Services for activities in group editing 'sage'

The project described in this thesis investigates the needs of a group of people working cooperatively in an OSI environment, and recommends tools and services to meet these needs. The project looks specifically at Services for Activities in Group Editing, and is identified as the `SAGE' project. The project uses case studies to identify user requirements and to determine common functionalities for a variety of group editing activities. A prototype is implemented in an X.400 environment to help refine user requirements, as a source of new ideas and to test the proposed functionalities. The conceptual modelling follows current CCITT proposals, but a new classification of group activities is proposed: Informative, Objective and Supportive application groups. It is proposed that each of these application groups have their own Service Agent. Use of this classification allows the possibility of developing three sets of tools which will cover a wide range of group activities, rather than developing tools for individual activities. Group editing is considered to be in the Supportive application group. A set of additional services and tools to support group editing are proposed in the context of the CCITT draft on group communication, X.gc. The proposed services and tools are mapped onto the X.400 series of recommendations, with the Abstract Service Definition of the operational objects defined, along with their associated component files, by extending the X.420 protocol functionality. It is proposed that each of the Informative, Objective and Supportive application groups should be implemented as a modified X.420 inter-personal messaging system.

A unified classification of manufacturing strategies and design processes

This article categorises manufacturing strategy design processes and presents the characteristics of resulting strategies. This work will therefore assist practitioners to appreciate the implications of planning activities. The article presents a framework for classifying manufacturing strategy processes and the resulting strategies. Each process and respective strategy is then considered in detail. In this consideration the preferred approach is presented for formulating a world class manufacturing strategy. Finally, conclusions and recommendations for further work are given.

Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.

A model of service performance enhancement:the role of transactional and transformational leadership

This paper is concerned with the ways in which transactional and transformational leadership styles can improve the service performance of front-line staff. Past literature on services marketing has indicated the importance of leadership but has largely ignored the parallel literature in which leadership styles have been conceptualized and operationalized (e.g., sales management, organizational psychology). This paper seeks to build upon existing services marketing theory by introducing the role of leadership styles in enhancing service performance. Consequently, a conceptual framework of the effect of transactional and transformational leadership styles on service performance, anchored in a crossdisciplinary literature review, is developed. Managerial implications and future research directions are also discussed.

Outcomes of service encounter quality in a business-to-business context

Service encounter quality is an area of growing interest to researchers and managers alike, yet little is known about the effects of face-to-face service encounter quality within a business-to-business setting. In this paper, a psychometrically sound measure of such service encounter quality is proposed, and consequences of this construct are empirically assessed. Both a literature review and a dyadic in-depth interview approach were used to develop a conceptual framework and a pool of items to capture service encounter quality. A mail survey of customers was undertaken, and a response rate of 36% was obtained. Data analysis was conducted via confirmatory factor analysis and structural equation modeling. Findings reveal a four-factor structure of service encounter quality, encompassing professionalism, civility, friendliness and competence dimensions. Service encounter quality was found to be directly related to customer satisfaction and service quality perceptions, and indirectly to loyalty. The importance of these findings for practitioners and for future research on service encounter quality is discussed.

Antecedents and consequences of service quality in consumer evaluation of self-service internet technologies

Advances in technology coupled with increasing labour costs have caused service firms to explore self-service delivery options. Although some studies have focused on self-service and use of technology in service delivery, few have explored the role of service quality in consumer evaluation of technology-based self-service options. By integrating and extending the self-service quality framework the service evaluation model and the Technology Acceptance Model the authors address this emerging issue by empirically testing a comprehensive model that captures the antecedents and consequences of perceived service quality to predict continued customer interaction in the technology-based self-service context of Internet banking. Important service evaluation constructs like perceived risk, perceived value and perceived satisfaction are modelled in this framework. The results show that perceived control has the strongest influence on service quality evaluations. Perceived speed of delivery, reliability and enjoyment also have a significant impact on service quality perceptions. The study also found that even though perceived service quality, perceived risk and satisfaction are important predictors of continued interaction, perceived customer value plays a pivotal role in influencing continued interaction.

GPCRTree:online hierarchical classification of GPCR function

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

An investigation into the nature, determinants, and dynamics of service quality expectations

On the basis of a review of the substantive quality and service marketing literature current knowledge regarding service quality expectations was found either absent or deficient. The phenomenon is of increasing importance to both marketing researchers and management and was therefore judged worthy of scholarly consideration. Because the service quality literature was insufficiently rich when embarking on the thesis three basic research issues were considered namely the nature, determinants, and dynamics of service quality expectations. These issues were first conceptually and then qualitatively explored. This process generated research hypotheses mainly relating to a model which were subsequently tested through a series of empirical investigations using questionnaire data from field studies in a single context. The results were internally consistent and strongly supported the main research hypotheses. It was found that service quality expectations can be meaningfully described in terms of generic/service-specific, intangible/tangible, and process/outcome categories. Service-specific quality expectations were also shown to be determined by generic service quality expectations, demographic variables, personal values, psychological needs, general service sophistication, service-specific sophistication, purchase motives, and service-specific information when treating service class involvement as an exogenous variable. Subjects who had previously not directly experienced a particular service were additionally found to revise their expectations of quality when exposed to the service with change being driven by a sub-set of identified determinants.

The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

The use of context in the classification of urban aerial imagery

Urban regions present some of the most challenging areas for the remote sensing community. Many different types of land cover have similar spectral responses, making them difficult to distinguish from one another. Traditional per-pixel classification techniques suffer particularly badly because they only use these spectral properties to determine a class, and no other properties of the image, such as context. This project presents the results of the classification of a deeply urban area of Dudley, West Midlands, using 4 methods: Supervised Maximum Likelihood, SMAP, ECHO and Unsupervised Maximum Likelihood. An accuracy assessment method is then developed to allow a fair representation of each procedure and a direct comparison between them. Subsequently, a classification procedure is developed that makes use of the context in the image, though a per-polygon classification. The imagery is broken up into a series of polygons extracted from the Marr-Hildreth zero-crossing edge detector. These polygons are then refined using a region-growing algorithm, and then classified according to the mean class of the fine polygons. The imagery produced by this technique is shown to be of better quality and of a higher accuracy than that of other conventional methods. Further refinements are suggested and examined to improve the aesthetic appearance of the imagery. Finally a comparison with the results produced from a previous study of the James Bridge catchment, in Darleston, West Midlands, is made, showing that the Polygon classified ATM imagery performs significantly better than the Maximum Likelihood classified videography used in the initial study, despite the presence of geometric correction errors.