A 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.

This 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.

Purpose: Although start-ups have gained increasing scholarly attention, we lack sufficient understanding of their entrepreneurial strategic posture (ESP) in emerging economies. The purpose of this study is to examine the processes of ESP of new technology venture start-ups (NTVs) in an emerging market context. Design/methodology/approach: In line with grounded theory guidelines and the inductive research traditions, the authors adopted a qualitative approach involving 42 in-depth semi-structured interviews with Ghanaian NTV entrepreneurs to gain a comprehensive analysis at the micro-level on the entrepreneurs' strategic posturing. A systematic procedure for data analysis was adopted. Findings: From the authors' analysis of Ghanaian NTVs, the authors derived a three-stage model to elucidate the nature and process of ESP Phase 1 spotting and exploiting market opportunities, Phase II identifying initial advantages and Phase III ascertaining and responding to change. Originality/value: The study contributes to advancing research on ESP by explicating the process through which informal ties and networks are utilised by NTVs and NTVs' founders to overcome extreme resource constraints and information vacuums in contexts of institutional voids. The authors depart from past studies in demonstrating how such ties can be harnessed in spotting and exploiting market opportunities by NTVs. On this basis, the paper makes original contributions to ESP theory and practice.

The 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.

This paper concerns the systematic testing of robotic control software based on state-based models. We focus on cyclic systems that typically receive inputs (values from sensors), perform computations, produce outputs (sent to actuators) and possibly change state. We provide a testing theory for such cyclic systems where time can be represented and probabilities are used to quantify non-deterministic choices, making it possible to model probabilistic algorithms. In addition, refusals, the inability of a system to perform a set of actions, are taken into account. We consider several possible testing scenarios. For example, a tester might only be able to passively observe a sequence of events and so cannot check probabilities, while in another scenario a tester might be able to repeatedly apply a test case and so estimate the probabilities of sequences of events. These different testing scenarios lead to a range of implementation relations (notions of correctness). As a consequence, this paper provides formal definitions of implementation relations that can form the basis of sound automated testing in a range of testing scenarios. We also validate the implementation relations by showing how observers can be used to provide an alternative but equivalent characterisation.

Several contributions in answering granulation challenges including the use of computer simulation and well thought out experimental analyses are being researched. Using a twin screw granulator (TSG) by design of experiments (DoE), comparisons on 1) equipment similarities i.e., continuous and 2) shear forces, are made to previous literature on continuous equipment and a Cyclomix. This study proposes that equipment specific DoE, better explains the contribution of parameters than investigating an identified parameter from the experimental findings from a specific equipment. Granule strength and structure are presented together with the contribution of process parameters, speed, temperature, and binder content. Seeded structures are present in all but the Extrudomix. Longer residence times within the Cyclomix facilitates seeded structures. Granule crushing strengths are higher in TSG than all other continuous equipment. Optimum condition for the formation of stronger granules with least variation is around 65.4 °C.

Drawing from 645 microfinance institutions across 56 countries, this paper examines the deposit-borrowing dynamic of microfinance institutions’ source of capital. We find that deposits and borrowings are substitutes rather than complements. We further find that the degree of substitutability is more pronounced among microfinance institutions operating in a developed financial sector where the level of information asymmetry is lower. Our findings represent novel contribution in understanding microfinance institutions’ funding behaviour that supports its quest for further growth and long-term sustainability.

The 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.

Missing 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.

Although prior research has examined the effects of different forms of disruptive market shift on organizational practice, structure, and performance, knowledge is lacking on its conceptual domain, antecedents, and organizational response outcomes. This study draws insights from an in-depth analysis of 23 organizations to conceptualize disruptive market shift and explore its antecedents and consequences. We find that digitization, technological advancements, political uncertainty and government regulations, competitive pressures, the media, and customer dynamism are major drivers of disruptive market shifts. Furthermore, evidence suggests that organizations establish collaborative relationships, initiate internal transformational processes, and develop innovative metrics and patterns to respond to disruptive market shifts. We discuss the theoretical and managerial implications of the findings.