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The use of silent substitution in measuring isolated cone- & rod- human electroretinograms. An electrophysiological study of human rod- and cone- photoreceptor activity derived using silent substitution paradigmAfter over a decade of its discovery, the Electroretinogram (ERG) still remains the objective tool that is conventionally used in assessment of retinal function in health and disease. Although there is ongoing research in developing ERG- recording techniques, interpretation and clinical applications, there is still a limited understanding on how each photoreceptor class contribute to the ERG waveform and their role and/or susceptibilities in various retinal diseases still remains unclear. Another limitation with currently used conventional testing protocols in a clinical setting is the requirement of an adaptation period which is time-consuming. Furthermore, the ERG responses derived in this manner are recorded under different stimulus conditions, thus, making comparison of these signals difficult. To address these issues and develop a new testing method, we employed silent substitution paradigm in obtaining cone- and rod- isolating ERGs using sine- and square- wave temporal profiles. The ERGs achieved in this manner were shown to be photoreceptor-selective. Furthermore, these responses did not only provide the functional index of photoreceptors but their contributions to their successive postreceptoral pathways. We believe that the substitution stimuli used in this thesis could be a valuable tool in functional assessment of individual photoreceptor classes in normal and pathological conditions. Furthermore, we speculate that this method of cone/rod activity isolation could possibly be used in developing faster and efficient photoreceptor-selective testing protocols without the need of adaptation.
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Contributions for Handling Big Data Heterogeneity. Using Intuitionistic Fuzzy Set Theory and Similarity Measures for Classifying Heterogeneous DataA huge amount of data is generated daily by digital technologies such as social media, web logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the archiving and storage of larger and larger datasets, many of which are multi-modal, or contain different types of data which contribute to the problem that is now known as “Big Data”. In the area of Big Data, volume, variety and velocity problems remain difficult to solve. The work presented in this thesis focuses on the variety aspect of Big Data. For example, data can come in various and mixed formats for the same feature(attribute) or different features and can be identified mainly by one of the following data types: real-valued, crisp and linguistic values. The increasing variety and ambiguity of such data are particularly challenging to process and to build accurate machine learning models. Therefore, data heterogeneity requires new methods of analysis and modelling techniques to enable useful information extraction and the modelling of achievable tasks. In this thesis, new approaches are proposed for handling heterogeneous Big Data. these include two techniques for filtering heterogeneous data objects are proposed. The two techniques called Two-Dimensional Similarity Space(2DSS) for data described by numeric and categorical features, and Three-Dimensional Similarity Space(3DSS) for real-valued, crisp and linguistic data are proposed for filtering such data. Both filtering techniques are used in this research to reduce the noise from the initial dataset and make the dataset more homogeneous. Furthermore, a new similarity measure based on intuitionistic fuzzy set theory is proposed. The proposed measure is used to handle the heterogeneity and ambiguity within crisp and linguistic data. In addition, new combine similarity models are proposed which allow for a comparison between the heterogeneous data objects represented by a combination of crisp and linguistic values. Diverse examples are used to illustrate and discuss the efficiency of the proposed similarity models. The thesis also presents modification of the k-Nearest Neighbour classifier, called k-Nearest Neighbour Weighted Average (k-NNWA), to classify the heterogeneous dataset described by real-valued, crisp and linguistic data. Finally, the thesis also introduces a novel classification model, called FCCM (Filter Combined Classification Model), for heterogeneous data classification. The proposed model combines the advantages of the 3DSS and k-NNWA classifier and outperforms the latter algorithm. All the proposed models and techniques have been applied to weather datasets and evaluated using accuracy, Fscore and ROC area measures. The experiments revealed that the proposed filtering techniques are an efficient approach for removing noise from heterogeneous data and improving the performance of classification models. Moreover, the experiments showed that the proposed similarity measure for intuitionistic fuzzy data is capable of handling the fuzziness of heterogeneous data and the intuitionistic fuzzy set theory offers some promise in solving some Big Data problems by handling the uncertainties, and the heterogeneity of the data.
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What is left for the youth at-risk? Honouring local peace dividends, rehabilitation and integration through the relational sensibility approach. An analysis of reintegration approaches and their effectiveness on youth at-risk of criminalisation – a Somalia case studyThe liberal peace approach guided the Disarmament Demobilisation and Reintegration (DDR) programmes under the auspices of the United Nations. While both practitioners and policymakers recognised that context fitted approaches are required, which resulted in the revision of DDR policy and practice, the driving principle approach remained the liberal peace theory, which creates a hierarchical relationship between the intervener and the intervened. I argue that applying the relational sensibility concept that places relations, dialogue, and hybridity in its focus can (potentially) contribute to a more effective locally designed, led, and implemented reintegration programme that is owned by the different stakeholders instead of imposed. Most reintegration programmes focused on the economic reintegration of ex combatants yielding limited results. I argue that social development for not only former combatants but also for youth at-risk of criminalisation is an essential element of reintegration. I probe the applicability of an alternative peace-building approach to the liberal peace that prioritises actions over relations by reviewing past DDR programmes and a specific case study in Somalia. I establish that an inclusive, community-based reintegration programme that focuses on the social rehabilitation and integration of vulnerable and at-risk youth by strengthening their social and spiritual capitals, as well as promotes restorative justice, can contribute to the decreased level of aggression at the individual level and the perceptions of the increased level of community security in Somalia. I conclude that DDR programmes both policy and practice, should look into more community-based approaches, inclusivity, and balancing between social and economic development opportunities.
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Female friendships in the workplace: A qualitative study of women's relationships in the Kuwaiti education sectorThis thesis draws upon the qualitative findings of 20 interviews undertaken with female teachers in order to explore the topic of workplace friendships between women in an all-female organisational setting. The interview data sheds light on these friendships within the all-female workplace context, examining how the workplace setting can influence the forms of friendship women build with one another. This thesis explores this topic across three main areas: 1) the way in which women develop workplace friendships, and the forms that women-women relations take in all-female workplaces; 2) the importance of workplace friendships and the meanings attached to these friendships; and 3) the blurred boundaries between family and friends, which result in unique workplace-friendship relationships. This study contributes to current knowledge on friendship development and, specifically, the issues associated with women’s development of friendships within the all-female workplace context. The findings highlight the difficulties that some women experience in creating and developing friendships based on cultural boundaries. The findings also emphasise the weaker utility in female friendships, which remains both unacceptable and unchallenged yet nonetheless recognised by women. Furthermore, women are argued to create “other-self” friends and to experience another form of suffusion process in the workplace context. This study also contributes to the current literature on the barriers and opportunities associated with female friendship-building by highlighting how female misogyny employed in the workplace and that workplace friendship is a surviving tool used, adopting a sociological perspective to explore and analyse the findings.
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Design, development and investigation of innovative indoor approaches for healthcare solutions. Design and simulation of RFID and reconfigurable antenna for wireless indoor applications; modelling and Implementation of ambient and wearable sensing, activity recognition, using machine learning, neural network for unobtrusive health monitoringThe continuous integration of wireless communication systems in medical and healthcare applications has made the actualisation of reliable healthcare applications and services for patient care and smart home a reality. Diverse indoor approaches are sought to improve the quality of living and consequently longevity. The research centres on the development of smart healthcare solutions using various indoor technologies and techniques for active and assisted living. At first, smart health solutions for ambient and wearable assisted living in smart homes are sought. This requires a detailed study of indoor localisation. Different indoor localisation technologies including acoustic, magnetic, optical and radio frequency are evaluated and compared. From the evaluation, radio frequency-based technologies, with interest in wireless fidelity (Wi-Fi) and radio frequency identification (RFID) are isolated for smart healthcare. The research focus is sought on auto-identification technologies, with design considerations and performance constraints evaluated. Moreover, the design of various antennas for different indoor technologies to achieve innovative healthcare solutions is of interest. First, a meander line passive RFID tag antenna resonating at the European ultra-high frequency is designed, simulated and evaluated. Second, a frequency-reconfigurable patch antenna with the capability to resonate at ten distinct frequencies to support Wi-Fi and worldwide interoperability for microwave access applications is designed and simulated. Afterwards, a low-profile, lightweight, textile patch antenna using denim material substrate is designed and experimentally verified. It is established that, by loading proper rectangular slots and introducing strip lines, substantial size antenna miniaturisation is achieved. Further, novel wearable and ambient methodologies to further ameliorate smart healthcare and smart homes are developed. Machine learning and deep learning methods using multivariate Gaussian and Long short-term memory recurrent neural network are used to experimentally validate the viability of the new approaches. This work follows the construction of the SmartWall of passive RFID tags to achieve non-invasive data acquisition that is highly unobtrusive.
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Development of Multiple Linear Regression Model and Rule Based Decision Support System to Improve Supply Chain Management of Road Construction Projects in Disaster RegionsSupply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas.
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Animals, Identity and Cosmology: Mortuary Practice in Early Medieval Eastern EnglandThe inclusion of animal remains in funerary contexts was a routine feature of Anglo-Saxon cremation ritual, and less frequently of inhumations, until the introduction of Christianity during the 7th century. Most interpretation has focused either on the animal as symbolic of identity or as an indication of pagan belief, with little consideration given to the interaction between these two aspects. Animals were a fundamental and ubiquitous part of early medieval society, and their contribution to mortuary practices is considered to be multifaceted, reflecting their multiple roles in everyday life. This project considers the roles of animals in mortuary practice between the 5th-7th centuries across five counties in eastern England – Norfolk, Suffolk, Lincolnshire, Cambridgeshire and Essex – in both cremation and inhumation rites. Animal remains have been recognised in 5th to 7th century burials in eastern England from an early date, and the quality of the existing archives (both material and written) is investigated and discussed as an integral part of designing a methodology to effectively summarise data across a wide area. From the eastern England dataset, four aspects of identity in mortuary practice are considered in terms of their influence on the role of animals: choice of rite (cremation/inhumation); human biological identity (age & gender); regionality; and changing expressions of belief and status in the 7th century. The funerary role of animals is argued to be based around broadly consistent cosmologies which are locally contingent in their expression and practice.
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The Civil Defence Debate in Britain 1957-1983. An account and critical analysis of the major issues in the debate about civil defence against nuclear attackThe thesis details the course of the civil defence debate in Britain, assesses the value of civil defence against nuclear attack and investigates other issues of concern to those involved in the debate. The thesis is divided into three parts. Part one deals with the course of the debate, the issues raised and the methods used to propogate them. The role of activists, academics and professionals is given particular emphasis. The period is charaterised as seeing the decline of civil defence, in many peoples' eyes, from a sine qua non of British defence to becoming almost an irrelevance to nuclear war. Part two, by means of the use of a reference scenario, looks in detail at the organisation and effectiveness of British civil defence against nuclear attack. It is concluded that civil defence in the long term, is unlikely to make any significant difference to the number of survivors of nuclear war. The developing knowledge and debate about the Nuclear Winter is also discussed. Part three deals with important issues in the debate which are not directly related to the effectiveness of civil defence in nuclear war. The issues, dealt with in turn, are: the current and potential effect of civil defence on civil liberties; the possible effect of civil defence on crisis stability in times of acute international tensions and the possible effectiveness of civil defence against non-nuclear attack. The conclusion offers a number of explanations as to why, given the apparent ineffectiveness of civil defence, successive governments have continued to develop it. This question is also looked at with reference to Kuhn's theory of scientific revolution and suggests that the understanding of civil defence is at present undergoing a paradigm shift.
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The Single Imputation Technique in the Gaussian Mixture Model FrameworkMissing data is a common issue in data analysis. Numerous techniques have been proposed to deal with the missing data problem. Imputation is the most popular strategy for handling the missing data. Imputation for data analysis is the process to replace the missing values with any plausible values. Two most frequent imputation techniques cited in literature are the single imputation and the multiple imputation. The multiple imputation, also known as the golden imputation technique, has been proposed by Rubin in 1987 to address the missing data. However, the inconsistency is the major problem in the multiple imputation technique. The single imputation is less popular in missing data research due to bias and less variability issues. One of the solutions to improve the single imputation technique in the basic regression model: the main motivation is that, the residual is added to improve the bias and variability. The residual is drawn by normal distribution assumption with a mean of 0, and the variance is equal to the residual variance. Although new methods in the single imputation technique, such as stochastic regression model, and hot deck imputation, might be able to improve the variability and bias issues, the single imputation techniques suffer with the uncertainty that may underestimate the R-square or standard error in the analysis results. The research reported in this thesis provides two imputation solutions for the single imputation technique. In the first imputation procedure, the wild bootstrap is proposed to improve the uncertainty for the residual variance in the regression model. In the second solution, the predictive mean matching (PMM) is enhanced, where the regression model is taking the main role to generate the recipient values while the observations in the donors are taken from the observed values. Then the missing values are imputed by randomly drawing one of the observations in the donor pool. The size of the donor pool is significant to determine the quality of the imputed values. The fixed size of donor is used to be employed in many existing research works with PMM imputation technique, but might not be appropriate in certain circumstance such as when the data distribution has high density region. Instead of using the fixed size of donor pool, the proposed method applies the radius-based solution to determine the size of donor pool. Both proposed imputation procedures will be combined with the Gaussian mixture model framework to preserve the original data distribution. The results reported in the thesis from the experiments on benchmark and artificial data sets confirm improvement for further data analysis. The proposed approaches are therefore worthwhile to be considered for further investigation and experiments.
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Developing a Corporate Brand in a Transitional EconomyThis DBA thesis explores the development of a corporate brand in a transitional economy, a previously under-explored context. The work employs a qualitative exploratory case-study research strategy involving a leader in the FMCG industry in Albania. This helps to set a positive example for the rest of the industry in the context of a transitional economy. Agna Group, the case study in focus, is a FMCG company operating in a domestic market, thus contributing to further insights in the area of corporate brand development and management from a business context which has been under-explored to date, as main focus of corporate branding has been on MNCs. A conceptual framework of corporate brand building and management is initially introduced, and its relevance in the transitional economy of Albania is further explored. The research benefits from the privileged access of the researcher in the case study organization, and the triangulation of a variety of data collected through in-depth interviews, documentation, and observations. The research advances our knowledge and practice of corporate brand building and management by adding to empirical work in the area. It indicates the constructs and practicalities involved in corporate brand development and management in a transitional economy context. This is an important contribution as the literature to date has predominantly focused on developed countries, i.e North America and Western Europe. The research showcases the six main constructs involved in corporate brand development and management and the three main drivers behind them. Research indicates that corporate brand building and management is strongly impacted by culture, as well as market context influence.
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Towards A Practice Theory of Goal-setting: Assessing the theoretical goal-setting of The Leprosy Mission in NigeriaGoal-setting is indispensable for effective healthcare management. Yet, literature evidence suggests many organisations worldwide do not know how to formulate ‘SMART’ goals. Evidence of how existing theories work in practice is scarce, and the practices in low-income countries are unknown. Therefore, this research explored how leprosy project goals were formulated to describe the theoretical practice framework of The Leprosy Mission Nigeria (TLMN). Using a case-study design, ten managers were interviewed individually concerning their goal-setting knowledge, experience and perspective; and documented goals of six projects were reviewed. A five-step constructionist thematic data analysis generated eleven theoretical frameworks from the concepts of the emergent core themes of ‘stakeholders’, ‘strategies’ and ‘statements.’ Further theorisation reduced them to one general framework. This revealed TLMN’s goal-setting practice as a four-stage centre-led, top-down, beneficiary-focused and problem-based process. The stages were national preparation, baseline needs-survey, centralised goal formulation and nationalised planning. The outcome was the formulation of assigned, ‘non SMART’ objective statements, which are then used for planning projects. Other theoretical models constructed included a Goal Effects Cycle, ‘SMARTA’ goal attributes and hierarchical criteria for differentiating goal-types. A theory developed from TLMN goal-setting postulates that: ‘Assigned non SMART goal formulation directly results from centralised goal-setting practice and is the predictor of unrealistic project planning.’ Therefore, I propose that goal statements will be ‘SMARTA’ and plans, more realistic and relevant if goal setting is done collaboratively by all stakeholders at all stages of the process. Also, ‘Change-Beneficiary-Indicator-Target-Timeframe’ and ‘Change- Beneficiary-Location-Timeframe’ frameworks are recommended as templates for writing SMART objectives and aims respectively.
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Investigating the expression and function of aldehyde dehydrogenases in prostate cancer. Probing the expression and function of ALDHs using chemical probes, drugs and siRNACastration-resistant prostate cancer (CRPC) remains an aggressive incurable disease in men mainly due to treatment resistance. Current treatments do not effectively eradicate cancer stem cells (CSCs), which play a pivotal role in tumour maintenance, progression and drug resistance. Aldehyde dehydrogenases (ALDHs) have been used in some tumour types as CSC markers. Their high expression and high functional activity found in CSCs is also associated with drug resistance. Emerging evidence suggests deregulation of certain ALDH isoforms have implications in cancer. The role of ALDHs in prostate cancer as potential biomarkers and therapeutic targets has not been fully explored yet. Accordingly, this study investigated the expression, regulation and function of selected ALDH isoforms in prostate cancer. This study showed that ALDH1A3, ALDH1B1, ALDH2 and ALDH7A1 are highly expressed in primary prostate cancer cells (n=9) compared to benign (n=9) prostate cells. The expression of ALDH1A3 was high in the stem cells (SCs) (n=3) as well as the more differentiated counterparts (n=16). Treatment of both benign and malignant primary prostate cancer cells with all-trans retinoic acid (atRA) also resulted in increased expression of ALDH1A3 and ALDH3A1, supporting a feedback loop between atRA and ALDHs. Furthermore, SerBob, Bob and LNCaP cells were sensitive to treatment with epigenetic drugs and led to significantly higher expression of ALDH1A2, ALDH3A1 and ALDH7A1 respectively. Importantly, siRNA suppression of ALDH1A3 and ALDH7A1 led to reduced SC properties of primary prostate cultures including reduced cell viability, migration and colony formation, and increased differentiation of transit amplifying (TA) cells to committed basal (CB) cells. Novel ALDH-affinic probes showed reduced cell viability of primary prostate epithelial cultures as a single agent and also when used in combination with docetaxel. The results indicate the potential of using ALDH-affinic compounds as single agents for therapeutic intervention or in combination with docetaxel to sensitise resistant cells to this anticancer drug. The data in this thesis provides novel findings, which supports ALDH1A2, -1A3 and -7A1 as potential biomarkers and/or therapeutic targets for drug intervention. Although, a study analysing a larger number of samples is necessary to fully understand ALDH isoform expression in CSC, TA and CB cells it is envisaged that an ALDH-targeted therapy have potential in future treatment strategies for prostate cancer.
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Engineering of Amorphous Active Pharmaceutical Ingredients by Sonoprecipitation and Spray Drying Pre-and Post-Processing Pharmaceutical Characterisation. Pre- and Post-Processing Physicochemical and Micromeritic Characterisation of Active Pharmaceutical IngredientsAmorphous active pharmaceutical ingredients remain in the research focus as an avenue to achieve a better solubility of drugs. Several processing techniques are applied to produce amorphous materials. Main two approaches applied to production of amorphous phases are comminution of crystalline materials in order to break down molecular long-range order of their crystal lattices and amorphous phase precipitation from solutions. This thesis is focused on processing challenges in preparation of amorphous API phases from solutions by spray drying and evaporative antisolvent sonoprecipitation. Budesonide (BUD) and simvastatin (SMV) were used as model poorly soluble APIs. Amorphous phases of relatively low-glass transition (Tg) APIs are physically unstable and crystallise upon storage and/or processing conditions. To tackle this issue, for the first time in this work a selection of polyvinylpyrrolidone vinyl acetate (PVP-VA) co-polymers has been applied to investigate impact of sonoprecipitation processing parameters and a composition of PVP-VA on physicochemical and micromeritic properties of BUD/PVP-VA nanoparticulate composites. Studies confirmed that in solid-state BUD is miscible with PVP-VA polymers. Application of factorial design revealed that processing parameters: polymer type, surfactant concentrations, time and amplitude of sonication impact the entrapment efficiency, drug loading, polydispersity and particle size properties of produced nanoparticles. The largest fraction of polymer to drug in produced nanoparticles has been achieved with PVP VA E-535. As it is known that polymer content in formulation of APIs may slow down its dissolution, novel approach to processing and dissolution enhancement of amorphous composites of SMV produced by spray drying has been applied. Introduction of easily crystallising inorganic salt- sodium chloride into spray drying feed rendered SMV-polyvinyl pyrrolidone (PVP) amorphous microparticles loaded with nanocrystalline NaCl. Addition of NaCl successfully facilitated generation of discrete microparticles post spray drying with low-Tg polymers, which otherwise were not processable as binary mixtures. In addition, NaCl content aided tabletability and dissolution of amorphous API composites with more viscous and high-Tg PVP polymers. Studies confirmed that application of factorial design facilitates robust design of production process of amorphous nanocomposites by sonoprecipitation as well as that introduction of soluble nanocrystalline phase into amorphous binary solid dispersion by spray drying aids its processing and dissolution.
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A Study On Employee’s Intention To Adopt Green Practices At The Workplace In The Context Of The Hotel IndustryThis study aims to examine the effect of organisational commitment and employee’s pro-environmental behaviour at home on their intention to adopt green practices at the workplace in the context of hotel industry, by taking the theory of planned behaviour as a conceptual framework. Hotel employees play a critical role that affects customers' experiences, which then affects the overall hotel performance. However, the mechanism that affects their behavioural intention has yet to be investigated properly. Thus, a survey was conducted to collect the data from employees working in green and non-green hotels in Malaysia. Overall, there were 407 responses received, which represented a response rate of 55.75 percent. Then, a set of hypotheses was tested using the structural equation modelling. The empirical results indicate that organisational commitments have a positive effect on the attitude for engaging in a green behaviour and subjective norm, which in turn influenced employees’ intention to adopt green practices at work. Meanwhile, employees’ pro environmental behaviour at home has an indirect impact on employee’s intention to adopt green practices in the workplace through their attitude for engaging in a green behaviour, subjective norms, and perceived behavioural control. The findings lead to a theoretical contribution by incorporating another theory into the theory of planned behaviour, which is the social bond theory through organisational commitment and spill-over effect through pro environmental behaviour at home. Subsequently, a practical recommendation from this research is attainable to policy makers and hotel providers in order for them to understand and increase employees’ willingness to adopt green practices at the workplace.
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Evaluating the ‘Success’ of The British Intervention in Sierra Leone 20 Years On: Implications for Sierra Leone, The UK, and Interventions GloballyOver the last two decades the frequency of humanitarian interventions in Africa, delivered by a wide range of actors, has increased. The British military intervention in the Sierra Leonean civil war in the early 2000s is often cited as an example of successful intervention and solidified Security Sector Reform (SSR) as a key component of state-building and development. Yet in-depth analysis of the long-term legacies of this ‘successful’ intervention are sparse and there remains a notable dearth in research exploring the British involvement from the perspectives of those directly involved or affected. This qualitative research provides a novel outlook by exploring micro-level experiences, thus addressing this lacuna through examining the legacies within Sierra Leone and in British foreign policy from an experiential perspective. The Responsibility to Protect (R2P) is used as a framework in order to draw out implications for global intervention practice, as arguably R2P must also be accompanied by a responsibility to fully understand the legacy of this social phenomenon. A themed analysis of original data explores the link between official narratives and the perspectives of those on the ground, often exposing a disconnect and identifying important nuances within the interpretation of the success of the British intervention. Through a critical analysis of these experiences significant questions are raised regarding the dynamics between intervening forces and the affected population; perceptions of legitimacy; accountability; and the implications for R2P more broadly.
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Managing Workforce Diversity in Canada: An Empirical Study of the Factors Affecting the Adoption and Success of Diversity Strategies in Canadian OrganisationsEquality, diversity and inclusion (EDI) in the workplace continues to be a dominant universal issue. Through its Employment Equity Act (EEA), Canada has acted as an exemplar in influencing equality legislation in other countries. The Canadian government’s thirtieth EEA annual report to Parliament presents a very positive picture of equality in employment for the four designated groups, DG: (women, Aboriginal peoples, persons with disabilities, and visible minorities) in the four industry sectors (Banking, Communication, Transportation, Other) federally regulated under the EEA’s legislated employment equity programme (LEEP). However, this claim of success is challenged in this study as specious in showing uniform take-up using aggregated LEEP data. A theoretical model is developed between variables representing external pressures, at the macro national level, internal pressures, at the meso-organisational level, and their hypothesized relationships with reactive and proactive EDI focused programmes pursued by LEEP organisations. This model is empirically validated by applying partial least squares structural equation path modelling to data collected from 440 LEEP organisations. Findings reveal that all four DGs are substantially under represented, relative to their labour market availability (LMA), in the majority of individual LEEP organisations, despite over three decades following EEA implementation. DG-LMA representation was also found to differ by industry sector. The main contribution to knowledge of this study is the introduction of a validated predictive EDI model developed and empirically validated for the four designated groups in the context of Canada. Applications of this generic model to other countries for benchmarking and comparative studies could contribute to EDI theory, practice and policy, internationally.
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A smart sound fingerprinting system for monitoring elderly people living aloneThere is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.
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Digital Education Resource Mining for Decision SupportNowadays education becomes a competitive and challenging domain, both nationally and internationally in terms of quality, visibility, experience of academic delivery affecting institutions, applicants, regulatory bodies. Currently data becomes more available for the general and public use, and plays also an increasingly significant role in decision support for education topics. For example, world university rankings (WUR) such as Quacquarelli Symonds (QS), Central World University Rankings (CWUR), Times Higher Education (Times) and national university rankings (e.g. the Guardian newspaper Best UK Universities and the Complete University Guide league tables) have published their data for many years now and are increasingly used in such decision making processes by institutions and general public.
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Towards The Total Synthesis Of Withanolide E And Physachenolide CWithanolides are a class of ergostane natural products found in plants of family Solanaceae. Plants of this family are used in traditional medicine in Asia and South America. Recently, a series of 17β-hydroxy withanolides were identified from high-throughput screens as inhibitors of androgen-induced changes in gene expression of prostate cancer cells. Therefore, these compounds may have important applications as new therapies against prostate cancer. We have devised a synthetic route to members of this family and their analogues which allows stereoselective introduction of C14, C17 and C20 hydroxyl groups in separate steps. This will allow preparation of differentially hydroxylated analogues so as to identify which contributes to the potency and thus gain a better understanding of the SAR of this class of bioactive molecules. As part of this we have shown that the stereochemical outcome of the epoxidation of Δ 14-15 cholestanes with m-CPBA is controlled by the steric bulk of a C17 substituent. When the C17 is in the β configuration, the epoxide is formed on the α face, whereas if the C17 is trigonal (flat) or the substituent is in the α configuration, the epoxide is formed on the β face. The presence of a hydroxyl substituent at C20 does not influence the stereochemical outcome of the epoxidation. We have successfully introduced aldehyde functionality to the lateral side chain 14 hydroxyl compound. This aldehyde compound is a key intermediate from which many of the withanolides can be made. We have also investigated the introduction of a hydroxyl at the C18 as an entry into the physachenolides. Finally, we have carried out an assessment of the potency of the synthesised compounds against hormone-insensitive prostate cancer cell line, PC-3.
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Shia Political Islam in Iran. A political and economic approachAlthough Islamism could be traced back to the seventh century when prophet Mohamad died and conflict between Shia and Sunni started, the recent growth of Islamism and emerging of new phenomena such as establishing Muslim Brotherhood in Egypt in 1920s, Iranian Islamic revolution in 1979, and some other Islamists groups like Hezbollah, and Al-Qaida highlighted the importance of Islamism. Moreover, forming the new Islamists in Syria, Iraq, and Yemen after the Arab uprising in 2010 which intensified the conflict between Shia and Sunni increased the concerns over increase in sectarianism in the Middle East. Considering the significance of Shia groups in recent movements, it is important to have a deep and comprehensive understanding of the nature and function of Shia Political Islam. Despite internal and external concerns, Shia Political Islam has emerged and continued to have control over power in Iran for more than four decades. The post-revolutionary Islamic government has been able to keep its power through reviving the Shia Movement since the 1979 Islamic revolution. Therefore, this thesis asks ‘how has Shia political Islam managed to survive in Iran over the past four decades? The answer to this question relies specifically on understanding the nature of Shia ideology and how the Shia control entities’ access to petrodollars. The study aims to clarify the concept of Shiism and explain the mechanism of the survival and continuation of the Shia movement in Iran through the lens of Social Movement Theory. This thesis argues that the essential mobilisers of the Shia Movement like the IRGC, the Basij and mosques have succeeded in sustaining the survival of the Shia Political Islam. The durability of this political approach lies in actively reviving the origins of the Shia movement, utilising Shia values, religious symbols and holy events such as Ashura, and financially rewarding the Movement with petrodollars. The mobilisers, especially the IRGC, use these values as a steering fuel to run the Shia Movement and suppress any security threat to its survival. For instance, after the 2009 presidential elections, the Green Movement was a serious security threat to the Islamic Republic and the political approach Shia political Islam. However, the IRGC and the Basij employed Shia symbols to mobilise their social base in a counter-movement in 2019 to overcome the threat of the Green Movement. The thesis concludes that if the Shia mobiliser organisations keep supporting the Shia Movement by utilising Shia values, religious symbols, and available economic resources such as petrodollars, Shia political Islam will stay resilient and survive.
