955 resultados para METHOD-R


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A number of systematic conservation planning tools are available to aid in making land use decisions. Given the increasing worldwide use and application of reserve design tools, including measures of site irreplaceability, it is essential that methodological differences and their potential effect on conservation planning outcomes are understood. We compared the irreplaceability of sites for protecting ecosystems within the Brigalow Belt Bioregion, Queensland, Australia, using two alternative reserve system design tools, Marxan and C-Plan. We set Marxan to generate multiple reserve systems that met targets with minimal area; the first scenario ignored spatial objectives, while the second selected compact groups of areas. Marxan calculates the irreplaceability of each site as the proportion of solutions in which it occurs for each of these set scenarios. In contrast, C-Plan uses a statistical estimate of irreplaceability as the likelihood that each site is needed in all combinations of sites that satisfy the targets. We found that sites containing rare ecosystems are almost always irreplaceable regardless of the method. Importantly, Marxan and C-Plan gave similar outcomes when spatial objectives were ignored. Marxan with a compactness objective defined twice as much area as irreplaceable, including many sites with relatively common ecosystems. However, targets for all ecosystems were met using a similar amount of area in C-Plan and Marxan, even with compactness. The importance of differences in the outcomes of using the two methods will depend on the question being addressed; in general, the use of two or more complementary tools is beneficial.

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Summary form only given. The Java programming language supports concurrency. Concurrent programs are harder to verify than their sequential counterparts due to their inherent nondeterminism and a number of specific concurrency problems such as interference and deadlock. In previous work, we proposed a method for verifying concurrent Java components based on a mix of code inspection, static analysis tools, and the ConAn testing tool. The method was derived from an analysis of concurrency failures in Java components, but was not applied in practice. In this paper, we explore the method by applying it to an implementation of the well-known readers-writers problem and a number of mutants of that implementation. We only apply it to a single, well-known example, and so we do not attempt to draw any general conclusions about the applicability or effectiveness of the method. However, the exploration does point out several strengths and weaknesses in the method, which enable us to fine-tune the method before we carry out a more formal evaluation on other, more realistic components.

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Low temperature, particularly during the reproductive stage of the development of rice, limits productivity in the Riverina region of New South Wales (NSW). This study primarily examined genotypic differences in cold damage that are associated with low temperature during reproductive development. Results from experiments in temperature-controlled rooms and the cold water facility were combined with four years of field experiments, which used natural exposure to low temperature to examine the response of over 50 cultivars from diverse origins. Plants were exposed to day/night air temperatures of 27°/13°C in temperature-controlled rooms and to a constant temperature of 19°C in the cold water facility. Low temperature treatments were imposed from panicle initiation (PI) to 50% heading. In field experiments several techniques were used to increase the likelihood of inducing cold damage such as sequential sowing dates (five to eight sowing dates each year), shallow water depths (5cm) and high nitrogen rates (e.g. 300kgN ha-1). Several cultivars were identified that were more cold tolerant than Australia’s commercial cultivars.

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Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

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In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.