Time complexity and convergence analysis of domain theoretic Picard method

We present an implementation of the domain-theoretic Picard method for solving initial value problems (IVPs) introduced by Edalat and Pattinson [1]. Compared to Edalat and Pattinson's implementation, our algorithm uses a more efficient arithmetic based on an arbitrary precision floating-point library. Despite the additional overestimations due to floating-point rounding, we obtain a similar bound on the convergence rate of the produced approximations. Moreover, our convergence analysis is detailed enough to allow a static optimisation in the growth of the precision used in successive Picard iterations. Such optimisation greatly improves the efficiency of the solving process. Although a similar optimisation could be performed dynamically without our analysis, a static one gives us a significant advantage: we are able to predict the time it will take the solver to obtain an approximation of a certain (arbitrarily high) quality.

Approaching questions in forensic authorship analysis

This chapter demonstrates diversity in the activity of authorship and the corresponding diversity of forensic authorship analysis questions and techniques. Authorship is discussed in terms of Love’s (2002) multifunctional description of precursory, executive, declarative and revisionary authorship activities and the implications of this distinction for forensic problem solving. Four different authorship questions are considered. These are ‘How was the text produced?’, ‘How many people wrote the text?’, ‘What kind of person wrote the text?’ and ‘What is the relationship of a queried text with comparison texts?’ Different approaches to forensic authorship analysis are discussed in terms of their appropriateness to answering different authorship questions. The conclusion drawn is that no one technique will ever be appropriate to all problems.

Feminist Post-structuralist discourse analysis: a new theoretical and methodological approach?

This chapter serves three very important functions within this collection. First, it aims to make the existence of FPDA better known to both gender and language researchers and to the wider community of discourse analysts, by outlining FPDA’s own theoretical and methodological approaches. This involves locating and positioning FPDA in relation, yet in contradistinction to, the fields of discourse analysis to which it is most often compared: Critical Discourse Analysis (CDA) and, to a lesser extent, Conversation Analysis (CA). Secondly, the chapter serves a vital symbolic function. It aims to contest the authority of the more established theoretical and methodological approaches represented in this collection, which currently dominate the field of discourse analysis. FPDA considers that an established field like gender and language study will only thrive and develop if it is receptive to new ways of thinking, divergent methods of study, and approaches that question and contest received wisdoms or established methods. Thirdly, the chapter aims to introduce some new, experimental and ground-breaking FPDA work, including that by Harold Castañeda-Peña and Laurel Kamada (same volume). I indicate the different ways in which a number of young scholars are imaginatively developing the possibilities of an FPDA approach to their specific gender and language projects.

Managing service productivity using data envelopment analysis

This chapter provides the theoretical foundation and background on data envelopment analysis (DEA) method. We first introduce the basic DEA models. The balance of this chapter focuses on evidences showing DEA has been extensively applied for measuring efficiency and productivity of services including financial services (banking, insurance, securities, and fund management), professional services, health services, education services, environmental and public services, energy services, logistics, tourism, information technology, telecommunications, transport, distribution, audio-visual, media, entertainment, cultural and other business services. Finally, we provide information on the use of Performance Improvement Management Software (PIM-DEA). A free limited version of this software and downloading procedure is also included in this chapter.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Balancing expectations in engineering education:enhancing student experiences through empirical research - a comparative analysis

This paper draws upon the findings of an empirical study comparing the expectations and concerns of engineering students with students enrolled on business and management programs. It argues that whilst the two groups of students have very similar expectations, motivations and concerns before their start their studies, once at university, engineering students are twice as likely to drop-out than are their compatriots in business studies. Drawing upon the study findings, recommendations are made as to what might be done to counteract this. The conclusion argues that there is a need for more in-depth research to be conducted in this area in order to identify the reasons behind the different attrition rates and to further enhance engineering undergraduate experience.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.