924 resultados para FLOWING STREAMS


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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Tracking the movement of migratory freshwater fish is essential to those invested in rebuilding declining fish populations. Using strontium isotopic signatures to match calcified fish tissues to streams where fish spawn is a useful method of tracking migratory fish where physical tracking methods such as radio, acoustic, or external tags, have proven unsuccessful. In this study, we develop tools to practice this method of tracking fish in Lake Roosevelt and its upstream tributaries in Washington State by analyzing the elemental concentrations and 87Sr/86Sr ratios of water samples, and mussel shell samples. This study evaluates whether mussel shells act as an appropriate proxy for water chemistry by comparing the 87Sr/86Sr isotope ratios of water samples to the 87Sr/86Sr isotope ratios of mussel shells sampled from the same, or nearby, locations. We compare concentrations of Ba, Ca, Cd, Cu, Fe, Mg, Pb, Sr, and U in the water and mussel shell samples to determine the feasibility of using mussel shells as a proxy for water chemistry. If it is determined that the concentrations of these elements in mussel shells reflect that of the surrounding water composition, the elemental composition of mussel shells can be compared to that of calcified tissues in fish, such as otoliths, to infer the location of the natal stream. We report analyses of water and mussel shell samples collected from Lake Roosevelt, Sanpoil River, Spokane River, Colville River, Kettle River, Pend Oreille River, Kootenay River, and Columbia River in Washington State. Each of these rivers is a tributary to Lake Roosevelt, and each flows through different geologic units. We hypothesize that the differences in the rock units of each stream’s watershed are reflected in the elemental concentrations and strontium isotopic ratios of water in each stream and in the lake. We also hypothesize that the composition of the mussel shells will match the composition of the water samples, therefore allowing us to use the mussel shells as a proxy for local water chemistry. Additionally, we hypothesize that the composition of the mussel shells will vary by location, and that we will be able to then infer where a fish is from by matching the composition of the fish in question to the mussels we have analyzed. We found that 87Sr/86Sr values for water and mussel hinge samples collected from tributaries east of Lake Roosevelt are significantly higher than the 87Sr/86Sr values for samples collected from tributaries west of Lake Roosevelt with averages of 0.7235 and 0.7089, respectively. The average 87Sr/86Sr ratios for water and mussel hinge samples collected within Lake Roosevelt is 0.7158, which is between the averages for samples collected east and west of the lake. Generally, older rocks are exposed on the east side of the lake, and younger rocks on the west side of the lake, so our 87Sr/86Sr values support the hypothesis that geologic units are a primary control on water chemistry, and that tributary compositions mix to form an average weighed by flow in Lake Roosevelt. The 87Sr/86Sr values for water and mussel shell samples collected from the same locations have a strong, positive linear correlation, suggesting that mussel shell 87Sr/86Sr ratios reflect the 87Sr/86Sr ratios of the ambient water. With these data, we can distinguish between different streams and the lake, but cannot distinguish between samples from within the same stream or within Lake Roosevelt. The Sr:Ca and Fe:Ca ratios of water samples show positive correlations with mussel shell compositions, with R2 values of 0.82 and 0.52, respectively. Ratios of Mg, Ba, Cu, Cd, Pb, and U to Ca showed little or no positive correlation between water and mussel shell samples. The elemental concentration data collected for this study do not demonstrate whether a correlation between elemental ratios in water samples and elemental ratios in mussel shell samples collected from the same location exists. Positive Sr:Ca and Fe:Ca correlations for water versus mussel shell samples indicate that perhaps for some elements, the composition of mussel shells are representative of the composition of ambient water. Using elemental concentration ratios to complement 87Sr/86Sr isotopic data may enhance our ability to identify correlations between water and mussel shell samples, and ultimately between mussel shell and otolith samples. The hinge part of a mussel shell may be used as a proxy for local water composition because the mussel shell composition reflects that of the local ambient water. The hinge of the mussel has the same composition as the whole mussel shell. We measured variation of 87Sr/86Sr ratios in the water among different streams and Lake Roosevelt. The 87Sr/86Sr values for samples collected in tributaries east of Lake Roosevelt, which erode older rocks, are higher for mussel shell and water samples than the average 87Sr/86Sr values for mussel shell and water samples collected in tributaries west of Lake Roosevelt, which flow through younger rocks.

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Concert Program

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A large number of mineral processing equipment employs the basic principles of gravity concentration in a flowing fluid of a few millimetres thick in small open channels where the particles are distributed along the flow height based on their physical properties and the fluid flow characteristics. Fluid flow behaviour and slurry transportation characteristics in open channels have been the research topic for many years in many engineering disciplines. However, the open channels used in the mineral processing industries are different in terms of the size of the channel and the flow velocity used. Understanding of water split behaviour is, therefore, essential in modeling flowing film concentrators. In this paper, an attempt has been made to model the water split behaviour in an inclined open rectangular channel, resembling the actual size and the flow velocity used by the mineral processing industries, based on the Prandtl's mixing length approach. (c) 2006 Elsevier B.V. All rights reserved.

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Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

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Collaborate Filtering is one of the most popular recommendation algorithms. Most Collaborative Filtering algorithms work with a static set of data. This paper introduces a novel approach to providing recommendations using Collaborative Filtering when user rating is received over an incoming data stream. In an incoming stream there are massive amounts of data arriving rapidly making it impossible to save all the records for later analysis. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively although an inevitable trade off between accuracy and amount of memory used is introduced. By adding a simple personalization step using a hierarchy of the items, it is possible to improve the predicted ratings made by each decision tree and generate recommendations in real-time. Empirical studies with the dynamically built decision trees show that the personalization step improves the overall predicted accuracy.

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One of critical challenges in automatic recognition of TV commercials is to generate a unique, robust and compact signature. Uniqueness indicates the ability to identify the similarity among the commercial video clips which may have slight content variation. Robustness means the ability to match commercial video clips containing the same content but probably with different digitalization/encoding, some noise data, and/or transmission and recording distortion. Efficiency is about the capability of effectively matching commercial video sequences with a low computation cost and storage overhead. In this paper, we present a binary signature based method, which meets all the three criteria above, by combining the techniques of ordinal and color measurements. Experimental results on a real large commercial video database show that our novel approach delivers a significantly better performance comparing to the existing methods.

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In recent years many real time applications need to handle data streams. We consider the distributed environments in which remote data sources keep on collecting data from real world or from other data sources, and continuously push the data to a central stream processor. In these kinds of environments, significant communication is induced by the transmitting of rapid, high-volume and time-varying data streams. At the same time, the computing overhead at the central processor is also incurred. In this paper, we develop a novel filter approach, called DTFilter approach, for evaluating the windowed distinct queries in such a distributed system. DTFilter approach is based on the searching algorithm using a data structure of two height-balanced trees, and it avoids transmitting duplicate items in data streams, thus lots of network resources are saved. In addition, theoretical analysis of the time spent in performing the search, and of the amount of memory needed is provided. Extensive experiments also show that DTFilter approach owns high performance.