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.

A peer-to-peer approach to content dissemination and search in collaborative networks

There are three key driving forces behind the development of Internet Content Management Systems (CMS) - a desire to manage the explosion of content, a desire to provide structure and meaning to content in order to make it accessible, and a desire to work collaboratively to manipulate content in some meaningful way. Yet the traditional CMS has been unable to meet the latter of these requirements, often failing to provide sufficient tools for collaboration in a distributed context. Peer-to-Peer (P2P) systems are networks in which every node is an equal participant (whether transmitting data, exchanging content, or invoking services) and there is an absence of any centralised administrative or coordinating authorities. P2P systems are inherently more scalable than equivalent client-server implementations as they tend to use resources at the edge of the network much more effectively. This paper details the rationale and design of a P2P middleware for collaborative content management.

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%.

Ocean data dissemination: new challenges for data integration

In the decade since OceanObs `99, great advances have been made in the field of ocean data dissemination. The use of Internet technologies has transformed the landscape: users can now find, evaluate and access data rapidly and securely using only a web browser. This paper describes the current state of the art in dissemination methods for ocean data, focussing particularly on ocean observations from in situ and remote sensing platforms. We discuss current efforts being made to improve the consistency of delivered data and to increase the potential for automated integration of diverse datasets. An important recent development is the adoption of open standards from the Geographic Information Systems community; we discuss the current impact of these new technologies and their future potential. We conclude that new approaches will indeed be necessary to exchange data more effectively and forge links between communities, but these approaches must be evaluated critically through practical tests, and existing ocean data exchange technologies must be used to their best advantage. Investment in key technology components, cross-community pilot projects and the enhancement of end-user software tools will be required in order to assess and demonstrate the value of any new technology.

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.

Fitness and dissemination of disinfectant-selected multiple-antibiotic-resistant (MAR) strains of Salmonella enterica serovar Typhimurium in chickens

Objectives: The aims of this study were to determine whether strains of Salmonella enterica serovar Typhimurium which had acquired low-level multiple antibiotic resistance (MAR) through repeated exposure to farm disinfectants were able to colonize and transmit between chicks as easily as the parent strain and, if such strains were less susceptible to fluoroquinolones, would high-level resistance be selected after fluoroquinolone treatment. Methods: Two mutants were compared with the isogenic parent. In the first experiment, day-old chicks were co-infected with both the parent and a mutant to determine their relative fitness. In the second experiment, parent and mutant strains (in separate groups of chicks) were assessed for their ability to transmit from infected (contact) to non-infected (naive) birds and with respect to their susceptibility to fluoroquinolone treatment. Birds were regularly monitored for the presence of Salmonella in caecal contents. Replica plating was used to monitor for the selection of antibiotic-resistant strains. Results: The parent strain was shown to be significantly fitter than the two mutants and was more rapidly disseminated to naive birds. Antibiotic treatment did not preferentially select for the two mutants or for resistant strains. Conclusions: The disinfectant-exposed strains, although MAR, were less fit, less able to disseminate than the parent strain and were not preferentially selected by therapeutic antibiotic treatment. As such, these strains are unlikely to present a greater problem than other salmonellae in chickens.

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.

Knowledge dissemination in the global service marketing community

Purpose– The evolution of the service marketing field was marked by the emergence of a global, vigorous and tolerant community of service marketing researchers. This paper seeks to examine the history of the service marketing community and argues that it may be an archetype for building the emergent global service research community. Design/methodology/approach– The study combines qualitative and quantitative approaches. The authors interviewed four pioneering service scholars and also collected descriptive data (e.g. Authorship, Affiliation, Title, Keywords) of all service related articles published in 13 top peer‐reviewed marketing and service journals over the last 30 years (5,432 articles; 6,450 authors). In a dynamic analysis the authors mapped global collaboration between countries over time and detected clusters of international collaboration. Findings– Findings suggest a growing international collaboration for the USA and the UK, while for other countries like Israel the global collaboration started from a high level and decreases now. Further, the service marketing community never became polarized and there were always contributions from researchers all over the world. Research limitations/implications– As the global service research community is developing, service marketing becomes a research neighborhood within the broader service research community. Simultaneously, other research neighborhoods are emerging within this new community (e.g. service arts, service management, service engineering, service science). Originality/value– Anchored on the social evolution and biological evolution metaphors, this study explains the evolution of the service marketing field from both qualitative and quantitative perspectives. Furthermore, it explains the development of the service marketing community as an archetype for building the global service research community.

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.