965 resultados para SELECTION INDEX


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A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.

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A new rock mass classification scheme, the Host Rock Classification system (HRC-system) has been developed for evaluating the suitability of volumes of rock mass for the disposal of high-level nuclear waste in Precambrian crystalline bedrock. To support the development of the system, the requirements of host rock to be used for disposal have been studied in detail and the significance of the various rock mass properties have been examined. The HRC-system considers both the long-term safety of the repository and the constructability in the rock mass. The system is specific to the KBS-3V disposal concept and can be used only at sites that have been evaluated to be suitable at the site scale. By using the HRC-system, it is possible to identify potentially suitable volumes within the site at several different scales (repository, tunnel and canister scales). The selection of the classification parameters to be included in the HRC-system is based on an extensive study on the rock mass properties and their various influences on the long-term safety, the constructability and the layout and location of the repository. The parameters proposed for the classification at the repository scale include fracture zones, strength/stress ratio, hydraulic conductivity and the Groundwater Chemistry Index. The parameters proposed for the classification at the tunnel scale include hydraulic conductivity, Q´ and fracture zones and the parameters proposed for the classification at the canister scale include hydraulic conductivity, Q´, fracture zones, fracture width (aperture + filling) and fracture trace length. The parameter values will be used to determine the suitability classes for the volumes of rock to be classified. The HRC-system includes four suitability classes at the repository and tunnel scales and three suitability classes at the canister scale and the classification process is linked to several important decisions regarding the location and acceptability of many components of the repository at all three scales. The HRC-system is, thereby, one possible design tool that aids in locating the different repository components into volumes of host rock that are more suitable than others and that are considered to fulfil the fundamental requirements set for the repository host rock. The generic HRC-system, which is the main result of this work, is also adjusted to the site-specific properties of the Olkiluoto site in Finland and the classification procedure is demonstrated by a test classification using data from Olkiluoto. Keywords: host rock, classification, HRC-system, nuclear waste disposal, long-term safety, constructability, KBS-3V, crystalline bedrock, Olkiluoto

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The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

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Divergent genetic selection for wool growth as a single trait has led to major changes in sheep physiology and metabolism, including variations in rumen microbial protein production and uptake of α-amino nitrogen in portal blood. This study was conducted to determine if sheep with different genetic merit for wool growth exhibit distinct rumen bacterial diversity. Eighteen Merino wethers were separated into groups of contrasting genetic merit for clean fleece weight (CFW; low: WG− and high: WG+) and fed a blend of oaten and lucerne chaff diet at two levels of intake (LOI; 1 or 1.5 times maintenance energy requirements) for two seven-week periods in a crossover design. Bacterial diversity in rumen fluid collected by esophageal intubation was characterized using 454 amplicon pyrosequencing of the V3/V4 regions of the 16S rRNA gene. Bacterial diversity estimated by Phylogenetic distance, Chao1 and observed species did not differ significantly with CFW or LOI; however, the Shannon diversity index differed (P=0.04) between WG+ (7.67) and WG− sheep (8.02). WG+ animals had a higher (P=0.03) proportion of Bacteroidetes (71.9% vs 66.5%) and a lower (P=0.04) proportion of Firmicutes (26.6% vs 31.6%) than WG− animals. Twenty-four specific operational taxonomic units (OTUs), belonging to the Firmicutes and Bacteroidetes phyla, were shared among all the samples, whereas specific OTUs varied significantly in presence/abundance (P<0.05) between wool genotypes and 50 varied (P<0.05) with LOI. It appears that genetic selection for fleece weight is associated with differences in rumen bacterial diversity that persist across different feeding levels. Moderate correlations between seven continuous traits, such as methane production or microbial protein production, and the presence and abundance of 17 OTUs were found, indicating scope for targeted modification of the microbiome to improve the energetic efficiency of rumen microbial synthesis and reduce the greenhouse gas footprint of ruminants.

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This paper brings a rural geographical lens to the study of education and rurality. Two key interrelated notions underpinning Australian educational scholarship on rurality are explored. That is, the concepts of the rural and of community. The adoption and mobilisation of these terms in a large proportion of rural educational research as unproblematic is at odds with contemporary theorising in rural geography. In order to advance studies of rural education, we point to the contestability, fluidity and fundamentally political nature of these core concepts. In doing so, we draw on a selection of extant geographical research and educational research concerned with the rural. In concluding the paper we highlight that as well as challenging orthodoxies in relation to notions of rurality and community, more recent rural geographical scholarship has also engaged a greater diversity of methodological approaches. We suggest that more robust and nuanced approaches to terms such as 'the rural' and 'the community' in educational research could be garnered by reference to this dynamic body of methodological writing.

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The control of shapes of nanocrystals is crucial for using them as building blocks for various applications. In this paper, we present a critical overview of the issues involved in shape-controlled synthesis of nanostructures. In particular, we focus on the mechanisms by which anisotropic structures of high-symmetry materials (fcc crystals, for instance) could be realized. Such structures require a symmetry-breaking mechanism to be operative that typically leads to selection of one of the facets/directions for growth over all the other symmetry-equivalent crystallographic facets. We show how this selection could arise for the growth of one-dimensional structures leading to ultrafine metal nanowires and for the case of two-dimensional nanostructures where the layer-by-layer growth takes place at low driving forces leading to plate-shaped structures. We illustrate morphology diagrams to predict the formation of two-dimensional structures during wet chemical synthesis. We show the generality of the method by extending it to predict the growth of plate-shaped inorganics produced by a precipitation reaction. Finally, we present the growth of crystals under high driving forces that can lead to the formation of porous structures with large surface areas.

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Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

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Conventional methods for determining the refractive index demand specimens of optical quality, the preparation of which is often very difficult. An indirect determination by matching the refractive indices of specimen and immersion liquid is a practical alternative for photoelastic specimen of nonoptical quality. An experimental arrangement used for this technique and observations made while matching the refractive indices of three different specimens are presented.

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The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

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Under certain special conditions natural selection can be effective at the level of local populations, or demes. Such interpopulation selection will favor genotypes that reduce the probability of extinction of their parent population even at the cost of a lowered inclusive fitness. Such genotypes may be characterized by altruistic traits only in a viscous population, i.e., in a population in which neighbors tend to be closely related. In a non-viscous population the interpopulation selection will instead favor spiteful traits when the populations are susceptible to extinction through the overutilization of the habitat, and cooperative traits when it is the newly established populations that are in the greatest danger of extinction.

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This paper reviews the recent research progress on multi-layer composite structures composed of variety of materials. The utilization of multi-layer composite system is found to be common in metal structures and pavement systems. The layer of composite structure designed to encounter heavy dynamic energy should have sufficient ductility to counteract the intensity of energy. Therefore, the selection of materials and enhancement of interface bonding become crucial and both are discussed in this paper. The failure modes have also been explored in conjunction with stresses at failures and inferred solutions are also revealed. The paper attempts to reveal all technical facts on multi-layer composite structure in a broad field.