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The aim of the studies in this thesis was to examine whether reminders of values and goals allow people to put stressors into perspective and thus reduce stress. Study 1 of the dissertation tested whether giving participants a chance to self-affirm, which means reminding them of important values other than the stress-causing stimulus, lowers the stressor's significance and makes them want to regulate their stress. In the second study, participants were reminded of important goals (versus less important goals or nothing in the control conditions) to determine whether reminders of important goals lower stress responses. Lastly, in Study 3, participants were instructed to put a stressor into perspective to see whether it would reduce stress on various explicit and implicit measures of stress and coping. The main findings of the thesis show that self-affirmation and instructed perspective taking have the potential to affect the experience of stress, especially when taking into account individual difference variables, such as trait anxiety or habitual perspective taking. The results are discussed in light of existing knowledge, and some recommendations are made for future research and specialists in the fields of social and clinical psychology.

Within higher education mathematics, the focus on the teaching of calculus is continuing to be highlighted. Researchers have long sought to enhance the quality of calculus teaching at the university level. Even so, there is little clarity regarding the Pedagogical Content Knowledge (PCK) of calculus teachers in higher education. This study seeks to go some way to closing this identified gap by investigating the PCK of calculus teachers. This study proposes a model of PCK for calculus teaching and uses this model to identify how calculus teachers articulate and demonstrate their PCK to achieve their teaching goals, to deliver the building blocks to construct and enable their students' mathematical understanding, to apply instructional strategies, and to utilise calculus connections with other academic subjects and wider applications. In order to understand the PCK of calculus teachers, this study is situated in higher education in Saudi Arabia. The sample group comprises calculus teachers of first-year university students. This study uses multiple cases and qualitative and quantitative data collected through a triangulated approach using survey, semistructured interview and observation of teaching. The analysis of the data employs a specially developed analytical framework for PCK for teaching calculus. Cross-case analysis identified, in detail, how these teachers articulate and demonstrate their PCK to develop learners' cognition of calculus; address the developmental aspects of the curriculum, apply instructional strategies to deliver their teaching aims and objectives, and to utilise calculus connections. This study's findings are steeped in fine detail and have appropriately addressed the research questions. It is significant in conceptualising, and analysing empirically, the PCK of calculus teachers. The findings identify that all the teachers showed their PCK in relation to how they taught calculus, it was also clear that not all aspects of PCK were equally evident among them. Some focused on specific instructional strategies to target learners' needs, others highlighted students' misconceptions about calculus in different ways. Knowledge of students' thinking about calculus concepts was narrow, while little effort about knowledge of calculus connections was identified. Although the teachers attempted to highlight real-world applications of calculus, identifying real-world connections that the students could understand was lacking. Significantly, calculus relating to other academic subjects was least identified. The findings pave the way for future developments of university calculus teaching and provides a model that can be developed and used widely within the field of calculus teaching in higher education. It is anticipated that this model will support the development of mathematics teaching in higher education in Saudi Arabia and elsewhere.

With the advent of high-throughput biology, large amounts of molecular data are available for purposeful analysis and evaluation. Extracting relevant knowledge from high-throughput biomedical datasets has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, the datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. This is evidenced by the limited success these methods have had in detecting robust and reliable biomarkers for cancers and other complicated diseases. This could also explain the lack of finding generic biomarkers among the identified published genes for identical diseases or clinical conditions. This thesis proposes and evaluates the efficacy of two novel feature mining models established on the basis of the evolutionary computation and deep learning paradigms to position and solve biomarker discovery as an optimisation problem. Deep learning methods lack the transparency and interpretability found in the evolutionary paradigm. To overcome the inherent issue of poor explanatory power associated with the deep learning, this research also introduces a novel deep mining model that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations to aid feature selection. As a result, salient biomarkers for breast cancer and the positivity of the Estrogen and Progesterone receptors are discovered robustly and validated reliably across a wide range of independently generated breast cancer data samples.

Confocal microscopy demonstrated its value in the diagnosis of Acanthamoeba and fungal keratitis which considered sight-threatening corneal diseases. However, it can be difficult to find and train confocal microscopy graders to accurately detect Acanthamoeba cysts and fungal filaments in the images. Use of an automated system could overcome this problem and help to start the correct treatment more quickly. Also, response to treatment can be difficult to assess in infectious keratitis using clinical examination alone, but there is evidence that the morphology of filaments and cysts may change over time with the use of correct treatment. An automated system to analyse confocal microscopy images for such changes would also assist clinicians in determining whether the ulcer is improving, or whether a change of treatment is needed. This research proposes a fully automated novel system with GUI to detect cysts and hyphae (filaments) and measure useful quantitative parameters for them through many stages; Image enhancement, image segmentation, quantitative analysis for detected cysts and hyphae, and registration and tracking of ordered sequence of images. The performance of the proposed segmentation procedure is evaluated by comparing between the manual and the automated traced images of the dataset that was provided by the Manchester Royal Eye Hospital. The positive predictive values rate of cysts for Acanthamoeba images was 76%. For detected hyphae in Fusarium images, many standard measurements were computed. The accuracy of their values was quantified by calculating the percent error rate for each measurement and which ranged from 23% to 49%.

The aim of this thesis was to study, using numerical simulation techniques, the possible effects of an additive noise on the firing properties of stochastic neural models, and the related first exit time problems. The research is divided into three main investigations. First, using SDELab, mathematical software for solving stochastic differential equations within MATLAB, we examine the influence of an additive noise on the output spike trains for the space-clamped Hodgkin Huxley (HH) model and the spatially-extended FitzHugh Nagumo (FHN) system. We find that a suitable amount of additive noise can enhance the regularity of the repetitive spiking of the space-clamped HH model. Meanwhile, we find the FHN system to be sensitive to noise, requiring that very small values of noise are chosen, in order to produce regular spikes. Second, under additive noise, we use fixed and exponential time-stepping Euler algorithms, with boundary tests, to calculate the mean first exit times (MFET) for one-dimensional neural diffusion models, represented by a stochastic space-clamped FHN system and the Ornstein-Uhlenbeck (OU) model. The strategies and theory behind these numerical methods and their convergence rates in the MFET are also considered. We find that, for different values of noise, these methods with boundary tests can improve the rate of convergence from order one half to order one, which coincides with previous studies. Finally, we look at spatially-extended systems, represented by the Barkley system with additive noise that is white in time and correlated in space, calculating mean nucleation times and mean lifetimes of traveling waves, using an efficient numerical simulation. A simple model of the dynamics of the underlying Barkley model is introduced, in order to compute the mean lifetimes, particulary for interacting waves. The reduced model is easy to use and allows us to explore the full dynamics of the kinks and antikinks, in particular over long periods. One application of the reduced model is to calculate the mean number of kinks at a given time and use this to obtain the probability that the system is excitable at a given position. With these three investigations into the effects of additive noise on stochastic neural models, we have demonstrated some of the interesting results that can be achieved using numerical techniques. We hope to extend this work, in the future, to include the effects of multiplicative noise.

Many studies have been conducted in the USA and Europe to explore the attitudes and beliefs of physicians toward the use of Electronic Medical Record (EMR) systems and to examine the impact of using EMR in the management of diabetes mellitus.  However, no such study has been conducted in the United Arab Emirates (UAE). This study was designed to fill this gap.  The study consists of two parts. The first examines the Primary Health care (PHC) physicians’ attitudes and beliefs towards the implementation of EMR and the second, examines the impact of EMR on the management of diabetes in a diabetes mini-clinic in a PHC health centre. The overall finding was that non-users of EMR had higher expectations of what computers could achieve than did users.  The majority of EMR users and non-users believed that (1) physicians should computerise their medical records; (2) EMR were a useful tool for physicians; and (3) using EMR will improve the quality of healthcare in the health centre and in the UAE overall.  A significantly higher proportion of non-users than EMR users believed that using EMR would enable them to accomplish tasks more quickly and reduce their risk of making medical errors. The intervention part of the study found that the introduction of an EMR system has significantly improved documentation and the performance of processes of care for diabetic patients.  However, its impact on outcomes was limited.  A significant improvement in the proportion of patients with blood pressure <140/80 mm Hg and a significant reduction in the proportion of patients with blood pressure >160/95 mm Hg was found.  An improvement in the proportion of patients with LDL-C<100 mg/dl was also found.  The limitations of the study should be considered before generalising these results.

By its nature, building maintenance requires an ability to respond to a demand that is random in time, random in nature and random in location. This in turn creates complex operational and logistical problems for management, specially if the property complex is large. The objective of this research is to assess the scope for and effectiveness of quantitative modelling, and the prediction of the outcome of alternative management action (policy), to assist in the management of building maintenance complexes of the size of a hospital. Both building and engineering equipments are encompassed within the study as appropriate. The research issues are split into three related phases; a demand study; a defect reduction study; and a maintenance management model. 1- The maintenance demand study: Based upon general statistics obtained, attempts have been made to identify and quantify both the major problems areas (in terms of cost and frequency of maintenance activities), and the nature and cause of the demand for maintenance. They have revealed no coherent picture in that the demand from wards and buildings seems independent of the patient throughput and the age of buildings. The demand for maintenance, for the main trades involved, has been estimated and used in the simulation models mentioned in below. 2- Demand reduction model: Accepting the current demand situation for maintenance, it was proposed to identify what is the cause of the demand and what possible actions could reduce the demand: Possibly through design modification, changes in materials used, change in practice of service/building user, development of Preventive Maintenance 'PM' or inspection system for component. Despite considerable effort, it proved not possible to progress this aspect of the study and the reasons are discussed. 3- Maintenance management models: Simulation models to the maintenance activities within the hospital has been developed using, Extended Control and Simulation Language, ECSLPLUS, to model the maintenance policies, and assessing any changes in operating procedures. The advantage of modelling is that the magnitude and nature of changes can be assessed and contemplated prior to any actual change in operating procedures. This is generally recognised as being most valuable. For specific problems and areas of operation identified, development of specific methods of deployments have been attempted. For instance, 'recieving one job at a time'; 'recieving a batch of jobs at a time'; and 'delaying non-urgent jobs and grouping them in time'. A number of maintenance management policies have been assessed using the above models, these are: 'Previewing' and 'not previewing' most of the defects before repair to identify the required resources; 'employing extra part-time tradesmen during the busy days'; 'working 7 days instead of 5 days a week'; 'no sickness policy'; and 'employing multi-skilled tradesmen option'. These models should be capable of indicating to management the gains and consequances, in terms of measures of interest to them such as the workforce and manhours required to meet the demand for maintenance per trade, changing operating practice, customs and timescales. That is, their decision variables.

Demand forecasting has been an area of study among scholars and businessmen ever since the start of the industrial revolution and has only gained focus in recent years with the advancements in AI. Accurate forecasts are no longer a luxury, but a necessity to have for effective decisions made in planning production and marketing. Many aspects of the business depend on demand, and this is particularly true for the Fast-Moving Consumer Goods industry where the high volume and demand volatility poses a challenge for planners to generate accurate forecasts as consumer demand complexity rises. Inaccurate demand forecasts lead to multiple issues such as high holding costs on excess inventory, shortages on certain SKUs in the market leading to sales loss and a significant impact on both top line and bottom line for the business. Researchers have attempted to look at the performance of statistical time series models in comparison to machine learning methods to evaluate their robustness, computational time and power. In this paper, a comparative study was conducted using statistical and machine learning techniques to generate an accurate forecast using shipment data of an FMCG company. Naïve method was used as a benchmark to evaluate performance of other forecasting techniques, and was compared to exponential smoothing, ARIMA, KNN, Facebook Prophet and LSTM using past 3 years shipments. Methodology followed was CRISP-DM from data exploration, pre-processing and transformation before applying different forecasting algorithms and evaluation. Moreover, secondary goals behind this paper include understanding associations between SKUs through market basket analysis, and clustering using KNN based on brand, customer, order quantity and value to propose a product segmentation strategy. The results of both clustering and forecasting models are then evaluated to choose the optimal forecasting technique, and a visual representation of the forecast and exploratory analysis conducted is displayed using R.

Abstract: This thesis examined whether the print media of New Zealand and British Columbia, in the immediate month campaign period leading into their respective November 6, 1993 and May 12, 2009 electoral reform referendums, provided a deliberative environment for citizens to make informed ballot choices. This is vital on such a vote. Grounded in theories of deliberative democracy, I evaluate the print media of NZ and B.C., specifically the New Zealand Herald, the Evening Post, the Vancouver Sun, and the Victoria Times Colonist broadsheets, against three indicators: quantity of coverage, balance of coverage, and presence and type of reasoning to support taken positions. Based on the gathered results, I conclude that both NZ and B.C.’s print media attempted to provide a deliberative environment so that citizens could make informed ballot decisions on competing electoral system options. However, New Zealand’s was stronger based on displaying a high quantity of coverage on their country’s referendum between FPTP and MMP, while British Columbia’s coverage was moderate on their province’s FPTP vs. STV vote.