This multi-method study presents an exercise in applied discourse analysis conducted within the broad framework of systemic-functional linguistics. The theoretical part of the work explores the relationship between one functional component of language, Halliday's notion of Theme, and the characterisation of a particular genre, the scientific research article (RA). Relevant literature on a variety of views of genre and Theme in the traditions of English for Academic Purposes (FAP) and systemic-functional linguistics is reviewed. The integration of these two levels of functional description is used as a basis for exploring the way in which Theme and generic structure relate to 'successful' processes and products of scientific research communication. The particular educational setting for the applied part of this work is the writing of first scientific RAs in English by NNS (non-native speaker) novice researchers. The primary method of study adopted here is corpus-based and initial discourse-functional analysis and description of marked and unmarked thematic choices are based on a corpus of 36 published RAs in the physical and life sciences written by 'experienced' NSs. This corpus represents a base 'norm' of thematic usage against which other corpora are compared, namely, published RAs written by 'experienced' NNS scientists and unpublished first and final RA drafts written by NNS novices. Major findings indicated that 'appropriate' thematic selections in the RA genre are constrained by the changing rhetorical purposes, signalled by means of moves, which operate throughout the different stages of scientific RA discourse; thus, the textual metafunction of Theme plays a significant role in the characterisation and dynamic wi thin-text structuring of the scientific RA genre. Furthermore, background surveys by means of questionnaires and interviews of the participants in the process of international research communication, in particular, of 'expert' NS journal editors, confirmed that 'appropriate' thematic control was clearly associated with the judgement of the merits of NNSs' RAs, and thereby, their 'successful' publication. With the pedagogical application of such theoretical insights in mind, the use of the teaching/research tool of Propositional Clusters (PCs) was explored in the EAP classroom as a heuristic for raising NNS novices' awareness about the manipulation of Theme in drafting and redrafting RA sections. Data collected from PCs exercises indicated their potential to raise awareness about the role of 'appropriate' thematic control in helping to create 'successful' texts. This study contributes to our understanding of aspects of the functional relationship between elements of discourse structure and lexica-grammatical components such as Theme/Subject. In addition, reflecting the social-semiotic perspective of a systemic-functional framework, this work strongly emphasises the social-constructionist nature of the processes involved in international research communication through the medium of the scientific research article.

published_or_final_version / abstract / toc / Linguistics / Master / Master of Philosophy

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Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.

Regression models with a scalar response and a functional predictor have been extensively studied. One approach is to approximate the functional predictor using basis function or eigenfunction expansions. In the expansion, the coefficient vector can either be fixed or random. The random coefficient vector is also known as random effects and thus the regression models are in a mixed effects framework. The random effects provide a model for the within individual covariance of the observations. But it also introduces an additional parameter into the model, the covariance matrix of the random effects. This additional parameter complicates the covariance matrix of the observations. Possibly, the covariance parameters of the model are not identifiable. We study identifiability in normal linear mixed effects models. We derive necessary and sufficient conditions of identifiability, particularly, conditions of identifiability for the regression models with a scalar response and a functional predictor using random effects. We study the regression model using the eigenfunction expansion approach with random effects. We assume the random effects have a general covariance matrix and the observed values of the predictor are contaminated with measurement error. We propose methods of inference for the regression model's functional coefficient. As an application of the model, we analyze a biological data set to investigate the dependence of a mouse's wheel running distance on its body mass trajectory.

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