Grassland leafhoppers (Hemiptera: Auchenorrhyncha) as indicators of habitat condition - a comparison of between-site and between-year differences in assemblage composition

The Auchenorrhyncha (leafhoppers) show great potential as indicators of grassland habitat quality, which would make them useful as a conservation tool. However, they are known to have labile populations. The relative importance of site identity and the year of sampling in the composition of leafhopper assemblages on chalk grassland are assessed for two sets of sites sampled twice. The study included a total of 95 sites (one set of 54, the other of 41), and demonstrated that for both sets the vegetation community and geographical location had high explanatory value, while the influence of year was small. The conclusion is that, notwithstanding population fluctuations, the leafhopper assemblages are a good indicator of habitat quality, and represent a potentially valuable tool in grassland conservation and restoration.

Intra-specific variation affects the structure of the natural enemy assemblage attacking pea aphid colonies

1. Intra-specific variation in plant defence traits has been shown to profoundly affect herbivore community structure. Here we describe two experiments designed to test whether similar effects occur at higher trophic levels, by studying pea aphid–natural enemy interactions in a disused pasture in southern England. 2. In the first experiment, the numbers and identity of natural enemies attacking different monoclonal pea aphid colonies were recorded in a series of assays throughout the period of pea aphid activity. 3. In the summer assay, there was a significant effect of clone on the numbers of aphidophagous hoverfly larvae and the total number of non-hoverfly natural enemies recruited. Clone also appeared to influence the attack rate suffered by the primary predator in the system, the hoverfly Episyrphus balteatus, by Diplazon laetatorius, an ichneumonid parasitoid. Colonies were generally driven to extinction by hoverfly attack, resulting in the recording of low numbers of parasitoids and entomopathogens, suggesting intense intra-guild predation. 4. To further examine the influence of clonal variation on the recruitment of natural enemies, a second experiment was performed to monitor the temporal dynamics of community development. Colonies were destructively sampled every other day and the numbers of natural enemies attacking aphid colonies were recorded. These data demonstrated that clonal variation influenced the timing, abundance, and identity of natural enemies attacking aphid colonies. 5. Taken together, these data suggest that clonal variation may have a significant influence on the patterns of interactions between aphids and their natural enemies, and that such effects are likely to affect our understanding of the ecology and biological control of these insect herbivores.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

Cnoc Coig: a Mesolithic shell midden assemblage

Recent work on a sample of the chipped stone from one of the late Mesolithic shell midden sites on the Inner Hebrides island of Oronsay has shown that the assemblage was created using bipolar and platform technologies, with an unexpected element of narrow blade technology shown in cores and core trimming elements, suggesting that there may have been links between this classic ‘Obanian’ assemblage and the more typical Mesolithic narrow blade technology. Findings raise questions about the relationship of settlement on the island to Mesolithic settlement rounds and to the transition to the Neolithic.

Catch-up strategies in the Indian auto components industry: domestic firms’ responses to market liberalization

Market liberalization in emerging-market economies and the entry of multinational firms spur significant changes to the industry/institutional environment faced by domestic firms. Prior studies have described how such changes tend to be disruptive to the relatively backward domestic firms, and negatively affect their performance and survival prospects. In this paper, we study how domestic supplier firms may adapt and continue to perform, as market liberalization progresses, through catch-up strategies aimed at integrating with the industry's global value chain. Drawing on internalization theory and the literatures on upgrading and catch-up processes, learning and relational networks, we hypothesize that, for continued performance, domestic supplier firms need to adapt their strategies from catching up initially through technology licensing/collaborations and joint ventures with multinational enterprises (MNEs) to also developing strong customer relationships with downstream firms (especially MNEs). Further, we propose that successful catch-up through these two strategies lays the foundation for a strategy of knowledge creation during the integration of domestic industry with the global value chain. Our analysis of data from the auto components industry in India during the period 1992–2002, that is, the decade since liberalization began in 1991, offers support for our hypotheses.

Auto ID-Bridging the physical and the digital on construction projects

This book looks at how auto-ID has evolved and how it can be used in the construction industry and across projects from the perspective of all the stakeholders, from owners to design consultants, contractors and the supply chain. It could help to improve efficiency, reduce costs, ensure quality, protect the environment, and enhance safety.

Scene understanding for auto-calibration of surveillance cameras

In the last decade, several research results have presented formulations for the auto-calibration problem. Most of these have relied on the evaluation of vanishing points to extract the camera parameters. Normally vanishing points are evaluated using pedestrians or the Manhattan World assumption i.e. it is assumed that the scene is necessarily composed of orthogonal planar surfaces. In this work, we present a robust framework for auto-calibration, with improved results and generalisability for real-life situations. This framework is capable of handling problems such as occlusions and the presence of unexpected objects in the scene. In our tests, we compare our formulation with the state-of-the-art in auto-calibration using pedestrians and Manhattan World-based assumptions. This paper reports on the experiments conducted using publicly available datasets; the results have shown that our formulation represents an improvement over the state-of-the-art.