A methodology for the generation and evaluation of biorefinery process chains, in order to identify the most promising biorefineries for the EU

The topic of bioenergy, biofuels and bioproducts remains at the top of the current political and research agenda. Identification of the optimum processing routes for biomass, in terms of efficiency, cost, environment and socio-economics is vital as concern grows over the remaining fossil fuel resources, climate change and energy security. It is known that the only renewable way of producing conventional hydrocarbon fuels and organic chemicals is from biomass, but the problem remains of identifying the best product mix and the most efficient way of processing biomass to products. The aim is to move Europe towards a biobased economy and it is widely accepted that biorefineries are key to this development. A methodology was required for the generation and evaluation of biorefinery process chains for converting biomass into one or more valuable products that properly considers performance, cost, environment, socio-economics and other factors that influence the commercial viability of a process. In this thesis a methodology to achieve this objective is described. The completed methodology includes process chain generation, process modelling and subsequent analysis and comparison of results in order to evaluate alternative process routes. A modular structure was chosen to allow greater flexibility and allowing the user to generate a large number of different biorefinery configurations The significance of the approach is that the methodology is defined and is thus rigorous and consistent and may be readily re-examined if circumstances change. There was the requirement for consistency in structure and use, particularly for multiple analyses. It was important that analyses could be quickly and easily carried out to consider, for example, different scales, configurations and product portfolios and so that previous outcomes could be readily reconsidered. The result of the completed methodology is the identification of the most promising biorefinery chains from those considered as part of the European Biosynergy Project.

Consumer brand equity : a model for the measurement, analysis and evaluation of consumer perceived value

The topic of my research is consumer brand equity (CBE). My thesis is that the success or otherwise of a brand is better viewed from the consumers’ perspective. I specifically focus on consumers as a unique group of stakeholders whose involvement with brands is crucial to the overall success of branding strategy. To this end, this research examines the constellation of ideas on brand equity that have hitherto been offered by various scholars. Through a systematic integration of the concepts and practices identified but these scholars (concepts and practices such as: competitiveness, consumer searching, consumer behaviour, brand image, brand relevance, consumer perceived value, etc.), this research identifies CBE as a construct that is shaped, directed and made valuable by the beliefs, attitudes and the subjective preferences of consumers. This is done by examining the criteria on the basis of which the consumers evaluate brands and make brand purchase decisions. Understanding the criteria by which consumers evaluate brands is crucial for several reasons. First, as the basis upon which consumers select brands changes with consumption norms and technology, understanding the consumer choice process will help in formulating branding strategy. Secondly, an understanding of these criteria will help in formulating a creative and innovative agenda for ‘new brand’ propositions. Thirdly, it will also influence firms’ ability to simulate and mould the plasticity of demand for existing brands. In examining these three issues, this thesis presents a comprehensive account of CBE. This is because the first issue raised in the preceding paragraph deals with the content of CBE. The second issue addresses the problem of how to develop a reliable and valid measuring instrument for CBE. The third issue examines the structural and statistical relationships between the factors of CBE and the consequences of CBE on consumer perceived value (CPV). Using LISREL-SIMPLIS 8.30, the study finds direct and significant influential links between consumer brand equity and consumer value perception.

Evaluation and development of drainage and pipeline construction processes

In 1974 Dr D M Bramwell published his research work at the University of Aston a part of which was the establishment of an elemental work study data base covering drainage construction. The Transport and Road Research Laboratory decided to, extend that work as part of their continuing research programme into the design and construction of buried pipelines by placing a research contract with Bryant Construction. This research may be considered under two broad categories. In the first, site studies were undertaken to validate and extend the data base. The studies showed good agreement with the existing data with the exception of the excavation trench shoring and pipelaying data which was amended to incorporate new construction plant and methods. An inter-active on-line computer system for drainage estimating was developed. This system stores the elemental data, synthesizes the standard time of each drainage operation and is used to determine the required resources and construction method of the total drainage activity. The remainder of the research was into the general topic of construction efficiency. An on-line command driven computer system was produced. This system uses a stochastic simulation technique, based on distributions of site efficiency measurements to evaluate the effects of varying performance levels. The analysis of this performance data quantities the variability inherent in construction and demonstrates how some of this variability can be reconciled by considering the characteristics of a contract. A long term trend of decreasing efficiency with contract duration was also identified. The results obtained from the simulation suite were compared to site records collected from current contracts. This showed that this approach will give comparable answers, but these are greatly affected by the site performance parameters.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Emerging perspectives on the design, use, and evaluation of mobile and handheld devices

Human-computer interaction is a growing field of study in which researchers and professionals aim to understand and evaluate the impact of new technologies on human behavior. With the integration of smart phones, tablets, and other portable devices into everyday life, there is a greater need to understand the influence of such technology on the human experience. Emerging Perspectives on the Design, Use, and Evaluation of Mobile and Handheld Devices is an authoritative reference source consisting of the latest scholarly research and theories from international experts and professionals on the topic of human-computer interaction with mobile devices. Featuring a comprehensive collection of chapters on critical topics in this dynamic field, this publication is an essential reference source for researchers, educators, students, and practitioners interested in the use of mobile and handheld devices and their impact on individuals and society as a whole. This publication features timely, research-based chapters pertaining to topics in the design and evaluation of smart devices including, but not limited to, app stores, category-based interfaces, gamified mobility applications, mobile interaction, mobile learning, pervasive multimodal applications, smartphone interaction, and social media use.