A study of the Hobson and Rogers volatility model

We firstly examine the model of Hobson and Rogers for the volatility of a financial asset such as a stock or share. The main feature of this model is the specification of volatility in terms of past price returns. The volatility process and the underlying price process share the same source of randomness and so the model is said to be complete. Complete models are advantageous as they allow a unique, preference independent price for options on the underlying price process. One of the main objectives of the model is to reproduce the `smiles' and `skews' seen in the market implied volatilities and this model produces the desired effect. In the first main piece of work we numerically calibrate the model of Hobson and Rogers for comparison with existing literature. We also develop parameter estimation methods based on the calibration of a GARCH model. We examine alternative specifications of the volatility and show an improvement of model fit to market data based on these specifications. We also show how to process market data in order to take account of inter-day movements in the volatility surface. In the second piece of work, we extend the Hobson and Rogers model in a way that better reflects market structure. We extend the model to take into account both first and second order effects. We derive and numerically solve the pde which describes the price of options under this extended model. We show that this extension allows for a better fit to the market data. Finally, we analyse the parameters of this extended model in order to understand intuitively the role of these parameters in the volatility surface.

Paracetamol metabolism in postoperative patients

Introduction: Despite being available for more than 50 years, there is still much to learn about paracetamol. Postoperative analgesic regimens that maintain good pain control while minimising exposure to opiates are beneficial and paracetamol has had a resurgence in this role since an IV formulation came to market. However there is evidence to suggest currently licensed doses are sub-therapeutic, especially when administered orally or rectally. Higher, unlicensed doses are now being advocated but, prior to this study, there was little evidence of their safety in surgical patients. When assessing drug safety in surgical patients a number of surgery and patient related factors influence results, and these must be considered. Methods: Major and intermediate surgical patients were recruited from two hospitals in Ireland. They were administered IV paracetamol at either 9g or 4g daily doses. In addition they received daily sub therapeutic doses of four other medicines to indicate the activity of their CYP450 enzymes that are involved in paracetamol metabolism. Urine and blood samples were collected to determine paracetamol pharmacokinetics, CYP450 activity, inflammatory cytokine concentration and for evidence of hepatotoxicity. Results: There were 33 patients that participated in the study. There was no evidence of clinically significant hepatotoxicity occurring in any patient during the study period, but there could have been changes following this time. Paracetamol disposition was shown to change, however half-life remained relatively constant. There were a number of changes to the way paracetamol was metabolised following surgery that maintained this rate of elimination. Conclusion: Doses of up to 9g per day given to major surgical patients for up to five days postoperatively produced no evidence of hepatotoxicity. Further research is warranted to determine the clinical utility of these higher doses

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Clinical autonomy and nurse/physician collaboration in emergency nurses

Aim: To investigate clinical autonomy and Nurse/Physician collaboration among emergency nurses and the relationship between these concepts, personal characteristics and organisational influences. Background: Nurses have been identified as having a significant role in addressing the challenges of providing modern healthcare. Emergency nurses have reported competence in a wide range of emergency care skills. However, there is evidence that Emergency Department (ED) nurses may have lower levels of clinical autonomy than other areas of practice. Levels of clinical autonomy appear to be influenced by levels of collaboration with physicians and the organisations in which nurses work Methods: A descriptive correlational study using a survey design with a purposive convenience sample of 141 ED staff nurses (response 70.9%) from 3 EDs in Ireland. Data were collected using the Dempster Practice Behaviours Scale (DPBS) the Nurse/Physician Collaboration Scale (NPCS) and the newly developed Organisational Influences on Nursing Scale. Demographic information was also sought from participants. Results: Participants were largely female (87%), relatively young (mean age 35.57, SD=7.83) and educated to degree level (48%) or higher (31%) with 40% posessing specialist emergency nursing qualifications. Participants reported moderate levels of clinical autonomy and Nurse/Physician collaboration. No relationships were found between sample characteristics and clinical autonomy and Nurse/Physician collaboration among emergency nurses. Relationships were found between levels of clinical autonomy and Nurse/Physician collaboration (r=-0.395, n=100, p<0.001), and organisational influence on nursing (r=0.455, p<0.001) and also between Nurse/Physician collaboration and organisational influence on nursing (r=-0.413, p<0.001). Discussion: Clinical autonomy of nurses has been linked with quality outcomes in healthcare. The quest for quality in modern healthcare in a challenging environment should acknowledge that strategies need to focus beyond education and skills provision and include essential elements such as Nurse/Physician collaboration and the organisational influence on nursing to ensure the greater involvement of nurses in patient care.

Early biomarkers to predict grade of encephalopathy following hypoxic ischaemic injury

The standard early markers for identifying and grading HIE severity, are not sufficient to ensure all children who would benefit from treatment are identified in a timely fashion. The aim of this thesis was to explore potential early biomarkers of HIE. Methods: To achieve this a cohort of infants with perinatal depression was prospectively recruited. All infants had cord blood samples drawn and biobanked, and were assessed with standardised neurological examination, and early continuous multi-channel EEG. Cord samples from a control cohort of healthy infants were used for comparison. Biomarkers studied included; multiple inflammatory proteins using multiplex assay; the metabolomics profile using LC/MS; and the miRNA profile using microarray. Results: Eighty five infants with perinatal depression were recruited. Analysis of inflammatory proteins consisted of exploratory analysis of 37 analytes conducted in a sub-population, followed by validation of all significantly altered analytes in the remaining population. IL-6 and IL-6 differed significantly in infants with a moderate/severely abnormal vs. a normal-mildly abnormal EEG in both cohorts (Exploratory: p=0.016, p=0.005: Validation: p=0.024, p=0.039; respectively). Metabolomic analysis demonstrated a perturbation in 29 metabolites. A Cross- validated Partial Least Square Discriminant Analysis model was developed, which accurately predicted HIE with an AUC of 0.92 (95% CI: 0.84-0.97). Analysis of the miRNA profile found 70 miRNA significantly altered between moderate/severely encephalopathic infants and controls. miRNA target prediction databases identified potential targets for the altered miRNA in pathways involved in cellular metabolism, cell cycle and apoptosis, cell signaling, and the inflammatory cascade. Conclusion: This thesis has demonstrated that the recruitment of a large cohortof asphyxiated infants, with cord blood carefully biobanked, and detailed early neurophysiological and clinical assessment recorded, is feasible. Additionally the results described, provide potential alternate and novel blood based biomarkers for the identification and assessment of HIE.

College adjustment, depressive symptoms and peer support among undergraduate university students

Background: College adjustment is a developmental milestone that can be stressful and may lead to mental health problems such as depression. Support during this adjustment period is seen as essential, however it is unknown if informal peer support from fellow students has any impact on either college adjustment or depressive symptoms. Aim: To identify levels of social and personal college adjustment, depressive symptoms and peer support among students, and to examine the relationship between the variables. Design: A quantitative correlational design was used Instruments: Data were collected using two subscales of the Student Adaptation to College Questionnaire; the Centre for Epidemiology Depressive Symptoms Scale and a subscale of the Peer Support Evaluation Inventory. Sample: The sample consisted of 417, first (n=188), second (n=134) and fourth (n=94) year nursing and midwifery students from one University in Ireland. Findings: The findings indicated that 20% of participants were poorly personally adjusted and 9% poorly socially adjusted. Furthermore, 34% of participants experienced significant depressive symptoms. Most students had good levels of peer support. Statistically significant relationships were found between all key variables, the strongest of which were between personal adjustment and depressive symptoms and social adjustment and depressive symptoms. Differences in adjustment and depressive symptom scores were found based on year of study, with second year students experiencing more depressive symptoms and having poorer personal adjustment scores. Participants who had poor relationships with their father’s experienced greater depressive symptoms and had more difficulties personally and socially adjusting to college. The alcohol consumption of participants had a statistically significant correlation with college adjustment, depressive symptoms and peer support, with higher consumption having a positive impact on the variables.

Self-neglect: development and evaluation of a self-neglect (SN-37) measurement instrument

Aim: To develop and evaluate the psychometric properties of an instrument for the measurement of self-neglect (SN).Conceptual Framework: An elder self-neglect (ESN) conceptual framework guided the literature review and scale development. The framework has two key dimensions physical/psycho-social and environmental and seven sub dimensions which are representative of the factors that can contribute to intentional and unintentional SN. Methods: A descriptive cross-sectional design was adopted to achieve the research aim. The study was conducted in two phases. Phase 1 involved the development of the questionnaire content and structure. Phase 2 focused on establishing the psychometric properties of the instrument. Content validity was established by a panel of 8 experts and piloted with 9 health and social care professionals. The instrument was subsequently posted with a stamped addressed envelope to 566 health and social care professionals who met specific eligibility criteria across the four HSE areas. A total of 341 questionnaires were returned, a response rate of 60% and 305 (50%) completed responses were included in exploratory factor analysis (EFA). Item and factor analyses were performed to elicit the instruments underlying factor structure and establish preliminary construct validity. Findings: Item and factor analyses resulted in a logically coherent, 37 items, five factor solution, explaining 55.6% of the cumulative variance. The factors were labelled: ‘Environment’, ‘Social Networks’, ‘Emotional and Behavioural Liability’, ‘Health Avoidance’ and ‘Self-Determinism’. The factor loadings were >0.40 for all items on each of the five subscales. Preliminary construct validity was supported by findings. Conclusion: The main outcome of this research is a 37 item Self-Neglect (SN-37) measurement instrument that was developed by EFA and underpinned by an ESN conceptual framework. Preliminary psychometric evaluation of the instrument is promising. Future work should be directed at establishing the construct and criterion related validity of the instrument.

Development and psychometric evaluation of the spirituality instrument (SpI-27) in a sample of people with chronic illness

Background: Spirituality is fundamental to all human beings, existing within a person, and developing until death. This research sought to operationalise spirituality in a sample of individuals with chronic illness. A review of the conceptual literature identified three dimensions of spirituality: connectedness, transcendence, and meaning in life. A review of the empirical literature identified one instrument that measures the three dimensions together. Yet, recent appraisals of this instrument highlighted issues with item formulation and limited evidence of reliability and validity. Aim: The aim of this research was to develop a theoretically-grounded instrument to measure spirituality – the Spirituality Instrument-27 (SpI-27). A secondary aim was to psychometrically evaluate this instrument in a sample of individuals with chronic illness (n=249). Methods: A two-phase design was adopted. Phase one consisted of the development of the SpI-27 based on item generation from a concept analysis, a literature review, and an instrument appraisal. The second phase established the psychometric properties of the instrument and included: a qualitative descriptive design to establish content validity; a pilot study to evaluate the mode of administration; and a descriptive correlational design to assess the instrument’s reliability and validity. Data were analysed using SPSS (Version 18). Results: Results of exploratory factor analysis concluded a final five-factor solution with 27 items. These five factors were labelled: Connectedness with Others, Self-Transcendence, Self-Cognisance, Conservationism, and Connectedness with a Higher Power. Cronbach’s alpha coefficients ranged from 0.823 to 0.911 for the five factors, and 0.904 for the overall scale, indicating high internal consistency. Paired-sample t-tests, intra-class correlations, and weighted kappa values supported the temporal stability of the instrument over 2 weeks. A significant positive correlation was found between the SpI-27 and the Spirituality Index of Well-Being, providing evidence for convergent validity. Conclusion: This research addresses a call for a theoretically-grounded instrument to measure spirituality.

Factors relating to motivation to change behaviour in individuals who are overweight: application of the health belief model

Background: Obesity is the most important health challenge faced at a global level and represents a rapidly growing problem to the health of populations. Given the escalating global health problem of obesity and its co-morbidities, the need to re-appraise its management is more compelling than ever. The normalisation of obesity within our society and the acceptance of higher body weights have led to individuals being unaware of the reality of their weight status and gravity of this situation. Recognition of the problem is a key component of obesity management and it remains especially crucial to address this issue. A large amount of research has been undertaken on obesity however, limited research has been undertaken using the Health Belief Model. Aim: The aim of the research was to determine factors relating to motivation to change behaviour in individuals who perceive themselves to be overweight and investigate whether the constructs of the Health Belief Model help to explain motivation to change behaviour. Method: The research design was quantitative, correlational and cross-sectional. The design was guided by the Health Belief Model. Data Collection: Data were collected online using a multi-section and multi-item questionnaire, developed from a review of the theoretical and empirical research. Descriptive and inferential statistical analyses were employed to describe relationships between variables. Sample: A sample of 202 men and women who perceived themselves to be overweight participated in the research. Results: Following multivariate regression analysis, perceived barriers to weight loss and perceived benefits of weight loss were significant predictors of motivation to change behaviour. The perceived barriers to weight loss which were significant were psychological barriers to weight loss (p =<0.019) and environmental barriers to physical activity (p=<0.032).The greatest predictor of motivation to change behaviour was the perceived benefits of weight loss (p<0.001). Perceived susceptibility to obesity and perceived severity of obesity did not emerge as significant predictors in this model. Total variance explained by the model was 33.5%. Conclusion: Perceived barriers to weight loss and perceived benefits of weight loss are important determinants of motivation to change behaviour. The current study demonstrated the limited applicability of the Health Belief Model constructs to motivation to change behaviour, as not all core dimensions proved significant predictors of the dependant variable.

Physical function, psychosocial adaptation, and hardiness post stroke

Introduction: Stroke is a chronic condition that significantly impacts on morbidity and mortality (Balanda et al. 2010). Globally, the complexity of stroke is well documented and more recently, in Ireland, as part of the National Survey of Stroke Survivors (Horgan et al. 2014). There are a number of factors that are known to influence adaptation post stroke. However, there is a lack of research to explain the variability in how survivors adapt post stroke. Hardiness is a broad personality trait that leads to better outcome. This study investigated the influence of hardiness and physical function on psychosocial adaptation post stroke. Methods: A quantitative cross-sectional, correlational, exploratory study was conducted between April and November 2013. The sample consisted of stroke survivors (n=100) who were recruited from three hospital outpatient departments and completed a questionnaire package. Results: The mean age of participants was 76 years (range 70-80), over half (56%) of the participants achieved the maximum score of 20 on the Barthel Index indicating independence in activities of daily living. The median number of days since stroke onset was 91 days (range 74-128). The total mean score and standard deviation for hardiness was 1.89 (0.4) as measured by the Dispositional Resilience Scale, indicating medium hardiness (possible range 0-3). Psychosocial adaptation was measured using the Psychosocial Adjustment to Illness Scale, the total weighted mean and standard deviation was 0.54 (0.3) indicating a satisfactory level of psychosocial adaptation (possible range 0-3). A hierarchical multiple linear regression was performed which contained 6 independent variables (hardiness, living arrangement, and length of hospital stay, number of days since stroke onset, physical function and self-rated recovery). Findings demonstrated that physical function (p<0.001) and hardiness (p=0.008) were significantly related to psychosocial adaptation. Altogether, 65% of the variation in psychosocial adaptation can be explained by the combined effect of the independent variables. Physical functioning had the highest unique contribution (11%) to explain the variance in psychosocial adaptation while self-rated recovery, hardiness, and living arrangements contributed 3% each. Conclusion: This research provides important information regarding factors that influence psychosocial adaptation post stroke at 3 months. Physical function significantly contributed to psychosocial adaptation post stroke. The personality trait of hardiness provides insight into how behaviour influenced adaptation post stroke. While hardiness also had a strong relationship with psychosocial adaptation, further research is necessary to fully comprehend this process.

Eleanor Roosevelt: supporting Zion, denying Palestine

Eleanor Roosevelt, as a renowned humanitarian, portrayed an inconsistency by supporting Zionist ambitions for a national homeland in Palestine while simultaneously ignoring the rights of the indigenous Palestinians. Because of this dichotomy, this dissertation explores her attitudes, her disposition and her position in light of the conflict in the region. It conveys how her particular character traits interplayed with the cultural influences prevalent in mid-century America and encouraged her empathy with the plight of European Jews after the Holocaust. As she evolved politically, initially under the tutelage of Franklin Roosevelt and latterly as a UN delegate, she outgrew the anti-Semitism of the period to become a committed Zionist. Judging the Palestinians as ‘primitives’ incapable of self-government and heartened by Jewish development, she supported the partition of Palestine in November 1947. After the 1948 Arab-Israeli war the 800,000 Palestinian refugees encamped in neighbouring Arab states threatened to destabilise the region. Her solution was to discourage repatriation and to re-settle them in Iraq – a plan that directly contravened the principles of the December 1948 Universal Declaration of Human Rights proclaimed by the UN committee she had chaired. No detailed work has been conducted on these aspects of Eleanor Roosevelt’s life; this dissertation reveals a complex person rather than a model of ‘humanitarianism’, and one whose activities cannot be so simply categorised. In the eight chapters that follow, her own thoughts are disclosed through her ‘My Day’ newspaper column, through letters to friends and to members of the public that petitioned her, through a scrutiny of her articles, books and autobiography. This information was attained as a result of archival research in the US and in The Netherlands and was considered against an extensive range of secondary literature. During the Cold War, to offset Soviet incursion, Eleanor Roosevelt promoted Jewish usurpation of Palestinian lands with equanimity in order that an industrious Western-style democracy would bring stability to the region. These events facilitated the exposure of a latent Orientalism and an imperialistic lien that fostered paternalism in a woman new to the nuances of international diplomacy.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Caregiver burden and resilience among Malaysian caregivers of individuals with severe mental illness

Little research has focused on caregiver burden experienced by Malaysian caregivers of individuals with mental illness, despite the fact that data in the Asian region shows almost threequarter of patients with mental illness live with family members. The aim of this research was to examine the levels of caregiver burden and resilience of caregivers of individuals with severe mental illness and to determine the influencing factors on caregiver burden. A quantitative, cross sectional, correlational design was used to measure burden and resilience and to explore the relationship between demographic variables, caregiver stressors, resilience and caregiver burden. This study was guided by the model of Carer Stress and Burden. Data collection was conducted over two months in summer 2014. A self-administered questionnaire that consisted of four sections measuring demographic data, primary stressors, caregiver burden and resilience was used to collect data. Two hundred and one caregivers of individuals with mental illness attending Psychiatric Outpatient Clinics in Malaysia were recruited. Samples were selected using non-probability, consecutive sampling. Factors that were found to be significantly associated with caregiver burden were caregivers’ age, gender, ethnic group, employment status, having a medical condition and current health status. The primary stressors found to be significantly associated with caregiver burden include the time spent for caregiving tasks, unavailability of support with caregiving tasks, lack of emotional support and patients’ behavioural disturbances. In addition, it was found that caregivers who were less resilient reported a higher level of caregiver burden. Findings from hierarchical multiple regression indicated that caregivers’ marital status, current health status, time spent for caregiving and resilience predicted caregiver burden. This research provides insight into caregiver burden among caregivers of individuals with mental illness in Malaysia. It highlights the important factors associated with caregiver burden and the significant role of resilience in reducing caregiver burden.