn catchment, northern England, UK is affected by historical mining and processing of lead ore. Quantitative geochemical loading determinations, measurements of interstitial water chemistry from the stream hyporheic zone and inundation tests of bank sediments were carried out. Results and discussion: High concentrations of Pb in the sediments from the catchment, identified from the British Geological Survey Geochemical Baseline Survey of the Environment (GBASE) data, demonstrate both the impact of mineralisation and widespread historical mining. The results from stream water show that the stream Pb load increased in the lower part of the catchment, without any apparent or significant contribution of point sources of Pb to the stream. Relative to surface water, the interstitial water of the hyporheic zone contained high concentrations of dissolved Pb in the lower reaches of the Rookhope Burn catchment, downstream of a former mine washing plant. Concentrations of 56 {mu}g l{sup -1} of dissolved Pb in the interstitial water of the hyporheic zone may be a major cause of the deterioration of fish habitats in the stream and be regarded as a serious risk to the target of good ecological status as defined in the European Water Framework Directive. Inundation tests provide an indication that bank sediments have the potential to contribute dissolved Pb to surface water. Conclusions: The determination of Pb in the interstitial water and in the inundation water, taken with water Pb mass balance and sediment Pb distribution maps at the catchment scale, implicate the

In situ bioassays with Chironomus riparius larvae to biomonitor metal pollution in rivers and to evaluate the efficiency of restoration measures in mine areas

Energy Technology Data Exchange (ETDEWEB)

Faria, Mafalda S. [CESAM and Departamento de Biologia da Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)], E-mail: mafaldafaria@sapo.pt; Lopes, Ricardo J. [CIBIO, Centro de Investigacao em Biodiversidade e Recursos Geneticos, Campus Agrario de Vairao, 4485-661 Vairao (Portugal); Malcato, Joao; Nogueira, Antonio J.A.; Soares, Amadeu M.V.M. [CESAM and Departamento de Biologia da Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

2008-01-15

In this study we evaluate the ability of an in situ bioassay with Chironomus riparius larvae, using larval development and growth as endpoints, to biomonitor water quality and to assess the biological recovery of metal contaminated freshwater ecosystems of mine areas that are subject of restoration measures. The bioassay was carried out in streams located near an abandoned goldmine in North Portugal, throughout an environmental rehabilitation of the mine (2002-2004). During this period, a decrease in the inhibition of larval growth in the metal contaminated stream was observed. The bioassay was also performed in streams located near an active tungsten mine in Central Portugal. Larval growth and development were highly inhibited in the stream that receives acid drainage from the tungsten mine and treated water from the AMD treatment station. The results indicate that the bioassay can be used to evaluate the efficiency of environmental restoration measures in mining areas. - In situ bioassays with Chironomus riparius larvae can be a suitable tool to monitor restoration efficiency after a long time of metallic sediment contamination.

In situ bioassays with Chironomus riparius larvae to biomonitor metal pollution in rivers and to evaluate the efficiency of restoration measures in mine areas

International Nuclear Information System (INIS)

Faria, Mafalda S.; Lopes, Ricardo J.; Malcato, Joao; Nogueira, Antonio J.A.; Soares, Amadeu M.V.M.

2008-01-01

In this study we evaluate the ability of an in situ bioassay with Chironomus riparius larvae, using larval development and growth as endpoints, to biomonitor water quality and to assess the biological recovery of metal contaminated freshwater ecosystems of mine areas that are subject of restoration measures. The bioassay was carried out in streams located near an abandoned goldmine in North Portugal, throughout an environmental rehabilitation of the mine (2002-2004). During this period, a decrease in the inhibition of larval growth in the metal contaminated stream was observed. The bioassay was also performed in streams located near an active tungsten mine in Central Portugal. Larval growth and development were highly inhibited in the stream that receives acid drainage from the tungsten mine and treated water from the AMD treatment station. The results indicate that the bioassay can be used to evaluate the efficiency of environmental restoration measures in mining areas. - In situ bioassays with Chironomus riparius larvae can be a suitable tool to monitor restoration efficiency after a long time of metallic sediment contamination

An Unsupervised Opinion Mining Approach for Japanese Weblog Reputation Information Using an Improved SO-PMI Algorithm

Science.gov (United States)

Wang, Guangwei; Araki, Kenji

In this paper, we propose an improved SO-PMI (Semantic Orientation Using Pointwise Mutual Information) algorithm, for use in Japanese Weblog Opinion Mining. SO-PMI is an unsupervised approach proposed by Turney that has been shown to work well for English. When this algorithm was translated into Japanese naively, most phrases, whether positive or negative in meaning, received a negative SO. For dealing with this slanting phenomenon, we propose three improvements: to expand the reference words to sets of words, to introduce a balancing factor and to detect neutral expressions. In our experiments, the proposed improvements obtained a well-balanced result: both positive and negative accuracy exceeded 62%, when evaluated on 1,200 opinion sentences sampled from three different domains (reviews of Electronic Products, Cars and Travels from Kakaku. com). In a comparative experiment on the same corpus, a supervised approach (SA-Demo) achieved a very similar accuracy to our method. This shows that our proposed approach effectively adapted SO-PMI for Japanese, and it also shows the generality of SO-PMI.

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

Energy Technology Data Exchange (ETDEWEB)

Steed, Chad A [ORNL; Drouhard, Margaret MEG G [ORNL; Beaver, Justin M [ORNL; Pyle, Joshua M [ORNL; BogenII, Paul L. [Google Inc.

2015-01-01

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

BLOSTREAM: A HIGH SPEED STREAM CIPHER

Directory of Open Access Journals (Sweden)

ALI H. KASHMAR

2017-04-01

Full Text Available Although stream ciphers are widely utilized to encrypt sensitive data at fast speeds, security concerns have led to a shift from stream to block ciphers, judging that the current technology in stream cipher is inferior to the technology of block ciphers. This paper presents the design of an improved efficient and secure stream cipher called Blostream, which is more secure than conventional stream ciphers that use XOR for mixing. The proposed cipher comprises two major components: the Pseudo Random Number Generator (PRNG using the Rabbit algorithm and a nonlinear invertible round function (combiner for encryption and decryption. We evaluate its performance in terms of implementation and security, presenting advantages and disadvantages, comparison of the proposed cipher with similar systems and a statistical test for randomness. The analysis shows that the proposed cipher is more efficient, high speed, and secure than current conventional stream ciphers.

Improve Data Mining and Knowledge Discovery through the use of MatLab

Science.gov (United States)

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

2011-01-01

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

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

OpenAIRE

Hayden Wimmer; Loreen Powell

2014-01-01

While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Baye...

Multiagent data warehousing and multiagent data mining for cerebrum/cerebellum modeling

Science.gov (United States)

Zhang, Wen-Ran

2002-03-01

An algorithm named Neighbor-Miner is outlined for multiagent data warehousing and multiagent data mining. The algorithm is defined in an evolving dynamic environment with autonomous or semiautonomous agents. Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new agents and mining agent associations in first-order logic from agent attributes and actions. While the Apriori algorithm uses frequency as a priory threshold, the new algorithm uses agent similarity as priory knowledge. The concept of agent similarity leads to the notions of agent cuboid, orthogonal multiagent data warehousing (MADWH), and multiagent data mining (MADM). Based on agent similarities and action similarities, Neighbor-Miner is proposed and illustrated in a MADWH/MADM approach to cerebrum/cerebellum modeling. It is shown that (1) semiautonomous neurofuzzy agents can be identified for uniped locomotion and gymnastic training based on attribute relevance analysis; (2) new agents can be discovered and agent cuboids can be dynamically constructed in an orthogonal MADWH, which resembles an evolving cerebrum/cerebellum system; and (3) dynamic motion laws can be discovered as association rules in first order logic. Although examples in legged robot gymnastics are used to illustrate the basic ideas, the new approach is generally suitable for a broad category of data mining tasks where knowledge can be discovered collectively by a set of agents from a geographically or geometrically distributed but relevant environment, especially in scientific and engineering data environments.

A Streaming Language Implementation of the Discontinuous Galerkin Method

Science.gov (United States)

Barth, Timothy; Knight, Timothy

2005-01-01

We present a Brook streaming language implementation of the 3-D discontinuous Galerkin method for compressible fluid flow on tetrahedral meshes. Efficient implementation of the discontinuous Galerkin method using the streaming model of computation introduces several algorithmic design challenges. Using a cycle-accurate simulator, performance characteristics have been obtained for the Stanford Merrimac stream processor. The current Merrimac design achieves 128 Gflops per chip and the desktop board is populated with 16 chips yielding a peak performance of 2 Teraflops. Total parts cost for the desktop board is less than $20K. Current cycle-accurate simulations for discretizations of the 3-D compressible flow equations yield approximately 40-50% of the peak performance of the Merrimac streaming processor chip. Ongoing work includes the assessment of the performance of the same algorithm on the 2 Teraflop desktop board with a target goal of achieving 1 Teraflop performance.

A Framework To Support Management Of HIVAIDS Using K-Means And Random Forest Algorithm

Directory of Open Access Journals (Sweden)

Gladys Iseu

2017-06-01

Full Text Available Healthcare industry generates large amounts of complex data about patients hospital resources disease management electronic patient records and medical devices among others. The availability of these huge amounts of medical data creates a need for powerful mining tools to support health care professionals in diagnosis treatment and management of HIVAIDS. Several data mining techniques have been used in management of different data sets. Data mining techniques have been categorized into regression algorithms segmentation algorithms association algorithms sequence analysis algorithms and classification algorithms. In the medical field there has not been a specific study that has incorporated two or more data mining algorithms hence limiting decision making levels by medical practitioners. This study identified the extent to which K-means algorithm cluster patient characteristics it has also evaluated the extent to which random forest algorithm can classify the data for informed decision making as well as design a framework to support medical decision making in the treatment of HIVAIDS related diseases in Kenya. The paper further used random forest classification algorithm to compute proximities between pairs of cases that can be used in clustering locating outliers or by scaling to give interesting views of the data.

Secure remote service execution for web media streaming

OpenAIRE

Mikityuk, Alexandra

2017-01-01

Through continuous advancements in streaming and Web technologies over the past decade, the Web has become a platform for media delivery. Web standards like HTML5 have been designed accordingly, allowing for the delivery of applications, high-quality streaming video, and hooks for interoperable content protection. Efficient video encoding algorithms such as AVC/HEVC and streaming protocols such as MPEG-DASH have served as additional triggers for this evolution. Users now employ...

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

Science.gov (United States)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

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

Effect Of Imposed Anaerobic Conditions On Metals Release From Acid-Mine Drainage Contaminated Streambed Sediments

Science.gov (United States)

Remediation of streams influenced by mine-drainage may require removal and burial of metal-containing bed sediments. Burial of aerobic sediments into an anaerobic environment may release metals, such as through reductive dissolution of metal oxyhydroxides. Mining-impacted aerob...

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

Science.gov (United States)

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

2018-04-01

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

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

Directory of Open Access Journals (Sweden)

Fengcai Qiao

2018-02-01

Full Text Available Frequent subgraph mining (FSM plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSiGraM (Spark based Single Graph Mining, a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, we conduct the subgraph extension and support evaluation parallel across all the distributed cluster worker nodes. In addition, we also employ a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support evaluation process, which significantly improve the performance. Extensive experiments with four different real-world datasets demonstrate that the proposed algorithm outperforms the existing GraMi (Graph Mining algorithm by an order of magnitude for all datasets and can work with a lower support threshold.

Predictive analytics on evolving data streams anticipating and adapting to changes in known and unknown contexts

NARCIS (Netherlands)

Pechenizkiy, M.

2015-01-01

Ever increasing volumes of sensor readings, transactional records, web data and event logs call for next generation of big data mining technology providing effective and efficient tools for making use of the streaming data. Predictive analytics on data streams is actively studied in research

Prospective study to assess the prevalence and work-related risk factors in the development of musculoskeletal disorders in the South African mining industry

CSIR Research Space (South Africa)

Schutte, PC

2003-09-01

Full Text Available stream_source_info Health702 summary.pdf.txt stream_content_type text/plain stream_size 4676 Content-Encoding UTF-8 stream_name Health702 summary.pdf.txt Content-Type text/plain; charset=UTF-8 Safety In Mines Research...,5%) and the foot region (4,6%). At the platinum mine the second highest number of complaints was for knee pain (17,%), followed by ankle pain (9,%) and neck pain (9,1%). At the colliery neck pain (13,5%) and foot pain (8,1%) were also common presentations...

Stretch-minimising stream surfaces

KAUST Repository

Barton, Michael; Kosinka, Jin; Calo, Victor M.

2015-01-01

We study the problem of finding stretch-minimising stream surfaces in a divergence-free vector field. These surfaces are generated by motions of seed curves that propagate through the field in a stretch minimising manner, i.e., they move without stretching or shrinking, preserving the length of their arbitrary arc. In general fields, such curves may not exist. How-ever, the divergence-free constraint gives rise to these 'stretch-free' curves that are locally arc-length preserving when infinitesimally propagated. Several families of stretch-free curves are identified and used as initial guesses for stream surface generation. These surfaces are subsequently globally optimised to obtain the best stretch-minimising stream surfaces in a given divergence-free vector field. Our algorithm was tested on benchmark datasets, proving its applicability to incompressible fluid flow simulations, where our stretch-minimising stream surfaces realistically reflect the flow of a flexible univariate object. © 2015 Elsevier Inc. All rights reserved.

Stretch-minimising stream surfaces

KAUST Repository

Barton, Michael

2015-05-01

We study the problem of finding stretch-minimising stream surfaces in a divergence-free vector field. These surfaces are generated by motions of seed curves that propagate through the field in a stretch minimising manner, i.e., they move without stretching or shrinking, preserving the length of their arbitrary arc. In general fields, such curves may not exist. How-ever, the divergence-free constraint gives rise to these \\'stretch-free\\' curves that are locally arc-length preserving when infinitesimally propagated. Several families of stretch-free curves are identified and used as initial guesses for stream surface generation. These surfaces are subsequently globally optimised to obtain the best stretch-minimising stream surfaces in a given divergence-free vector field. Our algorithm was tested on benchmark datasets, proving its applicability to incompressible fluid flow simulations, where our stretch-minimising stream surfaces realistically reflect the flow of a flexible univariate object. © 2015 Elsevier Inc. All rights reserved.

Expressive power of an algebra for data mining

NARCIS (Netherlands)

Calders, T.; Lakshmanan, L.V.S.; Ng, R.T.; Paredaens, J.

2006-01-01

The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can

Innovative use of a mist eliminator in mine ventilation

International Nuclear Information System (INIS)

Boyko, K.; Smith, T.

2010-01-01

The McArthur River Operation recently installed an upcast mine ventilation system to increase its total mine ventilation capacity. During commissioning, the amount of water discharged by the new system exceeded that which is normally observed in similar installations. Operation of the system was suspended, and a study was conducted to determine to most effective technology to significantly reduce or remove this water from the saturated air stream. A chevron-type mist eliminator was designed and installed to strip the water from the air, such that the condensate could be appropriately managed in the mine effluent treatment system. (author)

Mercury methylation influenced by areas of past mercury mining in the Terlingua district, Southwest Texas, USA

International Nuclear Information System (INIS)

Gray, John E.; Hines, Mark E.; Biester, Harald

2006-01-01

Speciation and microbial transformation of Hg was studied in mine waste from abandoned Hg mines in SW Texas to evaluate the potential for methyl-Hg production and degradation in mine wastes. In mine waste samples, total Hg, ionic Hg 2+ , Hg 0 , methyl-Hg, organic C, and total S concentrations were measured, various Hg compounds were identified using thermal desorption pyrolysis, and potential rates of Hg methylation and methyl-Hg demethylation were determined using isotopic-tracer methods. These data are the first reported for Hg mines in this region. Total Hg and methyl-Hg concentrations were also determined in stream sediment collected downstream from two of the mines to evaluate transport of Hg and methylation in surrounding ecosystems. Mine waste contains total Hg and methyl-Hg concentrations as high as 19,000 μg/g and 1500 ng/g, respectively, which are among the highest concentrations reported at Hg mines worldwide. Pyrolysis analyses show that mine waste contains variable amounts of cinnabar, metacinnabar, Hg 0 , and Hg sorbed onto particles. Methyl-Hg concentrations in mine waste correlate positively with ionic Hg 2+ , organic C, and total S, which are geochemical parameters that influence processes of Hg cycling and methylation. Net methylation rates were as high as 11,000 ng/g/day, indicating significant microbial Hg methylation at some sites, especially in samples collected inside retorts. Microbially-mediated methyl-Hg demethylation was also observed in many samples, but where both methylation and demethylation were found, the potential rate of methylation was faster. Total Hg concentrations in stream sediment samples were generally below the probable effect concentration of 1.06 μg/g, the Hg concentration above which harmful effects are likely to be observed in sediment dwelling organisms; whereas total Hg concentrations in mine waste samples were found to exceed this concentration, although this is a sediment quality guideline and is not directly

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Directory of Open Access Journals (Sweden)

Musaab Riyadh

2017-01-01

Full Text Available The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

Natural and anthropogenic sources and processes affecting water chemistry in two South Korean streams

Energy Technology Data Exchange (ETDEWEB)

Shin, Woo-Jin [Division of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongwon-gun, Chungbuk 363-883 (Korea, Republic of); Department of Geoscience, University of Calgary, Calgary, Alberta T2N 1N4 (Canada); Ryu, Jong-Sik [Division of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongwon-gun, Chungbuk 363-883 (Korea, Republic of); Mayer, Bernhard [Department of Geoscience, University of Calgary, Calgary, Alberta T2N 1N4 (Canada); Lee, Kwang-Sik, E-mail: kslee@kbsi.re.kr [Division of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongwon-gun, Chungbuk 363-883 (Korea, Republic of); Lee, Sin-Woo [Division of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongwon-gun, Chungbuk 363-883 (Korea, Republic of); Department of Geology, Chungnam National University, Yuseong-gu, Daejeon 305-764 (Korea, Republic of)

2014-07-01

Acid mine drainage (AMD) in a watershed provides potential sources of pollutants for surface and subsurface waters that can deteriorate water quality. Between March and early August 2011, water samples were collected from two streams in South Korea, one dominantly draining a watershed with carbonate bedrock affected by coal mines and another draining a watershed with silicate bedrock and a relatively undisturbed catchment area. The objective of the study was to identify the sources and processes controlling water chemistry, which was dependent on bedrock and land use. In the Odae stream (OS), the stream in the silicate-dominated catchment, Ca, Na, and HCO{sub 3} were the dominant ions and total dissolved solids (TDS) was low (26.1–165 mg/L). In the Jijang stream (JS), in the carbonate-dominated watershed, TDS (224–434 mg/L) and ion concentrations were typically higher, and Ca and SO{sub 4} were the dominant ions due to carbonate weathering and oxidation of pyrite exposed at coal mines. Dual isotopic compositions of sulfate (δ{sup 34}S{sub SO4} and δ{sup 18}O{sub SO4}) verified that the SO{sub 4} in JS is derived mainly from sulfide mineral oxidation in coal mines. Cl in JS was highest upstream and decreased progressively downstream, which implies that pollutants from recreational facilities in the uppermost part of the catchment are the major source governing Cl concentrations within the discharge basin. Dual isotopic compositions of nitrate (δ{sup 15}N{sub NO3} and δ{sup 18}O{sub NO3}) indicated that NO{sub 3} in JS is attributable to nitrification of soil organic matter but that NO{sub 3} in OS is derived mostly from manure. Additionally, the contributions of potential anthropogenic sources to the two streams were estimated in more detail by using a plot of δ{sup 34}S{sub SO4} and δ{sup 15}N{sub NO3}. This study suggests that the dual isotope approach for sulfate and nitrate is an excellent additional tool for elucidating the sources and processes

Natural and anthropogenic sources and processes affecting water chemistry in two South Korean streams

International Nuclear Information System (INIS)

Shin, Woo-Jin; Ryu, Jong-Sik; Mayer, Bernhard; Lee, Kwang-Sik; Lee, Sin-Woo

2014-01-01

Acid mine drainage (AMD) in a watershed provides potential sources of pollutants for surface and subsurface waters that can deteriorate water quality. Between March and early August 2011, water samples were collected from two streams in South Korea, one dominantly draining a watershed with carbonate bedrock affected by coal mines and another draining a watershed with silicate bedrock and a relatively undisturbed catchment area. The objective of the study was to identify the sources and processes controlling water chemistry, which was dependent on bedrock and land use. In the Odae stream (OS), the stream in the silicate-dominated catchment, Ca, Na, and HCO 3 were the dominant ions and total dissolved solids (TDS) was low (26.1–165 mg/L). In the Jijang stream (JS), in the carbonate-dominated watershed, TDS (224–434 mg/L) and ion concentrations were typically higher, and Ca and SO 4 were the dominant ions due to carbonate weathering and oxidation of pyrite exposed at coal mines. Dual isotopic compositions of sulfate (δ 34 S SO4 and δ 18 O SO4 ) verified that the SO 4 in JS is derived mainly from sulfide mineral oxidation in coal mines. Cl in JS was highest upstream and decreased progressively downstream, which implies that pollutants from recreational facilities in the uppermost part of the catchment are the major source governing Cl concentrations within the discharge basin. Dual isotopic compositions of nitrate (δ 15 N NO3 and δ 18 O NO3 ) indicated that NO 3 in JS is attributable to nitrification of soil organic matter but that NO 3 in OS is derived mostly from manure. Additionally, the contributions of potential anthropogenic sources to the two streams were estimated in more detail by using a plot of δ 34 S SO4 and δ 15 N NO3 . This study suggests that the dual isotope approach for sulfate and nitrate is an excellent additional tool for elucidating the sources and processes controlling the water chemistry of streams draining watersheds having different

Implementation of a Multichannel Serial Data Streaming Algorithm using the Xilinx Serial RapidIO Solution

Science.gov (United States)

Doxley, Charles A.

2016-01-01

In the current world of applications that use reconfigurable technology implemented on field programmable gate arrays (FPGAs), there is a need for flexible architectures that can grow as the systems evolve. A project has limited resources and a fixed set of requirements that development efforts are tasked to meet. Designers must develop robust solutions that practically meet the current customer demands and also have the ability to grow for future performance. This paper describes the development of a high speed serial data streaming algorithm that allows for transmission of multiple data channels over a single serial link. The technique has the ability to change to meet new applications developed for future design considerations. This approach uses the Xilinx Serial RapidIO LOGICORE Solution to implement a flexible infrastructure to meet the current project requirements with the ability to adapt future system designs.

Advanced land mine detection using a synthesis of conventional technologies

International Nuclear Information System (INIS)

Rappaport, C.M.

1998-01-01

A team at Northeastern University develops and optimizes land mine detection based on ground-penetrating radar, infrared thermography, electromagnetic induction (EM), and high frequency acoustic sensors. It implements sophisticated, physics-based mathematical models to describe the interaction of EM or acoustic waves with mines buried in realistic (electromagnetically loose, inhomogeneous) soil and as a result develops signal processing algorithms to identify and classify mines. These mathematical models are derived from actual soil and land mine measurements, and include detection statistics of the sensors. The novel aspects of Northeastern University's approach are: (1) to combine multiple sensors synergistically, yielding more information than would be available to any single sencor technology operating alone, and (2) to use signal-processing algorithms derived from physics-based models which take into account the actual sensor parameters as well as material and electrical characteristics of the soil and land mines

Computing Diameter in the Streaming and Sliding-Window Models (Preprint)

National Research Council Canada - National Science Library

Feigenbaum, Joan; Kannan, Sampath; Zhang, Jian

2002-01-01

We investigate the diameter problem in the streaming and sliding-window models. We show that, for a stream of n points or a sliding window of size n, any exact algorithm for diameter requires Omega(n) bits of space...

Dispersion and toxicity of metals from abandoned gold mine tailings at Goldenville, Nova Scotia, Canada

International Nuclear Information System (INIS)

Wong, H.K.T.; Gauthier, A.; Nriagu, J.O.

1999-01-01

As its name indicates, Goldenville was a famous gold mining area in Nova Scotia where large quantities of mercury were used in the gold recovery process. It is estimated that the 3 million tons of tailings left from the mining activities which lasted from 1860 to 1945 contain 470 kg of Cd, 37-300 kg of Pb, 6800 kg of Hg, 20-700 kg of As and 2600 kg of Tl. Analysis of metal contents of stream water, stream and lake sediments, tailings, and vegetation show that the tailings have been distributed over time across the stream basin to form a tailing field of approximately 2 km 2 . There is a continuous release of As, Hg, Pb, Tl and other metals from the tailing field, resulting in contamination of ecosystems downstream including the Gagogan Harbor of the Atlantic Ocean. Stream water and sediments of Lake Gagogan located downstream from the mine were found toxic to the benthic community. A loss of fish habitat was observed. Although the mines were closed over 50 years ago, sedimentary records of metal loadings into Lake Gagogan show that the release of metals from the tailings has not slowed down. Analysis of metal tolerant species in the area suggests that horsetails (Equisetum rubiaceae and E. sylvaticum) can be used in phytoremediation of sites contaminated with arsenic and mercury. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

Dispersion and toxicity of metals from abandoned gold mine tailings at Goldenville, Nova Scotia, Canada

Energy Technology Data Exchange (ETDEWEB)

Wong, H.K.T. [National Water Research Institute, P.O. Box 5050, Burlington (Canada); Gauthier, A. [Environmental Protection Branch, Environment Canada, Dartmouth, Nova Scotia (Canada); Nriagu, J.O. [Department of Environmental and Industrial Health, School of Public Health, University of Michigan, Ann Arbor, MI (United States)

1999-03-22

As its name indicates, Goldenville was a famous gold mining area in Nova Scotia where large quantities of mercury were used in the gold recovery process. It is estimated that the 3 million tons of tailings left from the mining activities which lasted from 1860 to 1945 contain 470 kg of Cd, 37-300 kg of Pb, 6800 kg of Hg, 20-700 kg of As and 2600 kg of Tl. Analysis of metal contents of stream water, stream and lake sediments, tailings, and vegetation show that the tailings have been distributed over time across the stream basin to form a tailing field of approximately 2 km{sup 2}. There is a continuous release of As, Hg, Pb, Tl and other metals from the tailing field, resulting in contamination of ecosystems downstream including the Gagogan Harbor of the Atlantic Ocean. Stream water and sediments of Lake Gagogan located downstream from the mine were found toxic to the benthic community. A loss of fish habitat was observed. Although the mines were closed over 50 years ago, sedimentary records of metal loadings into Lake Gagogan show that the release of metals from the tailings has not slowed down. Analysis of metal tolerant species in the area suggests that horsetails (Equisetum rubiaceae and E. sylvaticum) can be used in phytoremediation of sites contaminated with arsenic and mercury. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

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

Science.gov (United States)

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

2017-12-01

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

Cooperative organic mine avoidance path planning

Science.gov (United States)

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

2005-06-01

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

Transparent data mining for big and small data

CERN Document Server

Quercia, Daniele; Pasquale, Frank

2017-01-01

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to prac...

WEKA-G: Parallel data mining on computational grids

Directory of Open Access Journals (Sweden)

PIMENTA, A.

2009-12-01

Full Text Available Data mining is a technology that can extract useful information from large amounts of data. However, mining a database often requires a high computational power. To resolve this problem, this paper presents a tool (Weka-G, which runs in parallel algorithms used in the mining process data. As the environment for doing so, we use a computational grid by adding several features within a WAN.

Maximum error-bounded Piecewise Linear Representation for online stream approximation

KAUST Repository

Xie, Qing; Pang, Chaoyi; Zhou, Xiaofang; Zhang, Xiangliang; Deng, Ke

2014-01-01

Given a time series data stream, the generation of error-bounded Piecewise Linear Representation (error-bounded PLR) is to construct a number of consecutive line segments to approximate the stream, such that the approximation error does not exceed a prescribed error bound. In this work, we consider the error bound in L∞ norm as approximation criterion, which constrains the approximation error on each corresponding data point, and aim on designing algorithms to generate the minimal number of segments. In the literature, the optimal approximation algorithms are effectively designed based on transformed space other than time-value space, while desirable optimal solutions based on original time domain (i.e., time-value space) are still lacked. In this article, we proposed two linear-time algorithms to construct error-bounded PLR for data stream based on time domain, which are named OptimalPLR and GreedyPLR, respectively. The OptimalPLR is an optimal algorithm that generates minimal number of line segments for the stream approximation, and the GreedyPLR is an alternative solution for the requirements of high efficiency and resource-constrained environment. In order to evaluate the superiority of OptimalPLR, we theoretically analyzed and compared OptimalPLR with the state-of-art optimal solution in transformed space, which also achieves linear complexity. We successfully proved the theoretical equivalence between time-value space and such transformed space, and also discovered the superiority of OptimalPLR on processing efficiency in practice. The extensive results of empirical evaluation support and demonstrate the effectiveness and efficiency of our proposed algorithms.

Maximum error-bounded Piecewise Linear Representation for online stream approximation

KAUST Repository

Xie, Qing

2014-04-04

Given a time series data stream, the generation of error-bounded Piecewise Linear Representation (error-bounded PLR) is to construct a number of consecutive line segments to approximate the stream, such that the approximation error does not exceed a prescribed error bound. In this work, we consider the error bound in L∞ norm as approximation criterion, which constrains the approximation error on each corresponding data point, and aim on designing algorithms to generate the minimal number of segments. In the literature, the optimal approximation algorithms are effectively designed based on transformed space other than time-value space, while desirable optimal solutions based on original time domain (i.e., time-value space) are still lacked. In this article, we proposed two linear-time algorithms to construct error-bounded PLR for data stream based on time domain, which are named OptimalPLR and GreedyPLR, respectively. The OptimalPLR is an optimal algorithm that generates minimal number of line segments for the stream approximation, and the GreedyPLR is an alternative solution for the requirements of high efficiency and resource-constrained environment. In order to evaluate the superiority of OptimalPLR, we theoretically analyzed and compared OptimalPLR with the state-of-art optimal solution in transformed space, which also achieves linear complexity. We successfully proved the theoretical equivalence between time-value space and such transformed space, and also discovered the superiority of OptimalPLR on processing efficiency in practice. The extensive results of empirical evaluation support and demonstrate the effectiveness and efficiency of our proposed algorithms.

Uncertainty modeling for data mining a label semantics approach

CERN Document Server

Qin, Zengchang

2014-01-01

Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.

Evaluation of Heavy Metals in Stream Sediments from Abakaliki Pb ...

African Journals Online (AJOL)

PROF HORSFALL

Evaluation of Heavy Metals in Stream Sediments from Abakaliki Pb – Zn Ore Mining. Areas of Ebonyi ... produced both for local consumption and also for food supplies to other .... of deionised water using a pH-meter (Aqualytica. Model pH 17).

Improving clinical decision support using data mining techniques

Science.gov (United States)

Burn-Thornton, Kath E.; Thorpe, Simon I.

1999-02-01

Physicians, in their ever-demanding jobs, are looking to decision support systems for aid in clinical diagnosis. However, clinical decision support systems need to be of sufficiently high accuracy that they help, rather than hinder, the physician in his/her diagnosis. Decision support systems with accuracies, of patient state determination, of greater than 80 percent, are generally perceived to be sufficiently accurate to fulfill the role of helping the physician. We have previously shown that data mining techniques have the potential to provide the underpinning technology for clinical decision support systems. In this paper, an extension of the work in reverence 2, we describe how changes in data mining methodologies, for the analysis of 12-lead ECG data, improve the accuracy by which data mining algorithms determine which patients are suffering from heart disease. We show that the accuracy of patient state prediction, for all the algorithms, which we investigated, can be increased by up to 6 percent, using the combination of appropriate test training ratios and 5-fold cross-validation. The use of cross-validation greater than 5-fold, appears to reduce the improvement in algorithm classification accuracy gained by the use of this validation method. The accuracy of 84 percent in patient state predictions, obtained using the algorithm OCI, suggests that this algorithm will be capable of providing the required accuracy for clinical decision support systems.

Comparison of braided-stream depositional environment and uranium deposits at Saint Anthony underground mine

International Nuclear Information System (INIS)

Baird, C.W.; Martin, K.W.; Lowry, R.M.

1980-01-01

United Nuclear's Saint Anthony mine, located in the Laguna district, produces uranium ore from the Jackpile sandstone unit of the Morrison Formation. The Jackpile sediments were deposited in a fluvial environment characterized by aridity, gentle slope, distant source area, and limited flow volume. Resultant stratigraphy consists of an intricate assemblage of trough and tabular cross-stratification grading to near massive bedding at some locations. Interbedded with the Jackpile sands are green mudstones and siltstones that commonly display irregular thicknesses of less than 2 ft and that are laterally discontinuous. Major penecontemporaneous and postdepositional alteration of originally deposited sands, silts, and clays includes: 1) infiltration and filling of interstices by kaolinitic clays; 2) mobilization and relocation of organic carbonaceous material; and 3) geochemical alteration of mineral constituents and fixation of uranium ions in organic carbonaceous material. Mineralized zones of economic volume display a spatial relationship to bedding features indicative of loosely packed sand deposited in dune and trough foresets. This relationship indicates possible permeability control by initial stratigraphy upon the flow of mineralizing solutions. Additionally, the low-energy foreset environment facilitates the accumulation of low-specific-gravity carbonaceous material necessary for interaction with mineralizing solutions. Large volumes of loosely packed foreset sands accumulate in transverse bars in braided-stream environments. These structures have a great potential for conducting large volumes of mineralizing fluids and hosting economic quantities of uranium ore