21 resultados para pruning

em CentAUR: Central Archive University of Reading - UK


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The inability of a plant to grow roots rapidly upon transplanting is one of the main factors contributing to poor establishment. In bare-rooted trees, treatments such as root pruning or application of the plant hormone auxin [e.g., indole butyric acid (IBA)] can promote root growth and aid long-term establishment. There is little information on ornamental containerised plants, however, other than the anecdotal notion that 'teasing' the roots out of the rootsoil mass before transplanting can be beneficial. In the present study we tested the ability of various root-pruning treatments and application of IBA to encourage new root and shoot growth in two shrub species, commonly produced in containers - Buddleja davidii 'Summer Beauty' and Cistus 'Snow Fire'. In a number of experiments, young plants were exposed to root manipulation (teasing, light pruning, or two types of heavy pruning) and/or treatment with IBA (at 500 or 1,000 mg l-1) before being transplanted into larger containers containing a medium of 1:1:1 (v/v/v) fine bark, sand and loam. Leaf stomatal conductance (gl) was measured 20 min, and 1, 2, 4 and 6 h after root manipulation. Net leaf CO2 assimilation (A) was measured frequently during the first week after transplanting, then at regular intervals up to 8 weeks after transplanting. Plants were harvested 8 weeks after transplanting, and root and shoot weights were measured. In both species, light root pruning alone, or in combination with 500 mg l-1 IBA, was most effective in stimulating root growth. In contrast, teasing, which is commonly used, showed no positive effect on root growth in Buddleja, and decreased new root growth in Cistus. The requirement for exogenous auxin to encourage new root growth varied between experiments and appeared to be influenced by the age and developmental stage of the plants. There were no consistent responses between root treatments and net CO2 assimilation rates, and changes in root weight were not closely correlated with changes in assimilation. The mechanisms whereby new root growth is sustained are discussed.

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The relationship between shoot growth and rooting was examined in two, 'difficult-to root' amenity trees, Syringa vulgaris L. cv. Charles Joly and Corylus avellana L. cv. Aurea. A range of treatments reflecting severity of pruning was imposed on field-grown stock prior to bud break. To minimise variation due to the numbers of buds that developed under different treatments, bud number was restricted to 30 per plant. Leafy cuttings were harvested at different stages of the active growth phase of each species. With Syringa, rooting decreased with later harvests, but loss of rooting potential was delayed in cuttings collected from the most severe pruning treatment. Rooting potential was associated with the extent of post-excision shoot growth on the cutting but regression analyses indicated that this relationship could not entirely explain the loss of rooting with time, nor the effects due to pruning. Similarly, in Corylus rooting was promoted by severe pruning, but the relationship between apical growth on the cutting and rooting was weaker than in Syringa, and only at the last harvest did growth play a critical role in determining rooting. Another unusual factor of the last harvest of Corylus was a bimodal distribution of roots per cutting, with very few rooted cuttings having less than five roots. This implies that, for this harvest at least, the potential of an individual cutting to root is probably not limited by the number of potential rooting sites.

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This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.

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We introduce and describe the Multiple Gravity Assist problem, a global optimisation problem that is of great interest in the design of spacecraft and their trajectories. We discuss its formalization and we show, in one particular problem instance, the performance of selected state of the art heuristic global optimisation algorithms. A deterministic search space pruning algorithm is then developed and its polynomial time and space complexity derived. The algorithm is shown to achieve search space reductions of greater than six orders of magnitude, thus reducing significantly the complexity of the subsequent optimisation.

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A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

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Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

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In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

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The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pruning methods. Pruning methods use various metrics to truncate decision trees or to eliminate whole rules or single rule terms from a Prism rule set. For decision trees many pre-pruning and postpruning methods exist, however for Prism algorithms only one pre-pruning method has been developed, J-pruning. Recent work with Prism algorithms examined J-pruning in the context of very large datasets and found that the current method does not use its full potential. This paper revisits the J-pruning method for the Prism family of algorithms and develops a new pruning method Jmax-pruning, discusses it in theoretical terms and evaluates it empirically.

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The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

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Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms.

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The grapevine moth Lobesia botrana (Denis & Schiffermuller) (Lepidoptera: Tortricidae) is a key pest of grapevines in Greece. As part of a broader study on integrated pest management, the effects were investigated of different cultural methods on the establishment and survival of L. botrana, specifically: application of different nitrogen levels (30 and 100 units of ammonium sulfate or 70 units of Agrobiosol); summer leaf and shoot pruning; application of growth regulators (Regalis, probexadione-calcium; or Falgro, gibberellic acid). There were significant differences among the three levels of N application. The lowest L. botrana infestation rates were found in plots treated with 30 units of (NH4)(2)SO4 and plots that received some summer pruning. Following the application of plant growth regulators, the lowest L. botrana infestation levels occurred in the plots treated with Regalis or Falgro at the manufacturers' recommended concentrations. On vines where growth regulators had been applied, the clusters had fewer berries than those not treated with growth regulators.

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Improving plant quality and the uniformity of a crop are major objectives for growers of ornamental nursery stock. The potential to control excess vigour and to improve quality through regulated deficit irrigation (RDI) was investigated using a range of woody ornamental species. RDI regimes reduced vegetative growth consistently across different species and growing seasons. Plants adapted to reduced water supplies primarily via stomatal control, but also by osmotic adjustment when grown under the most severe RDI regimes. Only plants exposed to <= 25% of potential evapo-transpiration demonstrated any evidence of leaf injury, and the extent was slight. Growth inhibition increased as the severity of RDI increased. Improvements in quality were attained through a combination of shorter internodes and final shoot lengths, yet the number of 'formative' primary shoots remained unaffected. Compact, well-branched plants could be formed without a requirement for mid-season pruning. In addition to severity, the timing of RDI also influenced growth responses. Applying 50% ETp for 8 weeks during July-August resulted in the formation of good quality plants, which retained their shape until the following Spring. Re-positioning irrigation drippers within the pots of well-watered plants, in an attempt to induce a partial root drying (PRD) treatment, reduced growth, but not significantly. The adoption of irrigation scheduling, based on 50-100% ETp, has the potential to improve commercial crop quality across a range of ornamental species.