This thesis examines the impact of environmental regulations on trade competitiveness for South Asian countries. The study further investigates whether South Asian countries have become a pollutive haven of industrial exports to OECD countries during 1984-2004. The thesis also analyses whether tariff walls created by the governments to offsets stringent environmental regulations negatively affect pollutive industrial trade flows. This study has identified gaps in the literature after critically reviewing both competing trade theories and empirical literature surrounding the subject. Firstly, most of the empirical literature on the subject has focused on developed countries while ignoring less developed regions like South Asia. Second, several studies concluded trade competitiveness impact of environmental policy following a single estimation method when results are sensitive to the choice of the method used. Hence, for robust results, cross-methods analysis was imperative. Thirdly, the empirical literature on the subject focused on most pollutive industries and ignored the research on somewhat pollutive and least pollutive sectors as well as comparative analysis between those industries. This study has contributed to the literature by filling these gaps. Following the neo-classical theory, the central hypothesis of this thesis is that environmental regulations negatively affect different categories of pollutive industrial export competitiveness. By using the highest dis-aggregated ISIC level trade data and incorporating other socio-economic variables, this study has deployed comparative advantage trade models by Balassa (1965), competitiveness indicator by XU (1999), and bilateral RCA model by Grether and de Melo (2004). The study used the gravity model to control for un-observed effects over time on trade flows while capturing environmental regulations impact on pollutive industrial trade competitiveness. Accordingly, to avert endogeneity/data sensitivity issues and to ascertain robust estimates, the present research has among others computed Random Effect and Newey-West standard error models. The statistical modeling results show that while India gained trade competitiveness in most pollutive industrial trade, Pakistan and Bangladesh lost their trade competitiveness in the same category. The research finds evidence of most pollutive industries of South Asian countries increasing their bilateral RCAs and exports with OECD countries and reset of the world. A comparative analysis between most pollutive to less pollutive industries showed a lack of support for any systematic specialization patterns of trade for South Asia during 1984-2004. Nonetheless, this study findings based on gravity modeling clearly depicted a statistically significant negative impact of environmental regulations on total exports, most pollutive exports, and less pollutive industrial exports for South Asia and OECD countries. This study rejected the pollution haven hypothesis between South Asian pollutive industrial exports with OECD. It further concluded that tariff barriers created by countries to offsets environmental regulation costs would prove counterproductive to competitiveness. At the policy level, instead of lobbing for protectionism to balance out environmental regulatory costs, the governments in both developed and developing countries need to focus on forming better environmental policies fostering both competitiveness and environmental quality. Also, trade-offs between environmental regulations and competitiveness are challenging situations for South Asia and OECD countries. Therefore, sustainable production and trade policies combined with innovative and cost-effective environmental policies are needed to accomplish environmental gains and competitiveness.

A framework for the quantitative analysis of performance, security and database management within a network system (e.g. a cloud computing platform) is presented within this research. Our study provides a methodology for modelling and quantitatively generating significant metrics needed in the evaluation of a network system. To narrow this research, a study is carried-out into the quantitative modelling and analysis of performance, security and database management trade-offs in cloud computing platforms, based on Stochastic Activity Networks (SANs) and combined metrics. Cloud computing is an innovative distributed computing archetypal based on the infrastructure of the internet providing computational power, application, storage and infrastructure services. Security mechanisms such as: batch rekeying, intrusion detection, encryption/decryption or security protocols come at the expense of performance and computing resources consumption. Furthermore, database management processing also has an adverse effect on performance especially in the presence of big data. Stochastic Activity Networks (SANs) that offer synchronisation, timeliness and parallelism are proposed for the modelling and quantitative evaluations of ‘optimal’ trade-offs involving performance, security and database management. Performance modelling and analysis of computer network systems has mostly been considered of utmost importance. Quantification of performance for a while has been assessed using stochastic models with a rising interest in the quantification of security stochastic modelling being applied to security problems. Quantitative techniques that includes analytical valuations founded on queuing theory, discrete-event simulations and correlated approximations have been utilised in the examination of performance. Security suffers from the point that no interpretations can be made in an optimal case. The most consequential security metrics are in analogy with reliability metrics. The express rate at which data grows increases the prominence for research into the design and development of cloud computing models that manages the workload intensity and are suitable for data exploration. Handling big data especially within cloud computing is a resource consuming, time-demanding and challenging task that necessitates titanic computational infrastructures to endorse successful data exploration. We present an improved Security State Transition Diagram (SSTD) by adding a new security state (Failed/Freeze state). The presence of this new security state signifies a security position of the computing network system were the implemented security countermeasures cannot handle the security attacks and the system fails completely. In a more sophisticated security system, when the security countermeasure(s) cannot in any form categorise the security attack, the network system is moved to the Failed/Freeze security state. At this security state, the network system can only resume operation when restored by the system administrator. In this study, we propose a cloud computing system model, defined security countermeasures and evaluated the optimisation problems for the trade-offs between performance, security and database management using SANs formalism. We designed, modelled and implemented dependency within our presented security system, developing interaction within the security countermeasures using our proposed Security Group Communication System (SGCS). The choice of Petri-Nets enables the understanding and capturing of specified metrics at different stages of the proposed cloud computing model. In this thesis, an overview of cloud computing including its classification and services is presented in conjunction with a review of existing works of literature. Subsequently, a methodology is proposed for the quantitative analysis of our proposed cloud computing model of performance-security-database trade-offs using Möbius simulator. Additionally, numerical experiments with relevant interpretations are presented and appropriate interpretations are made. We identified that there are system parameters that can be used to optimise the presented abstract combined metrics but they are optimal for neither performance or security or database management independently. Founded on the proposed quantitative simulation model framework, reliable numerical experiments were observed and indicated scope for further extensions of this work. For example, the use of Machine Learning (ML) or Artificial Intelligence (AI) in the predictive and prevention aspects of the security systems.

Glaucoma is the leading cause of irreversible blindness worldwide but the effect of glaucoma on patients’ vision under suprathreshold conditions relevant to their natural visual environment is poorly understood. This project aimed to investigate and further understand the effects of glaucoma on three aspects of suprathreshold vision; apparent contrast of suprathreshold stimuli, detection and discrimination of image blur and crowding of peripheral vision. Psychophysical methods were employed to assess these three visual functions by measuring contrast matches of Gabor stimuli, blur detection and discrimination thresholds of edge stimuli and crowding ratios of Vernier targets. These measures were obtained from glaucoma observers tested within and outside of visual field defects and the data compared with healthy controls. Contrast matching ratios were similar between glaucoma and healthy age similar controls despite sensitivity loss in the glaucoma group. Blur detection and discrimination thresholds were similar between glaucoma observers’ tested within and outside of visual field defects and age-similar controls, though thresholds were slightly elevated for high contrast stimuli in the glaucoma visual field defect group. Crowding ratios were similar between participants with glaucoma and healthy young controls. The results demonstrate that aspects of suprathreshold visual function can be maintained in early glaucoma despite sensitivity loss at threshold. The results provide empirical evidence as to the asymptomatic nature of the disease in its early stages. It appears that in early glaucoma, there may be compensatory mechanisms at work within the visual system under suprathreshold conditions that can overcome loss of sensitivity at threshold.

This study has examined the implications of the internet for the IPOB movement, focusing on the extent to which it has empowered the movement to engage in collective action mobilisation, enhance identity construction, framing and discourses for an independent Biafran state against multiple forms of state repressions. The thesis adopted a flexible approach that incorporated three level analysis including a macro-level analysis, which looked at the socio political and institutional environment; a meso-level, which examined the organisational infrastructure; and a micro-level exploring how the social movement made sense of their reality. The thesis addresses the debate between technology and society, and between the agency and political opportunity structures in Nigeria. It discussed the radical and agnostic democratic potential of the internet for African social movements and the dialectic between Biafra activism and the socio-political rootedness of Nigerian democracy. This approach allowed for providing analogies and new perspectives from the research and interpreting implications of human action. The thesis has found inconsistent, sometimes contradicting, data on the implication of internet technology for the IPOB movement. While there is some clear and robust evidence suggesting that new media technology has enhanced the movement's capabilities to organise, coordinate and mobilise for Biafran cause on many levels. The study reveals some severe limitations in the appropriation of internet technology in IPOB’s collective action objectives. The implication of the findings is discussed.

Theoretical and empirical literature suggest that public expenditure plays very important role in economic growth, especially in the developing countries. Available statistics show that Nigeria’s 5-year average annual real public expenditure/GDP ratio grew during the greater part of the study period 1981-2015, while the 5-year average annual real GDP growth and real GDP per capita growth rates are positive during the same study period, except for 1981-1985 and 1986-1990, respectively. The incidence of poverty, however, maintained upward movement, except for 2006-2010. The foregoing interactions have been seldom, the focus of empirical studies in Nigeria. This study examines the effects of public expenditure on economic growth and poverty reduction in Nigeria from 1981-2015, using variants of two models and simulation exercise: augmented Solow growth model and growth-poverty model. Real public expenditure/GDP ratio is used as the policy variable and the simulation duration is for 5-years, 2016-2020. We use the autoregressive distributed lag (ARDL) bounds testing procedure by Pesaran et al. (2001) to estimate the two models, given that the annual data used for the models’ estimations were integrated of order I(1) and I(0) and small sample size. The results from the two models confirmed that public expenditure increases economic growth, though not significant, while economic growth does not reduce poverty. The same findings are confirmed through the simulation exercise. We, however, offer measures that would ensure growth and poverty reduction in Nigeria; public expenditure switch that encourages more investments in capital public expenditure, social sector public expenditure and private capital investment.

Pulmonary Arterial Hypertension (PAH) is a rare incurable condition affecting both the cardiac and respiratory systems. Patients living with PAH face the burden of both intensive medication regimens and debilitating disease symptoms. This study’s primary aim was to identify patients’ medication-taking behaviours and beliefs using a framework derived from the extended health belief model (EHBM), and to use this information to deliver personalised interventions to improve medication-taking behaviours. A mixed-methodology longitudinal study design recorded patients’ parameters over a 12-month period. Thirteen participants from Northern Ireland completed the study. The results showed that the level of high-adherence to PAH medicines, as assessed using the MARS questionnaire was 80%, but this value differed when assessed via pill counting and interview data. There was a trend to improvement in observed and predicted medication adherence over the study duration. Participants’ beliefs showed a non-statistical increase in the specific-necessity beliefs and a reduction in general-overuse belief. This study added to the EHBM new constructs of trust and support in being able to better predict nonadherent behaviours. Key medication-taking themes were self-confidence, perceived ranking of medicines, uncertainty and knowledge. This study developed important learning that can be applied to future research on behavioural health studies.

Life on Earth is strongly related to the Sun, which makes it a vital star to study and understand. To improve our knowledge of the way the Sun works, many satellites have been launched into space to monitor the Sun‟s activities where the one of main focus is the effect of these activities on the Earth‟s climate; PICARD is one such satellite. Due to the noise associated with SODISM images, the clarity of these images and the appearance of solar features are affected. Image denoising and enhancement are the main techniques to improve the visual appearance of SODISM images. Affective de-noising algorithm methods depend on a proper detecting of noise present in the image. The aim is to identify which type of noise is present in the image. To reach this point, supervised machine-learning (ML) classifier is used to classify the type of noise present in the image. Furthermore, this work introduces a novel technique developed to enhance the quality of SODISM images. In this thesis, the Modified Undecimated Discrete Wavelet Transform (M-UDWT) technique is used to de-noise and enhance the quality of SODISM images. The proposed method is robust and effectively improves the quality of SODISM images, and produces more precise information and clear feature are brought out. In addition, the non wavelet enhancement is developed as well in this thesis. The results of this algorithm is discussed. The new methods are also assessed using two different methods: subjective (by human observation) and objective (by calculation)

This thesis presents the first full account of the life and achievements of Bradford-born W. Riley (1866-1961), once internationally known as a popular and prolific Yorkshire author. Before becoming a famous writer, he was Managing Director of the successful Bradford Optical Lantern Company, Riley Brothers and was also, for 75 years, a Methodist local preacher and an important layman within northern Methodism. He wrote 39 books, published many stories and articles, and was a busy lecturer. Riley located most of his 30 novels in the Yorkshire Dales and has left a legacy of vivid portraits of people and places in the dales that he knew and loved. This biography of Riley draws upon material never seen hitherto, expanding upon the author's diffident autobiography. The complete bibliography of his extensive writings includes much new and long-lost material. In presenting Riley to a new generation, this account places him in context with his contemporaries. Riley proclaimed his Christianity sympathetically and attractively to his receptive public in much of his output. This thesis includes an insight into the spiritual life, outlook and thinking of a popular and much-respected committed and active Methodist local preacher. Riley's life story is the account of a remarkably successful, self-motivated Victorian. He was a household name in his time, both in Yorkshire and internationally. The research for this thesis has uncovered important material relating to Riley, which will be held in the W. Riley Archive, at the Special Collections Section of the University of Bradford J.P. Priestley Library.

Inter-organisational knowledge sharing between airport security organisations has become increasingly vital to maintain the highest standards of security and public safety. Social networks are considered a significant space for knowledge sharing within and across organisations. The purpose of this research is to investigate inter-organisational knowledge sharing in social media between key organisations in policing and airport security. A cross-sectional case study strategy combining qualitative and quantitative methods was employed to investigate the use of social media in inter-organisational knowledge sharing in the context of airport security in the UAE. Findings showed that the structural characteristics within knowledge sharing were highly centralised and polarised with low intensity in knowledge sharing. Social capital was constrained at a relational level due to cultural factors of trust, risk aversion and power distance that influenced a closed culture and reduced the scope for tacit knowledge sharing practices as well as low level cognitive capital. Analysis of dimensions of the SECI model for knowledge creation revealed that knowledge and the process of knowing was impacted by cultural distinctions that constrained socialisation, externalisation, combination and internalisation processes. The key barriers to knowledge sharing were identified and associated with trust, risk aversion, organisational culture, resource constraints and interoperability factors. This study makes a contribution to theory and practice in terms of the relationship between social capital dimensions and knowledge creation processes and the characteristics of knowledge-sharing within social media. The study further adds to knowledge on the antecedents of inter-organisational knowledge sharing, particularly in the Arabic context.

This study aimed to develop a novel end-to-end plant diagnosis model for the analysis of plant health conditions in near real-time to optimize the rate of production on farmlands for an intensive, yet environmentally safe farming production to preserve the natural environment. First, field research was conducted to determine the extent of the problems faced by farmers in agricultural production. This allowed us to refine the research statement and the level of technology involved in the production processes. The advantages of unmanned aerial systems were exploited in the continuous monitoring of farm plantations to develop automated and accurate measures of farm conditions. To this end, this thesis applies the Precision Agricultural technology as a data based management system that takes into account spatial variations by using the Global Positioning System, Geographical Information System, remote sensing, yield monitors, mapping, and guidance system for variable rate applications. An unmanned aerial vehicle embedded with an optic and radiometric sensor was used to obtain high spectral resolution images of plantation status during normal production/growth cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor to train images to develop an end-to-end feature detection and multiclass classification system for plant overall health’s conditions. Whereby previous works have concentrated on using CNN as off the shelf feature extractor and model training to detect only plant diseases from plants. To date, no research has yet been carried out to develop an end-to-end model for the overall plant diagnosis system. Previous studies focused on the detection of diseases at any given time, making it difficult to implement comprehensive real-time PA systems. Applying the pretrained model to the new images showed that the model can accurately predict any plant condition with an average of 97% accuracy.