36 resultados para Trace Rule


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In contrast to metallic alloys, the mechanical characteristics of superplastic ceramics are very sensitive to minor changes in levels of trace impurities. In the present study, the mechanical behavior of a 2 mol% yttria stabilized tetragonal zirconia was studied in tension and compression in two batches of material, with small variations in levels of trace impurities, to examine the influence of stress axis and impurity content on the deformation behavior. The mechanical properties of the material were characterized in terms of the expression: (epsilon)over dot proportional to sigma(n) where (epsilon)over dot is the strain rate, sigma is the stress and n is termed the stress exponent. The mechanical behavior of the ceramic was identical in tension and compression, for a material with a given level of impurity. The high purity specimens exhibited a transition from a stress exponent of similar to 3 to similar to 2 with an increase in stress, whereas the low purity material displayed only n similar to 2 behavior over the entire stress range studied. Detailed high resolution and analytical electron microscopy studies revealed that there was no amorphous phase at interfaces in both batches of material; however, segregation of Al at interfaces was detected only in the low purity material. The observed transition in stress exponents can be rationalized in terms of two sequential mechanisms: grain boundary sliding with n similar to 2 and interface reaction controlled grain boundary sliding with n similar to 3. The transition from n similar to 3 to similar to 2 occurred at lower stresses with an increase in the grain size and a decrease in the purity level.

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This paper describes the design and implementation of a high-level query language called Generalized Query-By-Rule (GQBR) which supports retrieval, insertion, deletion and update operations. This language, based on the formalism of database logic, enables the users to access each database in a distributed heterogeneous environment, without having to learn all the different data manipulation languages. The compiler has been implemented on a DEC 1090 system in Pascal.

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An efficient geometrical design rule checker is proposed, based on operations on quadtrees, which represent VLSI mask layouts. The time complexity of the design rule checker is O(N), where N is the number of polygons in the mask. A pseudoPascal description is provided of all the important algorithms for geometrical design rule verification.

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In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

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Trace elements zinc, copper, manganese, molybdenum and cobalt have been shown to have varying effects on growth and trace element composition of the silkworm. Results indicate the important role of manganese in the normal metabolism of the insect. Cobalt has been shown to exert a very favourable effect on growth and silk yield.

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Benzimidazole is toxic to silkworms due to its antagonistic action to purines. Cobalt and copper possess the capacity to reverse its toxicity because of their ability to form complexes with benzimidazole. For reasons unknown cobalt exerts a favourable effect in its free form rather than in the vitamin form on the insect growth.

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A two-stage iterative algorithm for selecting a subset of a training set of samples for use in a condensed nearest neighbor (CNN) decision rule is introduced. The proposed method uses the concept of mutual nearest neighborhood for selecting samples close to the decision line. The efficacy of the algorithm is brought out by means of an example.

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Fly ash is a waste by-product obtained from the burning of coal by thermal power plants for generating electricity. When bulk quantities are involved, in order to arrest the fugitive dust, it is stored wet rather than dry. Fly ash contains trace concentrations of heavy metals and other substances in sufficient quantities to be able to leach out over a period of time. In this study an attempt was made to study the leachabilities of a few selected trace metals: Cd, Cu, Cr, Mn, Pb and Zn from two different types of class F fly ashes. Emphasis is also laid on developing an alternative in order to arrest the relative leachabilities of heavy metals after amending them with suitable additives. A standard laboratory leaching test for combustion residues has been employed to study the leachabilities of these trace elements as a function of liquid to solid ratio and pH. The leachability tests were conducted on powdered fly ash samples before and after amending them suitably with the matrices lime and gypsum; they were compacted to their respective proctor densities and cured for periods of 28 and 180 days. A marked reduction in the relative leachabilities of the trace elements was observed to be present at the end of 28 days. These relative leachability values further reduced marginally when tests were performed at the end of 180 days.

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Trace of iron(III) are determined by differential pulse polarography in a medium of sodium hydroxide and sodium bromate using the catalytic current. Various cations do not interfere. The relative standard deviation is 2%.

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The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

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Trace processors rely on hierarchy, replication, and prediction to dramatically increase the execution speed of ordinary sequential programs. The authors describe some of the processors will meet future technology demands.