Root pruning as a means to encourage root growth in two ornamental shrubs, Buddleja davidii ‘Summer Beauty’ and Cistus ‘Snow Fire'

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.

Rooting cuttings of Syringa vulgaris cv. Charles Joly and Corylus avellana cv. Aurea: the influence of stock plant pruning and shoot growth

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.

Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres

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.

Search space pruning and global optimisation of Multiple Gravity Assist spacecraft trajectories

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.

Givens rotation based fast backward elimination algorithm for RBF neural network pruning

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.

On the utility of input selection and pruning for financial distress prediction models

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.

Parallel rule induction with information theoretic pre-pruning

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.

Induction of modular classification rules: using Jmax-pruning

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.

Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

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.

J-measure based hybrid pruning for complexity reduction in classification rules

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.

Conducting nanofibres produced by electrospinning

Electrospun fibres based on polypyrrole have been prepared. The incorporation of preformed polypyrrole into fibres electrospun from a carrier polymer can only be achieved when materials are prepared with particulates smaller than the cross-section of the fibre; even so there are some problems, with the substantial loss of material from the electrode tip. As an alternative approach, soluble polypyrroles can be prepared but these are not of sufficient viscosity to prepare electrospun fibres, once again a carrier polymer must be employed. More effective loadings are gained by the process of coating the outer surface of a pre-spun fibre; in this way electrospun fibres coated with polypyrrole can be prepared. This approach has been adapted to produce silver coated polymer fibres by the use of copolymers of styrene and 3-vinyl benzaldehyde.

Electrospinning atactic polystyrene: A neutron scattering study

Electrospinning is a method used to produce nanoscale to microscale sized polymer fibres. In this study we electrospin 1:1 blends of deuterated and hydrogenated atactic-Polystyrene from N,N-Dimethylformamide for small angle neutron scattering experiments in order to analyse the chain conformation in the electrospun fibres. Small angle neutron scattering was carried out on randomly orientated fibre mats obtained using applied voltages of 10kV-15kV and needle tip to collector distances of 20cm and 30cm. Fibre diameters varied from 3mm - 20mm. Neutron scattering data from fibre samples were compared with bulk samples of the same polymer blend. The scattering data indicates that there are pores and nanovoiding present in the fibres; this was confirmed by scanning electron microscopy. A model that combines the scattering from the pores and the labelled polymer chains was used to extract values for the radius of gyration. The radius of gyration in the fibres is found to vary little with the applied voltage, but varies with the initial solution concentration and fibre diameter. The values for the radius of gyration in the fibres are broadly equivalent to that of the bulk state.