994 resultados para Mega-mining


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Trace elements may present an environmental hazard in the vicinity of mining and smelting activities. However, the factors controlling trace element distribution in soils around ancient and modem mining and smelting areas are not always clear. Tharsis, Riotinto and Huelva are located in the Iberian Pyrite Belt in SW Spain. Tharsis and Riotinto mines have been exploited since 2500 B.C., with intensive smelting taking place. Huelva, established in 1970 and using the Flash Furnace Outokumpu process, is currently one of the largest smelter in the world. Pyrite and chalcopyrite ore have been intensively smelted for Cu. However, unusually for smelters and mines of a similar size, the elevated trace element concentrations in soils were found to be restricted to the immediate vicinity of the mines and smelters, being found up to a maximum of 2 kin from the mines and smelters at Tharsis, Riotinto and Huelva. Trace element partitioning (over 2/3 of trace elements found in the residual immobile fraction of soils at Tharsis) and soil particles examination by SEM-EDX showed that trace elements were not adsorbed onto soil particles, but were included within the matrix of large trace element-rich Fe silicate slag particles (i.e. 1 min circle divide at least 1 wt.% As, Cu and Zn, and 2 wt.% Pb). Slag particle large size (I mm 0) was found to control the geographically restricted trace element distribution in soils at Tharsis, Riotinto and Huelva, since large heavy particles could not have been transported long distances. Distribution and partitioning indicated that impacts to the environment as a result of mining and smelting should remain minimal in the region. (c) 2006 Elsevier B.V. All rights reserved.

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Toxic trace elements present an environmental hazard in the vicinity of mining and smelting activities. However. the processes of transfer of these elements to groundwater and to plants are not always clear. Tharsis mine. in the Iberian pyrite belt (SW Spain), has been exploited since 2500 BC, with extensive smelting, taking place front the 1850S until the 1920s. Sixty four soil (mainly topsoils) and vegetation samples were collected in February 2001 and analysed by ICP-AES for 23 elements. Concentrations are 6-6300 mg kg(-1) As and 14-24800 mg kg(-1) Pb in soils, and 0.20-9 mg kg(-1) As and 2-195 mg Pb in vegetation. Trace element concentrations decrease rapidly away from the mine. with As and Pb concentrations in the range 6-1850 mg kg(-1) (median 22 mg kg(-1)) and 14-31 mg, kg(-1) (median 43 mg, kg(-1)), respectively, 1 km away from the mine. These concentrations are low when compared to other well-studied mining and smelting areas (e.g. 600 mg kg(-1) As at 8 km from Yellowknife smelter, Canada; >100 mg kg(-1) Pb over 270 km(2) around the Pb-Zn Port Pirie smelter. South Australia: mean of 1419 mg kg(-1) Pb around Aberystwyth smelter, Wales, UK). The high metal content of the vegetation and the low soil pH (mean pH 4.93) indicate the potential for trace element mobility which Could explain the relatively low concentration of metals in Tharsis topsoils and cause threats to plans to redevelop the Tharsis area as an orange plantation.

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Mega-scale glacial lineations (MSGLs) are longitudinally aligned corrugations (ridge-groove structures 6-100 km long) in sediment produced subglacially. They are indicators of fast flow and a common signature of ice-stream beds. We develop a qualitative theory that accounts for their formation, and use numerical modelling, and observations of ice-stream beds to provide supporting evidence. Ice in contact with a rough (scale of 10-10(3) m) bedrock surface will mimic the form of the bed. Because of flow acceleration and convergence in ice-stream onset zones, the ice-base roughness elements experience transverse strain, transforming them from irregular bumps into longitudinally aligned keels of ice protruding downwards. Where such keels slide across a soft sedimentary bed, they plough through the sediments, carving elongate grooves, and deforming material up into intervening ridges. This explains MSGLs and has important implications for ice-stream mechanics. Groove ploughing provides the means to acquire new lubricating sediment and to transport large volumes of it downstream. Keels may provide basal drag in the force budget of ice streams, thereby playing a role in flow regulation and stability We speculate that groove ploughing permits significant ice-stream widening, thus facilitating high-magnitude ice discharge.

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Our recent paper [McMurtry, G.M., Tappin, D.R., Sedwick, P.N., Wilkinson, I., Fietzkc, J. and Sellwood, B., 2007a. Elevated marine deposits in Bermuda record a late Quaternary megatsunami. Sedimentary Geol. 200, 155-165.] critically re-examined elevated marine deposits in Bermuda, and concluded that their geological setting, sedimentary relations, micropetrography and microfaunal assemblages were inconsistent with sustained intertidal deposition. Instead, we hypothesized that these deposits were the result of a large tsunami that impacted the Bermuda island platform during the mid-Pleistocene. Hearty and Olson [Hearty, P.J., and Olson, S.L., in press. Mega-highstand or megatsunami? Discussion of McMurtry et al. "Elevated marine deposits in Bermuda record a late Quaternary megatsunami": Sedimentary Geology, 200, 155-165, 2007 (Aug. 07). Sedimentary Geol. 200, 155-165.] in their response, attempt to refute our conclusions and claim the deposits to be the result of a +21 m eustatic sea level highstand during marine isotope stage (MIS) 11. In our reply we answer the issues raised by Hearty and Olson [Hearty, P.J., and Olson, S.L., in press. Mega-highstand or megatsunami? Discussion of McMurtry et al. "Elevated marine deposits in Bermuda record a late Quaternary megatsunami": Sedimentary Geology, 200, 155-165, 2007 (Aug. 07). Sedimentary Geol. 200,155-165.] and conclude that the Bermuda deposits do not provide unequivocal evidence of a prolonged +21 m eustatic sea level highstand. Rather, the sediments are more likely the result of a past megatsunami in the North Atlantic basin. (c) 2008 Elsevier B.V. All rights reserved.

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In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

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We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

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We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

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Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

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In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

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Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

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Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

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This paper provides an extended analysis of the tensions that have surfaced between large-scale mine operators and artisanal miners in gold-rich areas of rural Tanzania. The literature on grievance is used to contextualise, these disputes, the underlying cause of which is artisanal miners' mounting frustration over not being able to secure viable concessions to work. Newly implemented legislation has, for the most part, empowered foreign large-scale mine operators, while simultaneously disempowering indigenous small-scale miners. In many cases, the former have addressed mounting security and community problems on their own. Until the country's major mine operators extend assistance to marginalised small-scale mining groups, the likelihood of violent conflict unfolding between these parties will increase.