997 resultados para NONDESTRUCTIVE TESTING


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The nondestructive determination of plant total dry matter (TDM) in the field is greatly preferable to the harvest of entire plots in areas such as the Sahel where small differences in soil properties may cause large differences in crop growth within short distances. Existing equipment to nondestructively determine TDM is either expensive or unreliable. Therefore, two radiometers for measuring reflected red and near-infrared light were designed, mounted on a single wheeled hand cart and attached to a differential Global Positioning System (GPS) to measure georeferenced variations in normalized difference vegetation index (NDVI) in pearl millet fields [Pennisetum glaucum (L.) R. Br.]. The NDVI measurements were then used to determine the distribution of crop TDM. The two versions of the radiometer could (i) send single NDVI measurements to the GPS data logger at distance intervals of 0.03 to 8.53 m set by the user, and (ii) collect NDVI values averaged across 0.5, 1, or 2 m. The average correlation between TDM of pearl millet plants in planting hills and their NDVI values was high (r^2 = 0.850) but varied slightly depending on solar irradiance when the instrument was calibrated. There also was a good correlation between NDVI, fractional vegetation cover derived from aerial photographs and millet TDM at harvest. Both versions of the rugged instrument appear to provide a rapid and reliable way of mapping plant growth at the field scale with a high spatial resolution and should therefore be widely tested with different crops and soil types.

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The most widely used methods to assess the nitrogen (N) status of winter wheat (Triticum aestivum L.) are the determination of plant total N by combustion, the testing of nitrate in the leaf tissue and the use of SPAD readings. However, due to their labor requirements or high costs these methods can hardly be applied to the huge wheat growing areas of the Northern China Plain. This study therefore examined an alternative method to measure the N status of wheat by using a digital camera to record the visible green light reflected from the plant canopy. The experiment was conducted near Beijing in a multi-factorial field trial with three levels of N. The intensity of green light reflected from the wheat canopy was compared to the total N concentration, to the nitrate concentration of the basal stem, and to the SPAD readings of leaves. The results show significant inverse relationships between greenness intensity, canopy total N, and SPAD readings at booting and flowering. At booting, sap nitrate <2000mgL^-1 was inversely related to greenness intensity and to sap nitrate concentration in the basal stem. At sap nitrate ~2000mgL^-1, the greenness intensity reached a plateau. At booting and flowering, significant inverse relationships between greenness intensity and shoot biomass were found. The results show the potential of the new method to assess the N status of winter wheat.

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In standard multivariate statistical analysis common hypotheses of interest concern changes in mean vectors and subvectors. In compositional data analysis it is now well established that compositional change is most readily described in terms of the simplicial operation of perturbation and that subcompositions replace the marginal concept of subvectors. To motivate the statistical developments of this paper we present two challenging compositional problems from food production processes. Against this background the relevance of perturbations and subcompositions can be clearly seen. Moreover we can identify a number of hypotheses of interest involving the specification of particular perturbations or differences between perturbations and also hypotheses of subcompositional stability. We identify the two problems as being the counterpart of the analysis of paired comparison or split plot experiments and of separate sample comparative experiments in the jargon of standard multivariate analysis. We then develop appropriate estimation and testing procedures for a complete lattice of relevant compositional hypotheses

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Resumen tomado de la publicaci??n

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I test the presence of hidden information and action in the automobile insurance market using a data set from several Colombian insurers. To identify the presence of hidden information I find a common knowledge variable providing information on policyholder s risk type which is related to both experienced risk and insurance demand and that was excluded from the pricing mechanism. Such unused variable is the record of policyholder s traffic offenses. I find evidence of adverse selection in six of the nine insurance companies for which the test is performed. From the point of view of hidden action I develop a dynamic model of effort in accident prevention given an insurance contract with bonus experience rating scheme and I show that individual accident probability decreases with previous accidents. This result brings a testable implication for the empirical identification of hidden action and based on that result I estimate an econometric model of the time spans between the purchase of the insurance and the first claim, between the first claim and the second one, and so on. I find strong evidence on the existence of unobserved heterogeneity that deceives the testable implication. Once the unobserved heterogeneity is controlled, I find conclusive statistical grounds supporting the presence of moral hazard in the Colombian insurance market.

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PowerPoint slides for Hypothesis Testing. Examples are taken from the Medical Literature

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PowerPoint Slides for Hypothesis testing Used in Research Skills for Biomedical Science

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The first set of Usability testing on EdShare (7th June). Tests where carried out using Silverback on the Mac. This is unedited footage - Each video is around 20minutes long.

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In this session we look at the sorts of errors that occur in programs, and how we can use different testing and debugging strategies (such as unit testing and inspection) to track them down. We also look at error handling within the program and at how we can use Exceptions to manage errors in a more sophisticated way. These slides are based on Chapter 6 of the Book 'Objects First with BlueJ'

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