The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland

A first step in interpreting the wide variation in trace gas concentrations measured over time at a given site is to classify the data according to the prevailing weather conditions. In order to classify measurements made during two intensive field campaigns at Mace Head, on the west coast of Ireland, an objective method of assigning data to different weather types has been developed. Air-mass back trajectories calculated using winds from ECMWF analyses, arriving at the site in 1995–1997, were allocated to clusters based on a statistical analysis of the latitude, longitude and pressure of the trajectory at 12 h intervals over 5 days. The robustness of the analysis was assessed by using an ensemble of back trajectories calculated for four points around Mace Head. Separate analyses were made for each of the 3 years, and for four 3-month periods. The use of these clusters in classifying ground-based ozone measurements at Mace Head is described, including the need to exclude data which have been influenced by local perturbations to the regional flow pattern, for example, by sea breezes. Even with a limited data set, based on 2 months of intensive field measurements in 1996 and 1997, there are statistically significant differences in ozone concentrations in air from the different clusters. The limitations of this type of analysis for classification and interpretation of ground-based chemistry measurements are discussed.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Biomass partitioning and growth efficiency in four naturally regenerated forest tree species

Current forest growth models and yield tables are almost exclusively based on data from mature trees, reducing their applicability to young and developing stands. To address this gap, young European beech, sessile oak, Scots pine and Norway spruce trees approximately 0 to 10 years old were destructively sampled in a range of naturally regenerated forest stands in Central Europe. Diameter at base and height were first measured in situ for up to 175 individuals per species. Subsequently, the trees were excavated and dry biomass of foliage, branches, stems and roots was measured. Allometric relations were then used to calculate biomass allocation coefficients (BAC) and growth efficiency (GE) patterns in young trees. We found large differences in BAC and GE between broadleaves and conifers, but also between species within these categories. Both BAC and GE are strongly age-specific in young trees, their rapidly changing values reflecting different growth strategies in the earliest stages of growth. We show that linear relationships describing biomass allocation in older trees are not applicable in young trees. To accurately predict forest biomass and carbon stocks, forest growth models need to include species and age specific parameters of biomass allocation patterns.

A cluster computing implementation of a Monte Carlo simulation of a particle growth mechanism

Clusters of computers can be used together to provide a powerful computing resource. Large Monte Carlo simulations, such as those used to model particle growth, are computationally intensive and take considerable time to execute on conventional workstations. By spreading the work of the simulation across a cluster of computers, the elapsed execution time can be greatly reduced. Thus a user has apparently the performance of a supercomputer by using the spare cycles on other workstations.

Simultaneous observations of reconnection pulses at Cluster and their effects on the cusp aurora observed at the Chinese Yellow River station

While the Cluster spacecraft were located near the high-latitude magnetopause, between 1010 and 1040 UT on 16 January 2004, three typical flux transfer event (FTE) signatures were observed. During this interval, simultaneous and conjugated all‐sky camera measurements, recorded at Yellow River Station, Svalbard, are available at 630.0 and 557.7 nm that show poleward‐moving auroral forms (PMAFs), consistent with magnetic reconnection at the dayside magnetopause. Simultaneous FTEs seen at the magnetopause mainly move northward, but having duskward (eastward) and tailward velocity components, roughly consistent with the observed direction of motion of the PMAFs in all‐sky images. Between the PMAFs meridional keograms, extracted from the all‐sky images, show intervals of lower intensity aurora which migrate equatorward just before the PMAFs intensify. This is strong evidence for an equatorward eroding and poleward moving open‐closed boundary associated with a variable magnetopause reconnection rate under variable IMF conditions. From the durations of the PMAFs, we infer that the evolution time of FTEs is 5–11 minutes from its origin on the magnetopause to its addition to the polar cap.

On merging gradient estimation with mean-tracking techniques for cluster identification

This paper discusses how numerical gradient estimation methods may be used in order to reduce the computational demands on a class of multidimensional clustering algorithms. The study is motivated by the recognition that several current point-density based cluster identification algorithms could benefit from a reduction of computational demand if approximate a-priori estimates of the cluster centres present in a given data set could be supplied as starting conditions for these algorithms. In this particular presentation, the algorithm shown to benefit from the technique is the Mean-Tracking (M-T) cluster algorithm, but the results obtained from the gradient estimation approach may also be applied to other clustering algorithms and their related disciplines.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Cluster analysis for multivariable process control

This paper describes the novel use of cluster analysis in the field of industrial process control. The severe multivariable process problems encountered in manufacturing have often led to machine shutdowns, where the need for corrective actions arises in order to resume operation. Production faults which are caused by processes running in less efficient regions may be prevented or diagnosed using a reasoning based on cluster analysis. Indeed the intemal complexity of a production machinery may be depicted in clusters of multidimensional data points which characterise the manufacturing process. The application of a Mean-Tracking cluster algorithm (developed in Reading) to field data acquired from a high-speed machinery will be discussed. The objective of such an application is to illustrate how machine behaviour can be studied, in particular how regions of erroneous and stable running behaviour can be identified.

Tree species’ tolerance to water stress, salinity and fire

According to climate change predictions, water availability might change dramatically in Europe and adjacent regions. This change will undoubtedly have an adverse effect on existing tree species and affect their ability to cope with a lack or an excess of water, changes in annual precipitation patterns, soil salinity and fire disturbance. The following chapter will describe tree species and proven-ances used in European forestry practice which are the most suitable to deal with water stress, salinity and fire. Each subchapter starts with a brief description of each of the stress factors and discusses the predictions of the likelihood of their occurrence in the near future according to the climate change scenarios. Tree spe-cies and their genotypes able to cope with particular stress factor, together with indication of their use by forest managers are then introduced in greater detail.

Adaptation of tree growth to elevated CO2: quantitative trait loci for biomass in Populus

Information on the genetic variation of plant response to elevated CO2 (e[CO2]) is needed to understand plant adaptation and to pinpoint likely evolutionary response to future high atmospheric CO2 concentrations.• Here, quantitative trait loci (QTL) for above- and below-ground tree growth were determined in a pedigree – an F2 hybrid of poplar (Populus trichocarpa and Populus deltoides), following season-long exposure to either current day ambient CO2 (a[CO2]) or e[CO2] at 600 µl l−1, and genotype by environment interactions investigated.• In the F2 generation, both above- and below-ground growth showed a significant increase in e[CO2]. Three areas of the genome on linkage groups I, IX and XII were identified as important in determining above-ground growth response to e[CO2], while an additional three areas of the genome on linkage groups IV, XVI and XIX appeared important in determining root growth response to e[CO2].• These results quantify and identify genetic variation in response to e[CO2] and provide an insight into genomic response to the changing environment

In vitro gas production and nitrogen degradability of tannin-containing tree fruits in response to incremental levels of polyethylene glycol

This study was designed to determine the response of in vitro fermentation parameters to incremental levels of polyethylene glycol (PEG) when tanniniferous tree fruits (Dichrostachys cinerea, Acacia erioloba, A. erubiscens, A. nilotica and Piliostigma thonningii) were fermented using the Reading Pressure Technique. The trivalent ytterbium precipitable phenolics content of fruit substrates ranged from 175 g/kg DM in A. erubiscens to 607 g/kg DM in A. nilotica, while the soluble condensed tannin content ranged from 0.09 AU550nm/40mg in A. erioloba to 0.52 AU550nm/40 mg in D. cinerea. The ADF was highest in P. thonningii fruits (402 g/kg DM) and lowest in A. nilotica fruits (165 g/kg DM). Increasing the level of PEG caused an exponential rise to a maximum (asymptotic) for cumulative gas production, rate of gas production and nitrogen degradability in all substrates except P. thonningii fruits. Dry matter degradability for fruits containing higher levels of soluble condensed tannins (D. cinerea and P. thonningii), showed little response to incremental levels of PEG after incubation for 24 h. The minimum levels of PEG required to maximize in vitro fermentation of tree fruits was found to be 200 mg PEG/g DM of sample for all tree species except A. erubiscens fruits, which required 100 mg PEG/g DM sample. The study provides evidence that PEG levels lower than 1 g/g DM sample can be used for in vitro tannin bioassays to reduce the cost of evaluating non-conventional tanniniferous feedstuffs used in developing countries in the tropics and subtopics. The use of in vitro nitrogen degradability in place of the favoured dry matter degradability improved the accuracy of PEG as a diagnostic tool for tannins in in vitro fermentation systems.