Introduction: There remains a lack of theoretical models which can adequately account for the key features of bipolar disorders (Power, 2005). Objectives: Firstly, to test the predictions made by the SPAARS model that mania is predominantly characterised by the coupling of happiness with anger, while depression (unipolar and bipolar) primarily comprises of a coupling between sadness and disgust. Secondly, to investigate and compare the coping strategies employed to regulate positive and negative emotion between bipolar, unipolar and control groups. Design: A cross sectional design was employed to examine the differences within and between the bipolar, unipolar and control groups in the emotions experienced and the strategies used to regulate emotion. Data were analysed using ANOVAs. Method: Psychiatric diagnoses in the clinical groups were confirmed using the SCID. Current mood state was measured using the BDI-II, STAI and the MAS. The Basic Emotion Scale was used to explore the emotional profiles and the Regulation of Emotion Questionnaire was used to measure coping strategies. Results: The results confirmed the predictions made by the SPAARS model about the emotions in mania and depression. Elevated levels of disgust were also found in the bipolar group generally. The clinical groups used internal dysfunctional strategies more often than the controls for negative emotion. The bipolar group used external dysfunctional strategies more frequently than the controls for positive emotion. Conclusion: The results support the predictions made by the SPAARS model and suggest that disgust plays a key role in bipolar disorder. Strengths and limitations are discussed and suggestions for future research are explored.

Flood literature presents an inconsistent account of post-disaster distress; debating whether distress is pathological or normal and attempting to understand distress in terms of disaster variables. The literature therefore provides little guidance as to how to formulate difficulties in a clinically meaningful way reflective of individual’s experiences. The SPAARS model is presented as a model by which to reconcile these differences and quantitative support for its concepts were studied within two flooded samples. Participants who were flooded in Carlisle in 2005 (n=32) and participants flooded in Morpeth in 2008 (n=29) provided two samples at different stages in flood recovery and facilitated a quasi-longitudinal sample for comparison of flood-related distress over time. Participants were asked to complete a survey pertaining to: basic emotions experienced during the flood event, basic emotions experienced after the flood, Impact of Events Scale-Revised (IES-R), Regulation of Emotions Questionnaire (REQ) and the Trauma Symptom Inventory (TSI). Findings suggest that a third of participants who were flooded experienced clinically significant levels of distress, even after four years. Both samples showed higher levels of impact symptoms on the IES compared to symptoms on the TSI. Anxiety and anger were significant in reported flood experiences both during and after the flooding. Flood-related variables and previous experiences had no effect on increased distress but greater use of internal-dysfunctional emotion regulation strategies was related to increased impact and distress symptoms. Study findings and the SPAARS model are discussed in relation to previous flooding and PTSD literature, as well as clinical implications for the treatment of post-disaster distress and for the future management of flood-affected populations.

Doctor of Philosophy (PhD) / This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks.

Doctor of Philosophy (PhD) / This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks.